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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, External Review
Draft
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EPA-452/D-23-002
May 2023
Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for
Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter, External Review Draft
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. Questions or
comments related to this document should be addressed to Ginger Tennant
(tennant.ginger@epa.gov), U.S. Environmental Protection Agency, Office of Air Quality Planning
and Standards, C504-06, Research Triangle Park, North Carolina 27711.
May 2023
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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 and Sulfur Oxides and
Particulate Matter 1-4
1.3.1 Nitrogen Oxides 1-4
1.3.2 Sulfur Oxides 1-5
1.3.3 Particulate Matter 1-7
1.3.4 Last Review of the Criteria and Secondary Standards for Nitrogen and Sulfur
Oxides 1-10
1.4 Current Review 1-12
1.5 Organization of This Document 1-13
References 1-16
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 2-3
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 of N, S, and PM 2-13
2.3.1 NOx Monitoring Networks 2-13
2.3.2 SO2 Monitoring Networks 2-15
2.3.3 PM2.5 Monitoring Networks 2-16
2.3.4 Other Monitoring Networks Relevant to N, S, and PM Deposition 2-18
2.4 Recent Ambient Air Concentrations and Trends 2-23
2.4.1 NO2 Concentrations and Trends 2-23
2.4.2 SO2 Concentrations and Trends 2-26
2.4.3 PM2.5 Concentrations and Trends 2-29
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2.5 Nitrogen and Sulfur Deposition 2-36
2.5.1 Estimating Atmospheric Deposition 2-36
2.5.2 Uncertainty in Estimates of Atmospheric Deposition 2-39
2.5.3 National Estimates of Deposition 2-41
2.5.3.1 Contribution from NH3 2-43
2.5.3.2 Contribution from International Transport 2-44
2.5.4 Trends in Deposition 2-45
References 2-52
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 3-2
3.3 General Approach for this Review 3-5
3.3.1 Approach for Direct Effects of the Pollutants in Ambient Air 3-8
3.3.2 Approach for Deposition-Related Ecological Effects 3-9
3.3.3 Identification of Policy Options 3-11
References 3-13
4 NATURE OF WELFARE EFFECTS 4-1
4.1 Direct Effects of Oxides ofN and S and ofPMin Ambient Air 4-1
4.2 Deposition-Related Ecological Effects 4-3
4.2.1 Acidification and Associated Effects 4-5
4.2.1.1 Freshwater Ecosystems 4-6
4.2.1.1.1 Nature of Effects and New Evidence 4-6
4.2.1.1.2 Freshwater Ecosystem Sensitivity 4-8
4.2.1.1.3 Key Uncertainties 4-12
4.2.1.2 Terrestrial Ecosystems 4-12
4.2.1.2.1 Nature of Effects and New Evidence 4-12
4.2.1.2.2 Terrestrial Ecosystem Sensitivity 4-13
4.2.1.2.3 Key Uncertainties 4-15
4.2.2 Nitrogen Enrichment and Associated Effects 4-16
4.2.2.1 Aquatic and Wetland Ecosystems 4-17
4.2.2.1.1 Nature of Effects and New Evidence 4-18
4.2.2.1.2 Aquatic Ecosystem Sensitivity 4-19
4.2.2.1.3 Key Uncertainties 4-21
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4.2.2.2 Terrestrial Ecosystems 4-21
4.2.2.2.1 Nature of Effects and New Evidence 4-22
4.2.2.2.2 Terrestrial Ecosystem Sensitivity 4-24
4.2.2.2.3 Key Uncertainties 4-25
4.2.3 Other Effects 4-27
4.2.3.1 Mercury Methylation 4-27
4.2.3.2 Sulfide Toxicity 4-27
4.2.3.3 Ecological Effects of PM Other Than N and S Deposition 4-28
4.3 Public Welfare Implications 4-28
References 4-34
5 EXPOSURE CONDITIONS ASSOCIATED WITH EFFECTS 5-1
5.1 Direct Effects of Oxides of N and S and of PM in ambient air 5-3
5.1.1 Sulfur Oxides 5-3
5.1.2 Nitrogen Oxides 5-4
5.1.3 Particulate Matter 5-6
5.2 Aquatic Ecosystem Acidification 5-6
5.2.1 Role of ANC as Acidification Indicator 5-7
5.2.2 Conceptual Model and Analysis Approach 5-13
5.2.2.1 Spatial Scale 5-14
5.2.2.2 Chemical Indicator 5-16
5.2.2.3 Critical Load Estimates Based on ANC 5-17
5.2.2.4 Critical Load-Based Analysis 19
5.2.2.5 Waterbody Deposition Estimates 5-20
5.2.2.6 Interpreting Results 5-20
5.2.3 Estimates for Achieving ANC Targets with Different Deposition Levels 5-22
5.2.3.1 National Scale Analysis 5-22
5.2.3.2 Ecoregion Analyses 5-27
5.2.3.3 Case Study Analyses 5-43
5.2.4 Uncertainty Analyses 5-44
5.2.5 Summary 5-46
5.3 Nitrogen Enrichment 5-48
5.3.1 Wetlands 5-48
5.3.2 Freshwater Lakes and Streams 5-49
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5.4 Terrestrial Ecosystems 5-50
5.4.1 Soil Chemistry Response 5-52
5.4.2 Effects on Trees 5-53
5.4.2.1 Steady-State Mass Balance Modeling 5-54
5.4.2.2 Experimental Addition Studies 5-56
5.4.2.3 Observational or Gradient Studies 5-57
5.4.3 Other Effects 5-61
5.4.3.1 Effects on Herbs and Shrubs 5-61
5.4.3.2 Effects on Lichen 5-63
5.5 Key Findings and Associated Uncertainties and Limitations 5-64
5.5.1 Aquatic Acidification 5-64
5.5.2 Other Aquatic Effects 5-68
5.5.3 Terrestrial Effects 5-68
5.5.3.1 Direct Effects on Plants and Lichens of Pollutants in Ambient Air... 5-68
5.5.3.2 Deposition and Risks to Trees 5-69
5.5.3.3 Deposition Studies of Herbs, Shrubs and Lichens 5-72
References 5-74
6 RELATIONSHIPS OF DEPOSITION TO AIR QUALITY METRICS 6-1
6.1 Overview 6-1
6.2 Relating Air Quality to Ecosystem Deposition 6-1
6.2.1 Class I Area Analyses 6-3
6.2.1.1 Evidence from Observations of Air Concentrations and Wet Deposition
6-7
6.2.1.2 Evidence from Chemical Transport Modeling 6-12
6.2.1.3 Evidence from Model-measurement Fusion 6-17
6.2.1.4 Conclusions 6-20
6.2.2 National-scale Zone of Influence Analyses 6-22
6.2.2.1 Approach 6-22
6.2.2.2 SO2 results 6-24
6.2.2.3 NO2 results 6-30
6.2.2.4 PM2.5 results 6-32
6.2.2.5 Conclusions 6-36
6.3 Air Quality Metrics for Consideration 6-38
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1 6.3.1 SO; Metrics 6-38
2 6.3.2 NO2 and PM2.5 Metrics 6-41
3 6.3.3 Key Uncertainties and Limitations 6-41
4 References 6-43
5 7 REVIEW OF THE STANDARDS 7-1
6 7.1 Evidence and Exposure/Risk Based Considerations for Direct Effects of the Pollutants in
7 Ambient Air 7-1
8 7.1.1 Direct Effects of SOx in Ambient Air 7-2
9 7.1.2 Direct Effects of N Oxides in Ambient Air 7-3
10 7.1.3 Particulate Matter 7-5
11 7.2 Evidence and Exposure/Risk-based Considerations for Deposition-related Effects 7-5
12 7.2.1 S Deposition and Oxides of S 7-6
13 7.2.1.1 Welfare Effects Evidence of Deposition-Related Effects 7-6
14 7.2.1.2 General Approach for Considering Public Welfare Protection 7-8
15 7.2.1.3 Relating Deposition-related Effects to Air Quality Metrics 7-13
16 7.2.2 N Deposition and Oxides of N and PM 7-16
17 7.2.2.1 Welfare Effects Evidence of Deposition-Related Effects 7-16
18 7.2.2.2 General Approach for Considering Public Welfare Protection 7-18
19 7.2.2.3 Relating Deposition-related Effects to Air Quality Metrics 7-21
20 7.3 Preliminary Conclusions 7-23
21 7.4 Key Uncertainties and Areas for Future Research 7-33
22 References 7-35
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25 APPENDIX 5A. AQUATIC ACIDIFICATION ANALYSES
26 APPENDIX 5B. ADDITIONAL DETAIL RELATED TO KEY TERRESTRIAL
27 ECOSYSTEM STUDIES
28 APPENDIX 6A. ADDITIONAL DETAIL RELATED TO KEY TERRESTRIAL
29 ECOSYSTEM STUDIES
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1 TABLE OF TABLES
2 Table 2-1. Average annual mean NO2 concentrations in select cities for the 1967-1971
3 period 2-26
4 Table 2-2. Change in total deposition by region between the 2000-2002 and 2019-2021
5 periods (U.S. EPA, 2022b): (a) total S deposition; (b) total, oxidized and
6 reduced N deposition 2-46
7 Table 5-1. Percentage of waterbodies nationally for which annual average S deposition
8 during the five time periods assessed exceed the waterbody CL for each of the
9 ANC targets 5-22
10 Table 5-2. Min, max, and median total S deposition for the 25 ecoregions included in the
11 analyses. Deposition values were determined by a zonal statistic for each
12 ecoregion 5-29
13 Table 5-3. Number of ecoregion-time period combinations with more than 10, 15, 20, 25,
14 and 30% of waterbodies exceeding their CLs for three ANC targets as a
15 function of ecoregi on-level estimates of annual average S deposition 5-31
16 Table 5-4. Percentage of ecoregion-time periods combinations with at least 90, 85, 80, 75
17 and 70% of waterbodies estimated to achieve an ANC at/above the ANC
18 targets of 20, 30 and 50 |ieq/L as a function of annual average S deposition for
19 18 eastern ecoregions (90 ecoregion-time period combinations) 5-36
20 Table 5-5. Annual average S deposition at/below which modeling indicates an ANC of 20,
21 30 or 50 |ieq/L can be achieved in the average, 70% and 90% of waterbodies in
22 each study area 5-44
23 Table 5-6. Acid deposition levels estimated for BC: A1 targets in 24-state range of red
24 spruce and sugar maple using steady-state simple mass balance model (2009
25 REA) 5-55
26 Table 5-7. Acidic deposition levels estimated for several BC:A1 ratio targets by steady -
27 state mass balance modeling for sites in northeastern U.S 5-56
28 Table 5-8. Tree effects and associated S/N deposition levels from observational studies 5-60
29 Table 6-1. Co-located CASTNET, NADP/NTN, and IMPROVE monitoring stations used
30 in this analysis of air concentration and deposition 6-5
31 Table 6-2. Relationship of deposition (S and N) to the various air quality metrics 6-24
32 Table 7-1. Summary of current standards and draft range of potential policy options for
33 consideration 7-32
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1 TABLE OF FIGURES
2 Figure 2-1. Schematic of most relevant individual pollutants that comprise oxides of
3 nitrogen, oxides of sulfur, and particulate matter 2-2
4 Figure 2-2. 2020 NOx emissions estimates by source sector (U.S. EPA NEI, 2023) 2-6
5 Figure 2-3. 2020 NOx emissions density across the U.S. (U.S. EPA NEI, 2023) 2-6
6 Figure 2-4. Trends in NOx emissions by sector between 2002 and 2022 2-7
7 Figure 2-5. 2020 SO2 emissions estimates by source sector (U.S. EPA NEI, 2023) 2-8
8 Figure 2-6. 2020 SO2 emissions density across the U.S. (U.S. EPA NEI, 2023) 2-9
9 Figure 2-7. Trends in SO2 emissions by sector between 2002 and 2022 2-10
10 Figure 2-8. 2020 NH3 emissions by source sector (U.S. EPA NEI, 2023) 2-11
11 Figure 2-9. NH3 Emissions density across the U.S. (U.S. EPA NEI, 2023) 2-11
12 Figure 2-10. Trends in NH3 emissions by sector between 2002-2022 2-12
13 Figure 2-11. Locations of NO2 monitors operating during the 2019-2021 period 2-15
14 Figure 2-12. Locations of SO2 monitors operating during the 2019-2021 period 2-16
15 Figure 2-13. PM2.5 mass monitors operating during the 2019-2021 period 2-17
16 Figure 2-14. PM2.5 speciation monitors operating during the 2019-2021 period 2-18
17 Figure 2-15. Location of NTN monitoring sites with sites active shown in blue and inactive
18 sites in white 2-19
19 Figure 2-16. Location of CASTNET monitoring sites and the organizations responsible for
20 collecting data 2-20
21 Figure 2-17. Location of AMoN monitoring sites with sites active shown in blue and
22 inactive sites in white 2-22
23 Figure 2-18. Primary NO2 design values (98th percentile of daily maximum 1-hourly
24 concentrations, averaged over 3 years; ppb) at monitoring sites with valid
25 design values for the 2019-2021 period 2-24
26 Figure 2-19. Primary and secondary NO2 design values (single year annual mean; ppb) for
27 2021 2-24
28 Figure 2-20. Distributions of annual 98th percentile, maximum 1-hour NO2 design values
29 (ppb) at U.S. sites across the 1980-2021 period 2-25
30 Figure 2-21. Distributions of annual mean NO2 design values (ppb) at U.S. sites across the
31 1980-2021 period 2-25
32 Figure 2-22. Primary SO2 design values (99th percentile of 1-hour daily maximum
33 concentrations, averaged over 3 years; ppb) for the 2019-2021 period at
34 monitoring sites with valid design values 2-27
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Figure 2-23. Secondary SO2 design values (2nd highest 3-hourly average; ppb) for the year
2021 at monitoring sites with valid design values 2-27
Figure 2-24. Distributions of 99th percentile of maximum daily 1-hour SO2 design values
(ppb) at U.S. sites across the 1980-2021 period 2-28
Figure 2-25. Distributions of annual average SO2 design values (ppb) at U.S. sites across the
2000-2021 period. Sites from Hawaii are not included 2-28
Figure 2-26. Map showing pie charts of PM2.5 component species at selected U.S.
monitoring sites based on 2019-2021 data 2-29
Figure 2-27. Primary and secondary annual PM2.5 design values (annual mean, averaged
over 3 years; |ig/m3) for the 2019-2021 period at monitoring sites with valid
design values 2-31
Figure 2-28. Primary and secondary 24-hour PM2.5 design values (98th percentile, averaged
over 3 years; |ig/m3) for the 2019-2021 period at monitoring sites with valid
design values 2-31
Figure 2-29. Average NO3 concentrations (|ig/m3) for the 2019-2021 period 2-32
Figure 2-30. Average SO42" concentrations (|ig/m3) for the 2019-2021 period 2-32
Figure 2-31. Trends in annual average concentrations for nitrate (NO3) from 2006 through
2021 2-33
Figure 2-32. Trends in annual average concentrations for sulfate (SO42") from 2006 through
2021 2-33
Figure 2-33. Distributions of annual mean PM2.5 design values (|ig/m3) at U.S. sites across
the 2000-2021 period 2-35
Figure 2-34. Distributions of the annual 98th percentile 24-hour PM2.5 design values (|ig/m3)
at U.S. sites across the 2000-2021 period 2-35
Figure 2-35. Data sources for calculating total deposition 2-38
Figure 2-36. Data sources for estimating dry deposition 2-38
Figure 2-37 Three year average of the total deposition of nitrogen (kg N/ha) across the
2019-2021 period 2-42
Figure 2-38. Three year average of the total deposition of sulfur (kg S/ha) across the 2019-
2021 period 2-42
Figure 2-39. Average percent of total N deposition in 2019-2021 as reduced N (gas phase
NH3 and particle phase NH4+) 2-44
Figure 2-40. Annual average concentrations of nitric acid in two years: 1996 (top) and 2019
(bottom) 2-47
Figure 2-41. Model-estimated dry deposition of nitric acid over two 3-year periods: 2000-
2002 (top) and 2016-2018 (bottom) 2-48
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1 Figure 2-42. Projected percent change in total N deposition in Class 1 areas from 2016,
2 based on a scenario for 2032 that includes implementation of existing national
3 rules on mobile and stationary sources (U.S. EPA, 2022a) 2-50
4 Figure 2-43. Projected percent change in total S deposition in Class 1 areas from 2016,
5 based on a scenario for 2032 that includes implementation of existing national
6 rules on mobile and stationary sources (U.S. EPA, 2022a) 2-51
7 Figure 3-1. Overview of general approach for review of the secondary N oxides, SOx, and
8 PM standards 3-7
9 Figure 3-2. General approach for assessing the currently available information with regard
10 to consideration of protection provided for deposition-related ecological effects
11 on the public welfare 3-9
12 Figure 4-1. Surface water ANC map, based on data compiled by Sullivan (2017) (ISA,
13 Appendix 8, Figure 8-11) 4-11
14 Figure 4-2. Conceptual model of the influence of atmospheric N deposition on freshwater
15 nutrient enrichment (ISA, Appendix 9, Figure 9-1) 4-18
16 Figure 4-3. Potential effects on the public welfare of ecological effects of N Oxides, SOx
17 and PM 4-33
18 Figure 5-1. Total macroinvertebrate species richness as a function of pH in 36 streams in
19 western Adirondack Mountains of New York, 2003-2005 5-8
20 Figure 5-2. Critical aquatic pH range for fish species 5-9
21 Figure 5-3. Number of fish species per lake versus acidity status, expressed as ANC, for
22 Adirondack lakes 5-11
23 Figure 5-4. Conceptual Model for Aquatic Acidification Analyses 5-13
24 Figure 5-5. Omernik Ecoregion II areas with ecoregion III subdivisions 5-15
25 Figure 5-6. Ecoregion III grouped in three acid sensitivity classes 5-21
26 Figure 5-7. Waterbodies for which annual average S only deposition for 2001-03 exceed
27 CLsfor ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 ueq/I. 5-23
28 Figure 5-8. Waterbodies for which annual average S only deposition for 2006-08 exceed
29 CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 ueq/I. 5-24
30 Figure 5-9. Waterbodies for which annual average S only deposition for 2010-12 exceed
31 CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 [j,eq/L 5-25
32 Figure 5-10. Waterbodies for which annual average S only deposition for 2014-16 exceed
33 CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 ueq/I. 5-26
34 Figure 5-11. Waterbodies for which annual average S only deposition for 2018-20 exceed
35 CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 ueq/I. 5-27
36 Figure 5-12. Locations of aquatic critical loads mapped across Ecoregions III 5-28
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Figure 5-13. 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 5-14. Percentage of waterbodies in each of the 18 eastern ecoregions exceeding their
CL for ANC values of 20, 30 and 50 |ieq/L, based on annual average S
deposition for 2014-2016 5-37
Figure 5-15. Percentage of waterbodies in each of the 18 eastern ecoregions exceeding their
CL for ANC values of 20, 30 and 50 |ieq/L, based on annual average S
deposition for 2018-2020 5-37
Figure 5-16. 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 5-39
Figure 5-17. 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 5-40
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 50 [j,eq/L 5-41
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 for East and 20 [j,eq/L for
the West 5-42
Figure 5-20. Location of the case study areas. Northern Minnesota (NOMN), Rocky
Mountain National Park (ROMO), Shenandoah Valley (SHVA), Sierra Nevada
Mountains (SINE) and White Mountain National Forest (WHMT) 5-43
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 6-1. 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. Locations of co-located CASTNET, NADP/NTN, and IMPROVE monitoring
sites, denoted by CASTNET site identifier 6-6
Figure 6-3. Dry and wet deposition of nitrogen and sulfur (2017-2019 annual average), for
locations listed in Table 6-1 6-6
Figure 6-4. Scatter plot matrix of annual average wet deposition measurements from
NADP/NTN (5 pollutants, units: kg/ha-yr) versus annual average
concentrations from IMPROVE (3 pollutants, units: |ig/m3) for 27 Class 1
areas from 1988-2018. 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 6-9
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Figure 6-5. Scatter plot matrix of annual average wet deposition measurements from
NADP/NTN (5 pollutants, units: kg/ha-yr) versus annual average
concentrations from CASTNET (2 pollutants, units: |ig/m3) for 27 Class 1
areas from 1988-2018. 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 6-10
Figure 6-6. Histograms of the ratios of the gas phase SO2 to particle SO42" (left) and the gas
phase HNO3 to particle NO3" (right) in CASTNET data 6-11
Figure 6-7. Annual average concentration (|ig/m3), deposition (kg/ha-yr), and the
deposition/concentration ratio for oxidized sulfur compounds, as estimated
using a 21-year (1990-2010) CMAQ simulation 6-13
Figure 6-8. Annual average concentration (|ig/m3), deposition (kg/ha-yr), and the
deposition/concentration ratio for nitrogen compounds, as estimated using a 21-
year (1990-2010) CMAQ simulation 6-14
Figure 6-9. Scatter plot matrix of annual average CMAQ-simulated total deposition (4
pollutants, units: kg/ha-yr) versus annual average CMAQ-simulated
concentrations (3 pollutants, units: |ig/m3) for 27 Class 1 areas from 1988-
2018. 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 6-16
Figure 6-10. Scatter plot matrix of annual average TDEP deposition (3 pollutants, units:
kg/ha-yr) versus annual average IMPROVE concentrations (5 pollutants, units:
|ig/m3) for 27 Class 1 areas with collocated IMPROVE and NADP/NTN from
1988-2018. 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 6-18
Figure 6-11. Scatter plot matrix of annual average TDEP deposition (3 pollutants, units:
kg/ha-yr) versus annual average CASTNET concentrations (5 pollutants, units:
|ig/m3) for 27 Class 1 areas with collocated CASTNET and NADP/NTN from
1988-2018. 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 6-19
Figure 6-12. TDEP sulfur deposition (vertical axis) and air concentration (horizontal axis)
for IMPROVE PM2.5 (left), IMPROVE S042" (center) and CASTNET total
sulfur (right) as three-year averages from 2002-2019 6-21
Figure 6-13. TDEP Nitrogen deposition (vertical axis) and air concentration (horizontal
axis) for IMPROVE PM2.5 (left), IMPROVE PM2.5 inorganic nitrogen (center),
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and CASTNET inorganic nitrogen (right) as three-year averages from 2002 -
2019 6-21
Figure 6-14
Figure 6-15
Figure 6-16
Figure 6-17
Figure 6-18
Figure 6-19
Figure 6-20
Figure 6-21
Figure 6-22
Figure 6-23
Figure 6-24
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Scatterplot of estimated 3-year average S deposition (ecoregion median) and
the weighted secondary SO2 design values from contributing upwind areas for
that ecoregion (EAQM) also averaged over 3 years 6-25
Scatterplot of estimated 3-year average S deposition (ecoregion median) and
the secondary SO2 design value over that 3-year period from the contributing
monitor with the maximum value for each ecoregion 6-26
Histogram of the ratio of secondary SO2 design value (ppb) from the
maximum contributing monitor for that ecoregion to the average of weighted
secondary SO2 design values (EAQM) (median = 4) 6-27
Scatterplot of 3-year average S deposition (ecoregion median) and the weighted
annual average SO2 concentrations from contributing upwind areas for that
ecoregion (EAQM) also averaged over 3 years 6-28
Scatterplot of estimated 3-year average S deposition (ecoregion median) and
the annual average SO2 concentration over that 3-year period from the
contributing monitor with the maximum value for each ecoregion 6-29
Histogram of the ratio of annual average SO2 concentration (ppb) averaged
over a 3-year period from the contributing monitor with the maximum value for
each ecoregion to the average of weighted annual average SO2 design values
(EAQM) over the same 3-year period 6-29
Scatterplot of estimated 3-year average N deposition (ecoregion median) and
the weighted secondary NO2 design values from contributing upwind areas for
that ecoregion (EAQM) also averaged over 3 years 6-30
Scatterplot of estimated 3-year average N deposition (ecoregion median) and
the secondary NO2 design value over that 3-year period from the contributing
monitor with the maximum value for each ecoregion 6-31
Histogram of the ratio of annual average NO2 concentration (ppb) averaged
over a 3-year period from the contributing monitor with the maximum value for
each ecoregion to the average of weighted annual average NO2 design values
(EAQM) over the same 3-year period 6-31
Scatterplot of estimated 3-year average S deposition (ecoregion median) and
the weighted annual average PM2.5 design values from contributing upwind
areas for that ecoregion (EAQM) also averaged over 3 years 6-33
Scatterplot of estimated 3-year average S deposition (ecoregion median) and
the average annual PM2.5 design value over that 3-year period from the
contributing monitor with the maximum value for each ecoregion 6-33
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Figure 6-25. Estimated 3-year average N deposition (ecoregion median) and average of
weighted annual average PM2.5 concentrations in 3-year period (EAQM) for
that ecoregion 6-34
Figure 6-26. Estimated 3-year average N deposition (ecoregion median) and annual average
PM2.5 concentration in 3-year period from maximum contributing monitor for
that ecoregion 6-34
Figure 6-27. Histogram of the ratio of average annual average PM2.5 concentration (|ig/m3)
in 3-year period from maximum contributing monitor for that ecoregion to the
average of weighted annual average PM2.5 concentrations (EAQM) in 3-year
period (median = 1.3) 6-35
Figure 6-28. Estimated 3-year average S+N deposition (ecoregion median) and average of
weighted annual average PM2.5 concentrations in 3-year period (EAQM) for
that ecoregion 6-35
Figure 6-29. Estimated 3-year average S+N deposition (ecoregion median) and average
annual average PM2.5 concentration in 3-year period from maximum
contributing monitor for that ecoregion 6-36
Figure 6-30. For ecoregions included in the Aquatic CL Analysis, estimated 3-year average
S deposition (ecoregion median) and weighted annual average SO2
concentrations (EAQM) in 3-year period for that ecoregion (r=0.94) 6-39
Figure 6-31. For ecoregions included in the Aquatic CL Analysis, estimated 3-year average
S deposition (ecoregion median) and average annual average SO2 concentration
in 3-year period from the maximum contributing monitor for the ecoregion
(r 0.69) 6-40
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1 INTRODUCTION
This document, Draft Policy Assessment for the Review of the Secondary National
Ambient Air Quality Standards for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter,
External Review Draft (hereafter referred to as draft PA), presents the draft 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, oxides of sulfur and particulate
matter (SOx and PM).12 In the context of the secondary standards for oxides of nitrogen, oxides
of sulfur and PM, the scope pertains to the protection of the public welfare from adverse effects
related to ecological effects this draft PA 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 Criteria (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, including background information on prior 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,
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), for PM10, 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 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 oxides of nitrogen, 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.
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any quantitative air quality, exposure or risk analyses based on the ISA findings, and related
limitations and uncertainties.3 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.4 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
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 draft 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 this draft PA will inform the evaluation and
conclusions in the final PA.
The PA is designed to assist the Administrator in considering the currently available
scientific and risk information and formulating judgments regarding the standards. The final PA
will inform the Administrator's decision in this review. Beyond informing the Administrator and
3 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).
4 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 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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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 Clean Air Act (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
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."5
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, "[attainability 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]). At the same time, courts
have clarified the EPA may consider "relative proximity to peak background ... concentrations"
as a factor in deciding how to revise the NAAQS in the context of considering standard levels
5 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."
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within the range of reasonable values supported by the air quality criteria and judgments of the
Administrator (,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.6
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 Clean Air Scientific Advisory
Committee (CAS AC) of the EPA's Science Advisory Board.
1.3 BACKGROUND ON CRITERIA AND SECONDARY STANDARDS
FOR NITROGEN AND SULFUR OXIDES AND PARTICULATE
MATTER
Secondary NAAQS were first established for oxides of nitrogen and oxides of sulfur in
1971 (36 FR 8186, April 30, 1971) based on evidence available regarding their effects on
vegetation. The secondary NAAQS for PM were first established in 1971 (36 FR 8186, April
30,1971). Since that time, the EPA has periodically reviewed the air quality criteria and
standards, with the most recent review being completed in 2012. The details of these reviews are
described in the subsections below.
1.3.1 Nitrogen Oxides
The EPA first promulgated identical primary and secondary NAAQS for nitrogen dioxide
(NO2) in April 1971 after reviewing the relevant science on the public health and welfare effects
associated with oxides of nitrogen in the 1971 Air Quality Criteria Document (AQCD). These
standards were set at a level of 0.053 parts per million (ppm) as an annual average (36 FR 8186,
April 30, 1971). In 1982, the EPA published Air Quality Criteria for Oxides of Nitrogen (U.S.
EPA, 1982), which updated the scientific criteria upon which the initial standards were based. In
6 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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February 1984, the EPA proposed to retain these standards (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).
The EPA began a second review of the oxides of nitrogen secondary standards in 1987.
In November 1991 the EPA released an updated AQCD for CAS AC and public review and
comment (56 FR 59285, November 25, 1991), which provided a comprehensive assessment of
the available scientific and technical information on health and welfare effects associated with
NO2 and other oxides of nitrogen. 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 "provides a
scientifically balanced and defensible summary of current knowledge of the effects of this
pollutant and provides an adequate basis for the EPA to make a decision as to the appropriate
NAAQS for NO2" (Wolff, 1993). The Air Quality Criteria for Oxides of Nitrogen was then
finalized (U.S. EPA, 1993). The EPA's Office of Air Quality Planning and Standards (OAQPS)
also prepared a Staff Paper that summarized and integrated the key studies and scientific
evidence contained in the revised AQCD for oxides of nitrogen and identified the critical
elements to be considered in the review of the NO2 NAAQS. CASAC reviewed two drafts of the
Staff Paper and concluded in a closure letter to the Administrator that the document provided a
"scientifically adequate basis for regulatory decisions on nitrogen dioxide" (Wolff, 1995).
In October 1995 the Administrator announced her proposed decision not to revise the
secondary NAAQS for NO2 (60 FR 52874; October 11, 1995). A year later, the Administrator
made a final determination not to revise the NAAQS for NO2 after careful evaluation of the
comments received on the proposal (61 FR 52852; October 8, 1996). The secondary NAAQS for
NO2 remains 0.053 ppm (100 micrograms per cubic meter [[j,g/m3] of air), annual arithmetic
average, calculated as the arithmetic mean of the 1-hour NO2 concentrations.
1.3.2 Sulfur Oxides
The EPA first promulgated secondary NAAQS for sulfur dioxide (SO2) in April 1971 (36
FR 8186, April 30, 1971). The 1971 secondary standards for SO2 were established solely on the
basis of evidence of adverse effects on vegetation available in the 1969 AQCD (U.S. DHEW,
1969a [1969 AQCD]). The secondary standards included a standard set at 0.02 ppm, annual
arithmetic mean, and a 3- hour average standard set at 0.5 ppm, not to be exceeded more than
once per year. In 1973, revisions made to Chapter 5 ("Effects of Sulfur Oxide in the Atmosphere
on Vegetation") of Air Quality Criteria for Sulfur Oxides (U.S. EPA, 1973) indicated that it
could not properly be concluded that the vegetation injury reported resulted from the average
SO2 exposure over the growing season, rather than from short-term peak concentrations.
Therefore, the EPA proposed (38 FR 11355, May 7, 1973) and then finalized (38 FR 25678,
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September 14, 1973) a revocation of the annual mean secondary standard. At that time, the EPA
was aware that then-current concentrations of oxides of sulfur in the ambient air had other public
welfare effects, including effects on materials, visibility, soils, and water. However, the available
data were considered insufficient to establish a quantitative relationship between specific
ambient concentrations of oxides of sulfur and such public welfare effects (38 FR 25679,
September 14, 1973).
In 1979, the EPA announced that it was revising the AQCD for oxides of sulfur
concurrently with that for PM and would produce a combined PM and oxides of sulfur criteria
document. Following its review of a draft revised criteria document in August 1980, CASAC
concluded that acid deposition was a topic of extreme scientific complexity because of the
difficulty in establishing firm quantitative relationships among (1) emissions of relevant
pollutants (e.g., SO2 and oxides of nitrogen), (2) formation of acidic wet and dry deposition
products, and (3) effects on terrestrial and aquatic ecosystems. CASAC also noted that acid
deposition involves, at a minimum, several different criteria pollutants: oxides of sulfur, oxides
of nitrogen, and the fine particulate fraction of suspended particles. CASAC 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.
For these reasons, CASAC recommended that 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. CASAC also suggested that a discussion of acid
deposition be included in the AQCDs for oxides of nitrogen and PM and oxides of sulfur.
Following CASAC closure on the AQCD for oxides of sulfur in December 1981, the EPA's
OAQPS published a Staff Paper in November 1982 (U.S. EPA, 1982), although the paper did not
directly assess the issue of acid deposition. Instead, the EPA subsequently prepared 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). These documents, though they were not considered criteria
documents and did not undergo CASAC review, represented the most comprehensive summary
of scientific information relevant to acid deposition completed by the EPA at that point.
In April 1988 (53 FR 14926, April 26, 1988), the EPA proposed not to revise the existing
secondary standards for SO2. This proposed decision with regard to the secondary SO2 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
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been decreased through ongoing research efforts, the EPA would draft and support an
appropriate set of control measures.
1.3.3 Particulate Matter
The EPA first established NAAQS for PM in 1971 (36 FR 8186, April 30, 1971), based
on the original AQCD (U.S. DHEW, 1969b) and recognition of effects on vegetation and to
match the primary standards that were set concurrently to protect human health.7 The secondary
standards were set at 150 |ig/m3, 24-hour average, from total suspended particles (TSP), not to
be exceeded more than once per year, and 60 |ig/m3, annual geometric mean.
In October 1979 (44 FR 56730, October 2, 1979), the EPA announced the first periodic
review of the air quality criteria and NAAQS for PM. Revised secondary standards were
promulgated in 1987 (52 FR 24634, July 1, 1987). In the 1987 decision, the EPA changed the
indicator for PM from TSP to PM10, and the level of the 24-hour secondary standard was set at
150 |ig/m3, and the form was one expected exceedance per year, on average over three years.
The level of the annual secondary standard was set at 50 |ig/m3, and the form was annual
arithmetic mean, averaged over three years.
In April 1994, the EPA announced its plans for the second periodic review of the air
quality criteria and NAAQS for PM, and in 1997 the EPA promulgated revisions to the NAAQS
(62 FR 38652, July 18, 1997). In the 1997 decision, the EPA determined that the fine and coarse
fractions of PM10 should be considered separately. 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 EPA revised the secondary standards by
setting them equal in all respects to the primary standards 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;8 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. Also, the EPA established a new reference
method for the measurement of PM2.5 in the ambient air and adopted rules for determining
7 Prior to the review initiated in 2007 (see below), the AQCD provided the scientific foundation (i.e., the air quality
criteria) for the NAAQS. Beginning in that review, the Integrated Science Assessment (ISA) has replaced the
AQCD.
8 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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attainment of the new standards. To continue to address the health effects of the coarse fraction
of PM10 (referred to as thoracic coarse particles or PM10-2.5; generally including particles with
a nominal mean aerodynamic diameter greater than 2.5 |im and less than or equal to 10 |im), the
EPA retained the primary annual PM10 standard and revised the form of the primary 24-hour
PM10 standard to be based on the 99th percentile of 24-hour PM10 concentrations at each
monitor in an area.
Following promulgation of the 1997 PM NAAQS, petitions for review were filed by
several parties, addressing a broad range of issues. In May 1999, the U.S. Court of Appeals for
the District of Columbia Circuit (D.C. Circuit) upheld the EPA's decision to establish fine
particle standards, holding that "the growing empirical evidence demonstrating a relationship
between fine particle pollution and adverse health effects amply justifies establishment of new
fine particle standards." American Trucking Associations, 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 PM10 standards, concluding that the EPA had not
provided a reasonable explanation justifying use of PM10 as an indicator for coarse particles.
American Trucking Associations v. EPA, 175 F. 3d at 1054-55. Pursuant to the D.C. Circuit's
decision, the EPA removed the vacated 1997 PM10 standards, and the pre-existing 1987 PM10
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 Associations 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. Regarding the cost issue, the court reaffirmed prior rulings
holding that in setting NAAQS the EPA is "not permitted to consider the cost of implementing
those standards." See generally, Whitman v. American Trucking Ass'ns, 531 U.S. 457, 465-472,
475-76 (2001). Likewise, "[attainability 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], cert, denied, 455 U.S. 1034
[1982]; accord Murray Energy Corp. v. EPA, 936 F.3d 597, 623-24 [D.C. Cir. 2019]). At the
same time, courts have clarified the EPA may consider "relative proximity to peak background
... concentrations" as a factor 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 (American Trucking Ass'ns, v. EPA, 283 F.3d 355,
379 [D.C. Cir. 2002], hereafter referred to as "ATA III").
In October 1997, the EPA published its plans for the third periodic review of the air
quality criteria and NAAQS for PM (62 FR 55201, October 23, 1997). After the CASAC and
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public review of several drafts, the EPA's National Center for Environmental Assessment
finalized the AQCD in October 2004 (U.S. EPA, 2004a and 2004b). The EPA's Office of Air
Quality Planning and Standards (OAQPS) finalized a Risk Assessment and Staff Paper in
December 2005 (Abt Associates, 2005, U.S. EPA, 2005).9 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 primary and secondary NAAQS for PM to provide
increased protection of public health and welfare, respectively (71 FR 61144, October 17, 2006).
With regard to the primary and secondary 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 primary and secondary standards for
PM10, the EPA retained the 24-hour standards, with levels at 150 |ig/m3, and revoked the annual
standards. 10 The Administrator judged that the available evidence generally did not suggest a
link between long-term exposure to existing ambient levels of coarse particles and health or
welfare effects. In addition, a new reference method was added for the measurement of
PM10-2.5 in the ambient air in order to provide a basis for approving federal equivalent methods
(FEMs) and to promote the gathering of scientific data to support future reviews of the PM
NAAQS.
Several parties filed petitions for review following promulgation of the revised PM
NAAQS in 2006. One of these petitions addressed 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 protection from visibility impairment. Id. at 528-32. The
9 Prior to the review initiated in 2007, the Staff Paper presented the EPA staff's 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 In the 2006 proposal, the EPA proposed to revise the 24-hour PMio standard in part by establishing a new PM10-2.5
indicator for thoracic coarse particles (i.e., particles generally between 2.5 and 10 (im in diameter). The EPA
proposed to include any ambient mix of PM10-2.5 that was dominated by resuspended dust from high density
traffic on paved roads and by PM from industrial sources and construction sources. The EPA proposed to exclude
any ambient mix of PM10-2.5 that was dominated by rural windblown dust and soils and by PM generated from
agricultural and mining sources. In the final decision, the existing PM10 standard was retained, in part due to an
"inability... to effectively and precisely identify which ambient mixes are included in the [PM10-2.5] indicator and
which are not" (71 FR 61197, October 17, 2006).
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EPA responded to the court's remands as part of the next 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 by issuing a call for information in the Federal Register (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), Risk and Exposure Assessment (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 only
the primary NAAQS for PM to provide increased protection of public health (78 FR 3086,
January 15, 2013). The PM secondary standards were established to provide protection against a
variety of PM-associated welfare effects, including effects on vegetation as well as visibility
impairment and materials damage (e.g., soiling, corrosion). The EPA generally retained the 24-
hour and annual PM2.5 standards, set at 35 |ig/m and 15 |ig/m and the 24-hour PM10 standard,
set at a level of 150 |ig/m3, to address visibility and non-visibility welfare effects.
1.3.4 Last Review of the Criteria and Secondary Standards for Nitrogen and Sulfur
Oxides
The EPA initiated the prior review in December 2005, with a call for information (70 FR
73236) for the development of a revised ISA. An Integrated Review Plan (IRP) was developed to
provide the framework and schedule as well as the scope of the review and to identify policy-
relevant questions to be addressed in the components of the review. The IRP was released in
2007 (U.S. EPA, 2007) for CAS AC and public review. The EPA held a workshop in July 2007
on the ISA to obtain broad input from the relevant scientific communities. This workshop helped
to inform the preparation of the first draft ISA, which was released for CASAC and public
review in December 2007; a CASAC meeting was held on April 2-3, 2008, to review the first
draft ISA. A second draft ISA was released for CASAC and public review in August 2008 and
was discussed at a CASAC meeting held on October 1-2, 2008. The final ISA (U.S. EPA,
2008a) was released in December 2008.
Based on the science presented in the ISA, the EPA developed the REA to further assess
the national impact of the effects documented in the ISA. The Draft Scope and Methods Plan for
Risk/ Exposure Assessment: Secondary NAAQS Review for Oxides of Nitrogen and Oxides of
Sulfur outlining the scope and design of the future REA was prepared for CASAC consultation
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and public review in March 2008. A first draft REA was presented to CAS AC and the public for
review in August 2008, and a second draft was presented for review in June 2009. The final REA
(U.S. EPA, 2009a) was released in September 2009.
A first draft PA was released in March 2010, and reviewed by CAS AC on April 1-2,
2010. In a June 22, 2010, letter to the Administrator, CAS AC provided advice and
recommendations to the Agency concerning the first draft PA (Russell and Samet, 2010a). A
second draft PA was released to CAS AC and the public in September 2010, and reviewed by
CAS AC on October 6-7, 2010. The CAS AC provided advice and recommendations to the
Agency regarding the second draft PA in a December 9, 2010 letter (Russell and Samet 2010b).
The CASAC and public comments on the second draft PA were considered by the EPA staff in
developing a final PA (U.S. EPA, 2011). CASAC requested an additional meeting to provide
additional advice to the Administrator based on the final PA on February 15-16, 2011. On
January 14, 2011, the EPA released a version of the final PA prior to final document production,
to provide sufficient time for CASAC review of the document in advance of this meeting. The
final PA, incorporating final reference checks and document formatting, was released in
February 2011. In a May 17, 2011, letter (Russell and Samet, 2011), CASAC offered additional
advice and recommendations to the Administrator with regard to the review of the secondary
NAAQS for oxides of nitrogen and oxides of sulfur.
On August 1, 2011, the EPA published a proposed decision to retain the existing annual
average NO2 and 3-hour average SO2 secondary standards, recognizing the protection they
provided from direct effects on vegetation (76 FR 46084, August 1, 2011). In the proposal, the
Administrator further concluded that the existing NO2 and SO2 secondary standards were not
adequate to protect against the adverse impacts of acidification of both aquatic and terrestrial
ecosystems or nutrient enrichment of terrestrial ecosystems, and proposed to revise the
secondary standards by adding secondary standards identical to the NO2 and SO2 primary 1-hour
standards set in 2010, noting that these new standards11 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 Administrator's final decision retained the
existing standards to address the direct effects on vegetation of exposure to gaseous oxides of
nitrogen and sulfur and did not set additional standards at that time to address effects associated
with deposition of oxides of nitrogen and sulfur on sensitive aquatic and terrestrial ecosystems
(77 FR 20218, April 3, 2012). The limitations and uncertainties in the available information were
11 The 2010 primary 1-hour standards include the NO2 standard set at a level of 100 parts per billion (ppb) and the
S02 standard set at a level of 75 ppb.
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judged to be too great to support establishment of a new standard that could be concluded to
provide the requisite protection for such effects under the Act. The Administrator concluded that
while the current secondary standards were not adequate to provide protection against potentially
adverse deposition-related effects associated with oxides of nitrogen and sulfur, it was not
appropriate under Section 109 to set any new secondary standards for such effects at that time.
The Administrator also determined that setting new secondary standards identical to the
existing 1-hour NO2 and SO2 primary standards would be neither necessary nor appropriate as, in
her judgment, such standards could not reasonably be judged to provide requisite protection of
public welfare. In addition, the Administrator decided that it was appropriate to retain the
existing NO2 and SO2 secondary standards to address direct effects of gaseous NO2 and SO2 on
vegetation. Thus, taken together, the Administrator decided to retain and not revise the current
NO2 and SO2 secondary standards: aNC>2 standard set at a level of 0.053 ppm, as an annual
arithmetic average, and a SO2 standard set at a level of 0.5 ppm, as a 3-hour average, not to be
exceeded more than once per year (77 FR 20281, April 3, 2012).
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 . . . ' "12 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 acidification13.
1.4 CURRENT REVIEW
In August 2013, the EPA's National Center for Environmental Assessment (NCEA)
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 (78 FR 53452,
August 29, 2013). Two types of information were called for: information regarding significant
new research studies to be considered for the ISA for the review, and policy-relevant issues for
consideration in this NAAQS review. Based in part on the information received in response to
12 Center for Biological Diversity, et al. v. EPA, 749 F.3d 1079, 1087 (2014).
13 Id. at 1088.
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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). In
developing the final IRP, the EPA expanded the review to include the ecological effects of PM.
Comments from the CASAC (Diez Roux and Fernandez, 2016) and the public on the draft IRP
were considered in preparing the final IRP (U.S. EPA, 2017).
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, which was then discussed at a CASAC meeting May 24-25, 2017. Comments from the
CASAC (Diez Roux, 2017) and the public were considered in preparing the second external
review draft (June 2018), which was then discussed at a CASAC meeting September 5-6, 2018
and April 27, 2020. The CASAC provided a final letter on the second draft ISA in May 2020
(Cox, 2020), and in October 2020, the EPA released the final ISA for N oxides, SOx, and PM
ecological criteria (U.S. EPA, 2020). In August 2018, the EPA published the Review of the
Secondary Standards for Ecological Effects of Oxides of Nitrogen, Oxides of Sulfur, and
Particulate Matter: Risk and Exposure Assessment Planning Document (U. S. EPA 2018) which
was available for public comment (83 FR 42497, August 22, 2018).
This draft PA will be reviewed by the CASAC and available for public comment, which
will inform completion of this document and development of the Administrator's proposed
decision in this review. The current timeline projects completion of the final PA in December
2023. 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 February 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 the results of quantitative
assessments based on that information presented and assessed in this document. Taken together,
this information informs staff conclusions and the identification of policy options for
consideration in addressing public and 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 and REA 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.
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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 reviews the basis for the existing NO2 and SO2 standards and outlines a general
approach for this review, including the additional PM secondary standard included in this
review.
In Chapter 4, we address questions related to linking ecological effects to measures that
can be used to characterize the extent to which such effects are reasonably considered to be
adverse to public welfare. This involves consideration of how to characterize adversity from a
public welfare perspective. In so doing, consideration is given to the concept of ecosystem
services, the evidence of effects on ecosystem services, and how ecosystem services can be
linked to ecological indicators.
Chapter 5 presents the exposure conditions associated with effects and the available
evidence providing quantitative information linking N oxides, SOx, and PM to deposition related
effects that can inform judgements on the likelihood of occurrence of such effects in air quality
conditions that meet the current standard. Quantitative analyses in this chapter help to identify
what effects for which the evidence is most established and robust for in regard to exposure-
response relationships between deposition and ecosystem effects.
Chapter 6 describes the relationships between the deposition S and N compounds and air
quality metrics for SOx, N oxides and PM, and other metrics with potential for effective
deposition-related standards. The analyses in this Chapter are intended to characterize the
relationships between ambient air concentrations and deposition particularly in rural areas, which
are of most concern for this review.
Chapter 7 presents an assessment of the adequacy of the current NO2 and SO2 secondary
standards. Consideration is given both to the adequacy of protection afforded by the current
standards for both direct and deposition-related effects, as well as to the appropriateness of the
fundamental structure and the basic elements of the current standards for providing protection
from deposition-related effects. In so doing, we address questions related to considering the
extent to which deposition-related effects that could reasonably be judged to be adverse to public
welfare are occurring under current conditions which are allowed by the current standards. We
also consider the ways in which the structures and basic elements of the current NO2 and SO2
secondary standards are inadequate to protect against such effects.
This document also includes several appendices providing additional information to
support the document. Appendix 5A provides an analysis conducted to compare aquatic
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1 acidification to terrestrial acidification. Appendix 5B includes additional details related to
2 terrestrial ecosystem studies. This encompasses discussion of additional studies of tree growth
3 and survival and species richness of herb and shrub communities. Appendix 6A details the
4 derivation of the ecoregion air quality metrics (EAQM) for each Ecoregion/pollutant pair using
5 historical air quality design value (DV) data. It also describes the methodology used to calculate
6 the air parcel trajectories that led to the zones of influence identification, as well as the
7 methodologies used to estimate the EAQM values.
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Available at: http://www.epa.gov/ttn/naaqs/standards/no2so2sec/cr pa.html
U.S. EPA. (2017). Integrated Review Plan for the Secondary NAAQS for Oxides of Nitrogen
and Oxides of Sulfur and Particulate Matter - Final. U.S. EPA. EPA-452/R-17-002.
January 2017. Available at: https://www.epa.gov/sites/default/files/2018-
08/documents/final integrated review plan for nox sox pm eco - 011817-final.pdf
U.S. EPA. (2018). REA Planning Document for the Secondary NAAQS for Oxides of Nitrogen
and Oxides of Sulfur and Particulate Matter. U.S. EPA. EPA-452/D-18-001. August
2018. Available at: https://www.epa.gov/sites/default/files/2Q18-
08/documents/rea plan final-080618.pdf
U.S. EPA. (2020). Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides of
Sulfur and Particulate Matter Ecological Criteria (Final Report, 2020). U.S.
Environmental Protection Agency, Washington, DC, EPA/600/R-20/278, 2020.
Wolff, G. T. 1993. CASAC closure letter for the 1993 Criteria Document for Oxides of Nitrogen
addressed to U.S. EPA Administrator Carol M. Browner dated September 30, 1993.
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1 Wolff, G. T. 1995. CASAC closure letter for the 1995 OAQPS Staff Paper addressed to U.S.
2 EPA Administrator Carol M. Browner dated August 22, 1995.
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2 AIR QUALITY AND DEPOSITION
This chapter begins with an overview of the atmospheric processes that are relevant for
the review of the welfare-based secondary NAAQS for oxides of nitrogen, oxides of sulfur,
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, S, 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 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, and how they can be
interconnected. Each of these three categories of species are discussed more fully below.
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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.
2.1.1 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 are defined here to include sulfur monoxide (SO),
sulfur dioxide (SO2), sulfur trioxide (SO3), disulfur monoxide (S2O), and sulfate (in particulate
form as S042")- As discussed in more detail in section 2.2, SOx is mostly emitted from
combustion processes in the form of SO2. SO2 is present at higher concentrations in the ambient
air than the other gaseous sulfur species and as a result the NAAQS uses SO2 as the indicator for
the larger group of SOx. Dry deposition is an important removal process for SO2. Although
particulate sulfate can dry deposit, it is more efficiently removed by precipitation (wet
deposition).
Once emitted to the atmosphere SO2 can react in both the gas phase and in aqueous
solutions such as clouds and particles to for SO42" (McMurry, 2004). There are multiple
pathways for this process to occur. In the daytime, atmospheric oxidation converts gas phase SO2
to sulfuric acid (H2SO4), which quickly and nearly completely condenses on existing particles or
forms new sulfate particles (generically referred to as SO42") The SO2 to sulfate conversion
typically occurs at rates of 0.1 to 5% per hour, with higher rates associated with higher
temperatures, sunlight, and the presence of oxidants. Another important pathway is aqueous
phase oxidation of SO2 in cloud droplets which can yield veiy fast rates of sulfate production.
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The conversion rates are determined by the availability of oxidants. Further reactions with
ammonia form ammonium sulfate (NH4)2S04. Sulfate particles contribute to PM2.5
concentrations. The atmospheric lifetime of sulfate particles is relatively long, ranging from 2 to
10 days. As such, sulfate concentrations tend to be regionally homogeneous (see section 2.4.2).
Dry deposition is an important removal process for SO2. Although particulate sulfate can dry
deposit, it is more efficiently removed by precipitation (wet deposition).
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 (NO), nitrogen dioxide (NO2) and all
other oxidized nitrogen-containing compounds formed from NO and NO2. The NAAQS
currently uses NO2 as the indicator for the larger group of oxides of nitrogen.
There are two main pathways of nitrate formation via oxidation of NO or NO2, one which
occurs during the day through reaction with the hydroxyl radical to produce HNO3 and the other
at night via reactions with other oxidants and water. Under the right thermodynamic conditions,
some of these compounds can move from the gas phase into the solid or liquid phases as
particulate nitrate (generically referred to as NO3") and contribute to PM2.5 concentrations. 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
Distinct from oxidized nitrogen, reduced nitrogen species can contribute to PM2.5
formation and lead to adverse deposition-related effects. Ammonia (NH3) 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
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temperatures and high relative humidity). Ammonia reacts with gas phase nitric acid (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 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 simultaneously impacted by
both chemical transformations and atmospheric transport processes 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, the
chemical lifetime of a pollutant is also a major factor 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 plant stomata), 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). Additionally, landscape characteristics
influence deposition processes.
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 originate from a combination of manmade and natural sources. Anthropogenic sources
of air pollutants that result in adverse deposition-related effects (i.e., 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.
1 https://www.epa.gov/air-emissions-inventories/national-emissions-inventorv-nei
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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 contain assumptions that may influence the
estimates of their magnitude 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.
2.2.1 NOx Emissions Estimates and Trends
Figure 2-2 shows the relative contributions of various sources to total U.S. NOx
emissions in 2020, based on estimates contained in the EPA NEI (2023). 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 plants and soil (biogenic) which represent 12% of the total NOx
emissions. In sum, fires (i.e., wild, prescribed, and agricultural) are estimated to represent 5% of
the overall emissions of NOx.
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
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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%
1
2 Figure 2-2. 2020 NOx emissions estimates by source sector (U.S. EPA NEI, 2023).
Nitrogen Oxides Emissions Density in tons/year/miA2 (# Counties)
3 ~ 0-1.9(1,356) ~ 2-4.9(1,130) ~ 5-9.9(412) ¦ 10-19.9(196) ¦ 20-648(127)
4 Figure 2-3. 2020 NOx emissions density across the U.S. (U.S. EPA NEI, 2023).
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Total NOx emissions have trended strongly downward across the U.S. between 2002 and
2022. 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.
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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. Of 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.
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.
S02 Emissions (1,845 kTon/year)
— Other 2%
Mobile Sources 1 %
Figure 2-5. 2020 SO2 emissions estimates by source sector (U.S. EPA NEI, 2023).
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~ 0-0.29(2,389) ~ 0.3-0.99(429) O 1-2.99(222) ~ 3-9.99(111) ¦ 10-329(70)
2 Figure 2-6. 2020 SO2 emissions density across the U.S. (U.S. EPA NEI, 2023).
3 Similar to NOx, and for many of the same reasons, SO2 emissions have declined
4 significantly since 2002. Figure 2-7 illustrates the emissions changes over the 2002-2022 period.
5 The data shows an 87% decrease in total SO2 emissions over the period, including reductions of
6 91% in emissions from EGUs and 96% in emissions from mobile sources.
7
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Inventory Year
Figure 2-7. Trends in SO2 emissions by sector between 2002 and 2022.
2.2.3 NH3 Emissions Estimates and Trends
Nlli is directly emitted, differing from other atmospheric N species (e.g., organic N,
NO2) that are formed through photochemical reactions. Figure 2-8 shows the percentage
contribution of specific source categories to the total anthropogenic (plus wildfires) NFh. In
2020, livestock waste (49%), fertilizer application (33%) and aggregate fires (11%) contributed
most significantly to total annual emissions (5.5 million tons NH3). While mobile source
contributions to total NFI3 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 a!.,
2017; Chen et al., 2022). Any underestimation in mobile source NH3 emissions would mostly
impact urban areas, where there is a lot of on-road mobile source traffic. Figure 2-9 shows the
NH3 emissions density in tons/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.
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Ammonia Emissions Density in tons/year/miA2 (# Counties)
^ ~ 0-1.99(1.905) ~ 2-4.99(1,076) H 5-9.99(187) ¦ 10-19.99(41) ¦ 20-71(12)
4 Figure 2-9. NH3 Emissions density across the U.S. (U.S. EPA NEI, 2023).
NH3 Emissions (5,485 kTon/year)
Livestock Waste 49%
Agricultural & Prescribed
Fires 5%
1
2 Figure 2-8. 2020 NBb emissions by source sector (U.S. EPA NEI, 2023).
Other 1 %
Stationary Fuel
Combustion 2%
Mobile Sources 2%
Waste Disposal 2%
Fertilizer Application 33%
Wildfires 6%
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Figure 2-10 shows NH3 emission trends from 2002-2022. In comparison with NOx and
SOx emission trends, which demonstrated dramatic decreases over the past few decades, the
annual rate of NH3 emissions remained relatively flat with even a noted upward trend in recent
years. However, there is greater uncertainty in NH3 emissions trends (ISA, Appendix 2, section
2.2.3). This is partly due to a lack of control programs nationally for agricultural sources of NH3.
It is worth noting that variabilities associated with local management practices related to animal
husbandry makes these emissions a bit more uncertain than emissions, for example, derived from
a mobile source model or direct measurements from EGU sources. The EPA has built improved
models for both livestock waste emissions and fertilizer application process to inform the 2020
NEI which is expected to have reduced these uncertainties. The reader is referred to our 2020
NEI Technical Support Document (TSD) (U.S. EPA, 2023).
Inventory Year
Figure 2-10. Trends in NFb emissions by sector between 2002-2022.
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2.3 MONITORING AMBIENT AIR CONCENTRATIONS AND
DEPOSITION OF N, S, AND PM
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. FRMs have been established and national
monitoring networks put in place 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. The
largest routinely operating network measuring 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 SLAMS3. 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 co-located measurements of SO2, NO,
NOy, and PM components including ammonium, nitrate, and sulfate, although with sparser
coverage than the FRM networks for SO2 or NO2. Because NOy is measured rather than NOx,
and because of collocated SO2 and SO42" measurements, ambient air concentrations of both NOy
and SOx can be determined from NCore data, so that these data 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 having a
requirement to measure NO2. The Near-road network is intended to capture short-term peak NO2
concentrations for comparison to the NAAQS. Many of the Near-Road sites are also required to
have collocation with PM2.5 and carbon monoxide (CO). 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, while many of the
areas where adverse deposition effects are of greatest concern tend to be in more rural areas.
2.3.1 NOx Monitoring Networks
There were 491 monitoring sites 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. This network relies on a chemiluminescent FRM and on multiple FEMs that use either
3 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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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 (U.S. EPA, 2020, p. 2-34)
the traditional chemiluminescence FRM is subject to potential measurement biases resulting
from interference by N oxides other than NO or NO2.4 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 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 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.
4 The N oxides other than NO and NO2 are often collectively abbreviated as NOz (i.e., NOy = NOx +NOz).
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® 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. Over 75% of the SO2 sites are part of the SLAMS network.
Measurements are made using ultraviolet fluorescence (UVF) 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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©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 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. 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 6lh day.
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©SLAMS (801) • NCORE (70) © NEAR ROAD (59) • SPM/OTHER (137)
Figure 2-13. PM2.5 mass monitors operating during the 2019-2021 period.
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 (3-day frequency) and IMPROVE (6-day frequency) sites reporting speciated PM2.5
data to the EPA during the 2019-2021 period are shown in Figure 2-14.
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» CSN (107) • IMPROVE (151) O NCORE (49) • OTHER (9)
Figure 2-14. PM2.5 speciation monitors operating during the 2019-2021 period.
2.3.4 Other Monitoring Networks Relevant to N, S, and PM Deposition
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
(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 2-15) 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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Canada
Mexico
Puertl
Figure 2-15. Location of NTN monitoring sites with sites active shown in blue and inactive
sites in white.
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In contrast, direct measurements of dry deposition flux are rare and difficult, and dry
deposi tion fluxes of gases and particles are estimated from concentration m easurements by an
inferential technique described in the 2008 ISA (U.S. EPA, 2008). Ambient air concentrations
are measured in the Clean Air Status and Trends Network (CASTNET), which was established
under the 1991 Clean Air Act Amendments to assess trends in acidic deposition. CASTNET is a
long-term environmental monitoring network with approximately 100 sites (see Figure 2-16 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 2-16. Location of CASTNET monitoring sites and the organizations responsible
for collecting data. (NPS = National Park Service, BLM = Bureau of Land
Management)
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).
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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
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 still be very useful if used in combination
with modeled data (Schwede et al., 2011) as discussed further in Section 2.5.
There are differences in the measurement techniques that require careful consideration
when used for analysis. IMPROVE and CSN 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 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, 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 co-located and compared to reference techniques, the
correlation between these measurement techniques depends on meteorological conditions. Due to
large measurement artifacts, IMPROVE no longer reports ammonium (NH4+), and CASTNET
reports the sum of nitric acid and particle nitrate (total NO3) as a more certain measurement.
The NADP also maintains the Ammonia Monitoring Network (AMoN) which is designed
to capture long-term trends in ambient air NH3 concentrations and deposition. There are
currently 106 AMoN sites covering 34 states (see Figure 2-17). The AMoN uses passive filter-
based samplers which are deployed for two-week periods. Both gaseous ammonia and particle
ammonium concentrations are measured.
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2 Figure 2-17. Location of AMoN monitoring sites with sites active shown in blue and
3 inactive sites in white. (There is an additional site in AK not shown here.)
ALBERTA
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2.4 RECENT AMBIENT AIR CONCENTRATIONS AND TRENDS
2.4.1 NO2 Concentrations and Trends
There are currently two forms of the primary NO2 NAAQS. One is based on the 98th
percentile of the 1-hour daily maximum concentrations averaged over 3 years and the level is set
at 100 ppb. The other is based on the annual mean and the level of the standard is set at 53 ppb.
The secondary NO2 NAAQS is also based on the annual mean with the same level of 53 ppb. As
shown in Figures 2-18 and 2-19, there are no locations with NO2 design values in violation of
these standards. The highest NO2 concentrations mostly occur in urban areas across the western
U.S. (e.g., Los Angeles, Phoenix, Las Vegas, Denver). The maximum 1-hour design value during
the 2019-2021 period was 80 ppb, while the annual design value for 2021 was 30 ppb. Both
maximum design values occurred at near-road sites in the Los Angeles metropolitan area. For the
2019-2021 period, the mean average hourly NO2 value, across valid monitoring sites, was 16.3
ppb.
NO2 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 2-20 and 2-21 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 forms of the NO2 standard. At the beginning of the trends record, it was not
uncommon for locations to exceed the NO2 NAAQS, especially the standard with the shorter
averaging time. However, the last violations of the NO2 standards occurred in 1991 (annual) and
2008 (hourly). 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 Section 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. 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 2-21). See
Table 2-1 for a summary of these older NO2 annual means.
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• 3 - 25 ppb (67 sites) © 26-50 ppb (222 sites) © 51 - 75 ppb (41 sites) © 76 - 100 ppb (1 sites)
Figure 2-18. Primary NO2 design values (98th percentile of daily maximum 1-hourly
concentrations, averaged over 3 years; ppb) at monitoring sites with valid
design values for the 2019-2021 period.
^ • 1 -10 ppb (297 sites) G 11-20 ppb (99 sites) © 21-30 ppb (8 sites)
6 Figure 2-19. Primary and secondary NO2 design values (single year annual mean; ppb)
7 for 2021.
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Z 180
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Number of N02 Sites
N02 NAAQS Level
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Figure 2-20. Distributions of annual 98th percentile, maximum 1-hour NOi design values
(ppb) at U.S. sites across the 1980-2021 period. The red line shows the number
of sites included in each boxplot per year.
70
65 -
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Number of N02 Sites
N02 NAAQS Level
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Figure 2-21. Distributions of annual mean NO2 design values (ppb) at U.S. sites across the
1980-2021 period. The red line shows the number of sites included in each
boxplot per year.
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Table 2-1. Average annual mean NO2 concentrations in select cities for the 1967-1971
period.
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
2.4.2 SO2 Concentrations and Trends
The primary SO2 standard is based on the 99th percentile of daily maximum 1-hour
concentrations, averaged over 3 years, and is currently set at a level of 75 ppb. The secondary
SO2 standard uses an averaging time of 3 hours with a level of 0.5 ppm (500 ppb) and the form
of the standard is that the level is not to be exceeded more than once per year. As shown in
Figure 2-22, 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 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 2-23 displays the second
highest 3-hourly SO2 values across the U.S. in 2021. All sites with valid secondary SO2 design
values were less than the 500 ppb level and the vast majority of sites had concentrations that
were less than 20 ppb. Like NO2, SO2 concentrations have been declining across the U.S. for
decades, primarily in response to emissions reductions at stationary fuel combustion sources.
Figure 2-24 shows the downward trend in design values for the primary SO2 NAAQS over the
past 40 years. 1994 was the last year in which the median site had a design value greater than the
current primary 1-hour standard of 75 ppb. Since then, the entire distribution of values has
continued to decline such that the median values 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. Finally,
Figure 2-25 shows the sharp downward trend in annual SO2 concentrations across the U.S.
Again, the highest values in the distribution in recent years are from the sites near industrial
sources.
May 2023
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2 Figure 2-22. Primary SO2 standard design values (99th percentile of 1-hour daily
3 maximum concentrations, averaged over 3 years; ppb) for the 2019-2021
4 period at monitoring sites with valid design values.
© 21 - 50 ppb (57 sites) © 101 - 200 ppb (3 sites)
6 Figure 2-23. Secondary SO2 standard design values (2nd highest 3-hourly average; ppb)
7 for the year 2021 at monitoring sites with valid design values.
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- 700
- 600
500
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300
-200
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2.4.3 PM2.5 Concentrations and Trends
There are three relevant standards for PM2.5. There are two standards based on annual
means, averaged over 3 years, with levels at 12.0 jig/'m3 (primary standard) and 15.0 ug/'m 3
(secondary standard). There is also a 24-hour standard (both primary and secondary) that is
based on the 98th percentile of daily PM2.5 values, averaged over 3 years, with a level of 150
jig/m3 that is not to be exceeded more than once per year. As discussed in Section 2.1, PM2.5 is a
mixture of substances suspended as small liquid and/or solid particles. Figure 2-26 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 sulfate (SO42"), nitrate (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 by nitrate. This regional
variability in PM2.5 composition has implications for the spatial nature of N and S deposition.
\ k SJ1® 1 % r7-r-'y--
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Figure 2-26. Map showing pie charts of PM2.5 component species at selected U.S.
monitoring sites based on 2019-2021 data.
Figures 2-27 and 2-28 show maps of the annual and 24-hour PM2.5 design values,
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 both the annual primaiy PM2.5 NAAQS of 12.0
ug/nv' and the 24-hour 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
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sites, mostly in California, were also violating the annual PM2.5 NAAQS (28 sites which exceed
the primary NAAQS level of 12.0 |ig/m3, and 9 sites which 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 2-29 and 2-30 display the average nitrate and sulfate concentrations over the U.S.
during the period 2019-2021. As discussed above, sulfate concentrations are highest in the Ohio
River valley and along the Gulf of Mexico, while nitrate concentrations are highest in the upper
Midwest, along the northeast urban corridor, and in parts of California. Figures 2-31 and 2-32
show trends in annual average concentrations for nitrate and sulfate 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 significant decreasing trends in nitrate concentrations in most of the
U.S., especially in areas where nitrate concentrations were historically highest. Similarly,
reductions in SO2 emissions have resulted in significant reductions in sulfate 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
significant 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.
The EPA has also promulgated standards for PM10 (a 24-hour primary and secondary
standard 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 PM10-2.5 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.
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• 1.8 - 6.0 ug/mA3 (102 sites) G 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) • 12.1 - 15.0 ug/mA3 (19 sites)
Figure 2-27. Primary and secondary annual PM2.5 design values (annual mean, averaged
over 3 years, 2019-2021 period) at monitoring sites with valid design values.
4
5
6
• 5 - 15 ug/mA3 (96 sites) © 26-35 ug/mA3 (87 sites)
O 16-25 ug/mA3 (511 sites) • 36-50 ug/mA3 (33 sites)
51-100 ug/mA3 (44 sites)
101 - 181 ug/mA3 (3 sites)
Figure 2-28. Primary and secondary 24-hour P1VI2.5 design values (98th percentile, averaged
over 3 years; 2019-2021 period) at monitoring sites with valid design values.
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• 0 - 0.49 ug/mA3 (158 sites) O 1-1.49 ug/mA3 (48 sites) • 2 - 3.42 ug/mA3 (8 sites)
j © 0.5 - 0.99 ug/mA3 (65 sites) Q 1.5-1.99 ug/mA3 (25 sites)
2 Figure 2-29. Average IN Or concentrations (jig/m?) for the 2019-2021 period.
• 0 - 0.49 ug/mA3 (99 sites) © 1-1.49 ug/mA3 (75 sites) • 2 - 2.37 ug/mA3 (2 sites)
3 9 0.5 - 0.99 ug/mA3 (127 sites) O 1.5-1.99 ug/mA3 (4 sites)
4 Figure 2-30. Average SO-i2" concentrations (jig/m3) for the 2019-2021 period.
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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 2-31. Trends in annual average concentrations for nitrate (NO.r) from 2006
through 2021.
t ~
C7
~ Decreasing > 0.1 ug/mA3/yr (108 sites) v Decreasing < 0.1 ug/mA3/yr (146 sites)
5 Figure 2-32. Trends in annual average concentrations for sulfate (SO-t2 ) from 2006
6 through 2021.
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The trends in total PM2.5 mass between 2000 and 2021 are shown in Figures 2-33 (annual
standard) and 2-34 (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.
2.4.4 Ammonia Concentrations and Trends
The AMoN network has collected measurements of ammonia gas since 2010 (NADP,
2011) and the number of sites within the network has increased over time. Figure 2-35 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 flat or slightly increasing trends in ammonia
emissions, we also see relatively unchanged NH3 concentrations over this 10 year period,
although there can be some variability from site to site.
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45 -
E 35
13 30
O 25 -
20 -
15
10 -
5 -
Number of PM2.5 Sites
PM2.5 NAAQS Level
, • •
• s
• • i •
0
IT
1500
- 1350
1050
h 900
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°.\ / • •* *•-
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• <04
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• ° 08-13
O 1.2 • 1.8
\ O 18-20
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• >24
2 Figure 2-35. Annual average ammonia concentrations as measured by the Ammonia
3 Monitoring Network in 2010 (top) and 2020 (bottom). Data source: NADP
4 (2012) and NADP (2021).
May 2023
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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 deposition. The focus of this review is on deposition-related impacts to ecological systems
from 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
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 combination 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. 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.
TDEP deposition estimates employ a combination of observations, computational
models, and statistical techniques (Schwede and Lear, 2014, with subsequent technical updates
available from NADP; ISA, Appendix 2, Section 2.6). Figure 2-36 provides a simple flowchart
of the process. For wet deposition, the approach is to combine the concentrations of nitrate,
ammonium and sulfate in precipitation as measured at NADP sites with precipitation amounts as
estimated in the (Parameter-elevation Relationships on Independent Slopes Model) PRISM
dataset. The result is a spatially complete wet deposition dataset at 4 km horizontal resolution.
The source of data for the dry deposition calculation is shown on the right side of Figure 2-36
and in more detail in Figure 2-37. Two intermediate datasets are created: an interpolated
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measurement and a bias-corrected simulation. The interpolated measurement dataset relies on the
CASTNET monitoring network, which measures gas-phase SO2 and nitric acid (HNO3) and
particle-phase SO42", nitrate (NO3"), and NH4. Samples are collected for one week and then
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. Each chemical species is
multiplied by the effective dry deposition velocity calculated from a 12-km Community
Multiscale Air Quality (CMAQ) model simulation. 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, as
meteorological processes have an influence on both the dry deposition velocity and the
concentration. The result is a set of point estimates of dry deposition. These are then summed to
an annual total. The final step is to apply inverse distance weighted interpolation 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 calculates a bias-corrected dry deposition dataset using the results of a CMAQ
simulation. The bias correction is estimated by calculating 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 dataset. 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 true 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.
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Wet deposition (no changes)
Dry deposition (re-calculated)
NADP wet
deposition
measurements
PRISM
precipitation
measurements
CASTNET
concentration
measurements
CMAQair
concentration
modeling
TDEPwet
deposition:
(SO/, NO,.
NH4')
Bias-corrected
dry deposition
dataset
CMAQdrv
deposition
velocity
Other species
CMAQ dry
deposition
TDEPdry
deposition
Figure 2-36. 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.
CASTNET
concentration
measurements
CMAQ dry
deposition
velocity
CMAQair
concentration
modeling
CASTNET
concentration
measurements
Weekly spatially
interpolated
CASTNET dry
deposition
Distance-
weighted TDEP
dry deposition
Seasonal bias-
corrected CMAQ
dry deposition
CMAQ dry
deposition
Figure 2-37. Data sources for estimating dry deposition. Dark blue indicates observations,
white boxes indicate chemical transport modeling results, and light blue boxes
are the results of model-measurement fusion. Note that the bias correction is not
applied to ammonia, in part, because the existing method must be modified to
account for its bidirectional flux.
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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
combining model results and measurements, some are from modeling results only, and a small
fraction is not included as part of TDEP. 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 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 co-located. However, because of the relatively large spatial
variability of NH3, these ammonia measurements 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. Most of the largest contributors of N and S dry deposition are measured at
CASTNET sties which serves to constrain the modeling uncertainties. The most significant
exception is ammonia dry deposition, which is estimated only using CMAQ modeling results.
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The CMAQ model is used to estimate the dry deposition velocity for chemical species
measured at CASTNET monitoring stations, the dry deposition in areas further from CASTNET
monitoring stations, and the dry deposition for species not measured by CASTNET. 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.
Concentration measurements from CASTNET and wet deposition measurements from
NADP NTN are used to assess bias in the modeled deposition values. For sulfur and oxidized
nitrogen, the concentration and wet deposition observations are within 25% of the simulated
values. Because nitrate and sulfate concentrations are bias adjusted in the TDEP model-
measurement fusion, these errors have less of an effect on the estimate of deposition in areas
near the measurement stations. However, in NFb concentration and NH4+ wet deposition bias can
be as high as 55%. 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 overestimate
ammonia dry deposition due to the overestimate in ammonia concentrations. This error is most
pronounced in regions near large ammonia emission sources, such as confined animal feeding
operations (CAFOs) and fertilized crops.
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
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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,
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. These data are
used to quantify ecosystem effects as discussed in the later sections of this assessment. Figure 2-
38 illustrates that nitrogen deposition is highest in and around large source regions. This mostly
includes regions of intensive crop and animal livestock production, which are large sources of
NH3 emissions. The total sulfur deposition is shown in Figure 2-39. Sulfur deposition is
generally higher in the eastern U.S. and near large emission sources like EGUs (section 2.2).
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Total deposition of nitrogen 1921
2 Source: V2022.1, data: CAS'I'NET/CMAQ/NADP USEPfl 11/21/22
2 Figure 2-38. Three year average of the total deposition of nitrogen (kg N/ha) across the
3 2019-2021 period.
Total S
(kg-S/ha)
¦
-0
-2
-4
-6
-8
- 10
- 12
a
-14
-16
-18
¦
->20
4
5
6
Source: V2022.1, data: CASTNET/CMAQ/NADP
Total deposition of sulfur 1921
USEPA 11/21/22
Figure 2-39. Three year average of the total deposition of sulfur (kg S/ha) 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 is not specifically within in the scope of the criteria pollutants that are
part of this review, and therefore it is necessary to quantify the contribution of ammonia to
nitrogen deposition separately from the other components of nitrogen deposition.
This review applies the CMAQ model with additional enhancements to track the
contribution of ammonia to both dry and wet deposition. First, for dry deposition, the CMAQ
model separately tracks the each of the main chemical species that include nitrogen, including
ammonia. This is important, because each of the chemical species has a different dry deposition
velocity, depending on that compound's physical properties. For wet deposition, CMAQ uses an
equilibrium approach. Based on the temperature, relative humidity, and relative concentration of
particle and gas-phase concentrations, CMAQ calculates the pH of the cloud droplets as well as
the equilibrium concentration of each species in the cloud water, in particle form, and in the gas
phase. The most thermodynamically favorable state is for nearly all the ammonia in the cloud
droplet to be in the form of ammonium ion (NH4+). From the model results alone, we would
attribute nearly all the wet deposition to be in the form of ammonium, rather than ammonia.
However, much of the nitrogen that enters the cloud droplet is in the gas-phase as ammonia. In
CMAQ, the contribution of ammonia to the cloud droplet ammonium is accounted for by taking
the difference between the gas-phase concentration of ammonia before the cloud and after the
cloud equilibrium calculation. This portion from ammonia is tracked in a separate variable. It
does not change the model calculations in any way; it is just used to account for the contribution
of ammonia to wet deposition of N.
The contribution of ammonia to total nitrogen deposition, as averaged over 2019 - 2021,
is shown in Figure 2-40. Deposition of ammonia is calculated as the sum of dry deposition of
ammonia and wet deposition of ammonia as described above. Total nitrogen deposition is the
sum of ammonia, ammonium, and oxidized nitrogen compounds. The contribution of ammonia
exceeds 70% in areas with large ammonia emissions, including areas of intensive livestock and
crops production in eastern North Carolina, parts of Iowa, Minnesota, Texas, and the Central and
Imperial valleys in California.
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Pet of total N as reduced N 1921
Source: V2022.1, data: CASTNET/CMAQ/NADP USEPA 11/21/22
Figure 2-40. Average percent of total N deposition in 2019-2021 as reduced N (gas phase
NEb and particle phase NH-t+)
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
unfertilized soils and lightning. Ammonia is emitted from unfertilized soils and from 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, 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 Nr deposition over the contiguous United States (CONUS) (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 can be attributed to
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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
for CONUS S deposition has declined by 68% and total N deposition has declined by 15% (U.S.
EPA, 2022b). See Tables 2-2a and 2-2b for a regional breakout of trends in total S, total N,
oxidized N, and reduced N deposition trends. The change in total N deposition is a combination
of declining oxidized N and increasing reduced N, which is similar to the trend 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). Figure 2-41 shows HNO3 ambient concentration data for a past and recent year (1996 and
2019) and then Figure 2-42 displays how those changes in concentrations have translated to
changes in model-estimated HNO3 dry deposition over similar time periods. 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).
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1 Table 2-2. Change in total deposition by region between the 2000-2002 and 2019-2021
2 periods (U.S. EPA, 2022b): (a) total S deposition; (b) total, oxidized and
3 reduced N deposition.
(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
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
Total Deposition of Oxidized
Nitrogen
(kg N ha-1)
North Central
Northeast
Pacific
4.1
7.7
2.4
2.6
2.9
1.4
-37
-62
-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
Total Deposition of Reduced
Nitrogen
(kg N ha-1)
North Central
Northeast
Pacific
4.4
2.7
1.4
6.9
3.3
1.7
+56
+22
+22
Rocky Mountain
1.1
1.8
+72
South Central
2.8
6.0
+111
Southeast
3.1
5.0
+63
4
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Source: CASTNET USEPA/CAMD 0700/07
Source: CASTNET USEPA/CAMD 11/17/20
Figure 2-41. Annual average concentrations of nitric acid in two years: 1996 (top) and 2019
(bottom).
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-2.0
Dry deposition of nitric acid 0002
USEPA 09/12/18
Source: CASTNET/CMAQ/NADP
HN03
(kg-N/ha)
-0.0
-2.0
-2.5
^3.0
1
2
3
HNO3
(kg-N/ha)
-0.0
-0.5
-10
-1.5
Figure 2-42. Model-estimated dry deposition of nitric acid over two 3-year periods: 2000-
2002 (top) and 2016-2018 (bottom).
Source: CASTNET/CMAQ/NADP
Dry deposition of nitric acid 1618
USEPA 10/21/19
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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 trends in reduced
N deposition will remain a concern. 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 percent change in total N and total S deposition projected to occur by the
model in 2032 (from a baseline 2016 scenario) within Class 1 areas is shown in Figure 2-43 and
Figure 2-44, 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.
The projected average deposition reduction for N and S is about 10%, with largest reductions
occurring in the East. The projected reduction in sulfur in the Pacific Coast states is relatively
minor, but there is already very little sulfur deposition and very few SO2 emission sources in this
region. Areas with relatively high levels of deposition in 2016 have the largest projected
reduction in deposition, but reductions in deposition are not limited to just these high deposition
areas, with deposition at nearly all Class I Areas expected to decline further. It should be noted
that there is considerable uncertainty in the change in future deposition due to the any revision to
the annual average PM2.5 primary standard. The emission sources that typically contribute most
to high 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,
States could elect to reduce emission sources that contribute to organic carbon PM2.5 which have
little impact on deposition.
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N Change in Deposition
scenario minus base case
> *
'• •
percent
K o%
-15%
&
1
2
3
4
Figure 2-43. 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
/ •
> •
• •
percent
Figure 2-44. 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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precipitation mapping in a mountainous watershed: ground truth for evaluating
uncertainty in a national precipitation dataset. Int J Climatol 37:124-37.
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Sensitivity of continental United States atmospheric budgets of oxidized and reduced
nitrogen to dry deposition parametrizations. Philosophical Transactions of the Royal
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for 1990-2015: 25 years of change in response to emission reductions of SO2 and NOx in
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A global simulation of tropospheric ozone and related tracers: Description and evaluation
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Clean Air Status and Trends Network nitrate and nitric acid measurements with data from
other monitoring programs, J. Air Waste Manage. Assoc., 59, 214-226,
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Lee, H.-M., Paulot, F., Henze, D. K., Travis, K., Jacob, D. J., Pardo, L. H., & Schichtel, B. A.
(2016). Sources of nitrogen deposition in Federal Class I areas in the U.S. Atmospheric
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Li, Y., Schichtel, B. A., Walker, J. T., Schwede, D. B., Chen, X., Lehmann, C. M. B., Puchalski,
M. A., Gay, D. A., and Collett, J. L.: Increasing importance of deposition of reduced
nitrogen in the United States, P. Natl. Acad. Sci. USA, 113, 5874-5879.
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(2021). Trends in precipitation chemistry across the U.S. 1985-2017: Quantifying the
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benefits from 30 years of Clean Air Act amendment regulation. Atmospheric
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McMurrv, P. H., Shepherd, M. F. & Vickery, J. S. (2004). Particulate Matter Science for Policy
Makers: a NARSTO Assessment (Cambridge University Press, 2004).
Nair, A. A., Yu, F., & Luo, G. (2019). Spatioseasonal Variations of Atmospheric Ammonia
Concentrations Over the United States: Comprehensive Model-Observation Comparison.
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2011 Annual Summary. Wisconsin State Laboratory of Hygiene, University of
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2021 Annual Summary. Wisconsin State Laboratory of Hygiene, University of
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Paulot, F., Malyshev, S., Nguyen, T., Crounse, J. D., Shevliakova, E., & Horowitz, L. W. (2018).
Representing sub-grid scale variations in nitrogen deposition associated with land use in a
global Earth system model: Implications for present and future nitrogen deposition fluxes
over North America. Atmospheric Chemistry and Physics, 18(24).
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Schwede, D., Zhang, L., Vet, R., & Lear, G. (2011). An intercomparison of the deposition
models used in the CASTNET and CAPMoN networks. Atmospheric Environment,
45(6), 1337-1346. https://doi.Org/10.1016/i.atmosenv.2010.l 1.050.
Schwede, D. B., & Lear, G. G. (2014). A novel hybrid approach for estimating total deposition in
the United States. Atmospheric Environment, 92, 207-220.
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Sun K, et al. (2017). Vehicle emissions as an important urban ammonia source in the United
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Ecological Criteria (Final Report, Dec 2008), EPA/600/R-08/082F, Available at:
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Ambient Air Quality Standards for Particulate Matter, EPA-452/P-22-001, Available at:
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001a, Available at: https://www.epa.gov/air-emissions-inventories/202Q-national-
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uncertainty in total reactive nitrogen deposition estimates for North American critical
load applications. Sci Total Environ. 10(690).
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tropospheric ammonia distribution as seen in the 13-year AIRS measurement record.
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atmospheric ammonia over the world's major agricultural areas detected from space:
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States: Modeling and Comparison with Observations. Journal of Geophysical Research:
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Zhang, L., Jacob, D. J., Knipping, E. M., Kumar, N., Munger, J. W., Carouge, C. C., van
Donkelaar, A., Wang, Y. X., & Chen, D. (2012). Nitrogen deposition to the United
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3 THE 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 and the ecological effects of PM 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"), 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 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 oxides of S and N 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 NO2 (100 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.
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. EPA, 1969; U.S. EPA, 1982; 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, visibility, soils, and water. 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). Accordingly, the existing
secondary standard for SOx was established with a focus on providing public welfare protection
related to the direct effects on vegetation of SOx in ambient air.
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, 1982; U.S. EPA, 1993; U.S.
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EPA, 2008). Since it was established in 1971, the secondary standard forN oxides has been
reviewed three times, in 1985, 1996, and 2012. Although those reviews identified additional
effects related to N deposition, they 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 standards
address an array of effects that include effects on visibility, materials damage, and climate
effects, as well as ecological effects, including those related to deposition. It is only the latter -
ecological effects, including those related to deposition - that fall into this review. The existing
PM secondary standards have not generally been established with ecological effects as their
focus, although prior reviews have generally concluded them to provide protection for such
effects (e.g., 78 FR 3086, January 15, 2013).
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
oxides of nitrogen and sulfur 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 oxides of nitrogen and sulfur 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 oxides of nitrogen and oxides of sulfur 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. 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 oxides of N and S in acidification and nutrient enrichment of aquatic
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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 oxides of N and S
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. While the
analyses indicated results of potential concern with regard to 2002 levels 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 (U.S. EPA, 2008, section 3.2.2.1 and 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 oxides of nitrogen and sulfur 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 nitrogen is taken up by the forests, with
most of the nitrogen retained in the soils (U.S. EPA, 2008, 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 (U.S. EPA, 2008,
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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 EPA concluded it had the greatest confidence in findings related to the aquatic
acidification-related effects of oxides of nitrogen and sulfur relative to other deposition-related
effects. Therefore, the PA focused on aquatic acidification effects from deposition of nitrogen
and sulfur in identifying policy options for providing public welfare protection from deposition-
related effects of oxides of N and S, 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
are considered adequate for these purposes. While the NAAQS have historically been set in
terms of an ambient atmospheric concentration or mixing ration, 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).1 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
1 These were among the ecoregion-specific factors that comprised the parameters, F1 through F4 in the AAI
equation (2011 PA, p. 7-37).
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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 Clean Air Act. In so doing, it was recognized that the
general structure of an AAI-based standard addressed the potential for contributions to acid
deposition from both oxides of nitrogen and of sulfur, and quantitatively described linkages
between ambient concentrations, deposition, and aquatic acidification, considering variations in
factors affecting in these linkages across the country. However, the limitations and uncertainties
in the available information were judged to be too great to support establishment of a new
standard that could be concluded to provide the requisite protection for such effects under the
Act (77 FR 20218, April 3, 2012). The Administrator concluded that while the current
secondary standards were not adequate to provide protection against potentially adverse
deposition-related effects associated with oxides of nitrogen and sulfur, it was not appropriate
under Section 109 to set any new or additional standards at that time to address effects associated
with deposition of oxides of nitrogen and sulfur on sensitive aquatic and terrestrial ecosystems
(77 FR 20218, 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.
The evaluations in the PA, including the scientific assessments in the ISA (building on
prior such assessments) augmented by quantitative air quality and exposure 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. 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:
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• 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.
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Adequacy of Current Standard(s)
I
Evidence-Based Considerations
'-Does avafaWe evidence and related uncertainties
strengthen or call into quesSon prior conclusions?
¦ Evidence of welfare efecs noc previously idenafed?
¦ Evidence of efecs at Sower teveis or for deferent
exposure circumstances?
• Evidence for effects from exposures allowed by the
current standard (s)?
• Uncertainies idenSfed previously are reduced or
new uncertainties have emerged?
1
Exposure and Risk-Based Considerations
^-Nature, magniude, and importance ofesSmated
exposures and risks associated wth meeang die
curren: standard (s)?
'-Uncertainties in the exposure and risk estimates?
Does the
available Information
call into question
the adequacy of
current standard(s)?
YES
Consider retaining
N0-*[ current
standard(s)
Consider Potential Alternative Standards
f
EJemens of Potential Aternaave Standards
r Indicator, Averaging Time, Form, Level
.
Q Potential Alternative Standards for Consideration
Figure 3-1. Overview of general approach for review of the secondary N oxides, SOx, and
PM standards.
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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
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. 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. 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.
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 questions articulated in section 3.3 above. Building from these considerations, the
PA will preliminarily conclude 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
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similar approach, based on the evidence presented in the current ISA and conclusions from the
2012 review of the PM NAAQS (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 oxides of nitrogen and oxides of sulfur 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.
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
associated uncertainties. These effects encompass both effects of airborne N oxides and SOx, as
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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
associated with 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 in air quality that meets the
current standard. 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 oxides of
S and N 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, 1982)2.
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, 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, section 4.6.1). 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
2 The role of historical deposition in current ecosystem circumstances (e.g., waterbody acidification and loss of
aquatic species, terrestrial acidification, and aquatic eutrophication) and the complications affecting recovery
have been noted in scientific assessments for NAAQS reviews ranging from the 1982 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.l 1, 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; 1982 AQCD, section 1.7 and Chapter 7).
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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 analyses (based on steady-state water quality modeling) to
describe the relationships between acid deposition and acid neutralizing capacity in U.S.
ecoregions.
In a parallel track, we have utilized 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 tracks which
inform an understanding of the relative contributions of source locations to individual ecoregions
in the U.S., we develop quantitative relationships of air pollutant concentrations to atmospheric
deposition rates. 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. This will consider existing standard metrics, as well
as other potential metrics for effective deposition-related standards. 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 (Chapter 7). Based on these considerations we
identify an array of policy options for consideration in this review
3.3.3 Identification of Policy Options
When final, this PA is intended to provide 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 are appropriate to consider. Considerations are discussed and conclusions
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reached with regard to protection from effects of the airborne pollutants and deposition-related
effects.
In considering potential alternative standards, as appropriate, we evaluate what the
current information, including emissions and air quality analyses available in Chapter 2, may
indicate regarding the relationships between N oxides, SOx, and PM and N/S deposition, the
influence of different averaging times on N/S deposition, and what the quantitative analyses
indicate regarding the extent to which one or more standards may have the 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 will present the staff
preliminary conclusions on whether 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, 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. Washington, DC: National Air Pollution Control Administration Pub.
No. AP-50.
U.S. EPA. (1971). Air Quality Criteria For Nitrogen Oxides [EPA Report], (AP-84). Washington
DC. U.S. Environmental Protection Agency, Air Pollution Control Office.
U.S. EPA. (1982). Review of the National Ambient Air Quality Standards for Sulfur Oxides:
Assessment of Scientific and Technical Information. OAQPS Staff Paper. EPA-450/5-82-
007.
U.S. EPA. (1993). Air Quality Criteria for Oxides of Nitrogen (Final Report, 1993). U.S.
Environmental Protection Agency, Washington, D.C., EPA/600/8-9l/049aF-cF.
December 1993.
U.S. EPA. (2007). Integrated Review Plan for the Secondary National Ambient Air Quality
Standards for Nitrogen Dioxide and Sulfur Dioxide. U.S. Environmental Protection
Agency, Research Triangle Park, NC, EPA-452/R-08-006.
U.S. EPA. (2008). Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur
Ecological Criteria (Final Report).
U.S. EPA. (2009). Risk and Exposure Assessment for Review of the Secondary National
Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur-Main
Content - Final Report. EPA-452/R-09-008a
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. U.S. Environmental
Protection Agency, Research Triangle Park, NC, EPA-452/R-1 l-005a, b. February 2011.
U.S. EPA. (2017). Integrated Review Plan for the Secondary NAAQS for Oxides of Nitrogen
and Oxides of Sulfur and Particulate Matter - Final. U.S. EPA. EPA-452/R-17-002.
January 2017.
U.S. EPA. (2020) Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides of Sulfur
and Particulate Matter Ecological Criteria (Final Report, 2020). U.S. Environmental
Protection Agency, Washington, DC, EPA/600/R-20/278, 2020.
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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. 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. We also address 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. 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 AND OF PM 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
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
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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 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
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. 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
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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).
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 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 4.2.3 below.
4.2 DEPOSITION-RELATED ECOLOGICAL EFFECTS
As summarized in section 2.5 above, oxides of N and S, and PM, in ambient air
contribute to deposition of N and S, which can affect ecosystem biogeochemistry, structure, and
function in multiple ways. These effects include nutrient enrichment, primarily associated with
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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).
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. 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.
Another complication specific to N deposition is its potential to increase growth and yield
of agricultural and timber crops, which may be judged and valued differentially than changes in
growth of some species in natural ecosystems (as noted in section 4.3 below). As discussed
further in section 4.2.2 below, 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 following sections draw from the ISA to provide an overview of the welfare effects
associated with N and S deposition ecosystems of the U.S. Section 4.2.1 focuses on acidification-
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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related effects, while 4.2.2 focuses on effects related to nitrogen enrichment. Lastly, section 4.3.2
provides an overview of other deposition-related effects. The summaries in the sections and their
subsections below are organized in a manner intended to address the following questions.
• What is the nature of the welfare effects associated with N and S and PM deposition?
Is there new evidence on welfare effects beyond those identified in the last review?
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?
4.2.1 Acidification and Associated 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, a hydrogen ion is 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 amount 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 SO4 2" 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
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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 4.2.2 below, and for terrestrial acidification in section 4.2.3 below.
4.2.1.1 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
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).
4.2.1.1.1 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
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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
(Ah) concentration (ISA, Appendix 8, Table 8-9).
The effects of aquatic 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).
Some of the most commonly 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 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 4.2.1.1.2 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, 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
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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 |ieq/L during the 1980s to 12 |ieq/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).
4.2.1.1.2 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 (ANC) 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). This indicator is defined as the molar sum of
strong base cations minus the molar sum of strong acid anions:
ANC = (Ca2+ + Mg2+ + K+ + Na+ + NH4+) - (S042" + N03" + CI")
While ANC is not the direct cause of acidification-related effects on aquatic biota, it serves as an
indicator of acidification-related risk. Water quality models are generally better at estimating
ANC than other indicators and ANC has been related to the health of biota and other surface
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water constituents like pH and A1 or watershed components like base cation weathering (BCw)
(ISA, Appendix 8, sections 8.1 and 8.3.6.3). Waterbody pH largely controls the bioavailability of
Al, which is very toxic to fish (ISA, Appendix 8, section 8.6.4).
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 4.2.1.1 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, section IS6.1.1.1; 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., 2006a; 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. 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.
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 (ieq /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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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
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, 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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0 300 600 Kilometers
1 s h—h—h
Puerto Rico
O
/' *s
Surface Water ANC
Entire U.S.
v
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Ocean
Atlantic
Ocean
Alaska
Projection: Lambert Conformal Conic, NAD 1983
Produced for National Park Service. Air Resources Division, 2011
Prepared by: E&S Environmental Chemistry
Note No ranking for Amencan Affilliated
Parks in PACN Not Shown
Surface Water ANC
Median (peq/L)
• <0
• 0-20
20-50
• 50-100
• > 100
Hawaii
Figure 4-1. Surface water ANC map, based on data compiled by Sullivan (2017) (ISA, Appendix 8, Figure 8-11).
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4.2.1.1.3 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 including weathering rates and leaching. 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.
4.2.1.2 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, sections 4.2.1.1 and 4.2.1.2; 2020 ISA, sections 4.1, 5.7.1 and 5.7.2).
4.2.1.2.1 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
impairment caused by Al toxicity (related to increased availability of inorganic Al in soil water)
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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, Bc:Al 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 5.4.2.3 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 a number of
species or species groups with S deposition metrics; positive and negative associates were
reported with N deposition (see section 5.4.2.3 and 5.5.3 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 does less well (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).
4.2.1.2.2 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
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cation supply (e.g., sandstone, quartzite), due mainly to low weathering, 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 documented in the evidence, biogeochemical sensitivity to
deposition-driven acidification (and eutrophication [see section 4.2.2 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). The BC:A1 ratio is commonly used, particularly
in mass balance modeling approaches, such as the simple mass balance equation (SMB), 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
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
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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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 base cation weathering (BCw) in support of simple mass balance (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).
4.2.1.2.3 Key Uncertainties
Although the evidence clearly demonstrates that acidifying deposition of N and S 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 4.2.1.2.2 above, modeling
analyses are commonly employed, with several inputs recognized as contributing to overall
uncertainty.
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
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response to acid deposition over a broad spatial scale, the primary source of uncertainty was
identified to be from components of BCw (Li and McNulty, 2007). The authors concluded that
improvements in estimates of these components 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, 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
deposition metrics are relatively low, may be influenced by impacts from past deposition.
4.2.2 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 terrestrial and aquatic ecosystems. Because of this, most species are adapted to
low nutrient conditions, and a much smaller fraction of species are adapted to high nutrient
availability. Therefore, when limiting nutrients become more available, whether from
atmospheric deposition, runoff, or episodic events, often selection leads to a shift in the
community from high diversity systems to low diversity systems. Thus, change in the availability
of an important nutrient, such as nitrogen, can, in nitrogen-limited systems, affect growth and
productivity, with ramifications on 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 occurs when increased
loading of the limiting nutrient (usually N or phosphorous) 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).
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Both N oxides and reduced forms of nitrogen (NHX) can contribute to N enrichment. 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).
4.2.2.1 Aquatic and Wetland Ecosystems
Nitrogen additions, including from atmospheric deposition, to freshwater, estuarine and
near-coastal ecosystems can contribution to eutrophication 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 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, 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
toN 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
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 in reduced forms such as NH4+ (ISA, section 4.3.6). Whether wetlands are a source
and/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
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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).
4.2.2.1.1 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 eutrophication alters freshwater biogeochemistry and can
impact physiology, survival, and biodiversity of sensitive aquatic biota (Figure 4-2).
{Changes in aquatic
plant assemblages
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 water bodies (ISA, 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
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the occurrence of harmful algal blooms (ISA, Appendix 9). More specifically, the availability
and form of N has been found to influence algal bloom composition and toxicity (ISA, Appendix
9, section 9.2.6.1). Such evidence 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).
4.2.2.1.2 Aquatic Ecosystem Sensitivity
Current evidence continues to support the conclusions of the previous review regarding
ecosystem sensitivity to nutrient enrichment. 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
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.
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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).
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. 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).
Estimates of the relative contribution of atmospheric deposition to total N loading of
estuarine systems vary, with analyses based on data extending across the past two to three
decades estimating that most estuaries receive 15-40% of N inputs from atmospheric sources
(ISA, section ES5.2; ISA, section 7, section 7.2.1). In coastal areas, N sources may include
atmospheric deposition to the water surface, coastal upwelling from oceanic waters, and
transport from watersheds. Freshwater inflows to estuaries often transport N from agriculture,
urban, wastewater, and atmospheric deposition sources (ISA, IS2.2.2; ISA, Appendix 7, section
7.2.1).
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
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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).
4.2.2.1.3 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 different
types of 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 et al., 2017b).
4.2.2.2 Terrestrial Ecosystems
It is long established that N 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
5 Ombrotrophic bogs develop in areas where drainage is impeded and precipitation exceeds evapotranspiration (ISA,
Appendix 11, section 11.1).
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deposition and the alteration of the physiology and growth of terrestrial organisms and the
productivity of terrestrial ecosystems (ISA, 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. Because N limitation is common, most terrestrial ecosystems are responsive
to increased levels of N. 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. Because over evolutionary time, low N
conditions were much more common than high N conditions, there are many more species
adapted to low N conditions compared with species adapted to high N conditions. Thus, there is
often a net loss of species as ecosystems receive more N, whether from atmospheric deposition
or otherwise. 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, community composition, and biodiversity in terrestrial ecosystems
(ISA, section IS.5.3 and Appendix 6, section 6.3).
4.2.2.2.1 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 link N 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. Eutrophication, 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).
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The currently available evidence base for the terrestrial ecosystem effects of N
enrichment, including eutrophication, 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 showing 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 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 that N deposition alters the physiology and growth of overstory
trees, and that N 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 species richness declining atN
deposition rates >11.6 kg N/ha/yr at sites with low soil pH but not having a negative effect, up to
deposition levels of 20 kg N/ha/yr, 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
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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,
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).
4.2.2.2.2 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 ofN 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, 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).
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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 CSS
ecosystems in southern California, makes these ecosystems sensitive to N 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., 2013b).
4.2.2.2.3 Key Uncertainties
Just as there are uncertainties associated with estimating N deposition associated with
ecological responses in aquatic systems (as summarized in section 4.2.2.1.3 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
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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 NOx
(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
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.
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4.2.3 Other 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).
4.2.3.1 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 Hg 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 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 methyl mercury 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).
4.2.3.2 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 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
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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).
4.2.3.3 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
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. 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.
4.3 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.
• Is there newly available information relevant to consideration of the public welfare
implications of S and N deposition-related welfare effects?
There is a large body of newly available evidence regarding the impacts of S and N
deposition on biological/ecological resources across a wide range of effects that can be used to
help inform public welfare considerations. The categories of effects identified in the CAA to be
included among welfare effects are in fact 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 numerous 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). 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. As noted in the last review of the secondary NAAQS for NOx 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).
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)).
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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 (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 For example, 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 03-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 NOx 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 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
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)).
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welfare significance to disruptions in ecosystem structure and function. The concept of
considering the extent to which a pollutant effect will contribute to such 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 4.2 and
4.3 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). 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. Other ecosystem services that can be affected are
summarized below in Figure 4-3 9 (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 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.
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However, available information does not yet provide a framework that can specifically tie
changes in a biological or ecological indicator (e.g., lichen abundance) from deposition and
broad 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 air quality and associated 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.
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3 Figure 4-3. Potential effects on the public welfare of ecological effects of N Oxides, SOx and PM.
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Stoddard, JL; Van Sickle, J; Herlihy, AT; Brahney, J; Paulsen, S; Peck, DV; Mitchell, R;
Pollard, AI. (2016). Continental-scale increase in lake and stream phosphorus: Are
oligotrophic systems disappearing in the United States? Environ Sci Technol 50: 3409-
3415. http://dx.doi.org/10.1021/acs.est.5b05950
Strengbom, J; Nordin, A; Nasholm, T; Ericson, L. (2001). Slow recovery of boreal forest
ecosystem following decreased nitrogen input. FunctEcol 15: 451-457.
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Sullivan, TJ, Driscoll, CT, Cosby, BJ, Fernandez, IJ, Herlihy, AT, Zhai, J, Stemberger, R,
Snyder, KU, Sutherland, JW, Nierzwicki-Bauer, SA, Boylen, CW, McDonnell, TC and
Nowicki, NA (2006). Assessment of the extent to which intensively-studied lakes are
representative of the Adirondack Mountain region. Final report. Corvallis, OR, E&S
Environmental Chemistry, Inc.
Sullivan. TJ. (2017). Air pollution and its impacts on U.S. national parks. Boca Raton, FL: CRC
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Sutherland, JW; Acker, FW; Bloomfield, JA; Boylen, CW; Charles, DF; Daniels, RA; Eichler,
LW; Farrell, JL; Feranec, RS; Hare, MP; Kanfoush, SL; Preall, RJ; Quinn, SO; Rowell,
HC; Schoch, WF; Shaw, WH; Siegfried, CA; Sullivan, TJ; Winkler, DA; Nierzwicki-
Bauer, SA. (2015). Brooktrout lake case study: Biotic recovery from acid deposition 20
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http://dx.doi.org/10.1021/es5036865
Thomas, R.Q., C.D. Canham, K.C. Weathers and C.L. Goodale. (2010). Increased tree carbon
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Thomas, RB; Spal, SE; Smith, KR; Nippert, JB. (2013). Evidence of recovery of Juniperus
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110: 15319-15324. http://dx.doi.org/10.1073/pnas.1308115110
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1 Williams, JJ; Lynch, JA; Saros, JE; Labou, SG. (2017a). Critical loads of atmospheric N
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3 Ecosphere 8: e01955. http://dx. doi. org/10.1002/ecs2.195 5
4 Williams, JJ; Chung, SH; Johansen, AM; Lamb, BK; Vaughan, JK; Beutel, M. (2017b).
5 Evaluation of atmospheric nitrogen deposition model performance in the context of US
6 critical load assessments. Atmos Environ 150: 244-255.
7 http://dx.doi.Org/10.1016/i.atmosenv.2016.l 1.051
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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 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.
Section 5.1 discusses the currently available information related to consideration of
exposure concentrations associated with direct welfare effects of nitrogen and sulfur oxides and
PM in ambient air. 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 of concern and
accordingly, the likelihood of occurrence of such effects in response to air quality
that meets the current standards?
Sections 5.2 through 5.4 address the more complex consideration of deposition-related
exposures, which was a major focus in the 2012 review of the secondary standards for oxides of
sulfur and nitrogen. In this regard, we consider the following policy-relevant question:
• To what extent does the available evidence provide quantitative linkages of S oxides,
N oxides and/or PM deposition and effects that can inform judgments on deposition
levels of concern and accordingly, the likelihood of occurrence of such effects in
response to air quality that meets the current standards?
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
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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 80s, surface water sulfate concentrations increased in response to S
deposition. Subsequently, and especially more recently, 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, 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 the categories of effects for which quantitative
assessment approaches for atmospheric deposition are the most established and robust. 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 the evidence base regarding atmospheric
deposition and nutrient enrichment has expanded since the 2012 review, this generally remains
the case in the current review. Accordingly, the chapter addresses the quantitative information
available for both acidification and nutrient enrichment, but there is more quantitative
information and associated discussion related to ecosystem acidification, and particularly aquatic
acidification.
Critical loads are frequently used in studies investigating associations between an array of
chemical, biological, ecological and ecosystem characteristics and a variety of N 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
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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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 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 approach is the methodology developed for the
analyses of aquatic systems and acidification, summarized in section 5.2.2 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 is explicitly
described in section 5.2.2.
While recognizing the inherent connections between watersheds and waterbodies (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. We also note that
recovery of aquatic ecosystem biota from aquatic acidification may in many locations be more
rapid than recovery of tree populations from terrestrial acidification (Driscoll et al., 2001).
Therefore, with regard to deposition-related effects, we focus first on the quantitative
information for aquatic ecosystem effects in sections 5.2 and 5.3. Section 5.4 then 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.
5.1 DIRECT EFFECTS OF OXIDES OF N AND S AND OF PM IN
AMBIENT AIR
5.1.1 Sulfur Oxides
As summarized in section 4.1 above, the direct 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
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 includes exposure concentrations is drawn from experimental studies or
observational studies in areas near sources, with the most studied effect being visible foliar
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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 or 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
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 two weeks, however, recovery had not occurred after significant
reduction in photosynthesis from six hours at 0.25 ppm. After shorter exposures to 0.25, 0.5 and
0.9 ppm, photosynthesis recovered within two weeks. After exposures to 0.9 and 1.5 ppm SO2
ppm for one to six hours, photosynthesis was significantly reduced and did not recover. The
second species tested was appreciably less sensitive, with photosynthesis not being affected for
lower exposures than six hours at a concentration of 0.5 ppm SO2 (Sanz et al., 1992).
5.1.2 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
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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-
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) 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 NCh'OSA, 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 .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
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illustrated in Figures 2-40 and 2-41 above (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.
5.1.3 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
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. 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.
5.2 AQUATIC ECOSYSTEM ACIDIFICATION
Changes in biogeochemical processes and water chemistry caused by deposition of
nitrogen and sulfur 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 4.2.1.1 above (ISA, Appendix 8, section 8.1). This acidifying deposition
infiltrates both terrestrial and aquatic systems and may result in changes to soils and water that
are harmful to biota. These changes are dependent on a number of factors that influence the
sensitivity of a system to acidification including weathering rates, bedrock composition,
vegetation and microbial processes, physical and chemical characteristics of soils and hydrology.
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The quantitative assessment of aquatic acidification risk performed for this review is
based on established modeling approaches, extensive databases of site-specific water quality
measurements and a commonly recognized indicator of acidification risk, ANC. Key aspects of
this assessment and its results are summarized in the following subsections, with details provided
in Appendix 5A. Section 5.2.1 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 5.2.2. Results for analyses at three scales are
presented in section 5.2.3 and a characterization of the analysis uncertainty is summarized in
section 5.2.4. Overall findings are summarized in section 5.2.5.
5.2.1 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 4.2.1.1.2 above,
surface water chemical factors such as pH, Ca2+, ANC, ionic metals concentrations, and
dissolved organic carbon (DOC) are affected by acid deposition and can profoundly affect the
structure and function of biological communities in lakes and streams (ISA, Section 8.3). The
most widely used measure of surface water acidification, and subsequent recovery under
scenarios with lower acidifying deposition, is ANC.
As summarized in section 4.2.1.1.2 above, the evidence of effects on biota from aquatic
acidification indicates a range of severity with varying 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
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
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of pH below 5 (which may correspond to ANC levels below 0 |icq/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 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, Section 8.3.3).
40
3S
30
2S
t/> 20
Oj
3
01
S- 15
i/l
"m
o 10
•
•ft •
non-impacted
•
• • •
•
•
ft ft ft
slight impact *
^ ^ ft
•
• •
ft
•
•
moderate impact
severe impact
y s 4.62x - 1.49
R2 s 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). 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
2 pH and ANC were related to one another using a generalized relationship base on equilibrium with atmospheric
CO2 concentration (Cole and Prairie, 2010)
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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 peq/L). 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 peq/L) indicated that the
trout had lost the ability to ionoregulate (ISA, Appendix 8, section 8.3.6.1). See Figure 5-2 for
fish species sensitivity based on observations from field studies with supporting bioassays.
Critical pH Ranges of Fish
JJ 1—i
Yellow perch
Brown bulhead
Pumpkjrtsoed
Northern p&e
-*•
Rock bass
Golden shiner
Atlantic salmon
Brown trout
Creek chub
Rainbow trout
Smallmouth boss
N. rebellied dace
Common sNner
Fathead minnow
4 0 j o 6.o pH
7.0
— Safe range, no acid-related effects occur
Uncertain range, acid related effects may occur
Critical range, acid-related effects likely
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 pFI 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)
Figure 5-2.
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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 [j,eq/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 [j,eq/L or less at base flow
may be 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 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 S absorption 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
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(Schreck, 1982; Wedemeyer et al., 1990). As noted in section 4.2.1.1.2 above, surveys in the
heavily impacted Adirondack mountains found that lakes and streams having an annual average
ANC < 0 ueq/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.
14 -
12 ¦
w in •
aj 10 •
o
IL
o 4
u
4) -
-O 2 -
£
z °-
•2
-4 ¦
-200 -100 0 100 200 300 400 500
ANC (ueq/L)
Acute
{/> m s Low
1? a
5 D O
® e 3
•
f #-y.v -•
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 jieq/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
with decreases in ANC below a threshold of approximately 50 to 100 ueq/L for lakes (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). Flowever,
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).
The key biological/ecological effects on aquatic organisms that have been observed in
field studies of different acidification levels. 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 jj,eq/L :
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• At ANC levels <0 [j,eq/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 lucius), and others (Sullivan et al., 2003, 2006; Bulger et
al., 2000), which is in most cases attributed to elevated inorganic monomeric Al
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 waterbodies of
Adirondacks and Appalachians. Such effects included reduced aquatic diversity (Kretser
et al., 1989, Lawrence et al., 2015; Dennis, 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, 1995).
• At an ANC between 50 to 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, 1995]). Reduced fish species richness
has also been reported to be affected in Adirondack streams at ANC 50 (Sullivan et al.,
2006).
• Values of ANC >80 [j,eq/L have not generally been associated with harmful effects on
biota (Bulger et al., 1999; Driscoll et al., 2001; Kretser et al., 1989; Sullivan et al., 2006).
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5.2.2 Conceptual Model and Analysis Approach
The impact of N and/or S deposition on aquatic acidification 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 characterize the risk of acidifying deposition on aquatic acidification
across the CONUS with a focus on acid sensitive areas.
This relationship 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 occuring under current
conditions across the U.S. The following schematic (Figure 5-4) represents the conceptual model
used in the analyses to link these factors.
Figure 5-4. Conceptual Model for Aquatic Acidification Analyses
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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 5.2.3 below). In general, relatively low CL values (i.e., less than 50
meq/m2/yr) indicate that the watershed has a limited ability to neutralize the addition of acidic
anions, and hence, it is susceptible to acidification. The higher the CL value, the greater the
ability of any given watershed to neutralize the additional acidic anions and protect aquatic life.
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.
Key aspects of the assessments described the subsections below include the spatial scales
of assessment (section 5.2.2.1), the chemical indicator (section 5.2.2.2), identification of CL
estimates for this assessment (section 5.2.2.3) and determining exceedances (section 5.2.2.4), as
well as sources of waterbody deposition estimates (section 5.2.2.5). Also discussed is the
approach for interpreting results, including with regard to ecosystems with sensitivity to acidic
deposition, ecosystems for which factors other than deposition are critical influences on
waterbody ANC, and systems for which nonzero CL estimates cannot be derived for ANC levels
of interest (section 5.2.2.6). Results of the assessments are presented in section 5.2.3. The
characterization of uncertainty is described in section 5.2.4 and key observations are summarized
in section 5.2.5.
5.2.2.1 Spatial Scale
For this review, we developed a multi-scale analysis to assess aquatic acidification at
three levels of spatial extent: national, ecoregion, and case study. For this analysis, the national
assessment included the CONUS only since there is insufficient data available for Hawaii,
Alaska, and the territories. The Omernik ecoregion classifications were used for the regional
assessments and case studies were selected for areas which were likely to be most impacted and
for which sufficient data was 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 a host of landscape factors, such as geology, hydrology,
soils, catchment scale, and vegetation characteristics that control whether a waterbody will be
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acidified by air pollution 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 Omemik 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
(Figure 5-5).
Eco_Leve{_»l_ Us
KA_urWM6
r I Ultimo HOUNOB
(=~ Ca-iTRALU5A?lA MS
] acODDfegftTS
I j&B»aL*0£S
~ tw?r£ WEST COAST FCHESI
I I ®: ft- J=UjJ'<¦ *4, JWDSOl^VCAET uSACQASMl F^AJMT. F .1 TEEAS4.0USWA COAST* PUtIM
~~1 MXED WOOD PiAWS |~~1 L»=Pg> 6U MajftAiNS
I i M.IH3 WXD SHELD I I CESfTTS
[ ] CEWCW(W>ITa-JIPF»HA;-I^MFCBgST5 | | '/SSTOiTW. SFM-AKD PRAIRIEi
1 3CHJTH CW»l sa-m P«WR£S | | 'AESTERN OWDUfflA
I I satmcASiiWN usa puns I I weaiuw
I I JLPA5.TE>A5 D PLMi
| iC3nEBRA.-E.*J CAlFQHMi*. | | TaMSATE PRAFE5
Omernik Ecoregion 91 Index Map
Figure 5-5. Omernik Ecoregion II areas with ecoregion III subdivisions
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 phenomena
that affect or reflect differences in ecosystem quality and integrity. Factors include geology,
physiography, vegetation, climate, soils, land use, wildlife, and hydrology.
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Three hierarchical levels were developed to distinguish coarser (more general) and finer
(more detailed) categorization. Level I is the coarsest level, dividing North America into 12
ecoregions. At level II, the continent is subdivided into 25 ecoregions. Level III is a further
subdivision of level II and divides CONUS into 105 ecoregions. Level IV is a subdivision of
level III, and divides CONUS into 967 ecoregions. For the analyses in this review, we used level
III ecoregions to give the greatest sensitivity for variation in ecoregion response while allowing
us to aggregate available water quality data to allow representativeness.
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. These areas were the Shenandoah National Park,
White Mountain National Forest, Voyagers National Park, Sierra National Forest, and Rocky
Mountain National Park. These parks and national forest vary in their sensitivity to acidification,
but represent high value or protected ecosystems, such as Class 1 areas, wilderness, and national
forests.
5.2.2.2 Chemical Indicator
The chemical indicator of acidification risk used in this assessment is ANC. Selection of
ANC provides a way to look most closely at those waterbodies for which deposition was the
main source of acidifying input as well as eliminating 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
threshold. Surface water ANC is also commonly used for estimating CLs for N and S in the U.S
as it is more stable and more easily modelled. Additionally, CL estimates generally are linearly
associated with ANC target, and, unlike some other indicators, ANC is not influenced by other
environmental factors such as CO2 levels in the surface water (ISA, section 7.1.2.5).
For the analyses described below, we evaluated CLs for three different ANC thresholds:
20 [j,eq/L, 30 [j,eq/L and 50 [j,eq/L . Selection of these target ANC values reflect 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 provides 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 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),
used an ANC threshold of 50 [j,eq/L for the eastern CONUS and 20 [j,eq/L for the western
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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 CAS AC
expressed its view that "[ljevels of 50 [j,eq/L and higher would provide 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 2012 review, ANC values at/above 50 were concluded to provide additional
protection although with increasingly greater uncertainty for values at/above 75
^eq/L (2011 PA, pp. 7-47 to 7-48).
5.2.2.3 Critical Load Estimates Based on ANC
Considerable new research on critical loads for acidification is available and both steady
state and dynamic models have been used to generate ANC based critical loads for much of the
U.S. 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
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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 applying the model to a date in the distant future. Since the 2008
ISA, studies utilizing dynamic modeling of CLs has generally been focused on the Adirondacks,
Appalachians, and the Rocky Mountains/Sierra Nevada (ISA, Appendix 8, section 8.5.4.1.2.2).
Empirical studies have also identified CLs for freshwater systems (ISA, Appendix 8,
Table 8-7). For example, in the Sierra Nevada mountains, total acidic deposition of about 74
eq/ha/yr was correlated with the decline in ANC observed in Moat Lake between 1920 and 1930
(Heard et al., 2014). Baron et al. (2011) estimated CLs to be about 571 eq N/ha/yr in the
Northeast and 286 eq N/ha/yr in the Rocky Mountains for N03 concentrations as triggers of
episodic acidification. In California, CLs for N deposition in California were estimated based in
part on changes in N03 leaching in stream water, which can cause or contribute to water
acidification (Fenn et al., 2008). Critical loads derived empirically and by the DayCent model for
N03 leaching were both 1,214 eq N/ha/yr (ISA, Appendix 8, section 8.6.8).
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 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 50-100 [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).
In the western U.S., Shaw et al. (2014) used the SSWC model to estimate CLs for 2008
lakes in Class I and II wilderness areas in the Sierra Nevada. For benchmark ANC values of 0, 5,
10, and 20 [j,eq/L, which span the range of minimum ANC values observed in Sierra Nevada
lakes, median CLs were estimated to be 217, 186, 157, and 101 eq (S + N)/ha/yr to achieve ANC
values of 0, 5, 10, and 20 [j,eq/L, respectively. The median CL for granitic watersheds based on a
critical ANC limit of 10 [j,eq/L was 149 eq/ha/yr (ISA, Appendix 8, section 8.5.4.1.2.1).
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
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et al., 2012a). The NCLD is comprised of CLs calculated from a host of common models. A
more detailed description of these models can be found in Appendix 5A. Figure 5-6 below shows
the unique locations for 14,000+ CLs used in this assessment. Critical loads have 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. Small to median size lakes (>200 Ha) and lower order streams tend to be the
waterbodies that are impacted by deposition driven acidification while rivers are not typically
impacted. Data in the NCLD is focused on waterbodies that are typically impacted by deposition
driven acidification. A waterbody 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 are averaged.
5.2.2.4 Critical Load-Based Analysis
In this analysis, we compared waterbody estimates to critical loads based on the three
ANC targets. A critical load exceedance was concluded when acidifying deposition estimate was
greater than the target CL. 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
acidication and thereby to any potential for exceedance of the acidification CL. The analyses
performed for this PA avoided this complexity by focusing on S only deposition.
The decision to focus on the S component of acidic deposition was based on the less
significant contribution of recent N deposition to acidification (compared to past decades). This
was concluded based on the finding for deposition in 2014-2016 and 2018-2020 of very few
exceedances driven by N. This means that adding N from leaching to the critical load
exceedances with S doesn't really change the percent of waterbodies exceeding their CL. To
confirm this assumption, analyses were performed to compare the percentage of CL exceedances
when both N and S were evaluated versus only S exceedances (see Appendix 5 A (Section
5A.2.1). This analysis supported the assumption being used in this assessment that most of the N
deposition entering the watersheds under the analyses time periods were retained within the
watershed and/or converted to gaseous N (e.g., N2O, N2, etc.). Additionally, there were two
different methods considered for determining the contribution of N deposition to aquatic
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acidification. Those methods and how they are handled in CL exceedance calculations are also
discussed in Appendix 5 A.
Critical loads and deposition estimates are uncertain and to have confidence in the
exceedance it is important that this uncertainty is factored into the calculation. Based on the CL
uncertainty analysis (see Appendix 5A, section 5A-2), on average the magnitude of the
uncertainty for aquatic CLs is 4.29 meq S/m2-yr or 0.69 Kg S/ha-yr with a confidence interval of
±2.15 meq/m2/yr or ±0.35 Kg S/ha/yr. For simplicity reasons, a 6.25 meq S/m2-yr or 1 Kg
S/ha/yr range of uncertainty was used in the exceedance calculation. Within this range, it is
unclear whether the CL is exceeded. For that reason, an exceedance was concluded when the S
deposition estimates were greater than the CLs by a margin of 3.125 meq S/m2-yr or 0.5 Kg
S/ha/yr. An exceedance was not concluded when the S deposition estimate is below the CL by at
least a margin of 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.
5.2.2.5 Waterbody Deposition Estimates
Estimates of waterbody deposition used in this assessment were based on the Total
Deposition (TDep) model (https://nadp.slh.wisc.edu/committees/tdep/) (Schwede and Lear,
2014). This model is discussed more fully in Section 2.5. Both total N and S deposition were
estimated at a resolution of a 4km grid cell for each stream reach or lake location. For each
waterbody, total N and S deposition was 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 was then calculated for each of these five periods and
summarized nationally and by ecoregion III (sections 5.2.3.1 and 5.2.3.2).
5.2.2.6 Interpreting Results
In order to focus our analyses on those areas which were likely to be impacted by
acidification and that were also driven primarily by deposition of N and S from ambient air, we
needed to look more closely at the ecoregions and their underlying characteristics. We also
needed to identify those ecoregions where, for various reasons, target ANC values could not be
achieved. These factors are discussed fully in Appendix 5A and summarized below.
The exceedance analysis was performed in waterbodies in 27 ecoregions (level III).
These ecoregions were selected (as described in Appx 5 A, section 5 A. 1.7) based on
consideration of their sensitivity to acidification, and their potential for natural (vs deposition-
driven) acidity (Figure 5-6). Thirty ecoregions were considered sensitive to acidification. Of
these 30 ecoregions, three were identified as having natural acidity, based on DOC as an
indicator of natural acidity. The acid sensitive ecoregions generally are areas with mountains,
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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 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). A more complete discussion of each ecoregion and its
sensitivity can be found in Appendix 5A (Table 5A-5).
| Most Acid Sensitive Ecoregions (<100 peq/L)
Moderately Acid Sensitive Ecoregions (<200 peq/L)
Low or Non-Acid Sensitive Ecoregions (>200
l&lllB Ecoregions with high level of natural acidity
Figure 5-6. Ecoregion III grouped in three acid sensitivity classes. The dark colors
indicate acid sensitive ecoregions. Points are ANC concentrations below 200
jieq/L. Crosshatched ecoregions are those with DOC driven acid sensitivity.
Estimates of CL less than zero indicate that no level of acidifying deposition would allow
those areas to reach a target ANC value. These areas, 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 (aeq/L under any deposition scenario. These areas were tracked
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separately from those areas with non-zero CL estimates. A more complete discussion of negative
CLs and results can be found in Appendix 5 A.
5.2.3 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 5.2.3.1 presents the results of the national scale analyses whereas
sections 5.2.3.2 and 5.2.3.3 present the results of the ecoregion scale and case study analyses
respectively.
5.2.3.1 National Scale Analysis
A total of 13,824 unique waterbodies across the CONUS had calculated CLs. Most of
those waterbodies had CLs that were less than 18 kg S/ha-yr across all the target ANC levels
(Appendix 5A, Table 5A-6). 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. Note that as discussed above, for the
purpose of this analysis we focused on CL>0 and S only. The 50/20 values reflect a threshold
ANC of 50 |aeq/L in the eastern portion of the U.S. and a target ANC of 20 |ieq/L in the west.
See discussion above for parameters used in developing this scenario.
Table 5-1. Percentage of waterbodies nationally for which annual average S deposition
during the five time periods assessed exceed the waterbody CL 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 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
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1 these systems to be acidic. Because these are waterbodies that are highly sensitive to
2 acidification and likely naturally acidic, they exceed the calculated CL at any deposition amount.
3 For these reasons, these sites have been removed from the assessment. For more information on
4 these areas see Appendix 5A, section 5A.2.1
ANC = 20 jjeq/L
5
6
7
< ' < . V-
Exeed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
d.
I* * '*
\ \ I !tLl
\\h~Jh
ANC = 50 (jeq/L
ANC = 50 peq/L 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 fieq/L.
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Exeed the Critical Load
Nearthe Critical Load (±3.125 meq/m2/yr)
d.
ANC = 50 peq/L East
ANC = 20 peq/L West
1
2 Figure 5-8. Waterbodies for which annual average S only deposition for 2006-08 exceed
3 CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 jieq/L.
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• Exeed the Critical Load
2 Figure 5-9. Waterbodies for which annual average S only deposition for 2010-12 exceed
3 CLs for ANC thresholds: a. 20, b. 30, c. 50, d, 50/20 |ieq/L.
ANC = 30 peq/L
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ANC = 50 jjeq/L
Exeed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
ANC = 50 |jeq/L East
ANC = 20 (jeq/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 |ieq/L.
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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.
Exeed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
ANC
ANC
= 50 ^eq/L East
= 20 ^ieq/L West
The results of the national scale analyses show a significant reduction in exceedances
over time as sulfur deposition has decreased (see 2.3.1 for deposition trends). It can also tell us
about the levels of deposition that occurred in those time periods and provide the foundation for
the additional analyses below to look at what impacts might be expected under different
geographic scales and deposition scenarios.
5.2.3.2 Ecoregion Analyses
Ecoregion-level analyses, summarized below, 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 ecoregion Ills (from this point on ecoregion, at the level III,
specification, will be referred to as ecoregions), except for ones that historically are known to be
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in acid sensitive areas since acid sensitive areas typically have been heavily sampled, hence,
contain many CLs (see Figure 5-12). These areas tend to be in the eastern ('ONUS in such
ecoregions as Central Appalachian, Atlantic Maritime Highlands, and the Blue Ridge. Areas in
the Rockies and Si erra Nevada also have been sampled extensively and contain many CLs. 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, ecoregions containing
greater than 50 CLs were the focus of this analysis.
For the CON US there are a total of 105 ecoregions of which 25 met the criteria of 50 or
more CLs (and excluding the three naturally acidic eastern ecoregions), yielding 18 in the east
and 7 in the west. The Northern Appalachian and Atlantic Maritime Highlands ecoregion had the
most CLs at 2,851 (see Appendix 5A, Table 5A-10).
Figure 5-12. Locations of aquatic critical loads mapped across Ecoregions III.
For each of the 25 ecoregions meeting the CL cri teria for this analysis, median annual
average S deposition was determined for each 3-year period using a GIS zonal statistic. 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
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18 eastern ecoregions had a median value of 11.0 Kg S/ha-yr in 2001-03 and 1.9 Kg S/ha-yr in
2018-20 (Table 5A-25). Total S deposition for the seven western ecoregions was lower in each
3-year period, ranging from a median of 1.14 Kg S/ha-yr in 2001-03 to 0.84 Kg S/ha-yr in
20180-20. For the period 2001-2003, 17 of the 25 ecoregions had a median total S deposition
over 10 Kg S/ha-yr while there were none over 10 Kg S/ha-yr in the period 2018-2020.
Ecoregions with the highest median total S deposition were Western Allegheny Plateau, Erie
Drift Plain, North Central Appalachians, Central Appalachians, Northern Piedmont, Eastern
Corn Belt Plains, Southwestern Appalachians, and Ridge and Valley, all in the Mid-Atlantic
region of the eastern U.S (see Appendix 5 A, Table 5A-14).
Table 5-2. Min, max, and median total S deposition for the 25 ecoregions included in the
analyses. Deposition values were determined by a zonal statistic for each
ecoregion.
'otal Sulfur Deposition (Kg S/ha-yr)
2001-03
2006-08
2010-12
2014-16
2018-20
Minimum
0.90
0.98
0.83
0.79
0.54
Maximum
18.08
15.05
7.24
4.70
3.64
Median
9.57
8.05
4.34
2.62
1.87
For waterbodies in the 25 ecoregions, this ecoregion-scale analysis compared the
ecoregion S deposition estimates in each of the five periods of deposition to the waterbody-
specific CLs and evaluate the exceedances per ecoregion (Appendix 5A, section 5A.2.2.1). There
were no exceedances of any of the ANC thresholds in the west, so we focus here on the eastern
ecoregions. We summarize these results for the 18 eastern ecoregions below, in terms of number
and percentage of waterbodies per ecoregion with CI exceedances in every ecoregion-time period
combination, using ecoregion deposition estimates as the organizing parameter. For example,
Table 5.3 presents the CL exceedance results of the ecoregion level analyses for the three ANC
target levels, summarized by ecoregion median annual average S deposition (regardless of the 3-
year period in which it occurred). For each kg S/ha-yr, Table 5.3 presents the number of
ecoregion-time period combinations with 10, 15, 20, 25 and 30% of waterbodies exceeding their
CL for the specified ANC target level.
For example, among the eastern ecoregion-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 an of the three ANC targets
(Table 5-3). In contrast, for annual average S deposition at or below 10 Kg S/ha-yr, there are 22
of the 90 ecoregion-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 with more than 30% of its
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1 waterbodies exceeding their CLs. The lowest annual average S deposition level associated with
2 any ecoregion-time period combinations having more than 30% of waterbodies exceeding their
3 CLs was 10 Kg S/ha-yr, for which one ecoregi on-time periods had more than 30% of the
4 waterbodies exceeding their CLs for all three ANC targets.
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Table 5-3. 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%
waterbodies exceeding CL for ANC
target of 20, 30 or 50 yeqlL
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 jjeq/L
ANC target of 30 jjeq/L
ANC target of 50 jjeq/L
<2
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
<2
52
0
<3
29
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
<3
55
0
<5
51
2
1
0
0
0
4
1
0
0
0
9
3
2
1
0
None of the 75 western ecoregion-
time periods in analysis had ecoregion
S deposition estimates above 3 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
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We also 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 5-3 above, using percentages instead of
absolute counts in the presentation. For example, rather than the number of ecoregion-time
periods, with a particular S deposition estimate, that have more than 10% of waterbodies
exceeding their CLs for an ANC target of 20 |ieq/L, Figure 5-13 presents the percentage of
ecoregion-time periods that have less than or equal to 10% 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
5-4 with the bins for percentage of waterbodies exceeding their CLs (>10, 15, 20, 25 and 30%)
flipped to be described as percentage of waterbodies that are at or below their CLs (i.e., can
achieve the ANC target). The complete results can be found in Appendix 5 A, Section 5A.2.2.
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100%
90%
„ 80%
c
I 70%
C£
.8 60%
50%
40%
30%
20%
10%
0%
100%
90%
80%
70%
S1 60%
or
"S 50%
40%
30%
20%
10%
0%
100%
90%
70%
| 60%
8 50%
UJ
| 40%
30%
20%
10%
0%
\ N. , »
-10%
-15%
20%
25%
-30%
-10%
-15%
-20%
25%
-30%
-10%
-is*
20%
25%
-30%
8 12
Sulfur Deposition (kg S/ha-yr)
16
20
2 Figure 5-13. Percentage of ecoregion-time period combinations with less than or equal to
3 10,15, 20, 25, and 30% of waterbodies exceeding their CLs for ANC of 20
4 (top), 30 (middle) and 50 jneq/L (bottom) for 18 eastern ecoregions.
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39
As noted above, Table 5-4 presents the same dataset with the bins for percentage of
waterbodies exceeding their CLs (>10, 15, 20, 25 and 30%) flipped to be described as percentage
of waterbodies that are expected to achieve an ANC at/above the specified target. Overall, the S
deposition levels in the 18 eastern ecoregions analyzed includes 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. 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/h-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/h-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 72% 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/h-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/h-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/h-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%
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1 of those combinations, respectively. The number of ecoregion-time period combinations
2 in this deposition bin is less than half the full dataset for the 18 eastern ecoregions.
3 • For the 75 western-time period combinations, all of which had an S deposition estimate
4 below 4 kg/ha-yr, at least 90% of waterbodies per ecoregion were estimated to achieve an
5 ANC at or above 50 |ig/L.
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Table 5-4. 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%
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To further describe how these results compare to recent conditions, we looked at sulfur
deposition for eastern ecoregions under the two most recent time periods, 20014-2016 and 2018-
2020 and the critical load exceedances that would be expected for the targeted ANC levels of 50,
30 and 20 ueq/L. As would be expected, given deposition trends, there were fewer exceedances
in the most recent time periods. Figures 5-14 and 5-15 show the results of these analyses.
OJ
c
T3
CD
CD
O
X
LU
C/D
CD
T3
O
M
i—
CD
16
14
12
10
8
6
4
2
0
•
•
+
•
;
•
•
•
• 4
•• . «
>
~
.. j
• «•
•
{
• •—
:• •
•
•
• .
•
• ANC 20 (jeq/L (CL>0)
ANC 30 |jeq/L (CL>0)
ANC 50 yieqll (CL>0)
0 1 2 3 4 5
Total S Deposition (Kg S/Ha-yr)
Figure 5-14. Percentage of waterbodies in each of the 18 eastern ecoregions exceeding their
CL for ANC values of 20, 30 and 50 jieq/L, based on annual average S
deposition for 2014-2016.
16
CD
14
c
'td
CD
12
CD
O
10
X
LU
CD
CD
8
"O
O
M
i.
6
CD
i
4
vO
2
0
•
•
•
•
•
#
•
•
• •
•
•
•
; t *
*1*4
• ANC
20
peq/L (CL>0)
ANC
30
peq/L (CL>0)
ANC
50
peq/L (CL>0)
0 1 2 3 4 5
Total S Deposition (Kg S/Ha-yr)
Figure 5-15. Percentage of waterbodies in each of the 18 eastern ecoregions exceeding their
CL for ANC values of 20, 30 and 50 jieq/L, based on annual average S
deposition for 2018-2020.
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1 Three or fewer of 18 eastern ecoregions have more than 10% of their waterbodies
2 exceeding the CI for all of the target ANC values for either time period. The median sulfur
3 deposition for eastern ecoregions included in the analyses for the 2014-2016 time period was 3.0
4 and for the 2018 2020 time period was approximately 1.9 kg/ha/yr. Figure 5-16 through 5-18
5 show the eastern ecoregions with exceedances of target critical loads under the two most recent
6 time periods. Figure 5-19 shows the ecoregions with exceedances for the entire U.S. for the most
7 recent time periods using an ANC target of 50 |ieq/L for the east and 20 for the west.
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2018 - 2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
1
2
3
Figure 5-16. Map of critical load exceedances for S only deposition from 2018-20 (top) and
2014-16 (bottom) for ANC threshold of 20 jieq/L.
Percent Exceedances
(ANC = 20 neq/L)
0-10%
10-15%
Bl >15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
| | Areas without critical loads
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2018 -2020 Sulfur Deposition Ecoregion Exceedances
1
2
3
Percent Exceedances
(ANC = 30 jjeq/L)
0-10%
| 10-15%
>15%
Ecoregions where critical loads are < 50 values
[x$5l 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 an ANC threshold of 30 neq/L.
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
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2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
1
2
3
Percent Exceedances
(ANC = 50 iieqlL)
0-10%
10-15%
>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 50 neq/L.
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2018 - 2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 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 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 jieq/L for East and 20 (teq/L
for the West.
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5.2.3.3 Case Study Analyses
The case study areas are geographically diverse acid sensitive areas across the ("ONUS
that have sufficient data to complete the quantitative analyses. Five case study areas were
identified that meet the criteria (Figure 5-20), three in the eastern U.S. (NOMN, SHVA and
WHMT) and two areas are in the western U.S. (ROMO and SINE). Three of the five areas
(SHVA, ROMO and SINE) are inclusive of Class I areas. Additional aquatic acidification
analyses using the case studies can be found in Appendix 5A. A total of 524 CLs were found in
the 5 case study areas, excluding SHVA which had complete coverage (4977 CLs). ROMO,
SINE, NOMN, and WHMT had 121, 139, 183, and 74 CLs respectively. For this discussion, we
will refer to analyses that looked at 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 (icq/L for
the eastern case studies and 20 jaeq/L for the western case studies.
Figure 5-20. Location of the case study areas. Northern Minnesota (NOMN), Rocky
Mountain National Park (ROMO), Shenandoah Valley (SHVA), Sierra
Nevada Mountains (SINE) and White Mountain National Forest (WHMT).
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Table 5-5. 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
(Meq/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: 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 (shading).
The steady-state mass balance modeling results summarized in Table 5-5 indicates the
average CL for achieving a target ANC of 20 |ieq/L in the five study areas ranges from about 10
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
|ieq/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 CO 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.
5.2.4 Uncertainty Analyses
Models used to estimate CLs, drawn from the NCLD, were derived using a variety of
commonly used models, including the steady-state mass-balance model, Steady State Water
Chemistry (SSWC) model, and dynamic models such as the Model of Acidification of
Groundwater In Catchment (MAGIC) run out to year 2011 or 3000. 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.
Uncertainty associated with runoff and surface water measurements is not characterized here.
The catchment supply of base cations from the weathering of bedrock and soils is the factor that
has the most 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
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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 CONUS
(e.g., Dupont et al., 2005 and others), the uncertainty in this estimate is unclear and could be
large in some cases. A quantitative uncertainty analysis was completed to evaluate the
uncertainty in the CL and exceedance estimation that were used in these analyses (as described
further in Appendix 5A, section 5A.3).
Monte Carlo analyses (described in detail in Appendix 5A, section 5A.3.1) were used to
describe the 5th and 95th confidence intervals around the CL for more than 14,000 waterbodies in
order to estimate the uncertainty around the CLs. The magnitude of the confidence interval for
the CLs was 7.68 meq S/m2-yr or 1.3 Kg S/ha/yr. The range based on the 5th to 95th magnitude
of the confidence interval was 0.37-33.2 meq/m2/yr or 0.1-5.3 Kg S/ha/yr giving a confidence
level of ±3.84 meq/m2/yr or ±0.65 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 (Appendix 5A, Table 5A-49). 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., western U.S.) had high uncertainty. Fifty-one ecoregions had
sufficient data to calculate the 5th to 95th percentile (Appendix 5A, Table 5 A-50). CLs with the
lowest uncertainty occurred in the eastern U.S., particularly along the Appalachian Mountains,
upper midwest, and Rockies Mountains (Appendix 5A, Figure 5 A-54). 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.
The magnitude of the error for the N leaching method used in the analyses was estimated
by quantifying the uncertainty of the flux of nitrate (NO3) to a given lake or stream. Water
quality data for the past 28 years from the EPA's Long-term Monitoring (LTM) program was
used to assess the uncertainty of the influx of nitrate (NO3"). The results of his uncertainty
analysis are summarized in (Appendix 5A, Table 5A-51) by region and time period. Overall,
nitrate flux varied between regions with Adirondacks lakes having the highest annual fluxes and
New England Lakes with the lowest fluxes. While a comprehensive analysis of uncertainty has
not been completed for these data prior to the analysis included in this review, expert judgment
suggested the uncertainty for combined N and S CLs is on average about ±0.5 kg/ha-yr (3.125
meq/m2/yr), which is consistent with the range of ± 2.30 to 3.77 meq/m2-yr determined from this
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analysis. Given this consistency, an uncertainty of ±3.125 meq/m2-yr was applied to the critical
load exceedances for the national, ecoregion, and case studies assessments.
Critical loads used in the national assessment analysis used different methods than those
in the ecoregion and case study analyses (see Appendix 5A, section 5A. 1.5). To understand
differences in the CLs calculated with different methods, waterbodies where it was possible to
use multiple methods were compared. There are three main CL approaches all based on
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 and 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.3.1 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.
5.2.5 Summary
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 current deposition levels, we focused on S
deposition solely. For these analyses ANC was used as the water quality indicator of
acidification, based on its longstanding use for this purpose. 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 III, and case studies. 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 2011 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
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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, only about 4% of waterbodies nationwide would not be
able to maintain an ANC of 50 |ig/L in the east and an ANC of 20 |ig/L in the west (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 ecoregi on median S deposition estimates in 2014-16 were below 5 kg/h-yr in all
ecoregions and the estimates for 2018-20 were all below 4 kg/h-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 ecoregi on 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 a range 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. The much higher deposition levels of the past are evident by the fact
that 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.
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/h-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/h-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
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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/h-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/h-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/h-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.
5.3 NITROGEN ENRICHMENT
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 4.2.2.1 above.3 Separate quantitative analyses were not performed for these
categories of effects in this review. As recognized above, quantitative analyses have been
performed for welfare endpoints for which the evidence is most robust, and for which the
available information, tools and assessment approaches is supportive of such analyses for the
purposes in this review. With regard to the effects related to N enrichment in various types of
aquatic ecosystems, such analyses were not performed 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. Quantitative information relating deposition to consideration of
ecosystem effects has been described below for two of these categories, for which the ISA
summarizes studies that have developed critical load estimates. These categories are effects
related to N enrichment in wetlands and freshwater lakes and streams.
5.3.1 Wetlands
Significant 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 (growth, species
composition, species competition, peat and peat water chemistry, decomposition, and nutrient
3 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). These categories of effects are described in section 4.2.3
above,
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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, Section 11.3). In U.S. coastal wetlands, two studies are available that have
considered N loads below 100 kg N/ha/yr. Wigand et al. (2003) estimated a critical load to
protect the community structure of salt marshes to be 63 to 400 kg N/ha/yr. Caffrey et al. (2007)
provided additional evidence that 80 kg N/ha/yr can alter microbial activity and
biogeochemistry. Two recent studies have described CLs for effects in freshwater wetlands. 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 (Greaver et al 2011). A critical load for purple
pitcher plants (Sarraceniapurpurea) has also been estimated (between 6.8-14 kg N/ha/yr) to
protect the population based on morphology and population dynamic endpoints.
A comparison of freshwater wetland CLs to observed ecological impacts of N from
recent studies (4.4-500 kg N/ha/yr) is provided in the ISA (Appendix 11, Figure 11-7). At the
lowest experimental addition level (16 kg N/ha/yr), there are observations of altered C and N
cycling and altered biodiversity. The endpoints affected include decreases in moss cover,
increased peat biomass, decreased N retention efficiency, and altered/damaged leaf stoichiometry
in vascular plants. However, this information is limited, and additional experimental evidence is
needed on critical loads for North American wetlands.
5.3.2 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). Exceedance estimates were as high as 48% of the Greater Yellowstone area study region,
depending on the threshold value of NO3 concentration in lake water selected as indicative of
biological harm.
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
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load of 4.1 kg/TN/ha/yr above which phytoplankton biomass P limitation is more likely than N
limitation was identified by Williams et al. (2017) for the western U.S. Modeled critical loads
ranged from 2.8 to 5.2 kg/TN/ha/yr, and a performance analysis indicated that a critical load of
2.0 kg/TN/ha/yr would likely reduce the occurrence of false negatives to near zero. However,
this evidence is geographically specific perhaps even waterbody specific and is not available for
most of the U.S.
5.4 TERRESTRIAL ECOSYSTEMS
As noted in the introduction to this chapter, quantitative analyses in the 2012 N oxides/
SOx 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 quantitative analyses in that 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). A more qualitative
approach was taken for nutrient enrichment in the 2012 review by simply describing deposition
ranges identified from observational or modeling research as associated with potential
effects/changes in species, communities and ecosystems and recognizing the 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 have taken the approach of drawing on prior analyses and published studies
recognized in the ISA that provide information pertaining to deposition levels associated with
effects related to terrestrial acidification and N enrichment. We reached this decision in
consideration of the available studies and with investigation into various assessment approaches.
As described in section 5.2 above, a full quantitative assessment has been performed, at multiple
scales, for consideration of aquatic acidification, an endpoint for which the available
information, tools and assessment approaches provides strong support of such analyses that are
targeted to the needs in this review. For terrestrial effects related to N and S deposition, this
section draws on quantitative information relating deposition to consideration of terrestrial
ecosystem effects, as described below and in the following subsections.
Since the 2012 N oxides/ SOx review, in addition to publications 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, several publications have analyzed large datasets
from field assessments of tree growth and survival, as well as understory plant community
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richness, with estimates of atmospheric N and/or S deposition. These 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. Both mass balance 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.
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
acidification indicator target). The complexities associated with site-specific aspects of
ecosystem recovery from historic depositional loading become evident through application of
dynamic models.
Observational studies, on the other hand, are inherently affected by 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, it may be reasonable to conclude, however, that there is
potential for such influence. This is an uncertainty associated with interpretation of the
observational studies regarding the deposition levels responsible for the observed variation in
plant or plant community measures. Thus, while observational studies contribute to the evidence
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base on the potential for N/S deposition to contribute to ecosystem effects (and thus are
important evidence in the ISA determinations regarding causality), 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 are considered in the sections below.
5.4.1 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 4.2.1.2 and 4.2.2.2 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 commonly used indicator for soil acidification in quantitative modeling analyses of the
effect of acidifying deposition on forests (see section 5.3.2 below) is the ratio of base cations to
aluminum (BC: Al), with higher ratios indicating a lower potential for acidification-related
biological effects (ISA, Table IS-2). The ratio 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 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 soil solution Ca: Al ratio all serve as
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).
There are many indicators of N enrichment and potential eutrophication, including N
accumulation, e.g., increased soil N concentrations or decreased C:N ratios (ISA, section
IS.5.1.1). Increases in soil N can, however, also 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
4 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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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 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 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.
5.4.2 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 5.4.1
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 (ISA, Appendix). 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., 1998a). 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, experimental addition studies and observational or gradient studies. As noted
in section 5.4. above, each of these categories of studies has associated strengths and
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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.
5.4.2.1 Steady-State Mass Balance Modeling
As for assessment of aquatic acidification (see section 5.2 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
somewhat 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.
The indicator most 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). Two meta-analyses are 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). The first analysis compiled findings from laboratory, greenhouse and
field studies, with growth matrices varying from water solution to sand to field soil (Sverdrup
and Warfvinge (1993).5 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:A1 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).6 In
consideration of these analyses, the BC:A1 targets used in the 2009 REA for identifying
5 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).
6 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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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:A1 target values differing by a factor of
nearly 20 (Table 5-6 and Table 5-7).
Table 5-6. 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, section 4.5.1.2).
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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
BC:A1 target of 10, this study reported a range of deposition estimates slightly higher than those
from the 2009 REA (see Table 5-7 below).
Table 5-7. 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
Modelinq 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:AL 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)
5.4.2.2 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, 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 yrs (dating back to 1988) was
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 yrs (beginning 1988) was associated with
reduced basal area (red spruce) or growth (red maple, tulip poplar and black cherry, red
pine) at sites in VT, MA, WV.
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• Additions of 25 kg N/ha-yr for 13 yrs (beginning in 1989) was 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).
5.4.2.3 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)7 sites across the U.S., and estimates of average deposition of S or N compounds at,
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; Dietz 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 5-8; Dietz 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 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 were also negatively associated with
7 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 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, 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 3 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.3.2.3).
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 metric (across full range or at the
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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
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). 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, section 5.2.1.3; 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, 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,
section 5.2.1.3).
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1 Table 5-8. Tree effects and associated S/N deposition levels from observational studies
2 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 S042 deposition
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 et al. (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 NO3 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 U-shaped 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 U-shaped
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 et al. (2018)
Growth of 2 species was negatively associated with N
deposition across all species' sites.
Growth of 2 other species (with U-shaped
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 U-shaped
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.
Details of information summarized here are provided in Appendix 5B, section 5B.2.2.3 and Tables 5B-2 and 5B -6.
A The two values below 5 kg S/ha-yr were for species with 60-80% of samples from the Northern Forests ecoregion.
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5.4.3 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 are discussed in the subsections below. The focus in these studies,
however, is predominantly on N deposition.
5.4.3.1Effects 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 plants8 and values of aN deposition metric at more than 15,000 forest, woodland,
shrubland and grassland sites across the U.S. (Appendix 5B, section 5B.4.2). The study grouped
the sites into open- or closed-canopy sites, with forest sites falling into the closed-canopy
category and the rest, open-canopy. The data for sites in each of the two categorized 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.4.2).
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).
8 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 communities9 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 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).
9 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, section 6.3; Appendix 5B, section
5B.4). 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).
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 numbers10
after 13 years (Clark and Tillman, 2008).
5.4.3.2 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 5.1.1 above, and the extent to which the effects relate to airborne SOx (v.s
associated acidic deposition) is not clear. Somewhat similarly, section 5.1.2 above 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,
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
the northeastern U.S. reported that "lichen metrics were generally better correlated with
10 Species number changes in control plots contributed to this finding (Clark and Tillman, 2008; Isbell et al., 2013).
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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 studies, newly available in the ISA, 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 5.1.2 above). Other
studies in Europe and Canada have not reported such associations with relatively large N
deposition gradients.
5.5 KEY FINDINGS AND ASSOCIATED UNCERTAINTIES AND
LIMITATIONS
5.5.1 Aquatic Acidification
Key findings related to deposition levels associated with aquatic acidification, and more
specifically to different waterbody buffering capacity targets, in terms of ANC, are summarized
below.
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• The most widely used indicator of surface water acidification, and subsequent recovery
under scenarios with lower acidifying deposition, is ANC.
• Considerable new research on critical loads for acidification is available and both steady
state and dynamic models have been used to generate ANC based critical loads for much
of the U.S. Empirical studies have also identified a range of critical loads over a wide
range of ANC levels for selected areas known to be sensitive to acidification.
• Quantitative assessments were developed for this review to evaluate the impact of
nitrogen and/or sulfur deposition on aquatic acidification across the U.S. using a CL
approach. This relationship between acidifying deposition of nitrogen and sulfur; water
chemistry changes reflected by changes in ANC; and waterbody health and biodiversity
are the basis for the quantitative assessments.
• Key design elements of the approach employed in the quantitative assessments include the
spatial scales, water quality indicator of acidification, how to define the CL and
exceedance parameters, data sources for deposition estimates, consideration of relative S
and N contributions to acidifying deposition, consideration of ecosystem sensitivity and
attainability of specific ANC targets and focus for quantitative uncertainty analyses.
These elements of the analyses are summarized here:
- Spatial Scale: National, Ecoregion III, and Case Study (Class I areas)
- Chemical Indicator: ANC, with target values of 20, 30 and 50 [j,eq/L
- Critical Load Sources: NCLD Database and empirical CL from ISA
- Exceedance Calculation: CLs are exceeded where deposition is above the CL+
3.125 meq S/m2-yr or 0.5 Kg S/ha/yr and are not exceeded where deposition is
below the CL - 3.125 meq S/m2-yr or 0.5 Kg S/ha/yr.
- Deposition Data Source and Time Periods: TDEP and three-year averages were
calculated for these periods: 2001-03, 2006-08, 2010-2012, 2014-16 and 2018-20
- Relative Contributions: Focus on S deposition CLs as analyses indicated
negligible contribution to acidification from N under most conditions
- Attainability of ANC targets: CLs<0 and those areas for which deposition was not
a driving factor were not used in the analyses
• Under recent (2018-2020) levels of S deposition, and available CL modeling, around 4%
of waterbodies nationwide for which we have sufficient data are not expected to attain an
ANC of 50 [j,eq/L.
• Ecoregion-level analyses of ANC-based CLs for the five periods from 2000-2002 through
2018-2020 provide further characterization of both spatial variability of acid sensitive
waterbodies across the U.S. and the magnitude of deposition driven acidification impacts.
- In the western ecoregions, for which the ecoregion S deposition estimates were
below 4 kg/h-yr, the analysis indicated an ANC at or above 50 [j,eq/L to be
achieved in all five time periods.
- Between the three-year period 2000-2002, which was the analysis year for the
2011 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
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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, about
96% of waterbodies nationwide would be able to maintain an ANC of 50 |ig/L
(see Table 5-1).
- Although the ecoregion S deposition estimates in the 15 eastern ecoregions
analyzed were all below 5 kg/ha-yr in the two most recent time periods (2014-16
and 2018-20), ecoregion deposition estimates for the full dataset of five time
periods range 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. The
much higher deposition levels of the past are evident by the fact that 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. 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 at or below 9 kg/h-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 at or below 5 kg S/h-yr,
these values are 96, 92 and 82% of combinations.
• The case study analyses of the CL modeling for waterbodies in those geographically
diverse locations include several Class I areas. In the three eastern case studies, the CL
modeling indicates that at an annual average S deposition of 9-10 kg/h-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/h-yr, 70% of the sites in the areas are
estimated to achieve an ANC at or above 20 |ieq/L. Lower S deposition values are
estimated to achieve higher ANC across more sites.
There are three major areas that contribute uncertainties to the results: (1) the linkage
between the biological/ecosystem response and acidification, (2) the linkage between specific
ecological impacts and the ecological indicator (ANC) and (3) the linkages between deposition
and ANC through the CL approach.
The first, the linkage between acidifying deposition and the ecosystem response has been
well documented over 40+ years of evidence (ISA, Appendix 8). Associations have been long
established between aquatic acidification (e.g. reduced pH, and elevated Al) and adverse
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ecosystem effects, including fish mortality, decreased species diversity, etc. (ISA, Appendix 8).
Variability in quantitative aspects of these associations, which generally relate to factors such as
climatological conditions, lake and stream size, other water quality parameters (e.g., dissolved
organic carbon, dissolved oxygen, etc), biological interactions, etc, complicate the quantitative
relationship of biological/ecological responses to acidification.
The second area of uncertainty is in associating specific levels of ANC with specific
biological/ecological effects. The water quality parameter, ANC, is the preferred indicator for
acidification because of its linear relationship with deposition driven acidification as opposed to
pH which is influenced by natural factors such as the level of dissolved CO2 in water. Surface
water levels of ANC, pH and A1 are controlled by well-defined aquatic equilibrium
chemistry. While the relationships between ANC and ecological impacts is well-known, 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.
The third point of uncertainty is associated with our understanding of the biogeochemical
linkages between deposition and ANC, and determination of steady-state CLs. This by far is the
largest uncertainty and the one that is most difficult to characterize and assess. There is
uncertainty associated with parameters 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 estimate and the
exceedance calculation relies 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
measurements is broadly understood, however, the ability to accurately estimate the catchment
supply of base cations to a water body is still difficult. This 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).
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. For this reason, an uncertainty analysis focused on this aspect of
state-steady CL modeling was performed (summarized in section 5.2.4 above).
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5.5.2 Other Aquatic Effects
Key findings related to deposition levels associated with other aquatic effects are
summarized below. There are several other effects to aquatic ecosystems from deposition of
nitrogen and/or sulfur for which there are a range of associated deposition levels. Most of these
impacts are associated with nitrogen deposition but some, such as sulfide toxicity, are primarily
related to sulfur. 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 less than 1 kg N/ha/yr for
impacts to diatom communities in high elevation lakes to over 500 kg N/ha/yr in some N
addition studies in wetlands. The information available on these types of impacts is sufficient for
causal determinations but often localized or otherwise limited for the purpose of quantitative
assessment relating deposition to waterbody response at an array of U.S. locations. For this
review, these impacts were considered from a qualitative perspective and contribute to the
evidence base described in Chapter 4.
5.5.3 Terrestrial Effects
Key findings related to ambient air concentrations and deposition levels associated with
terrestrial effects discussed in prior sections are summarized below.
5.5.3.1 Direct Effects on Plants and Lichens of Pollutants in Ambient Air
The evidence related to exposure conditions for direct 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. With regard to SO2, while most studies are
focused on visible foliar injury in sensitive plants (with exposures varying from 8 hours at 0.2
ppm S02to repeated hourly concentrations of 0.4 ppm), laboratory studies have also reported
reduced photosynthesis for repeated exposures of 3 to 4.2-hours/day to concentrations on the
order of 0.25 to 0.5 ppm SO2, and reduced soybean yield after repeated multi-hour exposures to
0.19 ppm SO2 (section 5.1.1 above). 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. Uncertainties relate to the extent to which effects observed in controlled
laboratory conditions may also be observed in the field.
With regard to oxides of N, the evidence includes reported effects on plant
photosynthesis and growth resulting from multiday exposures of six or more hours per day to
NO2 concentrations above 0.1 ppm. Effects occur at much lower exposures to HNO3. Laboratory
and field studies report effects that include effects on tree foliage at 50 ppb (-75 |ig/m3) HNO3 in
controlled exposures and on survival of several lichen species in the Los Angeles basin during
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the 1980s. The studies 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. Regardless, the elevated concentrations of NO2 and HNO3 in the
Los Angeles area in the 1970s-90s is well documented. 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 5.1.2). Ambient air concentrations of HNO3 in the Los Angeles metropolitan
area have declined markedly, as can be seen from Figure 2-40 (in section 2.5.4), which compares
concentrations at CASTNET monitoring sites between 2019 and 1996.
5.5.3.2 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:AL 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 5.4.2.1 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).
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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.3.1). 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).
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 shortness 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). 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., 2018u). Interestingly, survival for the same
9 species groups was also negatively associated with long-term average ozone (Dietze and
Moorcroft, 2011).
11 The study by Horn et al. (2018) constrained the S analyses to preclude a positive association with S.
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• 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'Vr"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
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 to 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_1yr"
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
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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
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.
5.5.3.3 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 5.4.3.1 above). Experiments involving additions of
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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
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 5.4.3.2 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 5.5.3.1 above, of information on exposure conditions associated with lichen
species effects of oxides of N such as HNO3.
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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 metrics for S oxides, N oxides and PM. This characterization is a key aspect of the
approach taken in this Policy Assessment (PA) for assessing deposition-related effects and the
adequacy of the current secondary standards, as summarized in section 3.2 above (Figure 6-1).
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.
6.2 RELATING AIR QUALITY TO ECOSYSTEM DEPOSITION
While many of the ecological effects examined in this review are associated with
deposition of S and N, the NAAQS are set in terms of an ambient atmospheric concentrations.
Therefore, an important part of this revi ew is to quantify the relationship between air
concentration and deposition. The goal of this section is to examine the relationship between air
concentrations and atmospheric deposition of S and N. Understanding more about this
relationship can then help inform how changes in air concentrations, and the emissions from
which they result, can lead to changes in the amounts of S and N deposited. This understanding
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can then help inform decisions on the best air quality metric(s) for a standard that protects
against N and S deposition-related effects.
Atmospheric deposition of a pollutant 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 (settling onto the surface in rain, 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 surface properties. Similarly, the rate of
wet deposition is influenced by the 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 can vary as the nitrogen and sulfur
containing compounds change in the atmosphere. For example, NO2 can oxidize to form nitric
acid (HNO3), which has a much higher dry deposition velocity than NO2. However, HNO3 can
also partition into the particle phase in the presence of ammonia to form ammonium nitrate
(NH4NO3). Fine particles, such as PM2.5, have a much slower dry deposition velocity and remain
in the atmosphere longer. On the other hand, HNO3 can also absorb onto larger, coarse particles,
whose dry deposition velocity is faster than smaller PM2.5. Thus, as the chemical and physical
forms of nitrogen and sulfur vary in the atmosphere, it leads to differences in the rate of
deposition, and causes variability in the relationship between total air concentrations and
atmospheric deposition. Furthermore, the dry deposition velocity is influenced by meteorological
conditions and their interaction with the deposition surface properties. 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 have an impact on
dry deposition of particles when they interact with surface features, such as friction velocity,
roughness height, and surface wetness (ISA, Appendix 2, section 2.5.2; Wesley, 2007).
For wet deposition, the chemical form plays a minor role, and the amount of nitrogen and
sulfur transferred to cloud water and falling precipitation is largely driven by the air
concentration. However, the vertical distribution of the pollutant is important. The air
concentration for the NAAQS has historically been measured near ground level where the health
and ecological effects occur. Atmospheric nitrogen and sulfur near the ground can settle onto
leaves, soils, buildings, and other surfaces by dry deposition. Sulfur and nitrogen higher in the
troposphere are scavenged by clouds and falling precipitation via wet deposition. While dry
deposition is directly related to the ground-level concentration, wet deposition is affected by
concentrations throughout the troposphere.
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For ground-level emission sources, much of the nitrogen and sulfur is near the surface
and most of the deposition can be attributed to dry deposition. Further from emission sources,
pollutants become well-mixed in the atmosphere, and wet deposition can play a larger role. The
frequency of precipitation is also important. 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 rainy years with high
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 cause
variability in the relationship between ground-level air concentrations and deposition.
The PA in the last review introduced the Transference Ratio, defined as the ratio of
deposition to air concentration (2011 PA, section 7.2.3). This was calculated from annual
average values and spatially averaged over eco-regions 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 has
some important uncertainties. For example, the transference ratio approach does 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). Furthermore, the results of the approach are influenced by which air quality
model is used in the analyses. Studies completed since the previous review 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).
This Policy Assessment recognizes these limitations, and as described in section 2, also
recognizes that emissions, air concentrations and deposition, have declined for sulfur and
oxidized nitrogen in recent years. The evolution of this trend is an opportunity to observe the
relationship of the change in deposition due to a change in emissions and air concentrations
using ongoing air concentration and wet deposition measurements. This assessment examines the
historical record of observations, multi-decadal CMAQ simulations, and merged model-
measurement TDEP data to assess the relationship between air concentration of a specific
compound or combination of compounds and estimates of N and S deposition in specific
locations. After examining those relationships, this section then looks at the recent and historical
relationships between air concentrations of S and N and estimates of S and N deposition by
TDEP across the U.S.
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6.2.1 Class I Areas - Collocated Site Analyses
In this first set of analyses, the focus is on understanding more about the deposition of S
and N in remote areas that are further away from most emission sources of S and N, as well as
from most SO2, NO2 and PM2.5 FRM/FEM monitors. These areas tend to be of particular interest
for ecological and legal reasons, as well. Class I areas have some special federal protections
(e.g., focus of efforts to reduce regional haze).1 For these analyses, this section analyzes
historical trends from measurements, CMAQ simulations, and model-measurement fusion data
(i.e., TDEP) to identify which S and N-related compounds are most closely related to S and N
deposition in these rural areas. Additionally, 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 more typical relationships. Data for deposition and air concentration are from both
observations and model simulations. The air concentrations are the annual average
concentrations. The deposition values are the sum of total deposition (wet + dry) for the same
year-long period. However, when assessing deposition estimates in this part of the analysis, the
assessment (i.e., section 6.2.1.1) relies on wet deposition as a proxy for total deposition since dry
deposition is not routinely measured.
The set of Class I areas with co-located CASTNET monitoring stations, chemically-
speciated PM2.5 from the IMPROVE network, and NADP/NTN wet deposition monitors have
been identified and listed in Table 6-1 and shown in the map in Figure 6-2. There are 27 areas
with co-located data. The wet, dry, and total deposition are TDEP estimates, and since these data
are at monitoring locations, the results are largely informed by the measured values. Figure 6-3
shows the range of wet and dry deposition levels across these 27 areas for the 2017-2019 period.
For these locations, in recent years, N deposition tends to be much greater than S deposition,
likely due to the fact that most of these locations are in the western U.S. and distant from S
sources, which are principally located in the eastern U.S. S deposition has also declined more
than N deposition over the last few decades (section 2). For nitrogen, dry deposition contributes
57% and wet deposition contributes 43%. The annual total deposition from 2017 - 2019 for
sulfur deposition is 60% wet deposition and 40% dry deposition.
1 Areas designated as Class I receive special protection status under the Clean Air Act (CAA), and 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.
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1 Table 6-1. Co-located CASTNET, NADP/NTN, and IMPROVE monitoring stations used
2 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
NDOO
THR01
Voyageurs
VOY413
MN32
VOYA2
Wind Cave
WNC429
SD04
WICA1
Yellowstone
YEL408
WY08
YELL2
Yosemite
YOS404
CA99
YOSE1
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OLY421"
• NCS415
• MOR409
' GLR468
• VOY413
• YEL408
• THR422
»WNC429
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30 N -
• BBE401
25°N-
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120 W
110 W
100-W
long
90W
80 "W
70=W
Figure 6-2. 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 6-1.
6-
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N Dry N Wet S Dry
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S Wet
7 Figure 6-3.
Dry and wet deposition of nitrogen and sulfur (2017-2019 annual average),
for locations listed in Table 6-1.
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In the following three subsections (6.2.1.1, 6.2.1.2, 6.2.1.3), the analyses focus on assessing
relationships between: (1) wet deposition measurements and air concentration measurements; (2)
CMAQ simulations, to understand the air concentration and total deposition relationship from
the perspective of a model that reflects known physical and chemical processes; and (3)
measured air concentrations and the total deposition estimated by TDEP at the same location.
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 (r), the
coefficient of determination (r2), the distribution of the residuals, and the uncertainty in the
assessment of the slope.
6.2.1.1 Evidence from Observations of Air Concentrations and Wet Deposition
This section assesses the relationships between wet deposition measurements and air
concentration measurements. Wet deposition is measured by the NADP/NTN network.
CASTNET measures particle sulfate and nitrate, gas phase SO2, and gas phase HNO3. The
IMPROVE network measures total PM2.5 and the sulfate and nitrate components of PM2.5. These
three types of monitors are collocated at 27 different sites listed in Table 6-1 and shown on the
map in Figure 6-2.
The comparison between annual average PM2.5 air concentration measurements from
IMPROVE and annual total wet deposition measurements from NADP/NTN is shown in Figure
6-3. For this subset of 27 Class I areas, the data indicates that wet S deposition is most highly
correlated with SO42" (r = 0.87). The correlation between wet S deposition and total PM2.5 is less
(r = 0.73) and, as expected, there is little correlation between wet S deposition and NO3" (r =
0.14). This figure also shows that the combined S and N wet deposition at these sites is very
highly correlated (r = 0.99) with wet S deposition. Turning attention to how wet N deposition
measurements relate to pollutant concentration data from IMPROVE, there is some moderate
positive correlation with observed SO42" (r = 0.70) and total PM2.5 (r = 0.64), but not with NO3" (r
= 0.28). The scatterplot for this pairing suggests that there are many location/years where the
annual average NO3" data are relatively high (e.g., 1-2 |ig/m3) but wetN deposition remains
relatively low. The low correlation between nitrate PM2.5 and N deposition may be due to
uncertainty in the nitrate PM2.5 measurement, which is larger than sulfate PM2.5 uncertainty, or to
a larger role for ammonium PM2.5 in N deposition (NH4+ not measured by IMPROVE). An
additional explanation is that nitric acid also contributes to N deposition and is an additional
source of variability not captured by PM nitrate.
To address the contribution of nitric acid, CASTNET air concentration measurements of
total sulfur and total nitrate (HNO3 + NO3") were compared to wet deposition measurements
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from NADP/NTN, as shown in Figure 6-5. As in the IMPROVE case, there is strong correlation
between S wet deposition and concentrations of total sulfate (r = 0.88) but again comparatively
weaker correlation between N wet deposition and measured concentrations of total nitrate (r =
0.38). Comparing the x-axes for IMPROVE NO3" (Figure 6-4) and CASTNET total nitrate
(Figure 6-5) shows that CASTNET total nitrate spans a factor of two larger range than
IMPROVE NO3", so we conclude that most of the total nitrate is in the form of nitric acid at these
sites. This is captured in Figure 6-5 which shows that, for these sites, the composition of S
concentration between SO2 and SO42" is more evenly split. This is an artifact of most of these 27
Class I areas being located in the western U.S. where S is generally low and concentrations of
SO2 and SO42" tend to be similar. (In other parts of the country SO2 tends to be higher near
emissions sources of SO2, with a greater chance of oxidation to SO42" higher at farther distances.)
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Figure 6-4.
1 I I I I r
0 10 20 30
i I I r
0.0 1.0 2.0
Scatter plot matrix of annual average wet deposition measurements from
NADP/NTN (5 pollutants, units: kg/ha-yr) versus annual average
concentrations from IMPROVE (3 pollutants, units: jig/m3) for 27 Class 1
areas from 1988-2018. 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. (Note for this plot
and all subsequent matrix plots: the x- and y- axes scales are shown on the
left and right sides of the plot for rows, and at the top and bottom of the plot
for columns.)
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NADP S + N
k.
0 2 4 6 8 12
J I I I I L
0 10 20 30
0 5 10 15
0.83
NADP N
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0.89
0.75
0.57
0.93
0.99
0.86
0.51
0.91
0.91
0.74
0.67
0.38
0.31
0.41
ii i i i i r
0 20 40 60
~i—i—i—r
0 2 4 6 8 10
i—i—r
0 10 30 50
12 3 4
Figure 6-5. Scatter plot matrix of annual average wet deposition measurements from
NADP/NTN (5 pollutants, units: kg/ha-yr) versus annual average
concentrations from CASTNET (2 pollutants, units: jig/m3) for 27 Class 1
areas from 1988-2018. 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.
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ratio S02 to S04 ratio HN03 to N03
Figure 6-6. Histograms of the ratios of the gas phase SO2 to particle SO-t2- (left) and the
gas phase HNO3 to particle NO3" (right) in CASTNET data. Each ratio is
calculated as the annual average concentration (2000-2019), converted to
moles of N or moles of S, for the 27 sites listed in Table 6-1.
One possible explanation for why particle sulfate is more strongly correlated with sulfur
wet deposition while particle nitrate has weaker correlation with nitrogen wet deposition is the
chemical properties of these compounds. Particle sulfate can be formed in clouds, it has
relatively low spatial variability, and SO2, while a minor contributor to wet deposition, is highly
correlated with particle sulfate (r = 0.91 at CASTNET sites, not shown in figures). Particle
nitrate concentrations have larger spatial variability as the partitioning between gas-phase nitric
acid and particle nitrate is controlled by temperature, relative humidity, and the availability of
cations such as ammonium. In the CASTNET measurements, the correlation between nitric acid
and particle nitrate is lower (r = 0.47, not shown in figures) and at CASTNET sites, nitric acid is
much more abundant than nitrate PM (Figure 6-6) although this interpretation should be
tempered due to uncertainties in the CASTNET measurement technique that make it difficult to
differentiate nitrate PM and nitric acid.
The evidence from observations of air concentrations and wet deposition (as a proxy for
total deposition) at 27 U.S. sites with collocated measurements of air quality and deposition
suggest that particle sulfate is strongly correlated with wet S deposition, but that particle nitrate
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and total nitrate (HNO3 + NO3") are not as strongly correlated with wet N deposition. Both wet
deposition of S and N are moderately correlated with total measured PM2.5.
6.2.1.2 Evidence from Chemical Transport Modeling
Since dry deposition flux is not routinely measured, models are often used to examine the
relationship between air concentration and total deposition. The Community Multiscale Air
Quality Modeling System (CMAQ) is a numerical air quality model that relies on scientific first
principles to predict the concentration of airborne gases and particles, and the deposition of these
pollutants back to Earth's surface. The results of a 21-year CMAQ simulation have been made
available, as described in Zhang et al. (2018). We utilize these model simulations to further
analyze relationships between air concentrations and deposition of S- and N-related compounds
as part of this review.
Figures 6-7 and 6-8 show spatial maps of the annual average SOx and NOy
concentrations (left panel), total S and N deposition (middle panel), and the
deposition/concentration ratio for oxidized sulfur and total nitrogen (right panel). For S oxides
(Figure 6-7), most of the U.S. exhibits deposition/concentration ratios of 0.5 to 3, most notably in
areas where local and regional sources of SO2 are prevalent. However, as an airmass moves
further away from emissions sources, the more rapidly depositing compounds are removed, and
pollutants are diluted by being mixed vertically in the atmosphere. In these locations, higher
deposition-to-concentration ratios for S oxides are modeled. Examples include parts of the
northeastern U.S. and at high elevation sites in the western U.S. These areas are farther away
from sources and ground-level air concentrations are low; however, sulfate can be transported in
clouds and deposited by falling rain, leading to a high level of deposition, relative to the ground-
level air concentration. ForN, the spatial patterns are similar, however the ratios are slightly
lower over most of the U.S. (i.e., ratios range from 0.5 to 2). Again, while the spatial distribution
of the concentration and deposition suggests that there is a strong correspondence, the ratio of the
two terms can vary spatially.
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Concentration
^ 0123456789 10 0 4 8 12 16 20 24 28 32 36 40 i 2 3 4 5 6 7
2 Figure 6-7. Annual average concentration (jig/m3), deposition (kg/ha-yr), and the deposition/concentration ratio for oxidized
3 sulfur compounds, as estimated using a 21-year (1990-2010) CMAQ simulation.
4
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0 1 2 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
2 Figure 6-8. Annual average concentration (jug/m3), deposition (kg/ha-yr), and the deposition/concentration ratio for nitrogen
3 compounds, as estimated using a 21-year (1990-2010) CMAQ simulation.
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Because there is evidence of variability in the deposition and concentration relationship,
it is important to rigorously assess potential deposition predictors. In order to compare the
CMAQ model results against the previous analysis of the concentration and deposition
relationships at 27 monitoring sites with collocated data, the EPA evaluated data from the grid
cells representing those 27 Class I areas. The matrix scatterplots of these results are displayed in
Figure 6-9.
Starting with a comparison of the wet deposition only results, it can be noted that the air
quality modeling data indicates strong correlation between total sulfate and wet S deposition (r =
0.90). In the model data, unlike what was observed from the measurement data, there is also
relatively strong correlation between total nitrate and wet N deposition (r = 0.76). For both S and
N, the correlation of wet deposition with total PM2.5 is slightly greater in the model data (r =
0.81, for both pollutants) than in the observed data (r = 0.73 and r = 0.64, respectively).
In Figure 6-9, it can be seen that total S and N deposition in the model output is strongly
correlated with wet deposition of S and N at these 27 sites (r = 0.98 and r = 0.86, respectively),
confirming our earlier assumption that most of the deposition at these locations likely occurs
through wet deposition. However, the advantage of the simulation data is that we can also
evaluate the relationships between concentration data and total deposition (wet + dry). As
expected, and consistent with previous results, total S deposition is strongly correlated with total
sulfur (r = 0.95) in CMAQ at these locations. Interestingly, the model data also show a strong
correlation between total N deposition and total nitrate (r = 0.94). As the modeling data includes
ammonium in total nitrate, unlike the IMPROVE and CASTNET data, this suggests that the
weaker correlations in the observed data may have been driven by incomplete measurements of
the total nitrate and/or uncertainties in the measurement data. For PM2.5, the data suggests strong
correlation in the model results with both total S deposition (r = 0.85) and total N deposition (r =
0.91).
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6.2.1.3 Evidence from Model-measurement Fusion
The TDEP approach described in section 2.5 estimates total deposition using a
combination of measurements from NADP/NTN and CASTNET fused with CMAQ simulation
results. This section compares the TDEP estimates of deposition with air concentration
measurements of PM2.5, total nitrogen, and total sulfur at the sites listed in Table 6-1.
Starting again with total S deposition, the comparison of IMPROVE (Figure 6-10) and
CASTNET (Figure 6-11) air quality concentrations and TDEP deposition data suggest that S
deposition is again reasonably well correlated with total sulfate, both in the IMPROVE data (r =
0.79) and CASTNET data (r = 0.87). For N deposition, the TDEP comparisons confirm the
observed wet deposition comparisons in section 6.2.1.1. That is, IMPROVE nitrate data is only
weakly correlated with total N deposition (r = 0.31). The strength of the relationship is improved
when total N deposition is compared again CASTNET total nitrate (i.e., with the inclusion of
nitric acid) but is still more weakly correlated (r = 0.65) than what is seen for sulfur. The
correlation between measured PM2.5 and TDEP deposition estimates (r = 0.78 for S, r = 0.73 for
N) is just slightly higher than what was determined when evaluating the wet deposition
observations, and just slightly less than what was noted from the CMAQ data. All three
evaluation approaches showed similar correlation between N and S deposition and PM2.5 data
(ranges from r = 0.64 to r = 0.81).
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Total Deposition
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correspondence with S deposition but there are a number of outliers where the PM2.5
concentration is high, but the S deposition is very low, especially in more recent years (colors).
These are likely cases where the PM2.5 is mostly composed of compounds other than sulfate.
For nitrogen (Figure 6-13), IMPROVE PM2.5, IMPROVE inorganic N PM2.5 (NO3" +
NH4+, |ig N m"3), and inorganic nitrogen measured at CASTNET monitoring sites (HNO3 + NO3"
+ NH4+, |ig N m"3) are the most closely associated with TDEP N deposition. IMPROVE
ammonium is estimated assuming that the nitrate and sulfate are fully neutralized by ammonia. A
large ratio of organic to inorganic PM2.5 may challenge this approach (Silvern et al., 2017).
However, this assumption may be adequate for IMPROVE sites, which are generally in the
western U.S. where there is a smaller contribution to PM2.5 from biogenic emissions. IMPROVE
PM2.5 has the widest prediction interval, while IMPROVE inorganic N PM2.5 and total inorganic
N measured at CASTNET have similar correlations to N deposition, with the CASTNET total
inorganic N having slightly fewer outliers.
6.2.1.4 Conclusions
The above analyses focus on characterizing relationships between various chemical
species that are the air quality components of S and N and deposition of S and N over longer
time periods (e.g., annual or 3-year averages) 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 S, the analyses suggest that in more rural locations, such as those represented
by these 27 Class I areas, S deposition is most strongly associated with measurements of both
sulfate and total sulfur. There is a slightly weaker association between S deposition and PM2.5 in
these rural locations, marked by more variability, as some percentage of the PM2.5 mass is
expected to be composed of compounds other than sulfate. These results suggest that 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. Thus, it is not surprising to see that sulfur can
transported as PM2.5 in these rural locations. These results also suggest that IMPROVE PM2.5,
IMPROVE sulfate, and total sulfur measured at CASTNET all could potentially be used to
predict S deposition, with CASTNET S showing the strongest relationship over recent years. For
N, these results suggest that N deposition in these rural areas is only somewhat correlated with
air concentrations of nitric acid and particulate nitrate. However, the results suggest that
IMPROVE PM2.5, IMPROVE approximated inorganic N PM2.5 (N03" + NH4+), and
approximated inorganic nitrogen measured at CASTNET monitoring sites (HN03 + N03" +
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NH4+) can be used to predict N deposition in these locations, with CASTNET N showing the
most consistent relationship over recent years.
£ io
a
S
IMPROVE PM2 5 (ug m-3)
IMPROVE PM2 5 S04 (ug m-3)
2 3 4
CASTNET S (ug S m-3)
Figure 6-12. TDEP sulfur deposition (vertical axis) and air concentration (horizontal axis)
for IMPROVE PM2.5 (left), IMPROVE SO-»2 (center) and CASTNET total
sulfur (right) as three-year averages from 2002-2019. Blue dots (2014-2016)
and red dots (2017-2019) show more recent data. A black dashed line denotes
the best fit using linear regression and the grey dashed lines denote the 90%
prediction interval.
IMPROVE PM2 5 (ug m-3)
0.5 1.0 1.5
IMPROVE PM2.5 TN (ug m-3)
CASTNET N (ug N m-3)
Figure 6-13.
TDEP Nitrogen deposition (vertical axis) and air concentration (horizontal
axis) for IMPROVE PM2.5 (left), IMPROVE PM2.5 inorganic nitrogen
(center), and CASTNET inorganic nitrogen (right) as three-year averages
from 2002 - 2019. Blue dots (2014-2016) and red dots (2017-2019) represent
more recent data. A black dashed line denotes the best fit using linear
regression and the grey dashed lines denote the 90% prediction interval.
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6.2.2 National-scale Sites of Influence Analyses
To broaden the geographical scope of our assessment, this section incorporates
information about deposition across the U.S. and analyzes the quantitative relationships between
1) the concentrations of S and N-related compounds measured at ambient monitors used to judge
attainment of the current secondary NAAQS for oxides of nitrogen, oxides of sulfur and PM and
2) the magnitude of S and N deposition.
6.2.2.1 Approach
Changes in measured concentrations of NO2, SO2, and PM at ambient monitors are an
indicator of the changes occurring in related sources of emissions. To better understand the
relationship between these measured air quality concentrations and S and N deposition in various
downwind locations of significance, this assessment uses the HYSPLIT air parcel trajectory
model to examine the transport of pollutant material from source to receptor. In this analysis, the
EPA utilized all NO2, SO2, and PM2.5 ambient air quality monitor locations for which valid
design values exist (i.e., from the SLAMS network described in section 2-3), in conjunction with
the HYSPLIT model, to identify meteorological patterns and estimate how pollution observed at
certain locations (referred to here as "sites of influence") could be transported to ecoregions
within the U.S. For PM, the analysis focuses on assessing the PM2.5 annual standard in the sites
of influence analyses because most deposition will transport in the smaller size fraction (i.e., as
PM2.5 rather than PM10 or greater) and because an annual average standard is more relevant to
assessing accumulating deposition than a standard with a form set to reduce peak concentrations
(i.e., PM2.5 24-hour standard with its 98th percentile form). The output from this analysis was
then postprocessed and associated with ambient measurements of concentrations of NO2, SO2,
and PM2.5, and TDEP estimates of S and N deposition for a range of years dating back to 2001.
By identifying which air quality monitors are potentially representative of the air quality that
leads to deposition in a particular ecoregion (see Figure 6A-1 for an example sites of influence
set), one can better understand the relationship between upwind ambient air concentrations and
downwind deposition.
After identifying the upwind geographic areas from which emissions potentially
contribute to N and S deposition in each Ecoregion III areas, the EPA analyzed air quality design
values within each Ecoregion's set of sites of influence to estimate a weighted-average design
value2, which we call an Ecoregion Air Quality Metric (EAQM). EAQM values were estimated
for each Ecoregion III area and for three separate pollutants: NO2, SO2, and PM2.5, and are
intended to provide a perspective of air quality levels in the upwind regions that potentially
2 For this, EPA calculates EAQM values for each Ecoregion by weighting the design value concentration at each
monitor by the percentage of HYSPLIT trajectories estimated to be linked to the Ecoregion III area.
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contribute to downwind deposition levels. For SO2, EPA also estimated EAQM values for SO2
using an annual average given the cumulative effect of deposition that might correspond best to a
longer averaging period.3
As shown in section 6.2.1, the linkage between air concentration and deposition can vary,
even at collocated sites. This variability can be influenced by meteorology, including frequency
of precipitation and micrometeorological factors relevant to the dry deposition velocity. This
analysis aimed to reduce biases due to meteorological variations by focusing on multiyear
averages of deposition. To provide information across a long time period that includes the
important reductions in emissions of N and S described in section 2.4, the assessment evaluates
data over 20 years, with a focus on the following set of years: 2001-2003, 2006-2008, 2010-
2012, 2014-2016 and 2018-2020.
The methodology used to calculate the air parcel trajectories that led to the sites of
influence identification, as well as the methodologies used to estimate the EAQM values for each
Ecoregion/pollutant pair using historical air quality design value (DV) data can be found in
Appendix 6A. In addition, to the EAQM values, EPA also extracted the highest monitored
design value in an area contributing pollution to each ecoregion. The EAQM is useful in
assessing how well measured air quality metrics for various S and N related pollutants are
correlated with estimated S and N deposition. Because the EAQM is a weighted metric of
concentration measurements from a number of monitors, it cannot be used alone to quantify how
the level of a design value at one monitor would correspond to a level of deposition in one area.
Similarly, the same is true for the information from the other analyzed metric - the maximum
design value from contributing monitors. However, used together, assessment of these two
metrics can help inform the range of levels associated with certain air quality metrics that might
be used to maintain S and N deposition at or below certain levels across the U.S. For example,
the EAQM can be viewed as providing information about a "typical" or "average" contributing
design value level, with some monitors measuring higher and some measuring lower yet being
associated with the same deposition level. On the other hand, the maximum design value from
contributing monitors can be viewed as providing information on the highest design value
associated with a particular deposition level. As shown in the figures below, the EAQM tends to
be better correlated with deposition, when compared to the maximum concentration at the
contributing monitor, for both S and N deposition and for all measured air quality metrics. This
is not a surprising result given that deposition is a function of accumulated deposition over
several years and contributed to by pollution from multiple locations. However, the measured
concentration at the maximum contributing monitor does also show a relationship for most of the
3 An annual average SO2 standard was established in 1971 but revoked in 1973 (38 FR 25678, September 14, 1973).
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1 air quality metrics. Table 6-2 shows the air quality metrics that were included in this assessment
2 and shown in Figure 6-14 to 6-26.
3
4 Table 6-2. Relationship of deposition (S and N) to the various air quality metrics.
Figure
Number
Y-Axis Metric
Pollutant
X-Axis Metric
6-14
Estimated 3-year average
S deposition (ecoregion
median)
S02
2nd highest
3-hr
average
EAQM, 3-year average
6-15
Maximum, 3-year average
6-16
Number of ratios
Bins of Maximum to EAQM
ratios, 3-year average
6-17
Estimated 3-year average
S deposition (ecoregion
median)
Annual
average
EAQM, 3-year average
6-18
Maximum, 3-year average
6-19
Number of ratios
Bins of Maximum to EAQM
ratios, 3-year average
6-20
Estimated 3-year average
N deposition (ecoregion
median)
N02
Annual
average
EAQM, 3-year average
6-21
Maximum, 3-year average
6-22
Number of ratios
Bins of Maximum to EAQM
ratios, 3-year average
6-23
Estimated 3-year average
S deposition (ecoregion
median)
PM25
Annual
average
EAQM 3-year average
6-24
Maximum, 3-year average
6-25
Estimated 3-year average
N deposition (ecoregion
median)
EAQM, 3-year average
6-26
Maximum, 3-year average
6-27
Number of ratios
Bins of Maximum to EAQM
ratios, 3-year average
6-28
Estimated 3-year average
S+N deposition (ecoregion
median)
EAQM 3-year average
6-29
Maximum, 3 year average
5
6 6.2.2.2 SO2 Results
7 Figure 6-14 displays a comparison of 3-year average sulfur deposition (i.e., median of
8 TDEP values within an ecoregion) against the 3-year EAQM for the current secondary SO2
9 standard (i.e., annual 2nd high of individual 3-hour SO2 averages). The data are binned into five
10 distinct time periods as shown in the legend. The figure reaffirms the decreasing trends in
11 ambient SO2 and S deposition discussed in section 2. Prior to the 2010-2012 period, it was not
12 uncommon for ecoregions to experience median S deposition values exceeding 5 kg/ha-yr. Since
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the 2014-2016 period, however, no regions have experienced median S deposition above that
level. At the same time, the secondary SO2 EAQM has also trended downward across the
ecoregions. There is a positive and moderately strong correlation (r = 0.75) between S deposition
in an ecoregion and the weighted design values of the current secondary SO2 standard in upwind
areas potentially affecting that ecoregion.
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Figure 6-14. Scatterplot of estimated 3-year average S deposition (ecoregion median) and
the weighted secondary SO2 design values from contributing upwind areas
for that ecoregion (EAQM) also averaged over 3 years.
As introduced above in section 6.2.2.1, this assessment also considered the relationship
between TDEP-estimated deposition in the ecoregions and the maximum design value monitored
anywhere within the set of sites of influence for the ecoregion over a three-year period. Figure 6-
15 displays this comparison for S deposition and the current secondary SO2 standard. Again, the
data are binned into the same five time periods. As can be seen by the expanded x-axis in Figure
6-15, the maximum secondary SO2 design values can be considerably higher than the weighted
averages of the EAQMs. Even for the more recent time periods, there are ecoregion-influencing
sites where the second-highest annual 3-hour S02 values exceeds 250 ppb (highest = 386 ppb).
While there is a positive correlation between S deposition and this potential indicator, it is less
than what was observed with the EAQM (r = 0.40), suggesting this metric is somewhat less
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useful in linking upwind concentrations to downwind deposition. Figure 6-16 shows the
relationship between the secondary SO2 EAQM values (i.e., weighted across all contributing
monitors) and the secondary SO2 DVs from the maximum contributing monitors. Most
maximum/EAQM ratios range from 2-5, although there are exceptions where the ratios can be
higher than 10. One possible cause for an exceptionally high ratio would be a situation in which
there are a number of potential contributing monitors to an ecoregion but where one of the
monitors is particularly affected by a single emissions source and consequently has a higher DV
than the other contributors. The value of the weighted EAQM approach is that it attempts to
account for the expected contribution of the outlier in this hypothetical relative to the other
contributing monitor locations, by evaluating the frequency of wind trajectories. However, as
discussed further in section 6.2.2.1, there is also some value in the maximum.
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Figure 6-15. Scatterplot of estimated 3-year average S deposition (ecoregion median) and
the secondary SO2 design value over that 3-year period from the contributing
monitor with the maximum value for each ecoregion.
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Figure 6-16. Histogram of the ratio of secondary SO2 design value (ppb) from the
maximum contributing monitor for that ecoregion to the average of weighted
secondary SO2 design values (EAQM) (median = 4).
As noted earlier, the EPA has in the past promulgated a secondary SO2 standard based on
an annual average. When considering deposition-related effects which are cumulative in nature,
there may be some advantage in linking annual average concentrations to the eventual
deposition. Figures 6-17, 6-18, and 6-19 repeat the analysis described above but using annual
average SO2 metrics (EAQM and maximum) instead of the current secondary SO2 standard (i.e.,
2nd highest 3-hour maximum value). The positive correlation between an EAQM based on annual
average SO2 concentrations and S deposition in the ecoregions is slightly stronger (r = 0.81) than
what was observed with the shorter-term form of the standard (r = 0.75). This suggests that
consideration of a longer averaging time might be an important consideration in any revised
NAAQS. Figure 6-17 displays the relationship. There are a subset of sites with very low
deposition (i.e., < 5 kg/ha-yr) where there is very little association between the upwind
concentrations and ecoregion deposition. This observation suggests that the relationship between
upwind SO2 concentrations and eventual downwind deposition may break down at lower
deposition levels (i.e., there may be factors other than contemporaneous air quality which
determine the deposition amounts). However, for ecoregions where S deposition is higher (i.e., >
5 kg/ha-yr), there is strong correlation. Figure 6-18 shows the comparison between ecoregion S
deposition and maximum measured annual average SO2 concentrations in potentially influencing
upwind areas. Again, the correlation is slightly stronger than what is seen with the current
secondary NAAQS (r = 0.50 vs. r = 0.40). Also, the use of the maximum observations as
opposed to the weighted EAQM again results in weaker associations (r = 0.50 vs. r = 0.81).
Finally, Figure 6-19 shows the ratios between the two terms (maximum/EAQM). The ratios
May 2023
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1 between these two terms is slightly lower when considering a longer averaging time but still
2 most often ranges from 2-4 and there can still be values in excess of 10.
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Figure 6-17. Scatterplot of 3-year average S deposition (ecoregion median) and the
weighted annual average SO2 concentrations from contributing upwind
areas for that ecoregion (EAQM) also averaged over 3 years
May 2023
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6.2.2.3 NC>2 Results
Similar analyses were completed assessing the relationship between the current
secondary NO2 standard (annual mean, level = 53 ppb). Based on the results of section 6.2.1, one
would expect it to be less likely that the existing NO2 NAAQS would be strongly correlated with
N deposition (due to the multiple pathways for N deposition, including ammonia-related
sources). Figure 6-20 displays a comparison of 3-year average N deposition estimates (TDEP)
against EAQM values for annual average NO;. While the data suggest that the ecoregions with
higher N depositions are associated with higher EAQM values, the correlation is less strong than
what was seen for SO2 (r = 0.58 vs. r = 0.75). However, unlike SO2, the positive association
appears to extend throughout the distribution of N deposition levels; that is, the correlation
between deposition and EAQM is similar whether N deposition values are greater than, or less
than, for example 10 kg/ha-yr. As was the case for SO2, Figure 6-21 illustrates that the switch to
consideration of the single highest NO2 DV from the set of contributing monitors, as opposed to
a weighted EAQM value, slightly reduces the correlation between deposition and concentration
(r = 0.35 vs. r = 0.58). The NO2 ratios between maximum DVs and EAQM values typically
range from 1.5 to 2.5 but can be as high as 6.5
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Figure 6-20. Scatterplot of estimated 3-year average N deposition (ecoregion median) and
the weighted secondary NO2 design values from contributing upwind areas
for that ecoregion (EAQM) also averaged over 3 years.
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6.2.2.4 PM2.5 Results
Finally, similar analyses were also completed assessing the relationship between S, N,
and S+N deposition and air quality design value data for the current secondary PM2.5 annual
standard.4 Figure 6-23 shows the relationship between upwind annual average PM2.5 EAQM
data and S deposition levels over the usual five periods. The data points can be divided into two
groups. There are a minority of data pairs where S deposition is extremely low yet PM2.5 EAQM
values are high. This is likely occuring in areas where the PM2.5 levels are driven by components
other than sulfate. Then there is a second set of data points where there is a positive association
between the upwind PM2.5 EAQM and downwind S deposition. Overall, the correlation for the
paired data is 0.67, which falls between the range seen for the S02 and N02 EAQM data. Figure
6-24 describes the comparison between S deposition levels and the annual PM2.5 DV from the
highest monitor in the ecoregions' sites of influence. The correlation between these two terms is
relatively low (r = 0.21).
However, there was very strong correlation between upwind PM2.5 EAQM and
downwind N deposition throughout the entire distribution (r = 0.98), as shown in Figure 6-25.
This strong correlation was diminished (r = 0.77) somewhat when moving from the weighted
EAQM to use of the maximum PM2.5 DV from the highest monitor in the ecoregions' sites of
influence (Figure 6-26). As shown in Figure 6-27, the ratios between the maximum PM2.5 DV in
an ecoregion's sites of influence and the weighted EAQM value typically ranges from 1.11 to
1.66. Finally, Figures 6-28 and 6-29 illustrate the relationship between PM2.5 design values and
total S+N deposition. The data suggest relatively strong correlation between PM2.5 EAQM data
and total S+N deposition (r = 0.88), but less correlation with the maximum DV (r = 0.50).
4 Given the cumulative nature of N and S deposition, it was expected that an air concentration metric with a longer
averaging time would be a more appropriate potential indicator of downwind deposition, thus the EPA restricted
the PM2.5 analysis to the annual standard and did not include analyses for the 24-hour standard.
Draft - Do Not Quote or Cite
May 2023
6-32
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Figure 6-24. Scatterplot of estimated 3-year average S deposition (ecoregion median) and
the average annual PM2.5 design value over that 3-year period from the
contributing monitor with the maximum value for each ecoregion.
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Figure 6-26. Estimated 3-year average N deposition (ecoregion median) and annual
average PM2.5 concentration in 3-year period from maximum contributing
monitor for that ecoregion.
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Figure 6-27. Histogram of the ratio of average annual average PM2.5 concentration
(jig/niJ) in 3-year period from maximum contributing monitor for that
ecoregion to the average of weighted annual average PM2.5 concentrations
(EAQM) in 3-year period (median = 1.3).
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Figure 6-28. Estimated 3-year average S+N deposition (ecoregion median) and average of
weighted annual average PM2.5 concentrations in 3-year period (EAQM) for
that ecoregion.
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Figure 6-29. Estimated 3-year average S+N deposition (ecoregion median) and average
annual average PM2.5 concentration in 3-year period from maximum
contributing monitor for that ecoregion.
~.2.2.5 Conclusions
For SO2, we examined both the 2nd highest 3-hour maximum and an annual average
metric. The results for the EAQM suggest that both metrics are correlated with S deposition,
with the stronger association being for the annual average metric. There is lower correlation
between the design values from the highest monitor within the ecoregion sites of influence for
both the 2nd highest 3-hour maximum and an annual average SO2 metrics. As shown by the ratio
information, this is likely due to the large concentration gradients seen across the SO2 monitors
in the U.S. (for example, see Figure 2-23), with the maximum contributing monitor between
generally 3 to 4 times higher than the EAQM. These figures also show that in the most recent
assessed time period of 2018-2020, the median S deposition in the Ecoregion III areas was below
5 kg/ha-yr when the annual average SO2 concentration, averaged over three years, at contributing
monitors was less than 22 ppb and the majority of monitors were below 10 ppb. Additionally, the
SO2 figures indicate that there can be high measured SO2 concentrations associated with low S
deposition (i.e., < 5 kg S/ha-yr) and that there is generally more scatter in the data at lower
deposi tion values. Both of these observations could be due to uncertainties in the TDEP
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calculations, uncertainties in our assessment methodology and/or a lack of correlation between
some SO2 monitor measurements and S deposition.
For NO2, the correlations between the measured annual NO2 concentrations and N
deposition are not as strong as they are between metrics for SO2 concentrations and S deposition.
This could be partially due to the fact that oxidized nitrogen only contributes to part of the total
N deposition estimate, and as discussed in section 2, the contribution of reduced nitrogen to total
N deposition has grown over the last few decades (e.g., Li et al., 2016). The figures also show
slightly less variability between the EAQM and maximum monitor concentrations for NO2
(when compared to SO2), with the NO2 maximum monitored values being typically about twice
as high as the calculated EAQM. This result suggests less variability and smaller gradients in
measured NO2 concentrations across the U.S. when compared to SO2. In the most recent time
period (2018-2020), median N deposition was generally maintained at 12 kg/ha-yr in Ecoregion
III areas while NO2 annual average, averaged over 3-years, monitored values were 30 ppb or
less.
For PM2.5, the assessment looks at correlations with S deposition, N deposition and S + N
deposition. The results show a clear and remarkably strong correlation (r=0.98) between
measurements of annual average PM2.5 and estimates of N deposition. This could be due to
measurements at PM2.5 monitors including both oxidized and reduced forms of N (i.e., NO3 and
NH4+), which contribute together to total N deposition. While not as strong, there is a correlation
between measurements of annual average PM2.5 and estimates of S deposition. However, the
results include data where the measured PM2.5 mass is high when S deposition is low (i.e., < 2 kg
S/ha-yr). This is similar to data seen in the figures assessing S deposition and SO2 air quality
metrics. However, this could also be due to PM2.5 mass at these contributing monitors having a
large fraction of non-S-containing compounds, such as NO3", NH4 and/or organic carbon (OC).
In looking at the relationship between measurements of annual average PM2.5 and estimates of
S+N deposition5, the results show a good correlation (r=0.88). For measurements of annual
average PM2.5 there is less difference between the EAQM metric and the maximum monitor
concentrations for annual average PM2.5. In the most recent time period (2018-2020), PM2.5
annual average, averaged over 3-years, contributing monitored values were less than 18 |ig/m3
and mostly less than 15 |ig/m3, corresponding to N and S deposition of approximately 6-12 kg
N/ha-yr and <5 kg S/ha-yr, respectively.
5 Total deposition is converted to units of milli-equivalent using the following equation: S+N deposition = (6.25*S
deposition) + (7.14*N deposition).
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6.3 AIR QUALITY METRICS FOR CONSIDERATION
Based on the information above, this section discusses how well various air quality
metrics relate to S and N deposition. Section 6.2.1 examines this relationship in important
ecological areas of the country, with a focus on a subset of Class 1 areas. Generally, this section
looks at co-located information and includes data from monitors and models. Section 6.2.2 then
examines, for SO2, NO2 and PM2.5 design value or design value-like metrics, the relationship
between measured upwind air quality concentrations and eventual downwind S and N
deposition. This analysis is particularly relevant given that the current secondary standards are
judged using design value metrics based on measurements at the current SO2, NO2 and PM2.5
FRM and FEM monitors. Most of these monitors are in the areas of higher pollutant
concentrations, and many are sited near SOx and NOx emissions sources. 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 monitors to measure
NO2 near larger roadways with a focus on mobile source emissions. Thus, this information can
help inform how changes in emissions relate to changes in deposition and how best to regulate
measured air quality concentrations through the NAAQS to maintain deposition at or below
certain levels.
6.3.1 SO2 Metrics
As introduced in section 2, S tends to deposit as SO2 close to sources of SO2 emissions
but as SO4 in areas further away, such as 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 SC>42+.
Section 6.2.2 examines the current form and averaging time of the SO2 secondary
NAAQS which is the 2nd highest 3-hour daily maximum for a year in the deposition to air quality
analyses. Additionally, given that the impacts examined in this review are associated with
deposition over some longer period of time (e.g., growing season, year, multi-year), section 6.2.2
also assesses an SO2 air quality metric 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 of all of the air quality and deposition metric and include multiple years of data to
better assess more typical relationships.
Based on the results of section 6.2.2, both the current standard form and averaging time,
as well as the annual average air quality metric, show a strong relationship with S deposition.
However, the annual average of SO2, averaged over 3-years, looks to have the strongest
correlation with S deposition averaged over 3-years. When further assessing these metrics, with a
focus on just the Ecoregion III areas used in the aquatic CL analyses (section 5.2) in Figures 6-30
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and 6-31, the same conclusions can be made. However, for these Ecoregion III areas there is less
variability in the relationships, with a very strong correlation (r=0.94) between S deposition and
annual average SO2 averaged over 3-years.
20
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Figure 6-30. For ecoregions included in the Aquatic CL Analysis, estimated 3-year
average S deposition (ecoregion median) and weighted annual average SO2
concentrations (EAQM) in 3-year period for that ecoregion (r=0.94).
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Figure 6-31. For ecoregions included in the Aquatic CL Analysis, estimated 3-year
average S deposition (ecoregion median) and average annual average SO2
concentration in 3-year period from the maximum contributing monitor for
the ecoregion (r=0.69).
Based on the information above, this section concludes that the quantitative analyses
support using either one of the two air quality metrics assessed to control S deposition: (1) 2nd
highest annual 3-hour daily maximum, averaged over 3-years or (2) annual average, averaged
over three years. Between these two metrics, the SO2 annual average, averaged over three years,
would likely be the better choice given that the analyses show the metric to be more strongly
related to S deposition.
When evaluating this information to assess a level at which one of these SO2 air quality
metrics might help maintain S deposition to an appropriate level, a few observations should be
considered. First, for SO2, the monitor concentrations can vary substantially across the U.S. This
is seen is the large ratio (i.e., 3-4) between the maximum contributing monitor concentration and
the EAQM. This large ratio means selecting a level based on the EAQM information alone will
lead to larger reductions than needed. Another observation is that there are a number of instances
where S02 concentrations are high, but S deposition is low. This is generally seen at S
deposition values of less than 5 kg/ha-yr. There is also a substantial scatter at these lower
deposition values, calling into question the ability to select an SO2 concentration level and metric
to maintain deposition below this 5 kg/ha-yr. However, it is worth noting that in the most recent
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assessed time period of 2018-2020, the median S deposition in the Ecoregion III areas was
maintained below 5 kg/ha-yr when the annual average SO2 concentration at contributing
monitors, averaged over three years, was less than 22 ppb. The majority of monitors were below
lOppb.
6.3.2 NO2 and PM2.5 Metrics
For N, the results in section 6.2.1 suggest that oxidized N deposition in rural areas is
mostly from deposition of air concentrations of nitric acid and particulate nitrate, rather than
NO2. Additionally, the results suggest that in some areas inorganic nitrogen (e.g., NH4+)
contributes to the N deposition, with higher contributions in areas near emission sources of NH3.
Section 6.2.2 examines the current form and averaging time of the NO2 secondary
NAAQS which is the annual average NO2 concentration. As in the assessments of the other
pollutants and air quality metrics, the analyses also focus on a 3-year average of NO2 and N
deposition and include multiple years of data to better assess more typical relationships. For
NO2, the correlations between annual average NO2 and N deposition were somewhat low (r=0.58
for EAQM). In addition, the ratios between the maximum contributing monitor and the EAQM
show variability, though less than was seen for SO2, across the measured annual average
concentrations of NO2 across the U.S., with a median ratio of 2. The correlation between annual
average PM2.5 and N deposition was much stronger (r=0.98 for EAQM). This is likely due to
HNO3, NO3 and NH4+ being the largest contributors to N deposition and being most closely
related to concentrations of PM2.5. Additionally, the ratios between the maximum contributing
monitors and the EAQM are lower for PM2.5 (compared to SO2 and NO2) with ratios closer to 1
suggesting lower variability of annual average PM2.5 across the U.S. Given this information and
these relationships, the PM2.5 annual average, averaged over three years, might be the better air
quality metric to control N deposition. Such a metric would also provide some control over S
deposition, as seen in the figures above. However, it is important to consider that this analysis
focuses on PM2.5 monitors that contribute to the S and N deposition across the U.S. and that
these monitors (and other PM2.5 monitors) also measure other non-S and N related pollutants as
part of the PM2.5 total mass.
6.3.3 Key Uncertainties and Limitations
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,
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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 6.2.1, 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 6.2.1 is the limited geographical coverage of the Class I areas that
were included. While these areas were selected from 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 do not
include many of the locations that were quantitatively assessed in section 5 for potential aquatic
acidification effects, given that few are located in the eastern U.S.
In section 6.2.2, 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
EPA analysis made subjective decisions as to what percentage of trajectory impacts warranted
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 receiving ecoregion deposition. An additional uncertainty that should also be
considered is the application of HYSPLIT to somewhat large area of the country (Ecoregion III
areas) which may have substantial spatial variability in deposition levels. In the analysis, a
median deposition level for each Ecoregion III area was used in considering the relationship
between deposition and air quality. To assess how these median data compare to those used in
the quantitative analysis (section 5.2), the median TDEP S deposition estimate for each
Ecoregion III area was compared to the median TDEP deposition estimate for each of the water
body locations used in each Ecoregion III area in the aquatic critical load analysis in section 5.
The comparison finds that the Ecoregion median can range from 31% higher to 22% lower than
the S deposition used in the aquatic analysis (and with a maximum difference of less than 3
kg/ha-yr) but, on average, is typically less than 7% different (see Appendix 6A, Table 6A-4).
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REFERENCES
Li, Y., Schichtel, B. A., Walker, J. T., Schwede, D. B., Chen, X., Lehmann, C. M. B., Puchalski,
M. A., Gay, D. A., & Collett, J. L. (2016). Increasing importance of deposition of
reduced nitrogen in the United States. Proceedings of the National Academy of Sciences,
773(21), 5874-5879. https://doi.org/10.1073/pnas.1525736113.
Silvern, R. F., Jacob, D. J., Kim, P. S., Marais, E. A., Turner, J. R., Campuzano-Jost, P., &
Jimenez, J. L. (2017). Inconsistency of ammonium-sulfate aerosol ratios with
thermodynamic models in the eastern US: A possible role of organic aerosol.
Atmospheric Chemistry and Physics, 77(8), 5107-5118. https://doi.org/10.5194/acp-17-
5107-2017.
Zhang, Y., Mathur, R., Bash, J. O., Hogrefe, C., Xing, J., Roselle, S. J. (2018). Long-term Trends
in Total Inorganic Nitrogen and Sulfur Deposition in the U.S. from 1990 to 2010.
Atmospheric Chemistry and Physics. 18:9091-9106. https://doi.org/10.5194/acp-18-
9091-2018.Zhang, Y., Mathur, R., Bash, J. O., Hogrefe, C., Xing, J., Roselle, S. J.
(2018). Long-term Trends in Total Inorganic Nitrogen and Sulfur Deposition in the U.S.
from 1990 to 2010. Atmospheric Chemistry and Physics. 18:9091-9106.
https ://doi. org/10.5194/acp-18-9091-2018.
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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:
• Does 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 PM ISA 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 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, the evidence and exposure-based questions regarding policy-relevant
aspects of the currently available information regarding effects, public welfare implications, the
current standards and as appropriate, consideration of potential alternatives are discussed in
sections 7.1 and 7.2. Section 7.1 addresses the questions in the context of direct effects of the
pollutants in ambient air and, in similar fashion, section 7.2 addresses policy-relevant questions
in the context of deposition related effects. Preliminary conclusions derived from the evaluations
presented in this draft PA are described in section 7.3. Section 7.4 identifies key uncertainties
and areas for future research.
7.1 EVIDENCE AND EXPOSURE/RISK BASED CONSIDERATIONS
FOR DIRECT EFFECTS OF THE POLLUTANTS IN AMBIENT AIR
In considering the currently available evidence and quantitative information pertaining to
direct effects of oxides of N and S and PM in ambient air, including what this information
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indicates regarding effects, and associated public welfare implications, that might be expected to
occur under air quality meeting the existing standards, we address the following questions.
• To what extent has the newly available information altered our scientific
understanding of the direct welfare 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 for the three criteria
pollutants in the subsections below.
7.1.1 Direct Effects of SOx in Ambient Air
As summarized in section 4.1 above, very little of the currently available information
regarding the direct effects of SOx in ambient air is newly available in this review. Among the
SOx, which 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
comprised of studies focused on SO2, documents its effects on vegetation, including foliar injury,
depressed photosynthesis and reduced growth or yield (ISA, Appendix 3, section 3.2).
In general, effects on plants occur at SO2 exposures higher than a 3-hour average
concentration of 0.5 ppm. The evidence derives from a combination of laboratory studies and
observational studies. A recent laboratory study reports some transient effects on lichen
photosynthesis for short exposures, with more long-lasting effects only observed for exposures
of nearly 1 ppm SO2, as summarized in section 5.1.1 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 of
past reviews. In large part they 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
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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 Direct Effects of N Oxides in Ambient Air
The currently available information on direct effects of N oxides in ambient air is
comprised predominantly of studies of NO2 and HNO3, and also of PAN, with regard to effects
on plants and lichens (as summarized in section 4.1 above). The very few studies newly available
in this review do not alter our prior understanding of effects of these N oxides, include visible
foliar injury and effects on photosynthesis and growth at exposures considered high relative to
current levels in ambient air (ISA, 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). Previously available
evidence for HNO3 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
5.1.2 above. Effects of HNO3 may be related to vapor exposures or deposition given its very high
deposition velocity (ISA, Appendix 3, section 3.4). The evidence includes studies of effects
related to historic conditions in the Los Angeles basin. A more recent 2008 reassessment of an
area in the Los Angeles basin in which there was a significant decline in species in the late 1970s
found that lichen communities have not recovered from the damage evident in the 1970s (ISA,
Appendix 3, section 3.4). The newer studies continue to support the findings 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, section
4.3).
With regard to the exposure concentrations, we note 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 which are "consistent with past
studies of plants with relatively high NO2 exposure" (ISA, Appendix 3, pp. 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
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range reported in summers during the 1980s in the Los Angeles Basin, as described in section
5.1.2 above (ISA, Appendix 3, section 3.4;). During that period, NO2 concentrations in the Basin
range 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
(-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 U.S. have met the existing secondary standard since around 1991 (Figure 2-21).
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 2-21). 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
5.1.2), as the 98th percentile of 1-hour concentrations rarely exceeded 0.2 ppm, as shown in
Figure 2-20.With regard to the potential for HNO3 concentrations occurring in conditions that
meet the current NO2 secondary standards to pose risk of effects, we consider the larger evidence
base in that regard. 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 N oxides documented during the 1970s
to 1990s (and likely also occurring earlier). Based on studies extending back to the 1980s, HNO3
has been suspected to have had an important role in these declines, as summarized in section
5.1.2 above. During that time period the Los Angeles metropolitan area experienced NO2
concentrations well in excess of the NO2 secondary standard. For example, annual average NO2
concentrations in Los Angeles ranged up to 0.078 ppm in 1979 and remained above the standard
level of .053 ppm into the early 1990s (Appendix 5B, section 5B.4.1). A resampling in the Los
Angeles Basin in 2008 reported that the impacts documented on lichen communities in the 1970s
remained at that time (ISA, Appendix 3, section 3.4). The extent to which this relates to lag in
recovery or concentrations of various air pollutants is unknown. Thus, the currently available
information is 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. 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. These include those related to limitations of the various study types. For example,
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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
may have contributed to the observations. With regard to the risk posed by N oxides, and
particularly HNO3, the evidence, as summarized in sections 5.1.2 and 5.4.3.2 above indicates the
potential for effects of air quality occurring during periods when the current secondary standard
was not met, which, depending on policy judgments, may be concluded to have public welfare
implications. The evidence is limited, however, with regard to support for conclusions related to
conditions meeting the current standard.
7.1.3 Particulate Matter
As summarized in section 5.1.3 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 last 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 were 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; 2012 PM ISA, section 9.4).
With regard to direct effects of PM in ambient air, the information on ambient air
concentrations 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 welfare effects of PM in exposure conditions likely to meet the current standards,
and that which is does not indicate effects to occur under those conditions.
7.2 EVIDENCE AND EXPOSURE/RISK-BASED CONSIDERATIONS
FOR DEPOSITION-RELATED EFFECTS
In this section, we consider the evidence and quantitative exposure/risk information
related to deposition-related ecosystem effects of oxides of S and N 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. While recognizing
there are multiple organizations that could be applied, we have adopted one that focuses first on
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consideration of S deposition (section 7.2.1) and then N deposition (section 7.2.2). Further,
within each of these sections, we first consider the evidence regarding deposition effects and the
evidence to support analysis of deposition levels associated with effects of potential public
welfare significance, and then the consideration of deposition levels that may be appropriate to
target for consideration of the public welfare protection appropriately afforded by the secondary
standards.
7.2.1 S Deposition and Oxides of S
To inform conclusions in this review related to the SOx secondary standards, we consider
a series of questions below that are intended to facilitate the evaluation of the linkages between S
oxides in ambient air with S deposition and its associated welfare effects. In considering these
questions, we draw on the available welfare effects evidence described in the current ISA, the
2008 NOx/SOx ISA, the 2009 PM ISA, and past AQCDs, and summarized in chapter 4 above.
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.
7.2.1.1 Welfare Effects Evidence of Deposition-Related Effects
The long-standing evidence documents the array of aquatic and terrestrial effects of S and
acidic deposition. This evidence, extending back many decades, has accrued in part through
study of ecosystem acidification that has resulted from historic acid deposition. As discussed in
prior chapters, both S and N compounds have contributed, with relative contributions varying
with both emissions, air concentrations and atmospheric chemistry, among other factors. The
ecosystem effects, documented comprehensively in waterbodies of the Adirondack and
Appalachian Mountains, and forests of the northeast, have ranged from the organism to
ecosystem-level scale. The focus in 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 on 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 provide support to characterization of the potential for effects, and of the protection that
might be afforded for such effects, under different air quality conditions. We do this in the
context of the following question.
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• To what extent does the currently available evidence base provide established
quantitative approaches for characterizing ecosystem responses to S deposition that
can inform judgments on the risk or likelihood of occurrence of ecosystem effects
under differing conditions of SOx air quality?
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. Aspects of the modeling
approaches that quantify processes that are the major determinants of the indicators have been
expanded and improved since the last review. Further, use of such modeling approaches for
characterizing potential risk of aquatic and terrestrial acidification is well established. Modeling
approaches vary in their complexity, precision, and limitations. Similarly, the evidence base
supporting risk characterization using the different acidification indicators also varies, with
associated uncertainties.
As recognized in Chapter 5 above, we have greatest confidence in the approach and tools
applied in the assessment of aquatic acidification. Although the approaches and tools for
assessing aquatic acidification are more generally utilized for S and N deposition in combination,
the approach taken in the analysis of aquatic ecosystem acidification summarized in section 5.2
above was to focus on S deposition. This decision was based on analyses indicating the relatively
greater role of S deposition under the more recent air quality conditions (as summarized in
section 5.2.1.4 above). The aquatic acidification assessment utilizes site-specific water quality
modeling that relates atmospheric deposition to ANC in a CL-based approach, as summarized in
section 5.2 above and described in more detail in Appendix 5A. The site-specific modeling
applications and associated estimates of CLs for different ANC targets are publicly available in
the NCLD. The modeling applications generally utilize mass balance and dynamic modeling
tools for watershed processes (e.g., fluxes that affect watershed concentrations of anions and
cations). In summary, the aquatic acidification assessment has utilized well-established site-
specific water quality modeling applications with a widely recognized indicator of aquatic
acidification.
Quantitative tools are also available for the assessment of terrestrial acidification related
to S deposition (using BC:A1 ratio as an indicator), as they were in the last review (section
5.4.2.1; 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 have not been performed in this review, the findings from
the 2009 analyses, have been considered in the context of more recently available evidence
(section 5.4.2.1; 2009 REA, section 4.3). Quantitative tools and approaches are not as developed
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for other deposition-related effects associated with SOx in ambient air, such as mercury
methylation and sulfide toxicity (summarized in sections 4.2.3.1 and 4.2.3.2 above).
In summary, as in the last review, we find the quantitative approaches and tools for
assessment of aquatic acidification (including that attributable to S deposition) to be the most
advanced. While recognizing the uncertainties associated with results of analyses utilizing these
tools, as described in section 5.2 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.
7.2.1.2 General Approach for Considering Public Welfare Protection
• 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 5.2.1 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. and Canada. In waterbodies with high 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 Al, protecting resident biota against A1
toxicity (ISA, Appendix 8, section 8.3.6.2). Accordingly, biota in such systems tolerate lower
ANC values than biota in waterbodies with low DOC. Thus, while the evidence 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 acid deposition in these areas. As noted in section 5.2 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 for purposes of judging a potential for ecosystem acidification effects (section
5.2.2.2).
As summarized in sections 4.2.1.1.2 and 5.2.1 above, there is longstanding evidence of an
array of significant impacts on aquatic biota and species richness reported in surface waters with
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ANC values below zero, and in waters with ANC values below 20 |ieq/L. This evidence derives
primarily from lakes and streams of the Adirondack Mountains and areas along the Appalachian
Mountains. The evidence base additionally indicates increased risk to resident biota of ANC
levels between 20 and 50 |ieq/L, as summarized in section 5.2.1 above. 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 |ieq/L have been
generally associated with high probability of low pH events, that have potential for death or loss
of fitness of sensitive biota (2008 ISA, section 5.2.2.1). 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 acid deposition impacted streams, and for consideration of the
set of targets analyzed in the quantitative aquatic acidification assessment: 20, 30, and 50 |ieq/L
(section 5.2 above).
• To what extent might waterbodies in sensitive ecoregions experiencing S deposition
across the range of recent time periods be expected to achieve ANC levels of interest?
What are associated uncertainties in these estimates?
In considering this question, we focus on the results of the quantitative aquatic
acidification assessment at three scales: national-scale, ecoregion-scale and focused case study-
scale, as described in section 5.2 above. An array of approaches are employed across the three
scales, all of which make use of water quality modeling-based CLs derived for three different
ANC targets. 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 with different patterns of S deposition. We also recognize that S
deposition that may be controlled by one or more NAAQS will vary across the U.S. such that
implementation of any new concentration based standard would be associated with a distribution
of different deposition levels across the U.S.
The national-scale analysis involved the 13,824 waterbodies for which a CL based on
ANC target was available. Unlike the case for the 2000-02 period analyzed in the last review,
few waterbodies are estimated to be receiving deposition in excess of their critical loads for
relevant ANC targets under recent deposition levels. More specifically, under deposition
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estimated for the most recent time period (2018-2020), generally below 4 kg/ha-yr, only 4% of
waterbodies nationally were estimated to exceed CLs for an ANC of 50 |ieq/L (Table 5-1).
The ecoregion analyses provided a dataset of five ecosystem deposition estimates (for the
five time periods from 2001-03 to 2018-20) for each of 18 eastern ecoregions that has been
summarized in terms of percentage of waterbodies estimated to achieve ANC at or above the
three ANC targets. We focused primarily on the results for the deposition bins representing half
or more of the full dataset (those for 5 kg/h-yr and for higher levels). Across the dataset of 90
combinations of eastern ecoregions and deposition time periods, with CL exceedances organized
by deposition bins, with the highest being 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 73% of the
ecoregion-time period combinations, and at or above 50 |ieq/L in 60% of the combinations
(Table 5-4). This summary contrasts with that for the 76 combinations 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 that for the 69
combinations for S deposition at or below 9 kg/ha-yr, for which 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 5-4). 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 5-4). Lastly, for the lowest bin comprised of at least half of the
full dataset, of the 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 |ieq/L in
96% of the combinations, and at or above 50 |ieq/L in 82% of the combinations
The ecoregion analysis results summarized for the deposition estimate bins at or below
11 kg/ha-yr (and at/below lower values), indicate the likelihood of appreciable improvements in
waterbody buffering capacity compared to that estimated for the set of ecoregion-time periods
reflecting deposition estimates as high as 18 kg/ha-yr. This improvement includes an appreciably
increased 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. Additionally, these estimates increase with bins
for lower deposition estimates, while also representing reductions in the size of the supporting
dataset.
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 5-5). For the Shenandoah National Park, one of the Class I areas, and the
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study area for which there are CLs available in the NCLD for all 4977 waterbody sites
(McDonnell et al., 2014), 70% of the area's waterbodies are estimated to be able to achieve an
ANC at or above 20 |ieq/L with annual average S deposition of 9.4 kg/h-yr; the comparable
value for 90% of the waterbodies in 7.1 kg/h-yr. The S deposition values for the 70th and 90th
percentile of waterbody CLs for the other less well studied areas, for which there are fewer
waterbodies for which modeling has been performed to estimate CLs, were consistently lower.
And, as one example of variability in estimates, and associated uncertainties, we observe that the
70th and 90th percentile waterbody CL estimates for an ANC target of 20 |ieq/L for the Sierra
Nevada study area, a much less well studied area than the eastern areas, are appreciably lower
than such estimates for all of the other case studies for any of the three ANC targets. Yet, we
note that the average of waterbody CLs for achieving ANC at or above each of the three targets
(20, 30 or 50 |ieq/L) within each of the five case studies were quite similar, ranging only from
9.4 to 12 kg/ha-yr.
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 impacted by a long history of acid deposition, such as waterbodies in
the Shenandoah National Park. In considering this information we also note the uncertainties
associated with such estimates, as in the last review. These include uncertainties associated with
the 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, with associated limitations and uncertainties. For example, as recognized in sections
4.2.1.1.3 and 5.2.4 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. 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 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.
• 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 much greater availability of site-specific water quality
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measurements than of soil quality measurements in sensitive areas across the U.S. The available
quantitative information related to terrestrial acidification summarized in Chapter 5 (and
presented in more detail on Appendix 5B) includes discussion of soil chemistry modeling
analyses (both those described in published studies and an analysis performed in the 2012 oxides
of N and S review), 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. 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 for several targets for a well-studied indicator of soil acidification, BC: A1
ratio (2009 REA, section 4.3). These estimates indicated a range of annual deposition rates under
which ratios at or above the intermediate target value of 1 were well above the CL estimates
associated with achieving various ANC targets in the aquatic acidification analyses discussed
above, and also above all of the ecoregion estimates (across the five time periods from 2001
through 2020) considered in the aquatic acidification analyses (Table 5-6). This is also the case
for the most conservative ratio value of 10. 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). We additionally note that published
studies 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.
With regard to the information available from studies involving S additions to
experimental forested areas, we note, as an initial matter, the somewhat limited number of tree
species that have been included in such experiments. Although limited in number, the more
widely recognized sensitive species, from field observations, have been included in such studies.
We note that the available studies have not reported effects on the trees analyzed with additions
below 20 kg/ha-yr (in addition to the background atmospheric addition 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
in regions of the eastern U.S. (section 5.4.2.3 and 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
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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 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).
Deposition at the sites with species for which growth or survival was negatively associated with
5 deposition in the second study ranged from a minimum below 5 kg/ha-yr to a site maximum
above 40 kg/ha-yr, with medians for these species generally ranged from around 5 to 12 kg/hr-yr
(Appendix 5B, section 5B.3.2.3; Horn et al., 2018).
As recognized in section 5.4.2 and Appendix 5B, the history of appreciable acidic
deposition in the eastern U.S. and the potential for the deposition patterns (e.g., locations of
relatively greater versus relatively lesser deposition) to be somewhat similar may be an influence
in the findings. This indicates an uncertainty with regard to the specific magnitude of deposition
that might be expected to elicit specific tree responses, such as those for which associations have
been found. As recognized by 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.
7.2.1.3 Relating Deposition-related Effects to Air Quality Metrics
In this review, we have explored how well various air quality metrics relate to S and N
deposition. The analyses examine, for design value or design value-like metrics, the relationship
between measured air quality concentrations and transported S and N deposition. This analysis is
particularly relevant given that the current secondary standards are judged using design value
metrics based on measurements at existing SO2, NO2 and PM2.5 FRM/FEM monitor locations.
Most of these monitors are located in areas of higher pollutant concentrations near emissions
sources. For example, many SO2 monitors are sited near large point sources of SO2 (e.g., electric
generating units). Accordingly, information from these monitoring sites can help inform how
changes in SO2 emissions 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. The details of these analyses are described in
Chapter 6 and Appendix 6A. In addressing the questions below, we consider the findings of
those analyses specific to S deposition associated with SOx and PM.
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• What does the available information and air quality analyses indicate regarding
relationships between air quality metrics related to the existing standards and S
deposition? What are the uncertainties in relationships using such metrics?
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. Based on the air quality information and analyses in Chapter 2 and 6, we
find that S tends to deposit as SO2 (in dry deposition) close to sources of SO2 emissions and as
SO4 in areas further away, such as in the 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 SO4.
The analyses in Chapter 2 and 6 assess SO2 concentrations using a metric based on the
current form and averaging time of the secondary SO2 NAAQS, which is the 2nd highest 3-hour
daily maximum in a year, as well as an annual average SO2 air quality metric. Additionally, 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 to
better assess more typical relationships. Specifically, the assessment includes data spanning 20
years, with a focus on the following set of 3-year periods: 2001-2003, 2006-2008, 2010-2012,
2014-2016 and 2018-2020.
The results suggest that a standard in the form of either metric analyzed (i.e., the 2nd
highest 3-hour maximum in a year, averaged over 3 years or an annual average, averaged over 3
years) might be expected to provide a level of control of S deposition across the U.S.
Additionally, of these two air quality metrics, the analyses suggest a potential for better control
with the annual average of SO2 concentrations, averaged over 3 years, given that the analyses
show this metric to be more strongly related to S deposition. This potential for better control
notwithstanding, we take note of two additional considerations. First, monitor concentrations of
SO2 can vary substantially across the U.S., in response to source emissions, complicating
consideration of how the maximum contributing monitor (as identified in the HYSPLIT analysis
described in section 6.2.2 above) relates to S deposition levels in downwind ecosystems. This
consideration is integral to identifying levels for a potential alternate standard that would avoid
over- (or under-) control. The other consideration is the finding of a number of instances in the
full dataset (spanning 20 years) of low S deposition associated with relatively higher SO2
concentrations. This generally involved S deposition values below 5 kg/ha-yr. At these lower
values, there is a substantial amount of scatter in the relationship between measured SO2
concentration and S deposition estimates, contributing increased uncertainty to the identification
of a levels for a SO2 metric for a potential secondary standard that might be expected to maintain
deposition at or below 5 kg/ha-yr. This scatter could relate uncertainties in the TDEP estimates,
particularly given that many of these sites tend to be in the western U.S. For these lower
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deposition values we find it appropriate to rely to a greater extent on air quality relationships
observed more recently. For example, for the most recent time period analyzed (i.e., 2018-2020),
the median S deposition in the Ecoregion III areas was maintained below 5 kg/ha-yr when the
annual average SO2 concentration, averaged over three years, at contributing monitors was less
than 22 ppb and the majority of monitors were below 10 ppb.
The analyses for PM2.5 show a positive relationship between measurements of annual
average PM2.5 and estimates of S deposition. However, similar to the SO2 air quality metrics, the
results also show that the measured PM2.5 mass can be high when S deposition is quite low (i.e.,
< 2 kg S/ha-yr). This could be due to PM2.5 mass at these contributing monitors being dominated
by non-S-containing compounds, such as NO3, NH4 and/or organic carbon (OC). However, it is
worth noting that in a recent time period (2018-2020), median Ecoregion III area S deposition
levels were at or below 5 kg S/ha-y when the PM2.5 annual standard levels at contributing
monitors were generally less than 15 |ig/m3 (i.e., the level of the current annual average,
secondary standard for PM2.5).
Lastly, as recognized in Chapter 6, we note multiple uncertainties with the analyses
approach that are important to interpretation of the results. It is unclear, however, how much and
in what way each of these uncertainties might impact the results. There are also uncertainties
inherent in the derivation of the TDEP estimates, which are discussed in more detail in Chapter
2. Another important uncertainty is associated with application of the HYSPLIT model to predict
transport trajectories between monitor locations and Ecoregion III areas, as well as the use of the
median TDEP deposition estimates across each Ecoregion III area in the assessment of the air
quality relationships. However, a comparison of the median Ecoregion III area S deposition
estimates used in the analyses to those used in the aquatic critical load analysis found the
difference to typically be less than 7%, with a maximum absolute difference of less than 3 kg/ha-
yr (as recognized in section 6.3.3 above).
• 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?
Chapter 6 also assessed relationships between co-located measurements and modeled
estimates in a subset of Class I areas that are mostly located in the western U.S. The analyses
calculated correlations between concentrations of air quality metrics in these locations for
indicators other than SO2 and S deposition in these locations. For example, these results suggest
the potential use of IMPROVE PM2.5, IMPROVE sulfate, and total sulfur measurements at
CASTNET monitoring sites to predict S deposition in those locations. Among those three
measurements, concentrations of total S (from SO2 and SO4"2 measurements) at CASTNET sites
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exhibited the strongest relationship over recent years. These results support the conclusion that S
deposition in rural areas (such as those in these Class I areas) is mostly comprised of sulfate and
SO2, which is consistent with our understanding of the chemical properties and physical transport
of these compounds (e.g., that fine particles, such as PM2.5, have a much slower dry deposition
velocity and remain in the atmosphere longer, allowing for transport and deposition in areas
more distant from sources). Given that this analysis is based on air concentrations and deposition
estimates at the same locations (distant from sources), use of one of these three combinations of
S compounds as the indicator of a new standard would entail use of a surveillance network
designed for this context. Further, a monitoring network for such a standard would also entail
development of sample collection and analysis FRMs. However, it is unclear whether such an
approach for a new standard would have advantages over options discussed above.
7.2.2 N Deposition and Oxides of N 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 welfare effects. In
considering the questions below, we draw on the available welfare effects evidence described in
the current ISA, the 2008 NOx/SOx ISA, the 2009 PM ISA, and past AQCDs, and summarized
in chapter 4 above. We do this in combination with the available quantitative information
summarized in chapters 5 and 6 above.
7.2.2.1 Welfare Effects Evidence of Deposition-Related Effects
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
ISA. As recognized in section 7.2.1.1 above, N deposition has played a role in acidic deposition
in both terrestrial and aquatic ecosystems and associated effects in the U.S. 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.
A particular focus of our consideration of the evidence relates to the evidence describing
quantitative relationships between deposition and ecosystem effects and the availability of
established approaches for estimating risk of such effects from deposition-related N enrichment.
The availability of such approaches that can be applied to inform our understanding of spatial
extent and magnitude of particular welfare effects associated with different air quality conditions
is important to informing decisions on standards that could provide the appropriate control on
deposition for the desired level of protection. As recognized in Chapter 5 and section 7.2.1
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above, the availability of established approaches for quantitatively relating atmospheric
deposition to effects on soil and water chemistry and relating those effects to specific welfare
effects varies for the different types of ecosystems and categories of effects.
We consider here the extent to which such information is available for effects associated
with N deposition, and particularly N enrichment-related effects, 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. 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,
particularly more recently (as described in section 5.2.1.4 above). In so doing, we note the
varying directionality of some effects of N enrichment, 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.
• To what extent does the currently available evidence base provide established
quantitative approaches for characterizing ecosystem responses to N deposition that
can inform judgments on the risk or likelihood of occurrence of ecosystem effects
under differing conditions of NOx and PM in ambient air?
With regard to acidification-related effects of N deposition, we recognize the approaches
and tools referenced in section 7.2.1 above with a focus on S deposition can be utilized for S and
N deposition in combination. The approach taken in the analysis of aquatic ecosystem
acidification summarized in section 5.2 above was to focus on S deposition, based on analyses
indicating the relatively greater role of S deposition under the more recent air quality conditions
(as summarized in section 5.2.1.4 above). Discussion of analyses relating acid deposition to
terrestrial acidification indicators is also presented in section 5.4 above.
With regard to quantitatively analyzing the linkages between N deposition and waterbody
eutrophication for the purposes of quantitatively relating N deposition to waterbody responses,
we take note of the waterbody-specific nature of such responses and the relative role played by
atmospheric deposition. 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. Thus, while the evidence is robust as to the ability for N loading to contribute to
waterbody eutrophication, which can affect waterbody biota, processes and functions, a variety
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of factors complicate our ability to quantitatively relate N deposition rates to eutrophication risks
in waterbodies ranging from small lakes and streams to large estuaries and coastal waters.
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.
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,
a variety of factors, including the history of deposition and variability of response across the
landscape 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..
7.2.2.2 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, 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.
Accordingly, there 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.
• 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 discussed in section 7.2.1 above), 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.
With regard to the information available from experimental addition studies, the ranges
of N additions that elicited increased growth overlapped with those that elicited reduced growth
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and increased mortality. In considering that these studies were conducted in the context of the
natural environment, with a backdrop of the air quality and atmospheric deposition occurring at
that time, we note that while some report observations based on additions over just a few years,
others extend over a decade or more. In general, they 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
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, covering overlapping areas of the U.S. (see Appendix
5B, Figure 5B-1), report associations of tree growth and/or survival metrics with various N
deposition metrics, providing 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 5.4.2.3 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 below, we note, as recognized in section 5.4.2 and Appendix 5B, the history of N
deposition in the eastern U.S. may be an influence in 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 mortality (or survival), the study by Dietze and Moorcroft (2011)
reported negative associations of tree mortality with average NO3" deposition (greater survival
with greater estimates of NO3" deposition) at sites across the eastern half of the contiguous U.S.
The associates were made for plant functional groups comprised of multiple species (Appendix
5B, Attachment 1). 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). At
the individual species level, the study by Thomas et al. (2010) reported negative associations of
N deposition (mean annual average for 2000-04) with survival (sites of higher deposition had
lower survival [higher mortality]) for eight of 23 species in northeastern and north-central U.S
and positive associations for three species. 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, which included temperature, precipitation and
tree size, did not include other pollutants (Thomas et al., 2010). The much larger study by Horn
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et al. (2018) of 71 species reported associations of tree survival 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). 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 shape
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'Vr"1. Values were below 9 kg N/ha-yr
for four of the 19 species; these species included at least half of their sample sites in the west or
in the Northern Forests ecoregion.
With regard to growth, the study by Thomas et al. (2010) reported positive associations
of N deposition (mean annual average for 2000-04) with tree growth in 11 of 23 species in
northeastern and north-central U.S and with negative associations in 3 species. Of the 39 species
for which Horn et al (2018) reported significant associations of growth with N deposition, the
association was negative across the full deposition range of their sites for two species, pitch pine
and bur oak. These species' sites were predominantly in the Atlantic coastal pine barrens and
northern plains and forests, respectively. The median deposition across all sites of these species
were nine and ten kg N ha"1 yr"1 (Appendix 5B, Figure 5B-5). The median deposition values for
the two other species, with hump shaped functions that were negative at the median, were seven
and eight kg N ha'Vr"1, respectively (Appendix 5B, Figure 5B-5).
A number of recently available studies have reported on addition experiments involving
herb and shrub community response, as summarized in section 5.4.3.1 and Appendix 5B, section
5B.3.1. The lowestN additions for which community effects have been reported include 10 kg
N/ha-yr. With this addition over a 10-year period, grassland species numbers declined; in a
subset of plots for which additions then ceased, relative species umbers increased, converging
with controls after 13 years (Appendix 5B, Table 5B-7; Clark 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 considering species richness in open- and closed-canopy communities
using database of site assessments conducted over 23-year period and average N deposition
estimates for 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 low pH
forested sites andN deposition above 11.6 kg N/ha-yr (section 5.4.3.1).
Observational studies have also analyzed variation in lichen communities in relation to
indicators of N deposition as summarized in section 5.4.3.2 and Appendix 5B, section 5B.4.2. In
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particular, a recent study in the Northwest focused on assessing relationships between metrics for
community composition and estimated N deposition. In this study the authors identified a
breakpoint associated with 33-43% fewer oligotrophic species and 3 to 4-fold more eutrophic
species when total N deposition estimates ranged from 3 to 9 kg N/ha-yr (Geiser et al., 2010).
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, S02 and historical air quality and associated deposition), as noted in section 5.4.3.2
above.
7.2.2.3 Relating Deposition-related Effects to Air Quality Metrics
As discussed above, in this review, we have explored how well various air quality metrics
relate to S and N deposition. The analyses examine, for design value or design value-like
metrics, the relationship between measured air quality concentrations and transported S and N
deposition. This analysis is particularly relevant given that the current secondary standards are
judged using design value metrics based on measurements at the current SO2, NO2 and PM2.5
FRM/FEM monitors. This information can help inform how changes in NO2 emissions relate to
changes in deposition and how best to regulate measured air quality concentrations through the
NAAQS to maintain N deposition at or below certain levels. The details of these analyses are
described in Chapter 6 and Appendices 2A and 6A. In addressing the questions below, we
consider the findings of those analyses specific to N deposition associated with N oxides and
PM.
• What does 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?
For N, the results in Chapter 6 suggest that oxidized N deposition in rural areas is mostly
from deposition of nitric acid and particulate nitrate, rather than NO2. Additionally, the results
suggest that in some areas inorganic nitrogen (e.g., NH4) contributes to the N deposition, with
higher contributions in areas near emission sources of NH3.
In considering policy options that might be expected to provide the desired protection of
the public welfare from N deposition related effects, we consider the current form and averaging
time of the secondary NO2 NAAQS which is the annual average of NO2. As in the assessments
of the other pollutants and air quality metrics, the analyses focus on a 3-year average for NO2
and N deposition and include multiple years of data to better assess more typical relationships.
For NO2, the correlations between annual average NO2 and N deposition in the analyses that
considered transport were somewhat low (as described in section 6.2.2 above), indicating some
uncertainty in the extent to which a standard set as this air quality metric might control N
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deposition by itself. However, the analyses also found that the correlation between annual
average PM2.5 and N deposition was much stronger, likely due to HNO3, NO3 and NH4 being the
largest contributors to N deposition and being most closely related to concentrations of PM2.5.
Given this information and these relationships, the results suggest the potential for a standard set
as the PM2.5 annual average, averaged over three years, to better control N deposition. Such a
metric would also provide some control over S deposition, as discussed above. Of course, it is
important to also keep in mind that PM2.5 monitors that contribute to the S and N deposition
across the U.S. also measure other non-S and N related pollutants as part of the PM2.5 total mass.
This and other uncertainties in the analyses are noted in Chapter 6. However, it is unclear how
much and in what way each of these uncertainties might impact the results.
• What does 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 between co-located
measurements and modeled estimates in a subset of Class I areas that are mostly located in the
western U.S. The analyses indicated correlations between concentrations of other air quality
metrics and N deposition levels in these locations. For example, these results suggest that N
deposition in these rural areas is fairly well correlated with air concentrations of nitric acid and
particulate nitrate. Additionally, the results suggest that IMPROVE PM2.5, IMPROVE
approximated inorganic N PM2.5 (NO3" + NH4+, |ig/m"3), and inorganic nitrogen measured at
CASTNET monitoring sites (HNO3 + NO3" + NH4+, |ig/m"3) can be used to predict N deposition
in these locations, with CASTNET N showing the most consistent relationship over recent years
(section 6.2.1.4).
As similarly discussed above for S, given that this analysis is based on air concentrations
and deposition estimates at the same locations (distant from sources), use of one or more of these
air quality metrics as the indicator of a new standard would entail use of a surveillance network;
attainment of such a standard would then be judged based on these monitor measurements in
these Class I or other similar locations using monitoring networks like CASTNET and/or
IMPROVE (e.g., with locations generally in rural areas, away from sources). A corresponding
FRM/FEM would need to be developed for these monitors and measurements and adequacy of
network coverage would need to be assessed. However, it is unclear whether there are
advantages to such a choice for a new or revised standard versus those options discussed above.
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7.3 PRELIMINARY CONCLUSIONS
This section describes preliminary conclusions for the Administrator's consideration in
this review of the secondary NAAQS for oxides of N and S and for PM standards. These
conclusions are based on consideration of the assessment and integrative synthesis of the
evidence (as summarized in the ISA, and the 2008 ISA and AQCDs from prior reviews), and the
quantitative information on exposure and air quality summarized above. Taking into
consideration the discussions above in this chapter, this section addresses the following
overarching policy question.
• 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, NOx 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 PAs,
involves, first, evaluation of the currently available information with regard to key considerations
for assessing risk of or protection against the effects of the criteria pollutant of focus, such as
discussed in section 3.4 above. In this evaluation, the PA considers the welfare effects of the
pollutant, associated public welfare implications, and also the quantitative information, such as
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, 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
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standard provides air quality that would be expected to achieve such protection and, as
appropriate, potential alternative options (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 form, averaging time and level of
the standard (or suite of standards), together, provide the requisite protection.
In NAAQS reviews in general, the extent to which the protection provided by the current
secondary standards for oxides of S and N and for PM are judged to be adequate depends on a
variety of factors, including science policy judgments and public welfare policy judgments.
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 the standard also include the interpretation of, and decisions
as to the weight to place on, different aspects of the quantitative analyses of air quality and
exposure and any associated uncertainties. Additionally, to the extent multiple policy options are
identified that would be expected to achieve a desired level of protection, decisions on the
approach to adopt falls within the scope of the Administrator's judgment. Thus, we recognize
that 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.
In the discussion below we address first the SO2 standard, and its adequacy with regard to
protection of the public welfare from the direct effects of SOx in ambient air. Next, we address
the extent of protection provided by the SO2 standard from 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 direct effects of N oxides in
ambient air, 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 of these discussions, we recognize limitations in the
available information and tools and associated uncertainties, which we recognize to vary in
specificity and significance.
As noted earlier in this draft PA, the 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
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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/short (a few
hours) exposure, with greater 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 meeting the existing standard (outside Hawaii, where air quality can be influenced
by volcanic emissions) during all years since 2000, 95% of the maximum annual 3-hour average
concentrations are below 0.2 ppm and 99% are below 0.3 ppm. Thus, 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. Accordingly, we conclude that it is appropriate to consider retaining the existing
standard for that purpose. In so doing, however we recognize the extensive evidence of the
contribution of SOx in ambient air to acidic deposition, particularly in aquatic ecosystems and
we next consider the adequacy of protection afforded by the existing SO2 standard from such
effects.
With regard to deposition-related effects, we note the range of median deposition values
estimated across U.S. ecoregions (in terms of level 3 specification) for the 20-year period
analyzed (2001-2020) 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, and when design values for the standard (second
highest 3-hour average in a year) ranged below 500 ppb (as discussed in section 6.2.2.2 above).
For example, in the earliest 3-yr period (2001-03), when some ecoregion max DV values ranged
below 400 ppb median S deposition in 4 ecoregions exceeded 15 kg/ha'Vi""1 and median S
deposition in more than 10 ecoregions exceeded 10-12 kg/ha" Vr"1.
Considering the aquatic acidification estimates of S deposition on the order of 12-15 kg
S/ha-yr levels occurring during periods when the existing standard has been met, and the aquatic
acidification analysis results for that level of S deposition, it is reasonably concluded that the
current evidence and quantitative analyses call into question the adequacy of the existing
standard. Thus, we have evaluated options for potential alternative standards that may be
indicated to provide appropriate control of S deposition and associated welfare effects.
For the purposes of evaluating options for potential alternative standards that may be
considered to provide an appropriate level of protection from deposition-related effects of S
oxides, we have drawn on the quantitative analyses and information described in Chapter 5 and
summarized in section 7.2.1.2 above. In this context and for our purposes within this PA, we
have focused on a range of S deposition levels below 12 kg S/ha-yr, extending down as low as 4
or 5 kg S/ha-yr. In focusing on this range, we draw primarily from the aquatic acidification
analyses. In so doing, however, we also note the linkages between watershed soils and
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waterbody acidification, as well as terrestrial effects. Such linkages indicate that a focus on
protecting waterbodies from reduced ANC 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 this range for our purposes here, we note there to be relatively greater uncertainty
associated with the lower levels. Moreover, we recognize that, in the end, judgments inherent in
identification of such a range, include judgements 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 considering options for a secondary standard that might be concluded to provide the
desired control of S deposition, we first note the complexity of identifying a national air quality
standard focused on protection from national deposition patterns (rather than air concentrations)
of concern to the public welfare. 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,
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 essential to identification of options expected to provide a particular level of
deposition control in sensitive ecosystems. Further, we recognize that to achieve the desired level
of S deposition control in sensitive ecosystems, SO2 emissions must be controlled at their
sources and that such control can be provided by the appropriate secondary standard measured at
regulatory SO2 monitors given that these monitors are generally sited near large SO2 sources to
provide control of these large sources of SO2. While recognizing the variation across the U.S. in
the processes that govern that transformation of source emissions to eventual deposition of S
compounds, we utilized a trajectory-based approach to account for the relationship between
upwind concentrations near sources and deposition in areas more distant, as described in section
6.2.2 above. Based on application of this approach, we observed that while there is some
variation (especially at lower deposition levels), there is generally a strong positive linear
correlation between deposition estimates and the trajectory-based concentration metrics. While
there is residual uncertainty in the relationship, its use facilitates the linking of pollutant
concentrations and the resultant N or S deposition, with the deposition-related welfare effects
associated with various deposition levels. With this linkage, the protectiveness of existing
standards can then be considered in terms of pollutant concentrations.
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Based on the analyses described in the preceding chapters and all of the above
considerations, we have identified options for potential alternative SO2 standards appropriate to
consider for providing control of S deposition associated with SOx in ambient air. The potential
alternatives include a SO2 standard of the same averaging time and form as the existing
secondary standard, with a level revised to within a range of 200 to 400 ppb. Additionally,
however, we recognize, in light of the 'not to be exceeded more than once per year' form of the
existing standard, which allows an average concentration above the standard level for which
there is no limit, and the relatively short averaging time, that this option might reasonably be
considered a relatively imprecise approach for controlling S deposition. Accordingly, we
conclude it may be more appropriate to consider adoption of a new SO2 standard with a different
averaging time and 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. For such a standard,
based on the air quality analyses and recognizing the various limitations and associated
uncertainties, a level on the order of 22 ppb to 10 ppb is identified. We additionally note that
whether such a range is concluded to be appropriate and/or what value within this range of levels
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. The 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 5.1.2 above.
We also recognize the evidence of NO2 concentrations well in excess of the standard that
occurred for more than a decade in areas of California where damage suspected to relate to N
oxides in air is well documented (as summarized in sections 5.1.2, 5.4.3 and Appendix 5B,
sections 5B.4). Given the extensive period of elevated concentrations above the standard, the
evidence 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 clearly documenting the potential for N
oxides 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 welfare 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 aspect 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 be considered to offer the potential for some desired additional
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protection from deposition-related ecosystem effects, and also the potential for increased
protection from effects related to airborne nitric acid effects on biota surfaces. Accordingly, in
addition to preliminarily concluding it is appropriate to consider retaining the existing NO2
standard, we additionally identify a revision option for the secondary standard for N oxides.
In considering options for revision of the secondary standard for N oxides, we have
further evaluated the information related to deposition-related effects on ecosystems. With regard
to the currently available information related to deposition -related effects of N oxides on
ecosystems, we recognize, as discussed in section 7.2.2 above, 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 of the associated uncertainty. Some
complications with regard to 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, for which there
are non-air contributing sources, we recognize the complexity of estimating the portion of N
inputs, and associated contribution to effects, derived from atmospheric sources. Lastly, as noted
above, there is additional complexity in risk management policy decisions for this category of
effects, including with regard to risk management targets or objectives for an ecosystem stressor
like N enrichment, particularly in light of historical deposition and its associated effects that have
influenced the current status of terrestrial ecosystems, their biota, structure and function.
Additionally, as discussed in section 7.2.2.3 above, and in more detail in Chapter 6,
several observations are made based on the air quality analyses of relationships between N
deposition and NO2 and PM2.5 air quality metrics. For NO2, the correlations between annual
average NO2 and N deposition in the analyses that considered transport were somewhat low (as
described in section 6.2.2 above), indicating some uncertainty in the extent to which a standard
set as this air quality metric might control N deposition by itself. These analyses found a much
stronger correlation between annual average PM2.5 and N deposition. This finding is likely due to
HNO3, NO3 and NH4 being the largest contributors to N deposition and also being most closely
related to concentrations of PM2.5. Given this information and these relationships, the results
suggest the potential for a standard set as the PM2.5 annual average, averaged over three years, to
be a better air quality metric for control N deposition than the NO2 metric assessed. Such a
metric would also be expected to provide some control of S deposition, as discussed above. In
recognizing this finding, however, we also note that PM2.5 across the U.S. varies with regard to
composition, including the contribution from other pollutants that are not S or N containing. The
potential influence of this and other uncertainties in the analyses (noted in Chapter 6) is unclear.
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Thus, 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 preliminarily conclude it is appropriate for the Administrator to consider an array of
policy options supported by the current scientific information and quantitative air quality,
exposure and risk analyses. 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 of direct effects of the
pollutants in ambient air and options to address protection of effects related to S deposition and
related to N deposition. A summary of these options is shown in Table 7-1 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 have identified two options, one involving
revisions to the existing SO2 standard to additionally afford protection for S deposition-related
welfare effects, and one involving adoption of a new SO2 standard. The option involving revision
of the existing standard is for a standard with a level revised to 200-400 ppb, as the 2nd highest
daily 3-hour maximum, and a form revised to be the average over three consecutive years. An
alternate option is to establish an additional SO2 annual mean standard, averaged across three
years, with a level within the range from 22 to 10 ppb, with greater uncertainty for lower levels.
With this option, it may be appropriate to consider revoking the current 3-hour standard.
With regard to protection from effects of N oxides 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 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
evidence base for ecosystem effects related to N deposition, such as N enrichment, as discussed
above, and with the air quality information related to the 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
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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 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. The option
based on all of these considerations 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.
To the extent different judgments are made, two options for revision are identified that
might be expected to provide protection from both direct effects of N oxides in ambient air and
from N deposition of potential concern. Based on the air quality information that suggests better
control of N-deposition with the annual PM2.5 versus NO2 standard, this option involves revision
to the level of the PM2.5 annual secondary standard. For this option, it may be appropriate to
consider revisions to the level of the current PM2.5 (annual) standard of 15 |ig/m3 down to a level
as low as 12 |ig/m3, recognizing increased uncertainty associated with lower levels. Such a
standard would potentially provide additional protection against S deposition. A second option
for revision is recognized, 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 include retaining or
revising the current secondary NO2 standard. For the revision option, it may be appropriate to
consider levels below 53 ppb. In considering such lower levels, potentially extending down to
perhaps, as low as about 40 ppb, however, we recognize appreciably greater uncertainty with
decreasing levels below 53 ppb.
In addition to the options identified above, we additionally recognize the potential value
in 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., NO3, SO4, NH4). In so doing, 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
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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, we also recognize the additional data collection and analysis needed to 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, we 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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1 Table 7-1. Summary of current standards and draft range of potential policy options for
2 consideration.
Current Standards Protect against Direct Effects of Pollutants in Ambient Air
Indicator
Level
Form
Avg Time
S02
0.5 ppm
Not to be exceeded more than once
per year
3 hours
no2
53 ppb
Annual
1 year
PM2.5
15 pg/m3
Annual, averaged over 3 years
1 year
35 pg/m3
98th percentile, averaged over 3 years
24 hours
PM10
150 pg/m3
Not to be exceeded more than once
per year on average over 3 years
24 hours
Draft Policy Assessment Range of Options for Consideration
Retain/Revise
to Address Direct Air-related Effects
Revise to Address Deposition-related Effects
Level
Form
Avg Time
S02
Retain
200-400 ppb
Not to be exceeded more than once
per year, averaged over 3-years
3 hours
OR:
10-22 ppb
Annual, averaged over 3 years
1 year
N02
Retain OR
Revise level of annual NO2 standard to <53 ppb to as low as 40 ppb
Provide increased protection using a revised PM2.5 standard
PM2.5
annual
standard
Retain
<15 pg/m3 to
as low as 12
|jg/m3
Annual, averaged over 3 years
1 year
OR Retain
PM2.5
24-hour
standard
Retain
Not assessed as most relevant metrics for N and S deposition
PM10
standards
We additionally recognize 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., NO3, SO4, NH4). A number of
uncertainties and complications are recognized with this option that include uncertainties in relationships between
concentrations near sources and in areas of deposition, as well as additional time and resources related to
establishment of regulatory monitoring networks and measurement methods.
3
4
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1 7.4 AREAS FOR FUTURE RESEARCH RELATED TO KEY
2 UNCERTAINTIES AND
3 In this section, we highlight key uncertainties associated with reviewing and establishing
4 the secondary standards for oxides of S, oxides of N and PM, and additionally recognize that
5 research in these areas may additionally be informative to the development of more efficient and
6 effective control strategies. Accordingly, areas highlighted for future welfare effects and
7 atmospheric chemistry research include model development, and data collection activities to
8 address key uncertainties and limitations in the current scientific evidence. These areas are
9 similar to those highlighted in past reviews, such as those that follow:
10 • Data and tools to relate concentrations of specific pollutants in ambient air with
11 deposition. This could include expansion of existing monitoring networks (either in
12 number or in the number of pollutants measured) to enable more geographically
13 representative comparisons of local deposition and local air quality concentrations.
14 • Research to further develop and improve modeling tools that relate atmospheric
15 deposition of specific compounds to changes in soil conditions, which influence
16 watershed aquatic impacts as well as effects on resident vegetation, in areas characterized
17 by different soil types and geology.
18 • Continued refinement of the TDEP methodology to estimate national total deposition.
19 This could include efforts to continually evaluate and improve the air quality model
20 simulation inputs to TDEP.
21 • Additional work to improve accuracy of estimates of BCw, a critical parameter in
22 modeling to characterize risks associated with aquatic and terrestrial acidification.
23 • To address uncertainty associated with characterizing risks associated with terrestrial
24 acidification, additional research might contributed to an improved understanding of
25 effects on sensitive vegetation of various levels of BC:A1 in different soil types.
26 • Improved understanding or relationship between soil N and C:N metrics and effects on
27 key ecological receptors.
28 • Although addition or exposure studies are somewhat limited, studies assessing important
29 tree species included in Horn et al 2018 would help improve confidence.
30 • Research to improve understanding of the linkages between deposition, geochemical
31 metrics and ecological effects of freshwater ecosystem eutrophi cation. Currently
32 available studies of waterbodies in the western U.S. have included investigations of
33 nutrient limitation and diatom assemblages. Studies in eastern lakes and streams have
34 primarily focused on N03 leaching. Information is limited for relationships between
35 additional ecological endpoints (e.g., effects on fish and invertebrate communities) and
36 N03 concentrations (or other chemical indicators).
37 • Research relating specific indicators of acidification or nutrient enrichment to ecological
38 effects and to ecosystem services (e.g., fish harvest, recreation, etc).
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• 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, DOC, 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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REFERENCES
Clark, CM; Tilman, D. (2008). Loss of plant species after chronic low-level nitrogen deposition
to prairie grasslands. Nature 451: 712-715.
Cox, RD; Preston, KL; Johnson, RF; Minnich, RA; Allen, EB. (2014). Influence of landscape-
scale variables on vegetation conversion to exotic annual grassland in southern
California, USA. Global Ecology and Conservation 2: 190-203.
http://dx.doi.Org/10.1016/i.gecco.2014.09.008
Dietze, M. C. and P. R. Moorcroft (2011). Tree mortality in the eastern and central United States:
Patterns and drivers. Global Change Biology 17(11): 3312-3326.
Fenn, ME, Lambert, KF, Blett, TF, Burns, DA, Pardo, LH, Lovett, GM, Haeuber, RA, Evers,
DC, Driscoll, CT and Jefferies, DS (2011). Setting limits: Using air pollution thresholds
to protect and restore U.S. ecosystems. Washington, DC, Ecological Society of America.
Geiser, LH; Jovan, SE; Glavich, DA; Porter, MK. (2010). Lichen-based critical loads for
atmospheric nitrogen deposition in Western Oregon and Washington Forests, USA.
EnvironPollut 158: 2412-2421. http://dx.doi.Org/10.1016/i.envpol.2010.04.001
Horn, K.J., R.Q. Thomas, C.M. Clark, L.H. Pardo, M.E. Fenn, G.B. Lawrence, S.S. Perakis,
E.A.H. Smithwick, D. Baldwin, S. Braun, A. Nordin, C.H. Perry, J.N. Phelan, P.G.
Schaberg, S.B. St. Clair, R. Warby, S. Watmough. (2018) Growth and survival
relationships of 71 tree species with nitrogen and sulfur deposition across the
conterminous U.S. PLoS ONE 13(10): e0205296.
https://doi.org/10.1371/journal.pone.0205296
McDonnell, TC, Sullivan, TJ, Hessburg, PF, Reynolds, KM, Povak, NA, Cosby, BJ, Jackson, W
and Salter, RB (2014). Steady-state sulfur critical loads and exceedances for protection of
aquatic ecosystems in the U.S. Southern Appalachian Mountains. J Environ Manage 146:
407-419. https://doi.Org/10.1016/i.ienvman.2014.07.019
McNulty, S.G., Boggs, J., Aber, J.D., Rustad, L., Magill, A. (2005). Red spruce ecosystem level
changes following 14 years of chronic N fertilization. For Ecol Manage 219: 279-291.
http://dx.doi.Org/10.1016/i.foreco.2005.09.004
Riddell, J; Padgett, PE; Nash, TH, III. (2012). Physiological responses of lichens to factorial
fumigations with nitric acid and ozone. Environ Pollut 170: 202-210.
http://dx.doi.Org/10.1016/i.envpol.2012.06.014
Thomas, R.Q., C.D. Canham, K.C. Weathers and C.L. Goodale. (2010). Increased tree carbon
storage in response to nitrogen deposition in the US. Nature Geoscience 3(1): 13-17.
U.S. EPA. (1987). National Air Quality and Emissions Trends Report, 1985. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. EPA 450/4-87-001.
Available at: https://www.epa.gov/air-trends/historical-air-qualitv-trends-reports
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5A APPENDIX
AQUATIC ACIDIFICATION ANALYSES
TABLE OF CONTENTS
5A.1 Aquatic Acidification and Overview of Analyses 5A-1
5A. 1.1 Analysis Scales 5A-3
5 A. 1.2 Method - Aquatic Critical Load Approach 5A-5
5A. 1.3 Ecological Risk and Response 5A-5
5A.1.4 Chemical Criterion and Critical Threshold 5A-11
5A. 1.4.1 Natural Acidic Waterbodies 5A-12
5 A. 1.5 Critical Load Data 5 A-12
5A. 1.5.1 Steady-State Water Chemistry (SSWC) model and F-Factor 5A-14
5A. 1.5.2 MAGIC Model and Regional Linear Regression Models for Estimating BCW
Input to SSWC 5A-15
5A.1.5.3 MAGIC model and Hurdle Modeling for Estimating BCW Input to SSWC ....16
5 A. 1.6 Critical Load Exceedance 5 A-17
5 A. 1.6.1 Deposition 5A-19
5A. 1.6.2 Acidifying Contribution of Nitrogen Deposition 5A-19
5A. 1.7 Ecoregions Sensitivity to Acidification 5A-25
5A.2 Analysis Results 5A-31
5A.2.1 Results of National Scale Assessment of Risk 5A-31
5A.2.2 Ecoregion Analyses 5A-59
5A.2.2.1 Ecoregion Critical Load Exceedances - Sulfur Only 5A-70
5 A.2.2.2 Ecoregion Summary - Percent Exceedances as a Function of Total S
deposition 5A-100
5A.2.3Analysis of Risk in Case Study Areas for Acidification 5A-125
5 A.2.3.1 Results 5A-126
5A.3 Key Uncertainties/Limitations 5A-134
5A.3.1 Results 5A-137
5A.3.1.1 Critical Load Model Comparison 5A-140
References 5A-145
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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. (McDonnell et al. 2012) 5A-16
Table 5A-2. Average annual nitrate concentrations for the EPA's Long-term Monitoring
(LTM) program for lakes and streams 5A-22
Table 5A-3. Regional aggregation for determine average N leaching for ecoregion II and III.
Water quality data based on National Critical Database v3.2 5A-24
Table 5A-4. Acid sensitive Categories and criteria used to define each one 5A-28
Table 5A-5. Ecoregion III results for acid sensitivity 5A-30
Table 5A-6. Percent of waterbodies with critical loads less than 2, 6, 12, and 18 Kg S/Ha for
critical loads based on an ANC limit of 20, 30, and 50 |ieq/L 5A-32
Table 5A-7. Summary of national aquatic critical load exceedances by ANC thresholds and
deposition periods 5A-32
Table 5A-8. National aquatic critical load exceedances based on all critical load values by
ANC thresholds and deposition periods 5A-33
Table 5 A-9. National aquatic critical load exceedances based on critical loads greater than 0
by ANC thresholds and deposition periods 5A-35
Table 5A-10. Summary of Sulfur only critical loads by Ecoregions III by ANC thresholds of
20 and 30 |ieq/L in Units = Kg S/ha-yr) 5A-62
Table 5A-l 1. Summary of Sulfur only critical loads by Ecoregions III by ANC thresholds of
50 and 50/20 |ieq/L in Units = Kg S/ha-yr) 5 A-64
Table 5A-12. Summary of total Sulfur deposition for 84 ecoregions with CLs in units of Kg
S/ha-yr 5A-66
Table 5A-13. Summary of the number of ecoregions with median deposition in the range of
<2, 2-5, 5-7, 7-10, >10 Kg S/ha-yr for the 84 ecoregions determined by GIS
zonal statistic 5A-66
Table 5A-14. Median total sulfur deposition (Kg S/ha-yr) of deposition estimates (based on
TDEP) across locations with CLs in each of the 69 ecoregions with at least one
CLs. Deposition based on TDEP 5A-66
Table 5A-15. Median sulfur deposition (Kg S/ha-yr) for the 84 ecoregions determined by GIS
zonal statistic. Deposition based on TDEP 5A-68
Table 5A-16. Summary of Ecoregion results for critical load (CL) exceedances (EX) for each
ANC threshold and time periods for the 58 Ecoregions with 10 or more values.
5A-72
Table 5A-17. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 20 |ieq/L for deposition years of 2018-20 and 2014-16 5A-73
Table 5 A-l 8. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 20 |ieq/L for deposition years of 2010-12, 2006-08 and 2001-03.
5A-75
Table 5A-19. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 30 |ieq/L for deposition years of 2018-20 and 2014-16 5A-77
Table 5A-20. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 30 |ieq/L for deposition years of 2010-12, 2006-08 and 2001-03.
5A-79
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Table 5A-21. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 50 |ieq/L for deposition years of 2018-20 and 2014-16 5A-81
Table 5A-22. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 50 |ieq/L for deposition years of 2010-12, 2006-08 and 2001-03.
5A-83
Table 5A-23. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 50/20 |ieq/L for deposition years of 2018-20 and 2014-16 5A-85
Table 5A-24. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 50/20 |ieq/L for deposition years of 2010-12, 2006-08 and 2001-03.
5A-87
Table 5A-25. Minimum, maximum, and median S deposition for ecoregions with at least 50
critical loads and with ecoregions with exceedances for the five deposition
periods and three ANC targets 5A-102
Table 5A-26. Number of ecoregion-time period combinations with more than 10, 15, 20, 25
and 30% of waterbodies exceeding their CLs for three ANC target of 50 |ieq/L.
Includes 18 ecoregions in the eastern U.S 5A-103
Table 5A-27. Cumulative percentage of ecoregion-time period combinations with more than
10, 15, 20, 25, and 30% of waterbodies per ecoregion meeting their CLs for the
ANC target of 50 |ieq/L as a function of total S deposition across all 5
deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20) 5A-104
Table 5A-28. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
>30%) as a function of total S deposition across all 5 deposition periods (2001-
03, 2006-08, 2010-12, 2014-06, 2018-20) 5A-106
Table 5A-29. 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) 5A-107
Table 5A-30. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
>30%o as a function of total S deposition across all 5 deposition periods (2001-
03, 2006-08, 2010-12, 2014-06, 2018-20) 5A-109
Table 5 A-31. 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) 5A-110
Table 5A-32. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
>30%o as a function of total S deposition across all 5 deposition periods (2001-
03, 2006-08, 2010-12, 2014-06, 2018-20) 5A-112
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) 5A-113
Table 5A-34. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
>30%o as a function of total S deposition across all 5 deposition periods (2001-
03, 2006-08, 2010-12, 2014-06, 2018-20) 5A-115
Table 5 A-3 5. 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) 5A-116
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Table 5A-36
Table 5A-37
Table 5A-38
Table 5A-39
Table 5A-40
Table 5A-41
Table 5A-42
Table 5A-43
Table 5A-44
Table 5A-45
Table 5A-46
Table 5A-47
Table 5A-48
Table 5A-49
Table 5A-50
Table 5A-51
Figure 5A-1.
Figure 5A-2.
Figure 5A-3.
Figure 5A-4.
Figure 5A-5.
Figure 5A-6.
Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
>30% as a function of total S deposition across all 5 deposition periods (2001-
03, 2006-08, 2010-12, 2014-06, 2018-20) 5A-118
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) 5A-119
Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
>30% as a function of total S deposition across all 5 deposition periods (2001-
03, 2006-08, 2010-12, 2014-06, 2018-20) 5A-121
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) 5A-122
Average, 70th and 90th percentile CL of S only (kg S/ha-yr) for each case study
area for ANC limits of 20, 30, 50, and 80 [j,eq/L 5A-126
Average, 70th and 90th percentile CL of S and S+N (meq/m2-yr) for each case
study area for ANC limits of 20, 30, 50, and 80 [j,eq/L 5A-127
The three-year historical periods used for each case study area 5A-129
For each three-year period described in Table 5A-41, this is the three-year
average deposition, spatially averaged across the case study area, for N and S
deposition 5A-130
Summary of correlation between observations of air concentration and NADP
deposition 5A-131
Correlation between CMAQ-simulated annual sum of total deposition and the
CMAQ-simulated annual average concentration for each case study 5A-132
Number and percent of case study waterbodies estimated to exceed their CLs
for specified ANC targets and air quality scenario 5A-133
Summary of S deposition levels to attain an ANC target of 20, 30, and 50 |ieq/L
for case study areas 5A-134
Parameters used and their uncertainty range 5A-135
Results of the Monte Carlo analysis for uncertainty broken down by confidence
interval 5A-137
Results of the Monte Carlo analysis for uncertainty broken down by ecoregion.
N/A indicates there was not sufficient data to calculate the percentile 5A-138
Results of the uncertainty analysis of Nitrate (N03-) in EPA's Long-term
Monitoring (LTM) program 5 A-140
TABLE OF FIGURES
Three scales of the analysis: National, Ecoregion III, and Case Study 5A-3
Omernik Ecoregion II areas with ecoregion III subdivisions 5 A-4
Total macroinvertebrate species richness as a function of pH in 36 streams in
western Adirondack Mountains of New York, 2003-2005 5A-7
Critical aquatic pH range for fish species 5A-8
Number of fish species per lake versus acidity status, expressed as ANC, for
Adirondack lakes 5 A-10
Unique waterbody locations with critical loads used in this assessment. Lower
critical load values are red and orange 5A-13
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Figure 5A-7. The EPA's Total Alkalinity regions (a) and ANC water quality measurements
across the CONUS (b) in units of |ieq/L 5A-27
Figure 5A-8. Ecoregion III grouped in three acid sensitivity classes 5A-29
Figure 5A-9. Critical load exceedance percentages by ANC thresholds and deposition years. .
5A-37
Figure 5A-10. Critical load exceedance (Ex) for S only total deposition from 2001-03 for an
ANC threshold of 20 [j,eq/L 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 5A-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 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... 5A-42
Figure 5A-14. Critical load exceedance (Ex) for S only total deposition from 2006-08 for an
ANC threshold of 20 [j,eq/L 5A-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 5A-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 5A-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... 5A-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 5A-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 5A-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 5A-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... 5A-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 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 5A-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 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... 5A-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 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 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 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... 5A-58
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Figure 5A-30. Critical load exceedance (EX) for S only deposition from 2018-20 for an ANC
threshold: a. 20, b. 30, c. 50, d. 50/20 ueq/I. for COM S 5A-59
Figure 5A-31. Locations of aquatic critical loads mapped across Ecoregions III 5A-61
Figure 5A-32. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2018-20 (top) and 2014-16 (bottom) for an ANC threshold of 20 [j,eq/L..
5A-89
Figure 5A-33. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2010-12 (top) and 2006-08 (bottom) for an ANC threshold of 20 [j,eq/L. .
5A-90
Figure 5A-34. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2001-02 for an ANC threshold of 20 [j,eq/L 5A-91
Figure 5A-35. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2018-20 (top) and 2014-16 (bottom) for an ANC threshold of 30 [j,eq/L. .
5A-92
Figure 5A-36. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2010-12 (top) and 2006-08 (bottom) for an ANC threshold of 30 [j,eq/L.
5A-93
Figure 5A-37. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2001-03 for an ANC threshold of 30 [j,eq/L 5A-94
Figure 5A-38. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2018-20 (top) and 2014-16 (bottom) for an ANC threshold of 50 [j,eq/L. .
5A-95
Figure 5A-39. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2010-12 (top) and 2006-08 (bottom) for an ANC threshold of 50 [j,eq/L.
5A-96
Figure 5A-40. Aggregated percent ecoregion critical load exceedances for S only deposition
from 2001-03 for an ANC threshold of 50 [j,eq/L 5A-97
Figure 5A-41. Aggregated percent ecoregion 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 5A-98
Figure 5A-42. Aggregated percent ecoregion critical load exceedances 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 5A-99
Figure 5A-43. Aggregated percent ecoregion critical load exceedances 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 5A-100
Figure 5A-44. Cumulative percentage of ecoregion-time period combinations with
exceedances >10, >15, >20, >25, >30% as a function of total S deposition
across all 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-20).
5A-105
Figure 5A-45. Cumulative percent of ecoregions with exceedances >10, >15, >20, >25, >30%
as a function of total S deposition across all 5 deposition periods (2001-03,
2006-08, 2010-12, 2014-06, 2018-20) 5A-108
Figure 5A-46. Cumulative percent of ecoregions with exceedances >10, >15, >20, >25, >30%
as a function of total S deposition across all 5 deposition periods (2001-03,
2006-08, 2010-12, 2014-06, 2018-20) 5A-111
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Figure 5A-47. Cumulative percent of ecoregions with exceedances >10, >15, >20, >25, >30%
as a function of total S deposition across all 5 deposition periods (2001-03,
2006-08, 2010-12, 2014-06, 2018-20) 5A-114
Figure 5A-48. Cumulative percent of ecoregions with exceedances >10, >15, >20, >25, >30%
as a function of total S deposition across all 5 deposition periods (2001-03,
2006-08, 2010-12, 2014-06, 2018-20) 5A-117
Figure 5A-49. Cumulative percent of ecoregions with exceedances >10, >15, >20, >25, >30%
as a function of total S deposition across all 5 deposition periods (2001-03,
2006-08, 2010-12, 2014-06, 2018-20) 5A-120
Figure 5A-50. Cumulative percent of ecoregions with exceedances >10, >15, >20, >25, >30%
as a function of total S deposition across all 5 deposition periods (2001-03,
2006-08, 2010-12, 2014-06, 2018-20) 5A-123
Figure 5A-51. Total S deposition (Kg S/Ha-yr) as a function of percent of waterbodies
exceeding the critical load for 2018-20 (upper) and 2014-16 (lower) for target
ANC = 20, 30, and 50 |ieq/L for positive critical loads (CL>0) 5A-124
Figure 5A-52. Location of the case study areas. Northern Minnesota (NOMN), Rocky
Mountain National Park (ROMO), Shenandoah Valley (SHVA), Sierra Nevada
Mountains (SINE) and White Mountain National Forest (WHMT) 5A-125
Figure 5A-53. Critical load maps of each case study area 5 A-128
Figure 5A-54. Critical load uncertainty analysis for 14,943 values across the CONUS of the
SSWC model 5A-136
Figure 5A-55. Critical load comparison between values based on MAGIC model (y-axis) and
values based on the SSWC F-factor model (Lynch et al. 2022) 5A-142
Figure 5A-56. 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) 5A-143
Figure 5A-57. A. Critical load comparison between values based on MAGIC model (y-axis)
and values based on the SSWC F-factor model (Lynch et al. 2020) (x-axis). B.
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) (x-axis) 5A-144
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5A.1 AQUATIC ACIDIFICATION AND OVERVIEW OF ANALYSES
Air emissions of sulfur oxides (SOx), nitrogen oxides (NOx), 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, NOx, 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 N and 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 (Ah+), 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 Ah+) 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 defined as the total amount of strong
base ions minus the total amount of strong acid anions as the differences between the equivalent
sum of base cations (SBC) plus ammonium (Ca2+ + Mg2+ + K+ + Na+ + NH4+) and the equivalent
sum of acid anions (SAA) (SO42" + NO3" + CI") (eqn. 5A-1):
ANC = SBC - SAA = (Ca2+ + Mg2+ + K+ + Na+ + NH4+) - (S042" + NO3" + CI") (5A-1)
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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 Continental U.S. (CONUS) but can be
higher in the eastern CONUS (ISA Appendix 7, 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
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 (i.e., less than 50 meq/m2/yr) may
mean that the watershed has a limited ability to neutralize the addition of acidic anions, and
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hence, it is susceptible to acidification. The greater the CL value, the greater the ability of the
watershed to neutralize the 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). For this analysis, the national-
scale assessment included the CONUS only since there is insufficient data available for Flawaii,
Alaska, and the territories. The Omernik ecoregion classifications were used for the ecoregion-
scale analyses. Case studies were selected for areas which were likely to be most impacted and
for which sufficient data was 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.
~ Example of Ecoregion III
V
'»•
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7
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• *
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• •
• •
Rock Mountain National Park
Figure 5A-1. Three scales of the analysis: National, Ecoregion III, and Case Study.
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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
taken into account in order to characterize sensitive populations of waterbodies and relevant
regions across the CONUS. The EPA's Omemik 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 dri ven acidification
(Figure 5A-2). There are 25 Ecoregion II categories in the CONUS, each of which are further
subdivided into a total of 84 Level III categories in the CONUS.
I I MSSS PP IMIUWH. JNDSOUTVCAST USACOhSTN. HJWB I I TBWSUWSWWi, COAST* PlJINI
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ico_Levet_ll_tJS
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Omernik Ecoregion il Index Map
Figure 5A-2. Omernik Ecoregion II areas with ecoregion III subdivisions
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
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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 phenomena
that affect or reflect differences in ecosystem quality and integrity. Factors 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. 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.
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. These areas were the Shenandoah National Park,
White Mountain National Forest, Voyagers National Park, Sierra National Forest, and Rocky
Mountain National Park. These parks and national forest vary in their sensitivity to acidification,
but represent high value or protected ecosystems, such as Class 1 areas, wilderness, and national
forests.
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 5A. 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 within the CL was also estimated and factored in the CL
exceedance determination.
5A.1.3 Ecological Risk and Response
Risk in aquatic systems is estimated based on the acidification indicator, ANC, and
changes in this water quality metric related to N and/or S deposition. The evidence relates ANC
and other water quality indicators of acidification to biological and ecological effects (ISA
Appendix 8.3). The connection between SO2 and NOx emissions, deposition of N and/or S, and
the acidification of surface waters is well documented in the eastern U.S. (ISA, Appendix
7;Driscoll et al., 2016).
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The biological impact of acidifying deposition is mediated through changes in water
quality that in turn impact biota. Deposition of N and/or S can effect biogeochemical changes
that may induce biologically harmful effects. Surface water chemistry is then a good indicator of
the effects 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 can affect the
structure and function of biological communities in lakes and streams (ISA Appendix 8.3).
The most widely used measure of surface water acidification, and subsequent recovery
under reduced acid deposition, is ANC. Inorganic A1 and pH are also affected by acidic
deposition. All three water quality parameters are indicators of aquatic acidification for which
there is evidence of effects on aquatic systems including physiological impairment, reduced
fitness or death, alteration of species richness, community composition and structure, and
biodiversity in freshwater ecosystems. 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).
As summarized in section 4.2.1.1.2 above, the evidence of effects on biota from aquatic
acidification indicates a range of severity with varying 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
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 (.icq/L)1 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
1 pH and ANC were related to one another using a generalized relationship base on equilibrium with atmospheric
CO2 concentration (Cole and Prairie, 2010)
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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 A5-3). 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).
Median pH
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 when stream
pH drops below 5.1 (ANC 0 (j,eq/L) indicating that trout lost 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
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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
peq/L). 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 [ieq/L) indicated that the trout had lost the ability to ionoregulate
(ISA, Appendix 8, section 8.3.6.1). See Figure A5-4 for fish species sensitivity based on
observations from field studies with supporting bioassays.
Critical pH Ranges of Fish
Central mud minnow
Yellow perch
Brown buMhoad
m Pumpkireeed
Largemouth bass
Northern pike
Brook trout
While sucker
Rock bass
Golden shiner
- Arctic char
Atlantic salmon
Brown trout
Creek chub
Rainbow trout
Smallmouth bass
Lake trout
Walloye
N. rebeMied dace
Slimy sculpin
Common shiner
Fathead minnow
Blacknose dace
Bluntnose minnow
4.0
50
6.0
PH
Blacknose shiner
7.0
Safe range, no acid-related effects occur
Uncertain range, acki related effects may occur
¦ Critical range, acid-related 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).
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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 [j,eq/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 [j,eq/L or less at base flow
may be 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 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).
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 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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14 -
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(0
« 6-
i£
o 4
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z °-
•2 -
-4 -
•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 with
decreases in ANC below a threshold of approximately 50 to 100 [ieq/L for lakes (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).
The key biological/ecological effects on aquatic organisms that have been observed in
field and laboratory studies of different acidification levels, as 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 }ieq/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 lucius), and others (Sullivan et al., 2003, 2006; Bulger et
al., 2000), which is in most cases attributed to elevated inorganic monomeric Al
Acute
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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, 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. Such effects included reduced aquatic diversity (Kretser et al., 1989,
Lawrence et al., 2015; Dennis, 1995) with many species missing such as Atlantic salmon
(Salmo salar) smolts, blacknose shiner (Notropis heterolepis) (Bulger et al., 2000,
Sullivan et al., 2006, Liebich et al., 2011). Comparatively, acid tolerant species, such as
brook trout may have moderate to healthy populations, (Kretser et al., 1989, Lawrence et
al., 2015; Dennis, 1995).
• At an ANC between 50 to 80 [j,eq L-l, the fitness and population size of only sensitive
species have been impacted. 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., Atlantic salmon smolts, blacknose shiner [Baldigo et al., 2009; Kretser
et al., 1989, Lawrence et al., 2015; Dennis, 1995]). Reduced fish species richness has also
been reported to be affected (Bulger et al., 2000 and Sullivan et al., 2006).
• Values of ANC >80 [j,eq/L have not generally 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 these
analyses, CLs were evaluated with respect to three different ANC thresholds: 20 [j,eq/L (minimal
protection), 30 (intermediate protection) and 50 [j,eq/L (moderate protection) that represent
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specified harmful ecological effects based on results from 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
provides 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, previous studies, and the
National Critical Loads Database (NCLD), used 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). 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 a host of 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. el., 2012; Shaw et al 2014). Sullivan et
el., (2012) using MAGIC model simulations for pre-industrial (1850), suggested that there were
no acidic (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 nearly all known cases, historical ANC levels are above 20 |ieq/L,
but not all waterbodies at the higher ANC levels of 50 |ieq/L are able to reach this level. In those
cases, the CL was evaluated, but was not included in the results and summary assessments.
5A.1.5 Critical Load Data
Aquatic CLs used in this assessment came from the National Critical Load Database
version 3.2.1 (Lynch et al., 2022), from studies identified in the ISA (e.g., Lawrence et al., 2015;
Fakhraei et al., 2014; Sullivan et al., 2012; Fakhraei et al., 2016). The NCLD is comprised of
CLs calculated from a host of common models: (1) steady-state mass-balance models such as the
Steady-State Water Chemistry (SSWC), (2) dynamic models such as Model of Acidification of
Groundwater In Catchment (MAGIC) (Cosby et al., 1985) or Photosynthesis EvapoTranspiration
Biogeochemical model (PnET-BGC) (Zhou et. al., 2011) run out to year 2100 or 3000 to model
steady-state conditions and (3) regional regression models that use results from dynamic models
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to extrapolate to other waterbodies (McDonnell et. al., 2012 and Sullivan et al. 2012). 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 F[a) 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 is focused on waterbodies that are typically 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.
Aquatic Critical Load (CLmaxS)
• Hightly Sensitive: <50
» Sensitive: 51 -100
o Low Sensivity101 - 200
• Not Sensitive: >201
State Boundary
Figure 5A-6. Unique waterbody locations with critical loads used in this assessment. Lower
critical load values are red and orange.
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5A.1.5.1 Steady-State Water Chemistry (SSWC) model and F-Factor
Critical loads were derived from present-day water chemistry 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). This model assumes a mass balance and that all SO42
in runoff originates from sea salt spray and anthropogenic deposition. In the Steady State Water
Chemistry (SSWC) model, CL of acidity, CL(A), 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):
CL(A) = BCdep + BCW + - Bcu - nANC crit (5A-2)
Where:
BC*deP (BC; Ca+Mg+K+Na) = the sea-salt corrected non-anthropogenic deposition of
base cations;
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 for these
studies was set to zero.
For these CLs, 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):
CL(A) = BCdep + 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.
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. Nu value varies depending on CL project.
See below section "Critical Load Exceedance" regarding how exceedance of Critical
Loads of S, N and Combined S and N Deposition are calculated. In addition, exceedance for
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these CLs can be determined using the Nie (Henriksen and Posch, 2001) after removing the N
terms from (Eq. 5A-4):
Ex(A) = Sdep + Nie - CL(A) (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 ([NC>3"]+[ NH4+])*QS.
Equation 5 A-4 determines the CL exceedance based on S deposition while incorporating
the present day measured (or assumed future) extent of N leaching.
5A.1.5.2 MAGIC Model and Regional Linear Regression Models for
Estimating BCw Input to SSWC
For Sullivan et al., (2012) and McDonnell et al., (2012), CLs were derived using a
modified form of the SSWC model (see Eq. 5A-3) Additionally, 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., 2012 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 watersheds 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
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output data to partition the inferred net internal sources of base cations between weathering and
base cation exchange.
Sullivan et al., (2012) and McDonnell et al., (2012) used the watershed-specific BCW to
develop a regional regression model for calculating watershed specific BCW 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 the calibrated MAGIC study watersheds was placed in an Ecoregion category
based on which Ecoregion contained the maximum 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 was collected during several
regional surveys, as compiled by Sullivan and Cosby, 2004). One water quality sample,
generally collected during the spring between 1985 and 2001, was used to characterize each
watershed. Water quality data were derived from several regional surveys, including 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.
Table 5A-1. Multiple Regression Equations to Estimate BCw from Either Water
Chemistry and Landscape Variables or from Landscape Variables Alone,
Stratified by Ecoregion. (McDonnell et al. 2012).
Ecoregion
n
Equation1
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
*SBC is the sum of base ca
ions; CALK is calcula
ted ANC
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 were derived using a modified
form of the SSWC model that excluded the N terms. Building on the framework of Sullivan et
al., (2012) 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 study expanded the area to include the full Southern Appalachian Mountain
(SAM) region and surrounding terrain from northern Georgia to southern Pennsylvania, and
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from eastern Kentucky and Tennessee to central Virginia and western North Carolina. As with
Sullivan et al., (2012) 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 locations in order
to develop a statistical model to predict ANC and BCW for all streams in the SAM 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", SO42") 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, soil,
and atmospheric deposition data to match current observed stream and soil chemistry conditions.
With the use of a random forest regression modeling technique, a continuous BCW layer was
regionalized using 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 SAM's domain and were upsloped averaged based on
the technique described in McDonnell et al., (2012). This resolution allowed for the creation of
"flowpaths" for the development of a topographically determined stream network. This
approach allowed for a total of 140,504 watersheds which were represented (i.e., delineated)
with the use of a hydrologically conditioned based on digital elevation models (DEM). CLs were
then calculated with SSWC (Henriksen and Posch, 2001) with estimates of BCdep, BCW, Bcu, Qs
and an ANC chemical criteria set to an value of 50 |ieq/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 ecological resources are likely to be harmed by
deposition.
If N and/or S deposition is less than the aquatic CL, adverse ecological effects (e.g.,
reduced reproductive success, stunted growth, loss of biological diversity) are not anticipated,
and recovery is expected over time if an ecosystem has been damaged by past exposure. When
pollutant exposure is higher than, or "exceeds," the CL and the ecosystem continues to be
exposed to damaging levels of pollutants. Critical loads and deposition estimates are uncertain
and to have confidence in the exceedance it is important that this uncertainty is factored into the
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calculation. Based on the CL uncertainty analysis (see section 5A-2), 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. For simplicity reasons, a 6.25 meq S/m2/yr or
1 Kg S/ha/yr range of uncertainty was used in the exceedance calculation. Within this range, it is
unclear whether the CL is exceeded. For that reason, 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. A detailed discussion of exceedances can be found in Chapter VII:
Exceedance calculation of the 2015 ICP Modelling and Mapping Manual (see
http://icpmappine.ore/Latest update Mapping Manual).
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. 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 CLN > Total N deposition, then
Ex(N+S) = Total S deposition - CLS (5A-8)
When minimum CLN < Total N deposition, then
Ex(N+S) = Total S + N deposition - CLS + minimum CLN (5A-9)
There are different methods for determining the contribution of N deposition to aquatic
acidification. The section below described the two most common methods and how they are
handled in the CL exceedance calculations.
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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: 2000-03, 2007-09, 2014-16 and 2018-20
to be used in the different analyses. Critical load exceedances were then calculated for each of
these four periods and summed nationally and by Ecoregion III.
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. Determining the contributions of N
deposition that acidifies is difficult to estimate and uncertain because of the underlining
processes that store and release N in a watershed is 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 used in CL
studies: 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., 2004). 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
9.1.1.2). 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
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
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be variable. Lovett et al., (2000) found immobilization of N to be 49% to 90% of the
atmospheric input based on N measured in stream water. The variation is because of a host 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. Gregor et
al., (2004) reported values of nitrogen immobilization for forest soil plots ranging from 2 to 5 kg
N ha/yr in colder climates and up to 10 kg N ha-yr in warmer climates.
Nitrogen is removed or exported from the watershed by being volatilized in fires,
denitrified or leached to drainage waters. 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, a host of factors control the rate of denitrification,
making it difficult to estimate at a site by site bases without directly measuring it. Dutch and
Ineson, (1990) ranged from 0.1-3.0 kg N/ha/yr while in well drained soils denitrification was
below 0.5 kg N/ha/yr, which is similar to Groffman et al., (2009) found denitrification in
temperate ecosystems had a mean value of 1.9 kg N ha/yr for forest soils. The remaining amount
of N that isn't volatilized, denitrified, or immobilized is leached in drainage water as nitrate or
dissolved organic nitrogen (DON) and has the potential to acidify surface waters. Nitrate
concentrations or DON in streams impacted by acidification (typically 1-3 order streams) is often
very low, near zero, during the growing season (Campbell et al., 2000; Perakis and Hedin 2002;
MacDonald et al., 2002 ; De Vries et al., 2007; Dise et al., 2009). This is because nearly all the
N entering the watershed is incorporated in the soil or vegetation.
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 7.1.5.1) showed 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). 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.
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 7.1.5.1) showed 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
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1 atmospheric deposition as the main driver of declines in total N (TN) deposition and lake
2 TN:total P (TP) ratios from 1990 to 2011. In additional, monitored lakes and streams as part of
3 the EPA's Long-term Monitoring (LTM) program have average annual nitrate concentrations of
4 9.5 and 7.64 |ieq/L, respectively, from 1990 to 2018 (Table 5A-3). Average annual nitrate
5 concentrations have decreased during the past decade to 7.19 and 4.40 |ieq/L. These areas
6 receive 5 to 8 kg N/ha/yr deposition annually.
7
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Table 5A-2. Average annual nitrate concentrations for the EPA's Long-term Monitoring
(LTM) program for lakes and streams.
Areas
Years
Average (95% CI)
(jjeq/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 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 that uses water quality and runoff data to estimate
the amount of 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. 5A-10):
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+ |aeq/L) in the runoff (Qs m/yr) as ([NC>3"]+[ NH4+])*Qs.
Factoring in the CL uncertainty Eq. 5A-11 is:
Ex(N+S) = ((Total S deposition + Nle)- CLS) > 3.125 meq S/m2-yr (5A-11)
The advantage of using a leaching estimate (Nle) is that for some waterbodies it is based
on a measured water quality value that integrates all the N processes occurring in the watershed.
However, it's a measurement of current conditions. Steady-state CLs are intended to represent
the long-term leaching amount, which may or may not be well represented under current
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2
3
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7
8
9
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13
14
15
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17
18
19
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23
24
25
26
27
28
29
30
31
32
33
34
35
36
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 (Vitousek and Reomers 1975,
Goodale et al. 2000), although this model has been questioned in recent years (Lovett et al.
2018). 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.
The Nle metric for calculating the contribution to acidification from N deposition is
based on the calculated flux of N to the waterbody estimated as the concentration of nitrate as N
within the waterbody and the annual surface water runoff to the waterbody. Actual measured N
leaching values are not typically collected across the U.S. For that reason, the only way to
estimate an annual leaching is to calculate it as a function of annual runoff (eq 5A-10), which we
recognize adds additional uncertainty into this estimated value. We chose to use an annual
runoff based on 30-year Normals that is included as a catchment parameter in NHD+2
(https://www.epa.eov/waterdata/nhdpliis-national-hydroeraphY-dataset-pliis). Site-specific
catchment annual runoff values were used for each waterbody with a CL. We decided to use
NHD+2 annual runoff value because it would better reflect long-term and temporal patterns in
runoff that reflects the mass-balance steady-state CL approach. In addition, the same runoff
value was used for the CL which provided consistency across both CL parameters.
Measurement of nitrate linked to the CL value in the NCLDv3.2 can date back to the
1980s and for that reason do not reflect the current N leaching rates. Also, many of the
waterbodies with CLs have no Nitrate water quality measurements, hence, no way to calculate
the leaching directly. Another limitation is that the water quality measurement is from a single
sample and may not reflect the variability of nitrate during the year and for that reason may over
or under-estimate it's contribution. For waterbodies with no or limited Nitrate water quality
measurements, a "regional approach" was used to estimate a value of leaching for that CL. We
recognize this regional approach provides additional uncertainty to the leaching estimate;
however, it at least 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 acidifies, however, those data are not readily available.
The regional aggregation was done at the ecoregion III and II levels. Water quality data
came from the NCLD3.2 associated with the CLs and was supplemented with data from EPA's
LTM program (https://www.epa.gov/power-sector/long-term-monitoring-temporally-integrated-
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monitoring-ecosystems, equaling 16,900+ measurements across the CONUS. We decide to focus
on the water quality data within the NCLD 3.2 because it represents 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 a single aggregated value, which was used to
replace the measured value for the CL. The ecoregion average for level III was used unless there
were less than 30 water quality measurements, in which case the level II ecoregion average was
used, and if there were less than 30 measurements in level II, the level I ecoregion average was
used. The ecoregion average could over-estimate the amount of leaching for a given waterbody
because most have very little leaching and near-zero values. See Table 5A-3 for the number of
measured used in the aggregation and N leaching value.
Table 5A-3. Regional aggregation for determine average N leaching for ecoregion II and
III. Water quality data based on National Critical Database v3.2
Name
Code
No.
Average
N Leaching
meq/m2/yr
Ecoregion III
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
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Name
Code
No.
Average
N Leaching
meq/m2/yr
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
Arizona/New Mexico Mountains
13.1.1
27
2.2
California Coastal Sage, Chaparral, and Oak Woodlands
11.1.1
25
0.7
Central Basin and Range
10.1.5
17
1.1
Western Allegheny Plateau
8.4.3
37
2.4
Northern Basin and Range
10.1.3
20
5.3
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
1 5A.1.7 Ecoregions Sensitivity to Acidification
2 Not all areas of the CONUS are sensitive to deposition driven acidification. The CONUS
3 areas known to be sensitive include the Northeast, Southeast, upper Midwest, Rocky Mountains,
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2
3
4
5
6
7
8
9
10
11
Sierra Never Mountains, and the Pacific Northwest (Figure 5A-7). Mountain regions are most
susceptible to acidification, particularly, the Appalachian Mountains from Maine to Georgia. In
order to appropriately characterize the level of acidification in sensitive areas across the CONUS,
ecoregions were used as the unit of spatial aggregation. The EPA's Total alkalinity of Surface
Water GIS layer (Omernik and Powers 1983) 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 (Figure 5A-7a) (Omernik and Powers 1983). Water quality ANC measurements were
collected from over 15,000 measurements from a host of water quality networks, programs,
groups across the CONUS for the period from 1990 to 2018 (Figure 5A-7b).
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2
3
4
5
6
7
8
9
10
11
a. Total Alkalinity
W
b. ANC
0'
j«r-
\
kMt'v. A*
ar. VHEa
w \Y.
. *•
\
[xeq/l
Most Sensitive
l
Not Sensitive
<50
50-100
100-200
200-400
>400
Figure 5A-7. The EPA's Total Alkalinity regions (a) and ANC water quality
measurements across the CONUS (b) in units of jieq/L. Red and orange
colors (regions or points) are those that are most acid sensitive with lighter
colors are those which are least sensitive. Data presented here was used to
determine which Ecoregion Ills are acid sensitive.
Water quality measurements and total alkalinity (Omernik and Powers 1983) were used
to classify CONUS ecoregion Ills (e.g., 84) into four acid sensitive classes: (1) most acid
sensitive (<50 |ieq/L), (2) acid sensitive (50-100 jieq/L), (3) moderately acid sensitive (100-200
jieq/L), and (4) low or no acid sensitivity (>200 jaeq/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
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2
3
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5
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9
10
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13
total of 23 ecoregions III were acid sensitive and another 7 ecoregions were moderately acid
sensitive for a total of 30. Fifty-four ecoregions had low or no evidence of acid 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 (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. 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.
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
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1
2
3
4
Acid Sensitive Ecoregion III
ANC (peq/L)
• 0-100
100-200
| Most Acid Sensitive Ecoregions (<100 Meq'L)
Moderately Acid Sensitive Ecoregions (<200 |Jeq/L)
Low or Non-Acid Sensitive Ecoregions (>200 peq/L)
1 Ecoregions with high level of natural acidity
Figure 5A-8. Ecoregion III grouped in three acid sensitivity classes. The dark colors
indicate acid sensitive ecoregions. Points are ANC concentrations below 200
jweq/L.
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1 Table 5A-5. Ecoregion III results for acid sensitivity.
No. ANC
Total
Ecoregion
III Code
No. Critical
Loads
Total No.
ANC Values
values
<200 )jeq/L
Alkalinity
Area (peq/L)
Acid Sensitive Category
5.2.1
839
1074
933
50
Most Acid Sensitive Ecoregions
5.3.1
2851
2053
2203
50
Most Acid Sensitive Ecoregions
5.3.3
216
242
290
50
Most Acid Sensitive Ecoregions
6.2.10
496
324
190
50
Most Acid Sensitive Ecoregions
6.2.12
353
359
503
50
Most Acid Sensitive Ecoregions
6.2.14
372
327
163
50
Most Acid Sensitive Ecoregions
8.1.7
565
488
289
50
Most Acid Sensitive Ecoregions
8.1.8
494
492
513
50
Most Acid Sensitive Ecoregions
8.3.5
390
432
352
50
Most Acid Sensitive Ecoregions
8.4.1
1292
1394
1192
50
Most Acid Sensitive Ecoregions
8.4.2
372
420
511
50
Most Acid Sensitive Ecoregions
8.4.4
1972
1136
1535
50
Most Acid Sensitive Ecoregions
8.5.3
142
228
247
50
Most Acid Sensitive Ecoregions
8.5.4
234
130
178
50
Most Acid Sensitive Ecoregions
6.2.5
162
155
120
50
Most Acid Sensitive Ecoregions
8.3.4
508
455
112
100
Most Acid Sensitive Ecoregions
6.2.7
179
244
209
50
Most Acid Sensitive Ecoregions
8.1.3
199
223
55
100
Acid Sensitive Ecoregions
8.3.7
153
165
58
50
Acid Sensitive Ecoregions
8.5.1
105
183
51
50
Acid Sensitive Ecoregions
8.4.8
42
73
61
50
Acid Sensitive Ecoregions
8.4.9
117
64
51
50
Acid Sensitive Ecoregions
6.2.13
96
139
87
100
Acid Sensitive Ecoregions
6.2.15
188
164
155
50
Moderately Sensitive Ecoregions
8.1.4
94
162
33
200
Moderately Sensitive Ecoregions
6.2.9
63
91
21
100
Moderately Sensitive Ecoregions
6.2.3
86
147
44
50
Moderately Sensitive Ecoregions
8.4.7
31
59
34
100
Moderately Sensitive Ecoregions
6.2.11
81
105
16
100
Moderately Sensitive Ecoregions
8.4.6
23
31
23
100
Moderately Sensitive Ecoregions
6.2.4
31
42
8
100
Low or Non-sensitive Ecoregions
8.3.1
231
211
9
100
Low or Non-sensitive Ecoregions
7.1.8
115
154
17
200
Low or Non-sensitive Ecoregions
8.4.3
35
114
2
100
Low or Non-sensitive Ecoregions
8.3.3
71
114
2
200
Low or Non-sensitive Ecoregions
6.2.8
27
43
1
>200
Low or Non-sensitive Ecoregions
7.1.7
38
51
10
200
Low or Non-sensitive Ecoregions
8.1.1
83
97
3
>200
Low or Non-sensitive Ecoregions
8.3.6
41
61
15
200
Low or Non-sensitive Ecoregions
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8
9
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18
19
20
No. ANC
Total
Ecoregion
III Code
No. Critical
Loads
Total No.
ANC Values
values
<200 )jeq/L
Alkalinity
Area (peq/L)
Acid Sensitive Category
13.1.1
25
64
3
>200
Low or Non-sensitive Ecoregions
8.3.2
18
115
10
100
Low or Non-sensitive Ecoregions
9.4.2
5
144
5
>200
Low or Non-sensitive Ecoregions
10.1.4
3
56
1
>200
Low or Non-sensitive Ecoregions
10.1.5
16
87
2
200
Low or Non-sensitive Ecoregions
5.2.2
2
26
2
>200
Low or Non-sensitive Ecoregions
10.1.3
20
80
4
>200
Low or Non-sensitive Ecoregions
8.1.5
15
80
2
>200
Low or Non-sensitive Ecoregions
8.2.1
10
38
2
>200
Low or Non-sensitive Ecoregions
9.6.1
0
7
2
>200
Low or Non-sensitive Ecoregions
8.1.6
33
131
0
>200
Low or Non-sensitive Ecoregions
8.4.5
56
111
0
>200
Low or Non-sensitive Ecoregions
9.4.5
26
96
0
>200
Low or Non-sensitive Ecoregions
9.2.3
26
180
0
>200
Low or Non-sensitive Ecoregions
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 Ecoregion Ills
(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. 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 deposition from
2018-20, 2014-16, 2010-12, 2006-08, and 2001-03 that is greater than the amount of deposition
the waterbodies could neutralize and still maintain the ANC thresholds for an ANC of 20, 30,
and 50 [j,eq/L.
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1 Table 5A-6. Percent of waterbodies with critical loads less than 2, 6,12, and 18 Kg S/Ha
2 for critical loads based on an ANC limit of 20, 30, and 50 jieq/L
Critical Load
Percent of Waterbodies
Kg/Ha (meq/m2-yr)
Grouped by A
sIC threshold
20 |jeq/L
30 |jeq/L
50 |jeq/L
2(12.5)
3%
5%
11%
6 (37.5)
14%
17%
25%
12 (75)
36%
39%
45%
18(112.5)
52%
55%
58%
3 Table 5A-7.
4
5
6
7
ANC
Sulfur Only
Sulfur and Nitrogen
Threshold
All Values
CL>0 Values
Only
All Values
CL>0 Values
Only
Deposition from 2018-20
20
2%
1%
2%
2%
30
3%
2%
4%
2%
50
9%
4%
9%
5%
50/20
7%
4%
8%
4%
Deposition from 2014-16
20
3%
3%
3%
3%
30
5%
4%
5%
4%
50
11%
6%
12%
7%
50/20
10%
6%
10%
7%
Deposition from 2010-12
20
5%
5%
6%
5%
30
8%
7%
9%
7%
50
15%
11%
16%
11%
50/20
14%
10%
15%
11%
Deposition from 2006-08
20
17%
16%
18%
17%
30
21%
19%
21%
20%
50
28%
24%
29%
25%
50/20
27%
23%
28%
24%
Deposition from 2001-03
20
22%
22%
23%
23%
30
26%
25%
27%
25%
50
33%
28%
33%
29%
50/20
31%
28%
32%
28%
Summary of national aquatic critical load exceedances by ANC thresholds
and deposition periods. The percent of modeled waterbodies where
deposition from 2018-20, 2014-16, 2010-12, 2006-08, and 2001-03 is above the
critical load and error of 3.125 meq/m2-yr. "All Values" includes all critical
loads. "CL>0 Values" includes only critical loads greater than 0.
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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 the lowest for the least protective CL of 20 [j,eq/L and the
highest rate for most protective CL 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 percent of
exceeded waterbodies for combined total S and N are slightly higher than S only percents at 2%,
4%, and 9% of the modeled waterbodies for CL thresholds of 20, 30, and 50 [j,eq/L based on
short-term leaching of nitrate to the surface water. 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 only slightly higher (1-2%)
when considering both N and S deposition compared to just S deposition only.
Table 5A-8. National aquatic critical load exceedances based on all critical load values by
ANC thresholds and deposition periods. The numbers and percent of
modeled waterbodies where deposition from 2018-20, 2014-16, 2010-12, 2006-
08, and 2001-03 is above the critical load and error of 3.125 meq/m2-yr.
Sulfur Only
Sulfur and Nitrogen
ANC
Threshold
Class
No.
Percent
No.
Percent
Deposition from 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%
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Sulfur Only
Sulfur and Nitrogen
ANC
Threshold
Class
No.
Percent
No.
Percent
50
>CL
1512
11%
1591
12%
CL
423
3%
465
3%
CL
748
5%
798
6%
CL
1122
8%
1192
9%
CL
2114
15%
2215
16%
CL
1918
14%
2013
16%
CL
2328
17%
2433
18%
CL
2845
21%
2962
21%
CL
3911
28%
4035
29%
CL
3710
2%
3825
28%
CL
3064
22%
3191
23%
CL
3587
26%
3694
27%
CL
4504
33%
4611
33%
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ANC
Threshold
Class
Sulfur Only
Sulfur and Nitrogen
No.
Percent
No.
Percent
CL
4313
31%
4410
32%
CL
182
1%
214
2%
CL
279
2%
323
2%
CL
566
4%
624
5%
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Sulfur Only
Sulfur and Nitrogen
ANC
Threshold
Class
No.
Percent
No.
Percent
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%
CL
696
5%
746
5%
CL
948
7%
1018
7%
CL
1475
11%
1576
12%
CL
1439
11%
1534
11%
CL
2276
17%
2381
17%
CL
2671
20%
2788
20%
CL
3272
25%
3396
26%
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Sulfur Only
Sulfur and Nitrogen
ANC
Threshold
Class
No.
Percent
No.
Percent
atCL
529
4%
523
4%
50/20
>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%
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Figures 5 A-10 to 5A-29 show mapped exceedances across the CONUS for S only for CL
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. These waterbodies exceed the
calculated CL at any deposition amount. For these reasons, these sites have been removed from
the assessment. At their given ANC threshold, exceedance maps for S and/or N combined are not
included because they show the same pattern of exceedances 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
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.
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a.
b.
Critical Load Exeedance for Sulfur
2001-2003
ANC = 20 peq/L
• Does not Exceed the Critical Load
c 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. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 meq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
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Critical Load Exeedance for Sulfur
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
Critical loads < 0
1
2 Figure 5A-11. Critical load exceedance (Ex) for S only total deposition from 2001-03 for
3 an ANC threshold of 30 fieq/L. a. Blue dots are waterbodies with sulfur
4 deposition below the CL and uncertainty (Ex < -3.125 ineq/m2-yr). b. Red
5 dots are waterbodies with sulfur deposition above the CL and uncertainty
6 (Ex < 3.125 nieq/ni2-yr). Yellow dots are near the CL and based on the
7 uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
8 meq/m2-yr).
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a.
Critical Load Exeedance for Sulfur
2001-2003
b.
V
i——
\ **3
Tw
1
* /••!
•
' - - ^ -
ANC = 50 peq/L
• Does not Exceed the Critical Load
c Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
v
Figure 5A-12.
Critical load exceedance (Ex) for S only total deposition from 2001-03 for
an ANC threshold of 50 jieq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 nieq/ni2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
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a.
Critical Load Exeedance for Sulfur
2001-2003
b.
ANC = 50 |jeq/L East
ANC = 20 (jeq/L West
• Does not Exceed the Critical Load
o 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 jieq/L for Western CONUS.
a. Blue dots are waterbodies with sulfur deposition below the CL and
uncertainty (Ex < -3.125 meq/m2-yr). b. Red dots are waterbodies with
sulfur deposition above the CL and uncertainty (Ex < 3.125 meq/m2-yr).
Yellow dots are near the CL and based on the uncertainty cannot be
determined if they exceed or not (-3.125 > Ex < 3.125 meq/m2-yr).
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a.
b.
Critical Load Exeedance for Sulfur
2006-2008
ANC = 20 |jeq/L
• Does not Exceed the Critical Load
o Near the Critical Load (+3.125 meq/m2/yr)
• Exceeds the Critical Load
Figure 5A-14.
Critical load exceedance (Ex) for S only total deposition from 2006-08 for
an ANC threshold of 20 fieq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 meq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
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a.
Critical Load Exeedance for Sulfur
2006-2008
b.
ANC = 30 peq/L
• Does not Exceed the Critical Load
o 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. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 meq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
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a.
b.
Critical Load Exeedance for Sulfur
2006-2008
ANC = 50 |jeq/L
• Does not Exceed the Critical Load
o 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. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 meq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
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1
2 Figure 5A-17. Critical load exceedance (Ex) for S only total deposition from 2006-08 for
3 an ANC threshold of 50 for the eastern and 20 fieq/L for Western CONUS.
4 a. Blue dots are waterbodies with sulfur deposition below the CL and
5 uncertainty (Ex < -3.125 meq/m2-yr). b. Red dots are waterbodies with
6 sulfur deposition above the CL and uncertainty (Ex < 3.125 meq/in2-yr).
7 Yellow dots are near the CL and based on the uncertainty cannot be
8 determined if they exceed or not (-3.125 > Ex < 3.125 meq/m2-yr).
a Critical Load Exeedance for Sulfur
ANC
ANC
= 50 peq/L East
= 20 peq/L West
Does not Exceed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
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Figure 5A-18.
Critical load exceedance (Ex) for S only total deposition from 2010-12 for
an ANC threshold of 20 peq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 ineq/ni2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/in2-yr).
O
Critical Load Exeedance for Sulfur
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
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Figure 5A-19.
Critical load exceedance (Ex) for S only total deposition front 2010-12 for
an ANC threshold of 30 jieq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/in2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 nieq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
Critical Load Exeedance for Sulfur
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
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Figure 5A-20.
Critical load exceedance (Ex) for S only total deposition from 2010-12 for
an ANC threshold of 50 peq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 ineq/ni2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/in2-yr).
a- Critical Load Exeedance for Sulfur
O
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
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2 Figure 5A-21. Critical load exceedance (Ex) for S only total deposition from 2010-12 for
3 an ANC threshold of 50 for the eastern and 20 fieq/L for Western CONUS.
4 a. Blue dots are waterbodies with sulfur deposition below the CL and
5 uncertainty (Ex < -3.125 meq/m2-yr). b. Red dots are waterbodies with
6 sulfur deposition above the CL and uncertainty (Ex < 3.125meq/in2-yr ).
7 Yellow dots are near the CL and based on the uncertainty cannot be
8 determined if they exceed or not (-3.125 > Ex < 3.125 meq/m2-yr).
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a.
Critical Load Exeedance for Sulfur
2014-2016
b.
ANC = 20 |jeq/L
• Does not Exceed the Critical Load
© 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. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 ineq/in2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 meq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
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a- Critical Load Exeedance for Sulfur
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 front 2014-16 for
an ANC threshold of 30 jieq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/in2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 meq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
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a.
b.
Critical Load Exeedance for Sulfur
2014-2016
J /
AL
/
-—
1 •
i
ANC = 50 peq/L
• Does not Exceed the Critical Load
o 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 fieq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 ineq/in2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 meq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
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a.
Critical Load Exeedance for Sulfur
2014-2016
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-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 CON US.
a. Bine dots are waterbodies with sulfur deposition below the CL and
uncertainty (Ex < -3.125 nieq/ni2-yr). b. Red dots are waterbodies with
sulfur deposition above the CL and uncertainty (Ex < 3.125 meq/m2-yr).
Yellow dots are near the CL and based on the uncertainty cannot be
determined if they exceed or not (-3.125 > Ex < 3.125 meq/in2-yr ).
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Figure 5A-26.
Critical load exceedance (Ex) for S only total deposition from 2018-20 for
an ANC threshold of 20 fieq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 meq/m2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/m2-yr).
O Near the Critical Load (±3.125 meq/m2/yr)
• Exceeds the Critical Load
Critical Load Exeedance for Sulfur
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a.
Critical Load Exeedance for Sulfur
2018-2020
ANC = 30 peq/L
• Does not Exceed the Critical Load
o 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 peq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 ineq/ni2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/in2-yr).
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Figure 5A-28.
Critical load exceedance (Ex) for S only total deposition from 2018-20 for
an ANC threshold of 50 peq/L. a. Blue dots are waterbodies with sulfur
deposition below the CL and uncertainty (Ex < -3.125 meq/m2-yr). b. Red
dots are waterbodies with sulfur deposition above the CL and uncertainty
(Ex < 3.125 ineq/ni2-yr). Yellow dots are near the CL and based on the
uncertainty cannot be determined if they exceed or not (-3.125 > Ex < 3.125
meq/in2-yr).
Critical Load Exeedance for Sulfur
Near the Critical Load (±3.125 meq/m2/yr)
Exceeds the Critical Load
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a.
Critical Load Exeedance for Sulfur
2018-2020
b.
ANC = 50 peq/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-29. Critical load exceedance (Ex) for S only total deposition front 2018-20 for
an ANC threshold of 50 for the eastern and 20 jieq/L for Western CONUS.
a. Blue dots are waterbodies with sulfur deposition below the CL and
uncertainty (Ex < -3.125 meq/m2-yr). b. Red dots are waterbodies with
sulfur deposition above the CL and uncertainty (Ex < 3.125 meq/m2-yr).
Yellow dots are near the CL and based on the uncertainty cannot be
determined if they exceed or not (-3.125 > Ex < 3.125 meq/ m2-yr).
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18
ANC = 50 jjeq/L
Exeed the Critical Load
Near the Critical Load (±3.125 meq/m2/yr)
Critical loads £ 0
d.
ANC = 50 peq/L East
ANC = 20 (jeq/L West
Figure 5A-30. Critical load exceedance (EX) for S only deposition from 2018-20 for an
ANC threshold: a. 20, b. 30, c. 50, d. 50/20 fieq/L for CONUS. Grey dots
are waterbodies where the critical load is zero or negative and was
excluded from the summary analysis. Red dots are waterbodies where
total sulfur is above the CL and uncertainty and yellow dots are where the
Ex is between -3.125 and 3.125 meq/in2-yr are near the CL and based on
the CL uncertainty cannot be determined if they exceed or not.
5A.2.2 Ecoregion Analyses
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 ecoregion Ills (from this point on ecoregion Ills will be referred to as
ecoregions), except for ones that historically are known to be in acid sensitive areas since acid
sensitive areas typically have been heavily sampled, hence, contain many CLs (see Figure 5A-
31). These areas tend to be in the eastern CONUS in such ecoregions as Central Appalachians,
Northern Appalachian and Atlantic Maritime Highlands, and the Blue Ridge. Areas in the
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20
Rockies and Sierra Nevada also have been sampled extensively and contain many CLs. 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, ecoregions containing
greater than 50 CLs were the focus of this analysis while ones with less than 10 values were
included in the summary tables but excluded from the analysis.
For the CONUS there are a total of 84 ecoregions, 69 of which had at least one CL.
Eleven ecoregions had 9 or less CLs and 58 ecoregions had 10 or more. Of the 58 ecoregions,
however, only 32 had 50 or more CLs. The Northern Appalachian and Atlantic Maritime
Highlands ecoregion had the most CLs at 2,858 (see Table 5A-10).
The 50th to 90th ecoregion CLs varied greatly among ecoregions from 4.4 to 136.1
Kg/ha/yr (27.3 to 850.6 meq/m2/yr) for sulfur with an ANC threshold of 20 |ieq/L to 3.9 to 134.9
Kg/ha/yr (24.6 to 843.1 meq/m2/yr) for sulfur with an ANC threshold of 50 |ieq/L. Lower
percentile ecoregion values indicate higher sensitivity and risk for acidification. 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 5 A-10 and 5A-
11 for 70th, 90th Ecoregion CLs.
May 2023
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| Ecoregion III
• Critical load Loactioris
Figure 5A-31. Locations of aquatic critical loads mapped across Ecoregions III.
May 2023
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1 Table 5A-10. Summary of Sulfur only critical loads by Ecoregions III by ANC thresholds
2 of 20 and 30 jieq/L in Units = Kg S/ha-yr). Included ecoregions with more
3 than 10 values.
Ecoregion III
ANC Threshold = 20
ANC Threshold = 30
|jeq/L
|jeq/L
Name
Code
No.
70th
90th
Min.
70th
90th
Min.
Northern Appalachian and Atlantic
Maritime Highlands
5.3.1
2851
9.7
4.8
0.0
00
CO
4.0
0.0
Ridge and Valley
8.4.1
1292
11.6
5.9
0.1
10.8
5.2
0.2
Blue Ridge
8.4.4
1972
9.2
5.4
0.1
7.7
4.2
0.3
Northern Lakes and Forests
5.2.1
839
5.1
3.0
0.7
4.7
2.6
0.1
Northeastern Coastal Zone
8.1.7
565
16.4
8.1
1.3
15.3
7.2
0.0
Middle Rockies
6.2.10
496
9.2
5.2
0.5
8.4
4.5
0.2
Acadian Plains and Hills
8.1.8
494
11.4
5.5
0.2
10.6
4.9
0.0
Piedmont
8.3.4
508
16.0
8.7
0.9
15.0
7.7
0.1
Southern Rockies
6.2.14
372
7.4
3.9
0.3
6.5
3.2
0.0
Central Appalachians
8.4.2
372
8.5
5.3
0.3
7.6
4.2
0.2
Sierra Nevada
6.2.12
353
5.3
1.9
0.2
5.5
0.9
0.0
Southeastern Plains
8.3.5
390
14.2
4.9
0.4
13.7
4.6
0.1
Atlantic Coastal Pine Barrens
8.5.4
234
6.3
2.1
0.4
6.3
1.8
0.0
Northern Piedmont
8.3.1
231
40.0
16.8
1.5
39.2
15.8
1.0
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
21.2
10.9
1.1
Idaho Batholith
6.2.15
188
10.4
5.7
1.7
9.4
4.2
0.2
Cascades
6.2.7
179
14.4
4.3
0.0
15.2
5.8
0.2
North Cascades
6.2.5
162
26.3
12.4
5.2
24.8
10.1
1.5
Southern Coastal Plain
8.5.3
142
4.4
1.6
0.2
4.3
1.4
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
16.6
8.2
1.6
15.7
7.3
0.9
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
19.9
7.1
0.1
20.6
7.2
1.1
Eastern Great Lakes Lowlands
8.1.1
83
51.5
19.8
4.2
50.6
18.9
3.9
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
11.2
4.1
0.8
10.3
3.3
0.3
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
8.1.6
33
11.5
5.8
2.1
10.8
4.4
1.3
Drift Plains
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Ecoregion III
ANC Threshold = 20
|jeq/L
ANC Threshold = 30
|jeq/L
Name
Code
No.
70th
90th
Min.
70th
90th
Min.
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 and Baja California Pine-Oak
Mountains
11.1.3
22
25.2
3.4
1.1
27.4
5.5
2.1
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
11.3
3.6
3.5
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
23.3
8.0
8.0
Southeastern Wisconsin Till Plains
8.2.1
10
136.1
16.7
15.0
135.7
14.8
13.6
May 2023
5A-63
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1 Table 5A-11. Summary of Sulfur only critical loads by Ecoregions III by ANC thresholds
2 of 50 and 50/20 jieq/L in Units = Kg S/ha-yr). Included ecoregions with
3 more than 10 values.
Ecoregion
ANC Threshold = 50
ANC Threshold =
|jeq/L
50/20 |jeq/
Name
Code
No.
70th
90th
Min.
70th
90th
Min.
Northern Appalachian and Atlantic
5.3.1
2851
7.5
2.7
0.0
7.5
2.7
0.0
Maritime Highlands
Ridge and Valley
8.4.1
1292
9.2
3.8
0.0
9.2
3.8
0.0
Blue Ridge
8.4.4
1972
5.3
2.0
0.0
5.3
2.0
0.0
Northern Lakes and Forests
5.2.1
839
3.9
1.7
0.1
3.9
1.7
0.1
Northeastern Coastal Zone
8.1.7
565
14.2
5.8
0.3
14.2
5.8
0.3
Middle Rockies
6.2.10
496
6.8
3.2
0.0
9.2
5.2
0.5
Acadian Plains and Hills
8.1.8
494
9.6
4.0
0.0
9.6
4.0
0.0
Piedmont
8.3.4
508
13.2
5.7
0.2
13.2
5.7
0.2
Southern Rockies
6.2.14
372
5.2
1.9
0.1
7.4
3.9
0.3
Central Appalachians
8.4.2
372
6.1
2.7
0.1
6.1
2.7
0.1
Sierra Nevada
6.2.12
353
7.5
1.8
0.0
5.3
1.9
0.2
Southeastern Plains
8.3.5
390
12.6
3.9
0.1
12.6
3.9
0.1
Atlantic Coastal Pine Barrens
8.5.4
234
5.9
1.6
0.1
5.9
1.6
0.1
Northern Piedmont
8.3.1
231
38.2
14.4
1.3
38.2
14.4
1.3
North Central Appalachians
5.3.3
216
11.9
5.5
0.4
11.9
5.5
0.4
Northern Allegheny Plateau
8.1.3
199
19.6
9.9
0.5
19.6
9.9
0.5
Idaho Batholith
6.2.15
188
7.8
1.7
0.2
10.4
5.7
1.7
Cascades
6.2.7
179
15.8
5.0
0.1
14.4
4.3
0.0
North Cascades
6.2.5
162
23.9
8.3
0.3
26.3
12.4
5.2
Southern Coastal Plain
8.5.3
142
4.7
1.2
0.1
4.7
1.2
0.1
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
15.9
6.1
1.0
15.9
6.1
1.0
Wasatch and Uinta Mountains
6.2.13
96
8.9
5.2
1.1
11.0
7.7
2.1
North Central Hardwood Forests
8.1.4
94
20.7
3.7
0.0
20.7
3.7
0.0
Columbia Mountains/Northern Rockies
6.2.3
86
20.6
6.0
0.2
19.9
7.1
0.1
Eastern Great Lakes Lowlands
8.1.1
83
50.9
17.1
0.7
50.9
17.1
0.7
Klamath Mountains
6.2.11
81
24.2
10.0
4.1
27.6
12.4
7.3
Interior Plateau
8.3.3
71
69.4
11.8
0.9
69.4
11.8
0.9
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
8.7
1.9
0.1
9.5
1.9
0.1
Ozark Highlands
8.4.5
56
58.6
11.1
0.0
58.6
11.1
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
28.9
8.0
1.6
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
8.1.6
33
9.5
3.8
0.3
9.5
3.8
0.3
Drift Plains
May 2023
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1
2
3
4
5
6
7
8
9
10
11
12
Ecoregion
ANC Threshold = 50
|jeq/L
ANC Thresho
50/20 |jeq/
Id =
Name
Code
No.
70th
90th
Min.
70th
90th
Min.
Arkansas Valley
8.4.7
31
12.5
4.7
1.2
12.5
4.7
1.2
Canadian Rockies
6.2.4
31
42.2
7.6
2.3
41.8
8.3
3.5
Western Corn Belt Plains
9.2.3
26
16.0
2.8
2.2
16.0
2.8
2.2
Cross Timbers
9.4.5
26
7.9
3.7
1.4
7.9
4.2
3.3
Eastern Cascades Slopes and
Foothills
6.2.8
27
19.7
4.7
0.5
21.5
6.6
3.6
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 and Baja California Pine-Oak
Mountains
11.1.3
22
25.8
4.5
0.5
25.2
3.4
1.1
Central Irregular Plains
9.2.4
21
11.3
2.2
1.5
11.3
2.2
1.5
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
18.1
14.1
5.5
19.1
10.1
3.2
Mississippi Alluvial Plain
8.5.2
19
8.3
1.1
0.4
8.3
1.1
0.4
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
50.4
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
11.6
1.2
0.3
11.6
1.2
0.3
East Central Texas Plains
8.3.8
10
20.2
5.6
5.6
16.6
1.2
0.3
Southeastern Wisconsin Till Plains
8.2.1
10
134.9
11.0
10.7
134.9
11.0
10.7
For ecoregions with CLs, minimum/maximum/average total S deposition were
summarized. Minimum to maximum range for ecoregion total S deposition 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 S/ha-yr for 2018-2020 to 2001-2003, respectively (Table 5A-12). Table 5A-13
shows the number of ecoregions with <2, 2-5, 5-7, 7-10, >10 Kg S/ha-yr. For the period 2001-
2003, 16 ecoregions had an average total S deposition over 10 Kg S/ha-yr while there were none
in the period 2018-2020. Median S deposition in Kg S/ha-yr are summarized in Table 5A-14
and 5A-15. in Ecoregions with the highest average total S deposition were Western Allegheny
Plateau, Erie Drift Plain, North Central Appalachians, Central Appalachians, Northern Piedmont,
Eastern Corn Belt Plains, Southwestern Appalachians, and Ridge and Valley, all in the Mid-
Atlantic region of the eastern U.S (Table 5A-14 and 5A-15).
May 2023
5A-65
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1
2
Table 5A-12.
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
3
4
5
Table 5A-13.
Summary of the number of ecoregions with median deposition in the range
of <2, 2-5, 5-7, 7-10, >10 Kg S/ha-yr for the 84 ecoregions determined by
GIS zonal statistic. Deposition based on TDEP.
Total Sulfur
Deposition
Number of ecoregions per deposition class
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
6 Table 5A-14. Median total sulfur deposition (Kg S/ha-yr) of deposition estimates (based
7 on TDEP) across CL locations for 69 ecoregions with at least one CL.
8 Deposition based on TDEP.
Ecoregion Name
Code
E/W
No. 2001-03
CLs (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
0.8436
0.8335
0.7607
0.6267
0.3812
Northern Basin and Range
10.1.3
w
20
0.9258
1.0470
1.0229
1.0412
0.7504
Wyoming Basin
10.1.4
w
3
0.7697
0.7580
0.6998
0.6846
0.5928
Central Basin and Range
10.1.5
w
16
0.8596
0.6641
0.6670
0.7573
0.5654
Colorado Plateaus
10.1.6
w
1
1.3223
1.4380
1.2534
1.3313
0.8433
Snake River Plain
10.1.8
w
2
0.7953
0.9315
0.9799
0.7949
0.5494
Southern and Central California
11.1.1
w
21
1.6483
1.2030
1.2634
0.9755
1.0646
Central California Valley
11.1.2
w
2
2.1744
1.7039
1.5387
1.4604
1.1912
Southern California Mountains
11.1.3
w
22
1.4499
1.2130
1.2395
1.0407
0.8588
Arizona/New Mexico Mountains
13.1.1
w
25
2.0662
2.5767
1.9619
1.4718
0.8100
Northern Lakes and Forests
5.2.1
E
839
4.0127
3.1001
2.3408
1.8413
1.3135
Northern Minnesota Wetlands
5.2.2
E
2
2.1930
2.2114
1.5100
1.1989
0.9099
Northeastern Highlands
5.3.1
E
2851
7.2925
6.1223
3.1183
2.2176
1.4778
North Central Appalachians
5.3.3
E
216
15.7250
13.3726
5.8302
3.1733
2.1716
Middle Rockies
6.2.10
W
496
1.4822
1.5250
1.3332
1.0605
0.8719
Klamath Mountains
6.2.11
W
81
0.9200
1.0682
1.0603
0.9866
0.8436
Sierra Nevada
6.2.12
W
353
1.4035
1.2449
1.2725
1.1730
1.0142
Wasatch and Uinta Mountains
6.2.13
W
96
1.7506
1.9154
1.6417
1.7174
1.1053
May 2023
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Ecoregion Name
Code
E/W
No. 2001-03
CLs (kg/ha-yr)
2006-08
(kg/ha-yr)
2010-12
(kg/ha-yr)
2014-16
(kg/ha-yr)
2018-20
(kg/ha-yr)
Southern Rockies
6.2.14
W
372
1.6341
1.7004
1.2931
1.1036
0.7382
Idaho Batholith
6.2.15
w
188
1.2067
1.5204
1.3871
1.1361
0.7188
Northern Rockies
6.2.3
w
86
1.1757
1.2198
1.0150
0.9288
0.6187
Canadian Rockies
6.2.4
w
31
1.2702
1.4281
1.0784
0.9907
0.7898
North Cascades
6.2.5
w
162
1.9444
1.8263
1.4712
1.4767
1.1931
Cascades
6.2.7
w
179
1.2474
1.5117
1.2483
1.2295
1.0687
Eastern Cascades Slopes and Foothills
6.2.8
w
27
0.6646
0.7499
0.7278
0.7420
0.6175
Blue Mountains
6.2.9
w
63
0.6261
0.6766
0.7181
0.8533
0.4616
Puget Lowland
7.1.7
w
38
2.2794
1.9380
1.5475
2.2492
1.3561
Coast Range
7.1.8
w
115
2.4906
2.3092
2.0744
2.0867
1.5174
Willamette Valley
7.1.9
w
24
1.7052
1.4381
1.4547
1.7550
1.0785
Eastern Great Lakes Lowlands
8.1.1
E
83
8.0352
6.5002
3.2588
2.1575
1.4427
Erie Drift Plain
8.1.10
E
14
18.6152
15.4885
7.8262
5.1417
2.8388
Northern Allegheny Plateau
8.1.3
E
199
11.6864
10.4483
4.6870
2.7008
1.7257
North Central Hardwood Forests
8.1.4
E
94
5.2991
3.7229
2.8589
2.1240
1.4780
Driftless Area
8.1.5
E
15
6.1584
5.3369
3.5647
2.7563
2.1083
Southern Michigan/Northern Indiana
8.1.6
E
33
10.3614
8.9903
5.4132
3.3507
2.3733
Northeastern Coastal Zone
8.1.7
E
565
9.2880
8.2813
3.7122
2.3007
1.9083
Acadian Plains and Hills
8.1.8
E
494
4.9838
5.4237
2.8289
1.9501
1.4418
Southeastern Wisconsin Till Plains
8.2.1
E
10
6.9406
5.7085
3.9255
2.7417
1.9581
Central Corn Belt Plains
8.2.3
E
2
10.6435
9.7895
5.9799
4.4423
2.5047
Eastern Corn Belt Plains
8.2.4
E
14
17.4280
13.4790
7.9019
4.7601
2.8735
Northern Piedmont
8.3.1
E
231
15.1825
12.9379
5.6325
3.3300
2.2078
Interior River Valleys and Hills
8.3.2
E
18
12.5930
11.0339
6.5440
4.2474
2.9442
Interior Plateau
8.3.3
E
71
13.1051
9.8406
5.5813
4.0054
2.7395
Piedmont
8.3.4
E
508
12.2634
10.1418
4.2405
2.6855
2.0337
Southeastern Plains
8.3.5
E
390
10.8772
9.1412
4.8250
3.4937
2.4093
Mississippi Valley Loess Plains
8.3.6
E
41
9.4020
7.6610
4.7177
4.4386
3.5668
South Central Plains
8.3.7
E
153
7.7690
7.1450
5.0275
4.6912
3.8806
East Central Texas Plains
8.3.8
E
10
6.3592
6.3713
4.6506
4.7800
3.7907
Ridge and Valley
8.4.1
E
1292
14.1834
11.9342
5.7140
3.3291
1.9421
Central Appalachians
8.4.2
E
372
17.0275
13.9833
7.2537
4.0873
2.4291
Western Allegheny Plateau
8.4.3
E
35
17.0756
14.1224
7.5947
4.1935
2.5646
Blue Ridge
8.4.4
E
1972
11.2890
9.5751
4.4112
2.6974
2.0560
Ozark Highlands
8.4.5
E
56
6.9470
6.1841
4.8676
3.2446
2.6634
Boston Mountains
8.4.6
E
23
6.2465
5.8956
4.6014
3.4302
2.7833
Arkansas Valley
8.4.7
E
31
5.7040
5.3761
4.2414
3.3530
2.9080
Ouachita Mountains
8.4.8
E
42
6.0931
5.7076
4.6545
4.0534
3.5802
Southwestern Appalachians
8.4.9
E
117
17.2703
14.4428
5.5887
4.1742
2.9277
Middle Atlantic Coastal Plain
8.5.1
E
105
14.1038
12.0745
5.3535
3.5822
2.4066
Mississippi Alluvial Plain
8.5.2
E
19
7.0222
5.4477
4.0627
3.6724
3.0482
Southern Coastal Plain
8.5.3
E
142
8.7020
5.9155
4.5581
4.1848
3.3530
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Ecoregion Name
Code
E/W
No. 2001-03
CLs (kg/ha-yr)
2006-08
(kg/ha-yr)
2010-12
(kg/ha-yr)
2014-16
(kg/ha-yr)
2018-20
(kg/ha-yr)
Atlantic Coastal Pine Barrens
8.5.4
E
234
13.8762
12.0097
5.4040
3.8876
2.8384
Western Corn Belt Plains
9.2.3
E
26
4.7163
4.0139
2.8494
2.3457
1.9949
Central Irregular Plains
9.2.4
E
21
5.5499
5.1167
3.9854
2.9482
2.2863
Northwestern Glaciated Plains
9.3.1
E
2
0.6676
0.7441
0.5393
0.5628
0.4593
Central Great Plains
9.4.2
E
5
4.3246
4.6747
2.8565
2.7344
2.4439
Flint Hills
9.4.4
E
7
4.4474
4.3621
2.9061
2.5660
2.2734
Cross Timbers
9.4.5
E
26
4.8935
4.4674
3.2464
3.1676
2.7247
Texas Blackland Prairies
9.4.7
E
3
6.5071
5.9520
4.4689
4.3720
3.6600
Western Gulf Coastal Plain
9.5.1
E
16
7.5945
6.9910
4.9189
5.3084
4.3375
1 Table 5A-15. Median sulfur deposition (Kg S/ha-yr) for the 84 ecoregions determined by
2 GIS zonal statistic. Deposition based on TDEP.
Ecoregion III
Tota
Median Sulfur Deposil
tion (Kg S/ha-yr)
Name
Code
2001-03
2006-08
2010-12
2014-16
2018-20
Columbia Plateau
10.1.2
0.4575
0.423
0.4339
0.5046
0.2873
Northern Basin and Range
10.1.3
0.3447
0.3736
0.5295
0.4769
0.2872
Wyoming Basin
10.1.4
0.6354
0.673
0.5232
0.5582
0.4235
Central Basin and Range
10.1.5
0.4931
0.4465
0.4682
0.5229
0.3402
Colorado Plateaus
10.1.6
0.7419
0.7448
0.5604
0.6091
0.3206
Arizona/New Mexico Plateau
10.1.7
0.8232
0.801
0.6367
0.5653
0.3321
Snake River Plain
10.1.8
0.4799
0.6588
0.5869
0.5938
0.381
Mojave Basin and Range
10.2.1
0.5775
0.4134
0.4165
0.4087
0.2954
Chihuahuan Deserts
10.2.10
1.2098
1.1233
1.1098
1.2169
0.8642
Sonoran Basin and Range
10.2.2
0.5358
0.4614
0.45
0.4381
0.2889
Southern and Central California
Chaparral and Oak Woodlands
11.1.1
1.12
0.9523
0.9446
0.8409
0.7363
Central California Valley
11.1.2
1.0886
0.9161
0.8184
0.7977
0.6555
Southern California Mountains
11.1.3
1.2321
1.0846
1.0668
0.9797
0.8343
Madrean Archipelago
12.1.1
1.1561
1.144
0.9214
0.9407
0.4895
Arizona/New Mexico Mountains
13.1.1
1.4323
1.4098
1.1677
1.0302
0.5954
Southern Florida Coastal Plain
15.4.1
5.9639
5.1638
4.199
4.3358
3.7554
Northern Lakes and Forests
5.2.1
4.2923
3.2398
2.4385
1.8937
1.3265
Northern Minnesota Wetlands
5.2.2
2.2819
2.1153
1.4503
1.1332
0.8588
Northeastern Highlands
5.3.1
6.457
5.7779
3.0112
1.9925
1.3362
North Central Appalachians
5.3.3
18.0836
15.0511
7.2364
4.0916
2.4012
Middle Rockies
6.2.10
1.0399
1.1392
0.9349
0.8577
0.7085
Klamath Mountains
6.2.11
0.9013
1.0525
1.022
1.0653
0.925
Sierra Nevada
6.2.12
1.3177
1.1446
1.2417
1.141
0.9773
Wasatch and Uinta Mountains
6.2.13
1.3604
1.3833
1.1818
1.2713
0.7741
Southern Rockies
6.2.14
1.1427
1.1821
0.9241
0.8462
0.5419
Idaho Batholith
6.2.15
0.8952
1.1615
1.0974
0.9326
0.5975
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Ecoregion III
Tota
Median Sulfur Deposil
tion (Kg S/ha-yr)
Name
Code
2001-03
2006-08
2010-12
2014-16
2018-20
Northern Rockies
6.2.3
0.8969
0.9757
0.829
0.7912
0.5242
Canadian Rockies
6.2.4
1.2218
1.348
0.9682
0.97
0.7759
North Cascades
6.2.5
1.643
1.5503
1.28
1.3852
1.0878
Cascades
6.2.7
1.6874
1.662
1.4078
1.506
1.2381
Eastern Cascades Slopes and
Foothills
6.2.8
0.4394
0.4902
0.4704
0.5498
0.471
Blue Mountains
6.2.9
0.4603
0.5022
0.523
0.6145
0.3557
Puget Lowland
7.1.7
2.1291
1.6318
1.3687
2.1064
1.2462
Coast Range
7.1.8
2.386
2.1428
1.9998
2.0311
1.5004
Willamette Valley
7.1.9
1.6079
1.4789
1.4314
1.7067
1.0764
Eastern Great Lakes Lowlands
8.1.1
10.9736
8.8203
4.0355
2.7074
1.6382
Erie Drift Plain
8.1.10
18.3931
15.104
8.0732
4.9947
2.8124
Northern Allegheny Plateau
8.1.3
11.9249
10.2437
4.8068
2.7891
1.6814
North Central Hardwood Forests
8.1.4
4.5736
3.4207
2.6262
2.0107
1.3864
Driftless Area
8.1.5
5.3867
4.9968
3.3673
2.6069
1.9468
Southern Michigan/Northern Indiana
Drift Plains
8.1.6
9.6189
8.3446
5.3163
3.2534
2.161
Northeastern Coastal Zone
8.1.7
9.5712
8.4241
3.8242
2.3994
1.8734
Acadian Plains and Hills
8.1.8
4.459
4.6136
2.3792
1.6499
1.2222
Southeastern Wisconsin Till Plains
8.2.1
7.0223
6.3692
3.977
2.7375
2.0215
Huron/Erie Lake Plains
8.2.2
9.8648
8.5901
5.2196
3.1513
2.1087
Central Corn Belt Plains
8.2.3
9.7812
8.9628
5.4209
4.1072
2.4466
Eastern Corn Belt Plains
8.2.4
14.8424
11.9827
7.0763
4.1088
2.5906
Northern Piedmont
8.3.1
14.9402
12.5778
5.3007
3.3249
2.1241
Interior River Valleys and Hills
8.3.2
10.5462
9.3038
6.2016
4.2898
3.03
Interior Plateau
8.3.3
13.5173
10.958
6.2411
4.1623
2.7293
Piedmont
8.3.4
11.7106
9.5824
4.3372
2.6194
1.8919
Southeastern Plains
8.3.5
9.6797
8.051
4.3423
3.4774
2.6347
Mississippi Valley Loess Plains
8.3.6
8.6391
6.6922
4.5953
3.9633
3.176
South Central Plains
8.3.7
7.3432
6.7813
4.9139
4.7025
3.6386
East Central Texas Plains
8.3.8
6.4132
5.1437
3.8214
4.4478
3.6219
Ridge and Valley
8.4.1
14.0986
11.8557
5.3063
3.2326
2.1381
Central Appalachians
8.4.2
16.2032
13.2761
7.0458
4.1165
2.3152
Western Allegheny Plateau
8.4.3
20.3503
16.3579
8.2608
4.7637
2.887
Blue Ridge
8.4.4
11.1204
9.2557
4.4061
2.6059
1.9493
Ozark Highlands
8.4.5
6.3057
5.8383
4.6492
3.1904
2.5927
Boston Mountains
8.4.6
5.9764
5.7193
4.4839
3.3348
2.7899
Arkansas Valley
8.4.7
5.5359
5.2017
4.1517
3.3783
2.9723
Ouachita Mountains
8.4.8
6.1981
5.8193
4.6709
4.0934
3.5246
Southwestern Appalachians
8.4.9
14.7077
11.5575
5.4669
3.4563
2.6107
Middle Atlantic Coastal Plain
8.5.1
10.5248
9.3374
5.0943
3.426
2.3606
Mississippi Alluvial Plain
8.5.2
7.3738
6.0578
4.2173
3.9128
3.1692
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Ecoregion III
Tota
Median Sulfur Deposil
tion (Kg S/ha-yr)
Name
Code
2001-03
2006-08
2010-12
2014-16
2018-20
Southern Coastal Plain
8.5.3
7.9369
6.0166
4.4339
3.9496
3.232
Atlantic Coastal Pine Barrens
8.5.4
14.0323
12.2726
5.6121
3.8026
2.747
Northern Glaciated Plains
9.2.1
2.0376
2.084
1.7364
1.3281
1.2238
Lake Agassiz Plain
9.2.2
1.9749
1.9895
1.4408
1.1922
1.066
Western Corn Belt Plains
9.2.3
4.521
4.2523
2.9805
2.5558
1.9268
Central Irregular Plains
9.2.4
5.8102
5.3365
4.1299
2.9768
2.2734
Northwestern Glaciated Plains
9.3.1
1.5672
1.6233
1.3799
1.2046
1.0888
Northwestern Great Plains
9.3.3
1.2028
1.3341
1.0109
0.8764
0.8163
Nebraska Sand Hills
9.3.4
1.6666
1.991
1.4768
1.3589
1.3629
High Plains
9.4.1
1.6
1.5156
1.2682
1.3302
0.983
Central Great Plains
9.4.2
3.058
2.9854
2.1563
2.1892
1.8397
Southwestern Tablelands
9.4.3
1.3027
1.2442
0.9851
1.116
0.6471
Flint Hills
9.4.4
4.4375
4.0337
2.8507
2.4599
1.9342
Cross Timbers
9.4.5
4.5832
3.9558
3.0153
3.0523
2.6092
Edwards Plateau
9.4.6
3.0692
2.7607
2.211
2.5378
2.1013
Texas Blackland Prairies
9.4.7
6.1482
4.8692
3.8494
4.0241
3.3937
Western Gulf Coastal Plain
9.5.1
6.9462
5.6366
4.3126
4.7353
4.3323
Southern Texas Plains
9.6.1
3.722
3.0284
2.5427
3.0882
2.3617
5A.2.2.1 Ecoregion Critical Load Exceedances - Sulfur Only
Critical load exceedances were evaluated for the 69 ecoregions that had at least one CL.
Of the 69, 58 ecoregions had 10 or more values, and were therefore used in this analysis. We
evaluated both All CLs and only positive values in the Ecoregion. Exceedances were evaluated
with respect to 2001-2003, 2006-2008, 2012-2014, 2014-2016, and 2018-2020 for S only and
combined N and/or S deposition. 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 (Noted as 50/20 |ieq/L).
Results of S only exceed are summarized in Table 5 A-16 for each endpoint and time period.
Results of S only exceedances are included in Tables 5A-17 and 5A-24. Results for N and S
deposition were not summarized into tables. See section titled "Critical Load Exceedances" for a
description of how exceedances were calculated.
This section describes the results for ecoregion CL exceedances considering S deposition
only (Tables 5A-7 to 5A-24 and Figures 5A-32 to 5A-43). A summary of ecoregion CLs and
exceedance results can be found in Table 5A-16 for each of the ANC thresholds and deposition
time periods. In addition, ecoregion averages, 5th and 95th percentiles and counts of EXs >5,
10, 15, and 25 are summarized for positive CLs.
Exceedances were low for the 58 ecoregions using ANC thresholds of 20 and 30 |ieq/L
for the most recent years (2018-2020 and 2014-2016). For the 58 ecoregions, 40 and 48 had no
May 2023
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
exceedances at all for 2018-2020 and 2014-2016, respectively. Of the remaining 29 and 21
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 Critical loads based on an ANC threshold of 30 |ieq/L had slightly
higher exceedances across all deposition periods. The Southeastern Plans, Southern Coastal
Plain, and Atlantic Coastal Pine Barrens 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 (Stauffer and Canfield Jr. 1992). Central Appalachians Acadian
Plains and Hills Northern Appalachian and Atlantic Maritime Highlands are ecoregions that have
documented deposition driven acidification.
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1 Table 5A-16. Summary of Ecoregion results for critical load (CL) exceedances (EX) for
2 each ANC threshold and time periods for the 58 Ecoregions with 10 or
3 more values. The range of CL and Sulfur (S) deposition in Kg S/ha/yr
4 represents values that exceed the CL. Average percent EX represents the
5 average EX found in the 58 ecoregions for a particular ANC threshold and
6 time period. The four numbers (#) represent the number of ecoregions that
7 have greater than 5%, 10%, 15%, and 25% EX within the ecoregion for a
8 given ANC threshold and time period.
Critical Load
S Deposition
Time Period
Average
(5th to 95th)
Average
(5th to 95th)
Ave. %
EX
# >5%
EX
#>10%
EX
#>15%
EX
#>25%
EA
ANC Threshold = 50/20
jeq/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 peq/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 peq/L
2018-2020
3.2(1.4-6.2)
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
Critical Load for ANC = 20 peq/L
2018-2020
3.8(1.8-7.1)
2 (0.9-3.4)
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(1.1-11.8)
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
9 Critical loads determined for ANC thresholds of 50 and 50/20 |ieq/L had higher percent
10 exceedances within ecoregions and more ecoregions with some level of exceedances particularly
11 for the early deposition periods of 2010-2012, 2006-2008, and 2001-2003 (Tables 5A-19 to 5A-
12 23 and Figures 5A-39 to 5A-44). For critical loads using an ANC threshold of 50 |ieq/L, 31, 25,
13 21,21, and 21 of the 58 Ecoregions had no critical load exceedances for the 5 deposition periods
14 2018-2020, 2014-2016, 2010-2012, 2006-2008, and 2001-2003. Of the remaining Ecoregions,
May 2023
5A-72
Draft - Do Not Quote or Cite
-------
1 13,17, 36, 43, and 44 had greater than 5% exceedances and 8, 9, 25, 33, and 35 Ecoregions had
2 exceedance percentage greater than 10%.
3 Table 5A-17. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
4 threshold of 20 jieq/L for deposition years of 2018-20 and 2014-16.
Sulfur only-A
NC = 20 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
201
8-20
2014-16
Name
#
No.
No.
%
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
May 2023
5A-73
Draft - Do Not Quote or Cite
-------
Sulfur only-A
NC = 20 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
201
8-20
2014-16
Name
#
No.
No.
%
All
CL>0
All
CL>0
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
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
May 2023
5A-74
Draft - Do Not Quote or Cite
-------
1 Table 5A-18. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
2 threshold of 20 jieq/L for deposition years of 2010-12, 2006-08 and 2001-03.
Sulfur only - ANC = 20 ueq/L
No. = 69
% Exceedances
Ecoregion
C
L<0
2010-2012
2006-2008
2001
-2003
Name
#
No.
No.
%
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
Strait of Georgia/Puget Lowland
7.1.7
38
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
May 2023
5A-75
Draft - Do Not Quote or Cite
-------
Sulfur only - ANC = 20 ueq/L
No. = 69
% Exceedances
Ecoregion
C
L<0
2010-2012
2006-2008
2001
-2003
Name
#
No.
No.
%
All
CL>0
All
CL>0
All
CL>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
May 2023
5A-76
Draft - Do Not Quote or Cite
-------
1 Table 5A-19. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
2 threshold of 30 jieq/L for deposition years of 2018-20 and 2014-16.
Sulfur only - Al^
C = 30 ueq/L
No. = 69
% Exceedances
Ecoregion
C
L<0
2018-2020
2014
1-2016
Name
#
No.
No.
%
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
Western Allegheny Plateau
8.4.3
35
0
0.0
0.0
0.0
0.0
0.0
May 2023
5A-77
Draft - Do Not Quote or Cite
-------
Sulfur only - Al^
C = 30 ueq/L
No. = 69
% Exceedances
Ecoregion
C
L<0
2018-2020
2014
1-2016
Name
#
No.
No.
%
All
CL>0
All
CL>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
May 2023
5A-78
Draft - Do Not Quote or Cite
-------
1 Table 5A-20. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
2 threshold of 30 jieq/L for deposition years of 2010-12, 2006-08 and 2001-03.
Sulfur only - ANC = 20 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2010-2012
2006-2008
2001-2003
Name
#
No.
No.
%
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
Strait of Georgia/Puget Lowland
7.1.7
38
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
May 2023
5A-79
Draft - Do Not Quote or Cite
-------
Sulfur only - ANC = 20 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2010-2012
2006-2008
2001-2003
Name
#
No.
No.
%
All
CL>0
All
CL>0
All
CL>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
8.1.6
33
0
0.0
6.1
6.1
15.2
15.2
21.2
21.2
Indiana Drift Plains
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
11.1.3
22
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pine-Oak Mountains
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
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Plains
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
May 2023
5A-80
Draft - Do Not Quote or Cite
-------
1 Table 5A-21. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
2 threshold of 50 jieq/L for deposition years of 2018-20 and 2014-16.
Sulfur only - ANC = 50 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2018-2020
2014-2016
Name
#
No.
No.
%
All
CL>0
All
CL>0
Northern Appalachian and Atlantic
5.3.1
2851
153
5.4
9.7
4.3
11.9
6.6
Maritime Highlands
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
Western Allegheny Plateau
8.4.3
35
0
0.0
0.0
0.0
2.9
2.9
May 2023
5A-81
Draft - Do Not Quote or Cite
-------
Sulfur only - ANC = 50 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2018-2020
2014-2016
Name
#
No.
No.
%
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
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
May 2023
5 A-82
Draft - Do Not Quote or Cite
-------
Table 5A-22. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
threshold of 50 jieq/L for deposition years of 2010-12, 2006-08 and 2001-03.
Sulfur only - ANC = 50 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2010-2012
2006-2008
2001-2003
Name
#
No.
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
May 2023
5A-83
Draft - Do Not Quote or Cite
-------
Sulfur only - ANC = 50 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2010-2012
2006-2008
2001-2003
Name
#
No.
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
May 2023
5 A-84
Draft - Do Not Quote or Cite
-------
1 Table 5A-23. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
2 threshold of 50/20 jieq/L for deposition years of 2018-20 and 2014-16.
Sulfur only - ANC = 50/20 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2018-2020
2014-2016
Name
#
No.
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
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
May 2023
5A-85
Draft - Do Not Quote or Cite
-------
Sulfur only - ANC = 50/20 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2018-2020
2014-2016
Name
#
No.
No.
%
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
May 2023
5A-86
Draft - Do Not Quote or Cite
-------
1 Table 5A-24. Percent Ecoregion Exceedances of aquatic CLs for Sulfur only by ANC
2 threshold of 50/20 jieq/L for deposition years of 2010-12, 2006-08 and 2001-
3 03.
Sulfur only - ANC = 50/20 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2010-2012
2006-2008
2001-2003
Name
#
No.
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
May 2023
5A-87
Draft - Do Not Quote or Cite
-------
Sulfur only - ANC = 50/20 ueq/L
No. = 69
% Exceedances
Ecoregion
CL<0
2010-2012
2006-2008
2001-2003
Name
#
No.
No.
%
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
11.4
11.4
25.7
25.7
37.1
37.1
Southern Michigan/Northern
8.1.6
33
1
3.0
15.2
12.1
27.3
24.2
27.3
24.2
Indiana Drift Plains
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
11.1.3
22
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pine-Oak Mountains
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
8.2.1
10
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Plains
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
May 2023
5A-88
Draft - Do Not Quote or Cite
-------
2018 - 2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
1
2
3
4
5
6
7
Percent Exceedances
(ANC = 20 peq/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-32. Aggregated percent ecoregion critical load exceedances for S only
deposition from 2018-20 (top) and 2014-16 (bottom) for an ANC threshold
of 20 jieq/L. Ecoregions with less than 50 critical loads are shaded and
ecoregions without any values are blank. 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.
May 2023
5A-89
Draft - Do Not Quote or Cite
-------
2010 - 2012 Sulfur Deposition Ecoregion Exceedances
2006 - 2008 Sulfur Deposition Ecoregion Exceedances
1
2
3
4
5
6
7
Percent Exceedances
(ANC = 20 peq/L)
0-10%
m 10 -15%
I >15%
Ecoregions where critical loads are < 50 values
7' Ecoregions with high level of natural acidity
Areas without critical loads
Figure 5A-33. Aggregated percent ecoregion critical load exceedances for S only
deposition from 2010-12 (top) and 2006-08 (bottom) for an ANC threshold
of 20 jieq/L. Ecoregions with less than 50 critical loads are shaded and
ecoregions without any values are blank. 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.
May 2023
5A-90
Draft - Do Not Quote or Cite
-------
2001 - 2003 Sulfur Deposition Ecoregion Exceedances
1
2
3
4
5
6
7
Percent Exceedances
(ANC = 20 Meq/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-34. Aggregated percent ecoregion critical load exceedances for S only
deposition from 2001-02 for an ANC threshold of 20 jieq/L. Ecoregions
with less than 50 critical loads are shaded and ecoregions without any
values are blank. 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.
May 2023
5A-91 Draft - Do Not Quote or Cite
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 30 |Jeq/L)
|0-10%
10-15%
>15%
Ecoregions where critical loads are < 50 values
; ' 1 Ecoregions with high level of natural acidity
Areas without critical loads
2 Figure 5A-35. Aggregated percent ecoregion critical load exceedances for S only
3 deposition from 2018-20 (top) and 2014-16 (bottom) for an ANC threshold
4 of 30 jieq/L. Ecoregions with less than 50 critical loads are shaded and
5 ecoregions without any values are blank. The Southern Coastal Plan
6 (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4) ecoregions are cross
7 hatched to indicate natural high level of acidity.
May 2023
5A-92
Draft - Do Not Quote or Cite
-------
2010 - 2012 Sulfur Deposition Ecoregion Exceedances
2006 -2008 Sulfur Deposition Ecoregion Exceedances
1
2
3
4
5
6
7
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-36. Aggregated percent ecoregion critical load exceedances for S only
deposition from 2010-12 (top) and 2006-08 (bottom) for an ANC threshold
of 30 jieq/L. Ecoregions with less than 50 critical loads are shaded and
ecoregions without any values are blank. 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.
May 2023
5A-93
Draft - Do Not Quote or Cite
-------
2001 -2003 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 30 Meq/L)
o -10%
10-15%
| >15%
Ecoregions where critical loads are < 50 values
[-] Ecoregions with high level of natural acidity
] | Areas without critical loads
2 Figure 5A-37. Aggregated percent ecoregion critical load exceedances for S only
3 deposition from 2001-03 for an ANC threshold of 30 fieq/L. Ecoregions
4 with less than 50 critical loads are shaded and ecoregions without any
5 values are blank. The Southern Coastal Plan (8.5.3) and Atlantic Coastal
6 Pine Barrens (8.5.4) ecoregions are cross hatched to indicate natural high
7 level of acidity.
May 2023
5 A-94 Draft - Do Not Quote or Cite
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
1
2
3
4
5
6
7
Percent Exceedances
(ANC = 50 peq/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-38. Aggregated percent ecoregion critical load exceedances for S only
deposition from 2018-20 (top) and 2014-16 (bottom) for an ANC threshold
of 50 fieq/L. Ecoregions with less than 50 critical loads are shaded and
ecoregions without any values are blank. 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.
May 2023
5A-95
Draft - Do Not Quote or Cite
-------
2010 -2012 Sulfur Deposition Ecoregion Exceedances
2006 - 2008 Sulfur Deposition Ecoregion Exceedances
1
2
3
4
5
6
7
Figure 5A-39. Aggregated percent ecoregion critical load exceedances for S only
deposition from 2010-12 (top) and 2006-08 (bottom) for an ANC threshold
of 50 fieq/L. Ecoregions with less than 50 critical loads are shaded and
ecoregions without any values are blank. 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.
Percent Exceedances
(ANC = 50 (Jeq/L)
0-10%
10-15%
I >15%
Ecoregions where critical loads are < 50 values
Ecoregions with high level of natural acidity
Areas without critical loads
May 2023
5A-96
Draft - Do Not Quote or Cite
-------
2001 -2003 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50 (Jeq/L)
~i 0-10%
' 10-15%
I >15%
Ecoregions where critical loads are < 50 values
[<>y>| Ecoregions with high level of natural acidity
~ Areas without critical loads
1
2 Figure 5A-40. Aggregated percent ecoregion critical load exceedances for S only
3 deposition from 2001-03 for an ANC threshold of 50 jieq/L. Ecoregions
4 with less than 50 critical loads are shaded and ecoregions without any
5 values are blank. The Southern Coastal Plan (8.5.3) and Atlantic Coastal
6 Pine Barrens (8.5.4) ecoregions are cross hatched to indicate natural high
7 level of acidity.
May 2023
5 A-97 Draft - Do Not Quote or Cite
-------
2018 -2020 Sulfur Deposition Ecoregion Exceedances
2014 - 2016 Sulfur Deposition Ecoregion Exceedances
Percent Exceedances
(ANC = 50/20 fjeq/L)
_]0-10%
| 10-15%
| >15%
Ecoregions where critical loads are < 50 values
j2x$<;l Ecoregions with high level of natural acidity
Areas without critical loads
2 Figure 5A-41. Aggregated percent ecoregion critical load exceedances for S only
3 deposition from 2018-20 (top) and 2014-16 (bottom) for an ANC threshold
4 of 50 jieq/L for East and 20 fieq/L for the West. Ecoregions with less than
5 50 critical loads are shaded and ecoregions without any values are blank.
6 The Southern Coastal Plan (8.5.3) and Atlantic Coastal Pine Barrens (8.5.4)
7 ecoregions are cross hatched to indicate natural high level of acidity.
May 2023
5A-98
Draft - Do Not Quote or Cite
-------
2010 -2012 Sulfur Deposition Ecoregion Exceedances
2006 - 2008 Sulfur Deposition Ecoregion Exceedances
1
2
3
4
5
6
7
Percent Exceedances
(ANC = 50/20 (Jeq/L)
0-10%
10-15%
>15%
Ecoregions where critical loads are < 50 values
[OxVf;! Ecoregions with high level of natural acidity
Areas without critical loads
Figure 5A-42. Aggregated percent ecoregion critical load exceedances 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. Ecoregions with less than
50 critical loads are shaded and ecoregions without any values are blank.
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.
May 2023
5A-99
Draft - Do Not Quote or Cite
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2001 - 2003 Sulfur Deposition Ecoregion Exceedances
1
2
3
4
5
6
7
Percent Exceedances
(ANC = 50/20 Meq/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. Aggregated percent ecoregion critical load exceedances for S only
deposition from 2001-03 for an ANC threshold of 50 jieq/L for East and 20
(xeq/L for the West. Ecoregions with less than 50 critical loads are shaded
and ecoregions without any values are blank. 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.
8 5A.2.2.2 Ecoregion Summary - Percent Exceedances as a Function of Total
9 S deposition
10 Ecoregions across the 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-
11 20) were summarized by the number of ecoregions with percent of EX over 10, 15, 20, 25, and
12 30% by total S deposition (e.g., 10% of all the CLs in the ecoregion EX). Ecoregions included in
13 this analysis contain at least 50 CLs. A total of 25 acid sensitive ecoregions across the CONUS
14 with 18 and 7 ecoregions in the eastern and western U.S. have greater than 50 CLs. Table 5A-25
15 summarizes the min, max, and median total S deposition for ecoregions included in this analysis
16 with 50 or more CLs and positive exceedances. Deposition levels were summarized for the 5
17 deposition periods and 3 CL thresholds (20, 30, and 50 |.ieq/L) for the eastern and western U.S.
18 separate and combined. Deposition for ecoregions in the eastern U.S. had a median value of 11.0
19 Kg S/ha-yr in 2001-03 and 1.9 Kg S/ha-yr in 2018-20. Total S deposition for ecoregions in the
May 2023
5A-100
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
western U.S. was lower from a median of 1.14 Kg S/ha-yr in 2001-03 to 0.84 Kg S/ha-yr in
20180-20.
This summary is intended to look at the percent exceedances as a function of total S
deposition. For example, at 2 Kg S/ha-yr across all ecoregions and deposition periods that are no
ecoregions that have >10% EX for ANC threshold 50 [j,eq/L (Tables 5A-26). The lowest
deposition level with EX>10% was at 3 Kg S/ha-yr, which had only 1 ecoregion. However, at 10
Kg S/ha-yr, there are 22 ecoregions across all deposition periods with >10% EX and 1 with
>30%) EX. At 6, 10, 15 Kg S/ha-yr, there were 13, 22, 33 ecoregions with EX>10%> and 2, 6, 14
ecoregions with EX>20%>. Ecoregions with the most severe exceedances (>30%) started at 10
Kg S/ha-yr with 1 ecoregion and had 7 ecoregions with EX>30% at 10 Kg S/ha-yr. This was
done for ANC thresholds 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.
Cumulative percent of ecoregions across the 5 deposition periods were also determined
and graphed. This shows the percent of ecoregions as a function of deposition. For example,
for ANC of 50 [j,eq/L and for the eastern U.S, 100% of ecoregions and time periods have no
exceedances > 10% at 2 Kg S/ha-yr while at 19 Kg S/ha-yr 60% or of ecoregions and time
periods have no EX > 10% (e.g 40% have > 10% EX) (Tables 5A-26, Figure 5A-44). Results
for the other ANC thresholds are summarized in Tables 5A-28, 5A-30, 5A-32, 5A-34, 5A-36,
5A-38. Cumulative results are graphed in Figure 5A-45 to 5A-50. Figure 5A-51 summarized
Total S deposition (Kg S/Ha-yr) as a function of percent of waterbodies exceeding the CLs for
2018-20 and 2014-16 for thresholds ANC = 20, 30, and 50 |ieq/L for positive CLS (CL>0). EX
>10%) fall between 4-5 Kg S/ha-yr.
May 2023
5A-101
Draft - Do Not Quote or Cite
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Minimum, maximum, and median S deposition for ecoregions with at least
50 critical loads and with ecoregions with exceedances for the five
deposition periods and three ANC targets. Deposition values were
determined by a zonal statistic for each ecoregion.
Median Sulfur Deposition (Kg S/ha-yr)
2001-03
2006-08
2010-12
2014-16
2018-20
ANC Target of 20 /jeq/L for the East U.S.
Min
4.3
3.2
2.4
1.6
1.2
Max
18.1
15.1
7.2
4.7
3.6
Median
11.0
9.3
4.6
3.0
1.9
ANC Target of 30 /jeq/L for the East U.S.
Min
4.3
3.2
2.4
1.6
1.2
Max
18.1
15.1
7.2
4.7
3.6
Median
11.0
9.0
4.6
2.8
1.9
ANC Target of 50 /jeq/L for the East U.S.
Min
4.3
3.2
2.4
1.6
1.2
Max
18.1
15.1
7.2
4.7
3.6
Median
11.0
9.0
4.5
3.0
1.9
ANC Target of 20 jjeq/L for the West U.S.
Min
0.90
0.98
0.83
0.79
0.54
Max
1.69
1.66
1.41
1.51
1.24
Median
1.14
1.14
0.93
0.86
0.84
ANC Target of 20 jjeq/L for the East/West U.S.
Min
0.90
0.98
0.83
0.79
0.54
Max
18.08
15.05
7.24
4.70
3.64
Median
9.57
8.24
4.34
2.61
1.51
ANC Target of 30/20 yeq/L for the East/West U.S.
Min
0.90
0.98
0.83
0.79
0.54
Max
18.08
15.05
7.24
4.70
3.64
Median
9.57
8.05
4.34
2.50
1.87
ANC Target of 50/20 yeq/L for the East/West U.S.
Min
0.90
0.98
0.83
0.79
0.54
Max
18.08
15.05
7.24
4.70
3.64
Median
9.57
8.05
4.34
2.62
1.87
5
May 2023 5A-102 Draft - Do Not Quote or Cite
1 Table 5A-25.
2
3
4
-------
1 Table 5A-26. Number of ecoregion-time period combinations with more than 10,15, 20,
2 25 and 30% of waterbodies exceeding their CLs for three ANC target of 50
3 jieq/L. Includes 18 ecoregions in the eastern U.S.
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
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
19
36
23
17
13
10
4
May 2023
5A-103
Draft - Do Not Quote or Cite
-------
1 Table 5A-27. Cumulative percentage of ecoregion-time period combinations with more
2 than 10,15, 20, 25, and 30% of waterbodies per ecoregion meeting their
3 CLs for the ANC target of 50 jieq/L as a function of total S deposition
4 across all 5 deposition periods (2001-03, 2006-08, 2010-12, 2014-06, 2018-
5 20). 100% indicates there were no ecoregions that had percent exceedances
6 above specified value. For the eastern U.S. (See Table 5A-27 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%
May 2023
5A-104
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
Percentage of Ecoregions Meeting Benchmarks
forANC 50 peq/L (Eastern U.S)
100%
90%
80%
70%
GO
§ 60%
CT5
CD
o 50%
o
LU
3 40%
30%
20%
10%
0%
8 12 16
Sulfur Deposition (kg S/ha-yr)
-10%
- 15%
20%
25%
-30%
20
Figure 5A-44. Cumulative percentage of ecoregion-time period combinations with
exceedances >10, >15, >20, >25, >30% 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 ecoregion that had percent exceedances
above >10, >15, >20, >25, >30% for that deposition level. Critical load
exceedances based on ANC threshold of 50 jieq/L for the eastern U.S. (See
Table 5A-27 for data).
May 2023
5A-105
Draft - Do Not Quote or Cite
-------
1 Table 5A-28. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
2 >30% as a function of total S deposition across all 5 deposition periods
3 (2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes 18 ecoregions in
4 the eastern U.S. and Critical load exceedances are based on an ANC
5 threshold of 30 jieq/L.
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
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
19
30
19
12
10
9
6
May 2023
5A-106
Draft - Do Not Quote or Cite
-------
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
threshold of 30 jieq/L for the eastern U.S. (See Table 5A-27 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%
7
8
1 Table 5A-29.
2
3
4
5
6
May 2023
5A-107
Draft - Do Not Quote or Cite
-------
Percentage of Ecoregions Meeting Benchmarks
for ANC 30 |jeq/L (Eastern U.S.)
100%
90%
80%
70%
m
g5 60%
at
o
lu 50%
40%
30%
20%
10%
0%
-10%
-15%
20%
25%
-30%
4 8 12 16
Sulfur Deposition (Kg S/ha-yr)
20
2
3
4
5
6
7
Figure 5A-45.
Cumulative percent of ecoregions with exceedances >10, >15, >20, >25,
>30% 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 ecoregion that had percent exceedances above >10, >15, >20, >25, >30%
for a given deposition level. Critical load exceedances based on ANC
threshold of 30 jweq/L for the eastern U.S. (See Table 5A-28 for data).
May 2023
5A-108
Draft - Do Not Quote or Cite
-------
1 Table 5A-30. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
2 >30% as a function of total S deposition across all 5 deposition periods
3 (2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes 18 ecoregions in
4 the eastern U.S. and Critical load exceedances are based on an ANC
5 threshold of 20 jieq/L.
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
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
19
24
17
11
9
7
20
24
17
11
9
7
6
May 2023
5A-109
Draft - Do Not Quote or Cite
-------
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
threshold of 20 jieq/L for the eastern U.S. (See Table 5A-29 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%
7
8
1 Table 5A-31.
2
3
4
5
6
May 2023
5A-110
Draft - Do Not Quote or Cite
-------
Percentage of Ecoregions Meeting Benchmarks forANC
20 |jeq/L (Eastern U.S.)
£=
O
CD
10, >15, >20, >25,
>30% 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 ecoregion that had percent exceedances above >10, >15, >20, >25, >30%
for a given deposition level. Critical load exceedances based on ANC
threshold of 20 jneq/L for the eastern U.S. (See Table 5A-30 for data).
May 2023
5A-111
Draft - Do Not Quote or Cite
-------
Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
>30% as a function of total S deposition across all 5 deposition periods
(2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes 7 ecoregions in the
western U.S. and critical load exceedances are based on ANC threshold of
20 jieq/L.
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
0
0
0
0
0
3
0
0
0
0
0
4
0
0
0
0
0
5
0
0
0
0
0
6
0
0
0
0
0
7
0
0
0
0
0
8
0
0
0
0
0
9
0
0
0
0
0
10
0
0
0
0
0
11
0
0
0
0
0
12
0
0
0
0
0
13
0
0
0
0
0
14
0
0
0
0
0
15
0
0
0
0
0
16
0
0
0
0
0
17
0
0
0
0
0
18
0
0
0
0
0
19
0
0
0
0
0
1 Table 5A-32.
2
3
4
5
May 2023
5A-112
Draft - Do Not Quote or Cite
-------
1 Table 5A-33.
2
3
4
5
6
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
100%
100%
100%
100%
100%
6
100%
100%
100%
100%
100%
7
100%
100%
100%
100%
100%
8
100%
100%
100%
100%
100%
9
100%
100%
100%
100%
100%
10
100%
100%
100%
100%
100%
11
100%
100%
100%
100%
100%
12
100%
100%
100%
100%
100%
13
100%
100%
100%
100%
100%
14
100%
100%
100%
100%
100%
15
100%
100%
100%
100%
100%
16
100%
100%
100%
100%
100%
17
100%
100%
100%
100%
100%
18
100%
100%
100%
100%
100%
19
100%
100%
100%
100%
100%
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 threshold of 20
iieq/L for the western U.S. (See Table 5A-31 for data).
May 2023
5A-113
Draft - Do Not Quote or Cite
-------
Percentage of Ecoregions Meeting Benchmarks for
ANC 20 |-ieq/L (Western U.S.)
100%
90%
80%
„ 70%
to
c
&0 60%
10, >15, >20, >25,
>30% 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 ecoregion that had percent exceedances above >10, >15, >20, >25, >30%
for a given deposition level. Critical load exceedances based on ANC
threshold of 20 ueq/L for the western U.S. (See Table 5A-33 for data).
-------
1 Table 5A-34. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
2 >30% as a function of total S deposition across all 5 deposition periods
3 (2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes 25 ecoregions
4 across the U.S. and critical load exceedances are based on ANC threshold
5 of 50 jieq/L for the east and 20 jieq/L for the west.
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
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
19
36
23
17
13
10
6
May 2023
5A-115
Draft - Do Not Quote or Cite
-------
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
threshold of 50 jieq/L for the east and 20 jieq/L for the west (See Table 5A-
33 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%
8
9
1 Table 5A-35.
2
3
4
5
6
7
May 2023
5A-116
Draft - Do Not Quote or Cite
-------
100%
90%
80%
70%
CD
£ 60%
o£5
cn
I 50%
en
10, >15, >20, >25,
>30% 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 ecoregion that had percent exceedances above >10, >15, >20, >25, >30%
for a given deposition level. Critical load exceedances based on ANC
threshold of 50 ,ueq/L for the east and 20 jieq/L for the west (50/20 jieq/L)
(See Table 5A-35 for data).
May 2023
5A-117
Draft - Do Not Quote or Cite
-------
1 Table 5A-36. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
2 >30% as a function of total S deposition across all 5 deposition periods
3 (2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes 25 ecoregions
4 across the U.S. and critical load exceedances are based on ANC threshold
5 of 30 jieq/L for the east and 20 jieq/L for the west.
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
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
19
30
19
12
10
9
6
May 2023
5A-118
Draft - Do Not Quote or Cite
-------
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 threshold of 30
jieq/L for the east and 20 jieq/L for the west (See Table 5A-35 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%
7
8
1 Table 5A-37.
2
3
4
5
6
May 2023
5A-119
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
100%
90%
Percentage of EcoRegions & Years Meeting Benchmarks for ANC 30/20
|jeq/L (Eastern and Western U.S.)
80%
70%
8 60%
>-
oO
tn
c
o
en
(D
CC
o
o
50%
40%
30%
-10%
-15%
20%
25%
-30%
20%
10%
0%
5 10 15
Sulfur Deposition (Kg S/ha-yr)
20
Figure 5A-49. Cumulative percent of ecoregions with exceedances >10, >15, >20, >25,
>30% 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 ecoregion that had percent exceedances above >10, >15, >20, >25, >30%
for a given deposition level. Critical load exceedances based on ANC
threshold of 30 jneq/L for the east and 20 fieq/L for the west (50/20 fieq/L)
(See Table 5A-37 for data).
May 2023
5A-120
Draft - Do Not Quote or Cite
-------
1 Table 5A-38. Number of ecoregions with percent of exceedances of >10, >15, >20, >25,
2 >30% as a function of total S deposition across all 5 deposition periods
3 (2001-03, 2006-08, 2010-12, 2014-06, 2018-20). Includes 25 ecoregions
4 across the U.S. and critical load exceedances are based on ANC threshold
5 of 20 jieq/L for both the east the west U.S.
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
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
19
24
17
11
9
7
6
May 2023
5A-121
Draft - Do Not Quote or Cite
-------
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 threshold of 20
jieq/L for the east and west U.S. (See Table 5A-37 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%
7
8
1 Table 5A-39.
2
3
4
5
6
May 2023
5A-122
Draft - Do Not Quote or Cite
-------
Percentage of EcoRegions & Years Meeting Benchmarks for
ANC 20 |jeq/L (Eastern and Western U.S.)
100%
90%
80%
03
CD
>-
o£5
CO
d
o
CT5
10, >15, >20, >25,
>30% 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 ecoregion that had percent exceedances above >10, >15, >20, >25, >30%
for a given deposition level. Critical load exceedances based on ANC
threshold of 20 jtieq/L for the east and west U.S. (See Table 5A-39 for data).
May 2023
5A-123
Draft - Do Not Quote or Cite
-------
2
3
4
5
05
16
14
"g 12
05
O
x 10
LU
cn
CD
T3
O
-O
OS
33
6
4
2
0
cn
16
14
"S 12
CD
lS 10
CD
gj
th
o
-Q
a3
03
8
6
4
2
0
•
•
•
•
#
#
#
#
• #
•
#
- 1
•
L •
•• » & *
—*—• • * •* M «• «
• ANC 20 |jeq/L (CL>0)
ANC 30 |jeq/L (CL>0)
ANC 50 peq/L (CL>0)
12 3 4
Total S Deposition (Kg S/Ha-yr)
% #
• ~ \
• •
» •
• ANC 20 jjeq/L (CL>0)
ANC 30 |jeq/L (CL>0)
ANC 50 |jeq/L (CL>0)
••
/
—•—•—
12 3 4
Total S Deposition (Kg S/Ha-yr)
Figure 5A-51. Total S deposition (Kg S/Ha-yr) as a function of percent of waterbodies
exceeding the critical load for 2018-20 (upper) and 2014-16 (lower) for
target ANC = 20, 30, and 50 jieq/L for positive critical loads (CL>0).
May 2023
5 A-124
Draft - Do Not Quote or Cite
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
5A.2.3 Analysis of Risk in Case Study Areas for Acidification
The case study areas represent geographic diverse acid sensitive areas across the CONUS
that have sufficient data to complete a quantitative analysis. This includes the necessary air
quality information to assess varying levels of deposition, including monitoring and deposition
information. In addition, the deposition levels across these set of case studies should generally
reflect the range of concentration and deposition levels found across the CONUS. Five case
study areas were identified that meet the criteria (Figure 5A-52), 3 in the eastern U.S. (NOMN,
SHVA and WHMT) and 2 areas are in the western U.S. (GILA, ROMO and SINE).
Figure 5A-52. Location of the case study areas. Northern Minnesota (NOMN), Rocky
Mountain National Park (ROMO), Shenandoah Valley (SHVA), Sierra
Nevada Mountains (SINE) and White Mountain National Forest (YVIIMT).
This section presents a summary of the CL assessment for deposition scenarios
representing just meeting the current annual secondary PM2.5 NAAQS. Using the same
methodology described in section 5A.1.2 above, this assessment estimated potential CL
exceedances for three air quality (AQ) scenarios. Not all case study areas had sufficient water
May 2023
5A-125
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
quality or aquatic CL data to provide an in-depth analysis. Aquatic CLs for the 5 case study areas
are summarized below using the following steps:
(1) CLs were extracted from the NCLD for each of the 5 case study areas for the following
ANC thresholds: 20, 30, 50, and 80 [j,eq/L.
(2) CLs were summarized for each area in terms of the average, 70th and 90th percentile.
This was done in terms of kg S/ha-yr for S only analyses and meq/m2-yr for N and S
analyses.
(3) Exceedances were calculated for each of the AQ scenarios for all 4 ANC thresholds for S
only and N+S.
(4) The exceedances were summarized as the percent of waterbodies that were exceeding in
each area for all CLs and for the 70th and 90th percentiles.
5A.2.3.1 Results
A total of 524 CLs were found in the 5 case study areas, excluding SHVA which had
complete coverage (4977 Total CL with 704 sensitive CLs). ROMO, SINE, NOMN, and
WHMT had 121, 139, 183, and 74 CLs respectively (Figure 5A-53). Despite the relatively high
number of aquatic CLs for these four case studies, they do not represent a complete coverage of
water resources and the summary of the CLs and exceedances only represent the waterbodies
that have been modelled. Table 5A-40 provides average, 70th and 90th percentile CLs for S only
for each case study areas in units of Kg S/ha-yr. Table 5A-41 also provides the same
information but also includes CLs for S and N but in units of meq/m2-yr. 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. The below summary is based on an ANC threshold of
50 [j,eq/L. Average S only CL values range from 6.6 to 9.8 kg S/ha-yr or 41.3 to 61.3 meq/m2-yr
for waterbodies with CLs within each of the case study areas. The 90th percentile CLs for S
only are similar among the case studies and range between 0.1 to 4.1 kg S/ha-yr or 0.1 to 25.8
meq/m2-yr.
Table 5A-40. Average, 70th and 90th percentile CL of S only (kg S/ha-yr) for each case
study area for ANC limits of 20, 30, 50, and 80 jieq/L.
20 |jeq/L
30 |jeq/L
50 |jeq/L
80 |jeq/L
Ave.
70th
90th
Ave.
70th
90th
Ave.
70th
90th
Ave.
70th
90th
Sul
ur (S) only
ROMO
9.5
5.4
3.6
8.5
4.5
2.6
6.6
2.7
0.5
4.3
0.1
0.1
SINE
12.0
4.1
1.8
11.0
2.8
0.5
9.3
0.6
0.1
7.5
0.1
0.1
NOMN
10.8
5.5
4.2
10.4
5.3
3.9
9.8
4.7
3.2
8.2
3.8
2.3
WHMT
10.6
6.9
4.4
9.6
6.1
3.3
7.4
4.1
0.7
4.7
0.4
0.1
SHVA
12.4
9.4
7.1
11.4
8.4
6.3
9.4
6.3
4.1
6.6
3.2
1.3
May 2023
5A-126
Draft - Do Not Quote or Cite
-------
1 Table 5A-41. Average, 70th and 90th percentile CL of S and S+N (meq/m2-yr) for each
2 case study area for ANC limits of 20, 30, 50, and 80 jieq/L.
20 |jeq/L
30 |jeq/L
50 |jeq/L
80 |jeq/L
Ave.
70th
90th
Ave.
70th
90th
Ave.
70th
90th
Ave.
70th
90th
Sulfur (S) only
ROMO
59.1
34.0
22.6
5.30
28.4
16.1
41.2
16.7
3.4
26.7
0.1
0.1
SINE
75.0
25.4
11.0
68.7
17.3
2.9
58.4
3.5
0.1
47.1
0.1
0.1
NOMN
67.4
34.5
26.0
65.3
32.4
29.1
61.0
29.3
20.1
54.6
23.8
14.4
WHMT
66.3
43.4
27.8
59.7
38.3
20.8
46.3
25.6
4.4
29.6
2.3
0.1
SHVA
77.4
58.9
44.6
71.3
52.4
39.1
59
39.5
25.8
41.4
20.3
8.0
Sulfur and Nitrogen
N and S)
ROMO
100.9
76.2
63.1
94.8
70.1
57.3
83.1
58.8
46.6
68.5
46.1
37.9
SINE
120.4
66.9
49.6
114.1
61.1
42.6
103.8
47.8
38.8
92.4
41.7
28.0
NOMN
110.4
76.0
67.7
108.3
74.2
65.9
104.0
70.4
62.1
97.6
64.7
56.2
WHMT
104.4
83.7
68.5
97.6
75.1
59.7
84.1
62.1
47.9
81.2
47.0
37.6
SHVA
3
May 2023
5A-127
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
11
12
Rocky Mountain
National Park (ROMO)
Northern Minnesota (NOMN)
White Mountain
National Forest
(WHMT)
a #. K* r
i • • • • h 1 • e
c • "
. ¦ • ® • »
® o
i, C • ' o
Figure 5A-53. Critical load maps of each case study area. Critical load for sulfur (S) using
an ANC threshold is mapped with units of meq/m2-yr. Upper left to right
is Rocky Mountain National Park (ROMO), Northern Minnesota (NOMN),
and White Mountains National Forest (WHMT). Lower left to right is
Sierra Nevada Mountains, (SINE) and Shenandoah Valley Area (SIIVA).
Critical load exceedances were calculated for several air quality scenarios that reflected
an area meeting the most controlling2 current secondary NAAQS for that area (of those 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
area3 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
2 The scenarios selected had air quality for which the PM2.5 design value for the highest monitor was just equal to
the current secondary standard.
3 The area of influence is defined as the region where a change in emissions leads 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). To ensure that emissions and
concentrations in the area of influence are relevant, this analysis uses a maximum radius of 500 km.
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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 each of the selected air
quality periods, the TDEP data were extracted for S and N. For one case study area, SINE,4 the
air quality and TDEP data were adjusted slightly downwards to reflect a relevant air quality
scenario. For some locations, it was not possible to select a three-year historical period as PM2.5
concentrations, currently and in the past, have not been as high as the threshold for that scenario.
The air quality periods analyzed, and associated deposition levels are shown in Table 5A-42 and
Table 5A-43.
Table 5A-42. The three-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
M9 m-3
Coastal South Carolina
2004-2006
2007-2009
2011-2013
Gila National Forest
PM2.5 concentrations have not
been this high
2002-2004
2005-2007
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: 0.70
N deposition: 0.72
S deposition: 0.56
N deposition: 0.57
S deposition: 0.46
N deposition: 0.48
White Mountain
National Forest
2000-2002
2005-2007
2009-2011
4 For the Sierra Nevada case study, there is no historical period that is at or near the target PM2.5 concentration, so it
is not possible to use a historical dataset of deposition. Instead, this assessment approximates the change in
deposition due to a change in PM2.5 concentration at the maximum monitor. A linear model was fit using air
concentration and total (wet + 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 when the PM2.5
concentration at the highest monitor was reduced 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, which reflects differences in the relationship between PM2.5 and deposition. To be
clear, this is not meant to be a prediction, but rather a plausible deposition scenario associated with maximum
PM2.5 concentrations for each target level.
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Table 5A-43. For each three-year period described in Table 5A-41, this is the three-year
average deposition, spatially averaged across the case study area, for N and
S deposition. These values are calculated from the TDEP dataset.
Case study
Mean N deposition (kg N ha1 year1)
Mean S deposition (kg S ha1 year1)
15 pg/m3
12 pg/m3
10 pg/m3
15 pg/m3
12 pg/m3
10 pg/m3
Northern Minnesota
NA
6.8
6.0
NA
3.4
3.0
Rocky Mountain National Park
NA
NA
6.6
NA
NA
2.3
Shenandoah National Park
11
8.7
8.3
10
5.0
3.1
Sierra Nevada
4.9*
3.9*
¦k
CO
CO
0.80*
0.64*
0.53*
White Mountain National Forest
(New Hampshire)
7.6
6.7
5.2
7.2
7.1
3.8
*The air quality and associated deposition estimates for 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.
The case study areas, when taken as a group, represent a large range of PM2.5 and
deposition conditions. For all case study areas, the correlation between the sulfate PM2.5 and
nitrate PM2.5 measured within the case study area and the PM2.5 monitor that measures the
highest concentrations within the area of influence are shown in Table 5A-44. The measured
sulfate and nitrate PM2.5 is found to be highly correlated with the maximum PM2.5 monitor values
for these case study areas. Table 5A-44 also lists the correlation between wet deposition and air
concentrations within the case study area. Wet deposition of sulfur is correlated with air
concentrations of sulfur in the eastern U.S. case study areas, but the correlation is absent for
some case study areas in the West. Correlations for air concentration of nitrate PM2.5 and wet
deposition of nitrogen are low for most case study areas. The case study areas in the western U.S.
have greater inter-annual variability in precipitation, which adds variability to the air
concentration-deposition relationship. Furthermore, the measured wet deposition provides only
part of the deposition budget; dry deposition is not routinely measured, and models are needed to
complete the assessment of the air concentration-deposition relationship.
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. Each of these vary across the different case study areas. For
case study areas in the eastern U.S., where SO2 and NOx emissions have declined the most,
measurements of PM2.5 within the area of influence and wet deposition within the case study area
show a strong correlation. For case study areas 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 concentrations.
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As some locations do not show a correlation of air concentrations and wet deposition, it is
necessary to investigate further with models which can also estimate the dry deposition
component. Table 5A-45 shows the correlation calculated between simulated PM2.5
concentrations at each case study area, where the annual average concentration and annual total
deposition are shown as calculated by a 21-year CMAQ simulation (Zhang et al., 2018). The
CMAQ simulation provides a more complete quantification of the relationship between air
concentrations and deposition because both wet and dry deposition are included. However, the
model inherently lacks some of the variability that arises from making measurements of air
concentration in the field. Nevertheless, the correlations between the CMAQ-simulated annual
average air concentration and total annual deposition are higher than the comparisons between
observed air concentration and wet deposition, which suggests the air concentrations and
deposition are more tightly linked than can be estimated from the observational dataset, which
does not include dry deposition.
Table 5A-44. Summary of correlation between observations of air concentration and
NADP deposition.
Case Study Areas
Correlation between
sulfate PM and total
PM2.5 mass
Correlation
between nitrate
PM and total
PM2.5 mass
Correlation
between wet
deposition and
total sulfur air
concentrations
Correlation
between wet
deposition and
total nitrate air
concentrations
Northern Minnesota
0.95
0.87
0.78
0.07
Rocky Mountain National Park
0.86
0.92
0.41
0.13
Shenandoah Valley Area
0.99
0.96
0.93
0.71
Sierra Nevada Mountains
0.78
0.85
-0.03
-0.08
White Mountain National Forest
0.99
0.88
0.81
0.61
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Table 5A-45. Correlation between CMAQ-simulated annual sum of total deposition and
the CMAQ-simulated annual average concentration for each case study.
The correlation is calculated by computing the annual average
concentration and annual total deposition from 21-year CMAQ simulation
(Zhang et al., 2018). This table compares total (wet + dry) deposition and
air concentrations.
Correlation between:
Total nitrate air
Total sulfur air
concentration
concentration
Case Study Area
and N deposition
and S deposition
Northern Minnesota
0.42
0.71
Rocky Mountain National Park
0.60
0.68
Shenandoah Valley
0.93
0.88
Sierra Nevada: Sequoia National Park
0.94
0.74
White Mountain National Forest
0.80
0.91
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 for N and/or S were calculated for all case study areas except for
SHVA. Table 5A-46 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 for N and/or S deposition. The
highest percent exceedances occurred for the ANC limit of 80 [j,eq/L while lower percent
exceedances occurred for ANC of 20 [j,eq/L, as expected, for all scenarios.
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1 Table 5A-46. Number and percent of case study waterbodies estimated to exceed their CLs for specified ANC targets and
2 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
Sulfur Only
No. | Percent
Sulfur and
Nitrogen
No. | Percent
ANC Target = 20 jjeq/L
ANC Target = 30 jjeq/L
ANC Target = 50 jjeq/L
ANC Target = 80 /.leq/L
10
ROMO
3
2%
6
5%
6
5%
16
13%
25
21%
37
31%
60
50%
69
57%
SINE
1
1%
1
1%
3
2%
3
2%
13
9%
13
9%
33
24%
33
24%
NOMN
2
1%
2
1%
2
1%
2
1%
3
2%
4
2%
16
9%
16
9%
WHMT
3
4%
5
7%
9
12%
10
14%
18
24%
19
26%
36
49%
39
53%
SHVA
g
2%
11
2%
20
4%
107 23%
12
ROMO
SINE
1
1%
1
1%
9
6%
9
6%
34
24%
34
24%
61
44%
61
44%
NOMN
2
1%
6
3%
2
1%
11
6%
6
3%
21
11%
27
15%
47
26%
WHMT
21
28%
30
41%
25
33%
36
49%
37
50%
48
65%
48
65%
57
77%
SHVA
16
3%
19
4%
68
15%
192
41%
15
ROMO
SINE
2
1%
2
1%
11
8%
11
8%
38
27%
38
27%
62
45%
62
45%
NOMN
WHMT
23
31%
35
47%
27
36%
41
55%
38
51%
49
66%
48
64%
61
82%
SHVA
156
34%
202
44%
279
60%
366
79%
3
4
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The results are summarized in Table 5A-47 for each of the case study areas. Across the
case studies and ANC thresholds, S deposition would need to be on average between 7.4 to 12
kg/ha-yr and 4.1 to 9.4 and 0.7 to 7.1 to attain a 70 and 90 percentile, respectively.
Table 5A-47. Summary of S deposition levels to attain an ANC target of 20, 30, and 50
jieq/L for case study areas.
ANC
(Meq/L)
—Eastern —
-Western-
Northern
MN
White Mtns
Shenandoah
Rocky Mtns
Sierra NV Mtns
Based on Averaqinq of All Sites Achievinq 20 ueq/L
20
11
11
12
9.5
12
30
10
10
11
50
10
7.4
9.4
Based on 70% of sites Achievinq 30 ueq/L
20
5.5
6.9
9.4
5.4
4.1
30
5.3
6.1
8.4
50
4.7
4.1
6.3
Based on 90% of sites Achievinq 50 ueq/L
20
4.2
4.4
7.1
3.6
1.8
30
3.9
3.3
6.3
50
3.2
0.7
4.1
5A.3 KEY UNCERTAINTIES/LIMITATIONS
There is uncertainty associated with the parameters in the steady-state critical load model
used to estimate aquatic CLs. The strength of the CL estimate and the exceedance calculation
relies on the ability 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 measurements is well known. However, the
ability to accurately estimate the catchment supply of base cations to a waterbody can be
uncertain. This is important because the catchment supply of base cations from the weathering of
bedrock and soils is the factor that has the most influence on the CL calculation and also has the
largest uncertainty (Li and McNulty, 2007). 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 CONUS (e.g., Dupont et al., 2005
and others), the uncertainty in this estimate is unclear and could be large in some cases. For this
reason, an uncertainty analysis of the state-steady CL model was completed to evaluate the
uncertainty in the CL and exceedance estimation.
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
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input parameter varies according to specified probability distributions and their range of
uncertainty (Table 5A-48). The purpose of the Monte Carlo methods was to propagate the
uncertainty in the model parameters in the steady-state CL model.
Table 5A-48. Parameters used and their uncertainty range. The range of surface water
parameters (e.g., CA, MG, CL, NA, NO3, SO4) were determined from
surface water chemistry. Runoff(Q) based on min. and max value from
long-term water quality data. Acidic Deposition were set at 25%.
Parameter
Units
Uncertainty range
Distribution
Q
m/yr
1971-2000 annual
runoff
Normal
CA
|jeq/L
M
n. and Max
Normal
MG
|jeq/L
M
n. and Max
Normal
CL
|jeq/L
M
n. and Max
Normal
NA
|jeq/L
M
n. and Max
Normal
N03
|jeq/L
M
n. and Max
Normal
S04
|jeq/L
M
n. and Max
Normal
Acidic Deposition
(NOx & S04)
meq/m2-yr
25%
Lognormal
Within the Monte Carlo analysis, model calculations were run enough times (i.e. 5,000
times) to capture the range of behaviors represented by all SSWC model parameters (Table 5A-
48). The parameter uncertainty ranges were determined by various methods. For runoff (Q), the
1971-2016 annual runoff (m/yr) (Wieczorek et al. 2018) was used for each waterbody. Water
quality uncertainty range was based on the minimum and maximum data range for a waterbody
where 6-years of water quality data exits. For waterbodies with insufficient water quality data,
the minimum and maximum range was based on a range determined from regional long-term
water quality data from the EPA's Long-term Monitoring (LTM) program
(https://www.epa.eov/airmarkets/monitorine-siirface-water-chemistry) and other local programs.
Regions were defined for New England, Adirondacks, Central Appalachia Mountains, and Mid-
Alantic. The Monte Carlo analysis was done in R. A total of 14,943 waterbodies in the CONUS
were analyzed (Figure 5A-54).
The magnitude of the error for the N leaching (method A) was determined by quantifying
the uncertainty of the flux of nitrate (NO3") to a given lake or stream. Water quality data for the
past 28 years from the EPA's Long-term Monitoring (LTM) program was used to assess the
uncertainty of the influx of nitrate (NO3"). Lakes or streams are sampled weekly to quarterly
depending on the site and program. Annual flux of nitrate was calculated using annual
concentration of NO3" for a given monitoring site and multiplied by annual runoff (m/yr)
(Wieczorek et al. 2018) for the watershed and year. Confidence intervals were calculated for
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monitoring sites for a given region (i.e., New England, Adirondack^ Mountains, and
Appalachian Mountains) and for four time periods (i.e., 1990-2018, 1990-1999, 2000-2009,
2010-2018).
Critical Load Uncertainty for SSWC
• 0 - 0.25 Kg/ha/yr (0 - 1.53 meq/m2/yr)
• 0.25 - 0.5 kg/ha/yr (1.53 - 3.0625 meq/m2/yr)
: 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-54. 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.
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 all based on 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. 2012 and McDonnell et al. 2014), and (3) Dynamic
Models (MAGIC or Pnet-BGC). Critical load values were compared between these models to
determine model biases.
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5A.3.1 Results
A Monte Carlo analysis was used to estimate the uncertainty around the CL. 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. The range based on the 5th to 95th magnitude of the confidence interval was 0.37-33.2
meq/m2/yr or 0.1-5.3 Kg S/ha/yr giving a confidence level of ±3.84 meq/m2/yr or ±0.65 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-49). 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. western U.S.)
had high uncertainty. CLs with the lowest uncertainty occurred in the eastern U.S., particularly
along the Appalachian Mountains, upper midwest, and Rockies Mountains (Figure 5A-55). 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.
Table 5A-49. 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-50 shows the average and 5th to 95th percentiles by ecoregions. Fifty-one
ecoregions had sufficient data to calculate the 5th to 95th percentile. Ecoregions in the
Appalachian Mountains on average (e.g. Northeastern 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 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 high uncertainty.
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1 Table 5A-50. Results of the Monte Carlo analysis for uncertainty broken down by
2 ecoregion. N/A indicates there was not sufficient data to calculate the
3 percentile.
Ecoregion
Ave. 5th - 95th percentile
Code
Name
No. Values
Kg S/ha/yr
meq/m2/yr
5.3.1
Northeastern 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)
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)
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Ecoregion
Ave. 5th - 95th percentile
Code
Name
No. Values
Kg S/ha/yr
meq/m2/yr
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)
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
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Table 5A-51. Results of the uncertainty analysis of Nitrate (NO3) in EPA's Long-term
Monitoring (LTM) program. Unit are meq N/m2-yr.
Average
(meq/m2/yr)
S.D.
(meq/m2/yr)
5th to 95th
(meq/m2/yr)
Magnitude & Confident
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)
The results of the uncertainty analysis of NO3" flux (N leaching) based on the EPA's
LTM monitoring program are summarized in (Table 5A-51) by region and time period. 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 (0.5
to 1.6 Kg N/ha/yr). The ranges of confidence interval for the NO3" flux differed some across the
monitoring sites from 0.15 to 1.62 meq/m2/yr (0.02 to 0.23 Kg N/ha/yr). A combined S and N
confident interval was ± 2.30 to 3.77 meq/m2-yr which is equivalent to 0.37 to 0.60 Kg S/ha-yr
or 0.32 to 0.53 Kg N/ha-yr. While a comprehensive analysis of uncertainty has not been
completed for these data prior to the analysis include in this review, expert judgment suggested
the uncertainty for combined N and S CLs is on average about ±0.5 kg/ha-yr (3.125 meq/m2/yr),
which is consistent with the range of ± 2.30 to 3.77 meq/m2-yr determined from this analysis.
Given this consistency, an uncertainty of ±3.125 meq/m2-yr will be applied to the critical load
exceedances for the national, ecoregion, and case studies assessments. Watersheds determined
to exceed the critical load are those with exceedances above +3.125 meq/m2/yr while those that
do not exceed will be below -3.125 meq/m2-yr. Those that fall between ±3.125 meq/m2/yr will
be noted as "at the CL."
5A.3.1.1 Critical Load Model Comparison
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
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England and Adirondacks lakes, the MAGIC and the SSWC - F-Factor (Scheffe et al. 2014,
Lynch et al. 2022) 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-55). The Statistical Regression Model (Sullivan et al.
2014) CL estimates were also comparable to the SSWC - F-Factor model with a R2 = 0.9815
(Figure 5 A-56A). A bias towards higher values for the Statistical Regression Model (Sullivan et
al. 2014) was observed (Figure 5A-56B). However, this bias was not pronounced for CLs in the
range of 0 and 150 meq/m2/yr, where CL exceedance occur at current deposition levels.
For streams in the Appalachian Mountains, strong agreement was found between the
SSWC - F-Factor, Statistical Regression, and MAGIC models. McDonnell et al. (2014) found a
highly correlated relationship (R2 = 0.92 and RMSE = 9-11 meq/m2/yr) between base cation
weathering estimates determined by MAGIC compared to the predictions based on weathering
rates using water quality or landscape factors. Additionally, CLs determined by MAGIC
compared well to the SSWC - F-Factor were also highly correlated with a R2=0.9887 and RMSE
of 24 meq/m2/yr (Figure 5A-57A). However, the comparison was not as strong (R2=0.8861)
between CLs based on Statistical Regression Model (McDonnell et al. 2014) and the SSWC - F-
Factor model (Scheffe et al. 2014, Lynch et al. 2022), indicating less agreement between those
methods (Figure 5A-57B). Overall, good agreement between the three methods used to calculate
CLs was found, indicating there was not a systematic bias between the methods and that they
should produce comparable results when used together.
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a.
b.
_ 350
CO
-Q
T3
ro
o
ro
(_>
100
50
•2 0
u
_ 350
a>
| 300
0 250
<
^ 200
c
o
td 150
aj
to
ro
-e 100
¦o
ro
3 50
"ro
.y o
u
J
y = 1.1676X
R2 = 0.9796
50 100 150 200 250 300 350
y = l.lb/bX
R2 = 0.9796
••
y/
••'1
•*
••Ij/
50 100 150 200 250 300 350
Critical Load Based on SSWC F-Factor Model
(Lynch et al. 2022)
Figure 5A-55. Critical load comparison between values based on MAGIC model (y-
axis) and values based on the SSWC F-factor model (Lynch et al.
2022). Units are meq/m2/yr. a. New England Lakes and b.
Adirondacks Lakes.
May 2023
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c
o
'
3 450
3
on
200
-50
y = 1.3159X
r->2 r\ no 1 i-
• .i
R = 0.9815
•
yl
• >
A
***
1
•y'
./I
J
sy
•jJ
w*
-50 200 450 700 950 1200 1450
Critical Load Based on SSWC F-Factor Model
(Lynch et al. 2022)
-------
a.
250
OJ
"O
o
200
u
O
<
150
c
o
¦g 100
i/l
03
T3
03
O
50
05
£ 0
u
y = 0.9409x
R2 = 0 9887
1 \ v. _/UU /
•
•
V
• •
50
100 150 200 250
b.
T3
O
c
.2 *
^ o
QJ O
ob ™
c a;
o —
"O
o £
03
u
y = 0.7396x
R2 =0.8861
•
/ •
•
•
•
r
[•••"* •
• •
50 100 150 200 250
Critical Load Based on SSWCF-Factor Model
(Lynch et al. 2022)
Figure 5A-57. A. Critical load comparison between values based on MAGIC model (y-
axis) and values based on the SSWC F-factor model (Lynch et al. 2020)
(x~axis). B. 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) (x-axis). Units are meq/m2/yr.
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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-3
5B.2.2.1. Dietze and Moorcroft (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-31
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-36
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
Moorcroft, 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
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
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1 Table 5B-9. Lichen endpoints and associated deposition estimates 5B-38
2 TABLE OF FIGURES
3 Figure 5B-1. Study areas of three observational studies utilizing FIA plot data 5B-4
4 Figure 5B-2. Location of FIA plots, based on survival analysis of Horn et al. (2018) 5B-12
5 Figure 5B-3. Average measurement interval S deposition at sites of species with negative
6 growth associations with S deposition metric (drawn from Horn et al.,
7 2018) 5B-16
8 Figure 5B-4. Average measurement-interval S deposition at sites of species with negative
9 survival associations with S deposition metric (drawn from Horn et al.,
10 2018) 5B-16
11 Figure 5B-5. Average measurement-interval deposition at sites of species with negative
12 associations of growth with N deposition metric at median (drawn from Horn
13 et al.. 2018) 5B-17
14 Figure 5B-6. Average measurement-interval deposition at sites of species with positive
15 associations of growth with N deposition metric at median (drawn from Horn
16 et al.. 2018) 5B-18
17 Figure 5B-7. Average measurement-interval deposition at sites of species with negative
18 associations of survival with N deposition metric (drawn from Horn et al.,
19 2018) 5B-19
20 Figure 5B-8. Average measurement-interval deposition at sites of species with positive
21 associations of survival with N deposition metric (drawn from Horn et al.,
22 2018) 5B-20
23 Figure 5B-9. Annual mean wet SO4 deposition in the U.S. for 1989-1991 (top panel) and
24 2014-2016 (bottom panel) (U.S. EPA, 2023; NADP, 2018) 5B-26
25 Figure 5B-10. Annual mean wet NO3 deposition in the U.S. for 1989-1991 (top panel) and
26 2014-2016 (bottom panel) (U.S. EPA, 2023; NADP, 2018) 5B-27
27 Figure 5B-1 l.Wet plus dry deposition of total sulfur over 3-year periods. Top: 2000-2002;
28 Bottom: 2016-2018 5B-28
29 Figure 5B-12. Wet plus dry deposition of total nitrogen over 3-year periods. Top: 2000-2002;
30 Bottom: 2016-2018 5B-29
31 Figure 5B-13.Sites included in analysis by Simkin et al. (2016) 5B-33
32
33 ATTACHMENTS
34 1. Species by Plant Functional Group, Drawn from Dietze and Moorcroft (2011) "Tree
35 mortality in the eastern and central United States: patterns and drivers"
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1 2A. Species-specific Sample Distribution across Ecoregions for Species with Statistically
2 Significant Associations of Growth with N/S, from Horn et al 2018 Supplemental
3 Information Dataset
4 2B. Species-specific Sample Distribution across Ecoregions for Species with Statistically
5 Significant Associations of Survival with N/S, from Horn et al 2018 Supplemental
6 Information Dataset
7
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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, 2014).
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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; and Wallace et al., 2007). Further, some multiyear S/N addition
(>20 kg/ha-yr) experiments with small set of eastern species including sugar maple, aspen, white
spruce, yellow poplar, black cherry have not reported growth effects (Bethers et al., 2009; Moore
and Houle, 2013; Jung and Chang, 2012; Jensen et al., 2014).
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1 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 kg N ha-1yr1.
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 ha-1yr1 over
14 years starting in 1988 (as
NH4CI).
Background deposition was 10
kg N ha-1yr1
Reductions in total live basal area
(low N-J, 18%; high N |40%vs
control|9%), indicating reduced
growth rates; increased red spruce
mortality in high N.
Bear Brook,
ME
(Elvir et 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 ha-1yr1 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
(Houle and
Moore, 2008;
Moore and
Houle, 2013)
Three year N addition (approximately
3x and 10x estimates of concurrent
deposition), beginning in 2001, across
9 plots in black spruce and balsam fir
boreal forests. Studies assessed NO3"
leaching and tree growth.
9 and 30 kg N ha1yr1 (spruce
sites); 18 and 60 kg N ha-1yr1 (fir
sites) (from ammonium nitrate
additions) (as NH4NO3)
Background wet deposition of 3
kg N ha-1yr1 (black spruce forest)
and 5.7 kg N ha-1yr1 (balsam fir)
After 3 years, no significant
changes in growth rates for black
spruce or balsam fir. After 8 years,
no effect on sugar maple basal area
growth.
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 NO3- leaching, tree
growth and mortality.
100 kg N ha-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.
Fenrow
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 ha-1yr1 and 40 kg S ha-
1yr1 starting in 1989 (as
(NH4)2S04)
Background deposition was
approximately 15 kg N ha-1yr1
and 20 kg S ha-1yr1
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.
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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 the influence of different sets of additional factors
(e.g., related to climate, other air pollutants, topography and stand characteristics).
Table 5B-2 below summarizes 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 approximately 5 year 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.
Thomas et a). (2010)
Dietze and Moorcraft (2011)
~ ] Horn etal. (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.
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Other observational studies in the recently available evidence have investigated
relationships of tree growth with estimates of SOx and NOx emissions. For example, increases in
eastern redcedar growth in West Virginia has 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 NOx 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).
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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 in1984 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)
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1 Table 5B-3. Recent gradient/observational studies of associations between tree growth and
2 survival or mortality and S or N deposition: larger-scale FIA data studies.
Study Description | Summary
Larger Regional and National Scales
and 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 SO4- 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 SO4- 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 3 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.
3
4 5B.2.2.1. Dietze and Moorcroft (2011)
5 The study by Dietze and Moorcroft (2011) statistically analyzed patterns of tree mortality
6 in the eastern and central U.S. using FIA data from 1971 to 2005. The total sample size was 3.4
7 million tree measurements and 750,000 plot level measurements. Mortality was quantified as a
8 binary metric (lived or died) based on resampling of FIA plots after intervals of 5 to 15 years.
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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"1 yr"1) 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 SO4" (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).
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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 SO4 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).
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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. Additional ecological attributes
included in the study, but not in the models relating growth or survival to the N deposition
metric, were plant functional type (deciduous hardwood or evergreen conifer) and mycorrhizal
fungi association (arbuscular versus ectomycorrhizal). The Akaike Information Criteria (AIC)
was 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
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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,
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 was similar in approach to Thomas et al. (2010), and investigated associations
between variation in tree growth and survival and atmospheric deposition of N and S across the
plots for each species. 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
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).
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\
¦ ftrh -- ''
¦ . .
¦ V+K" '
• 13
• f ¦ *.
. | 'V-
. J
j
- j-"''" ••
K \ {"•
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 individual 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, survi val 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 estim ates
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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 above 10 have been presented in the literature as a
threshold for high collinearity, the authors used VIF < 3 as a criterion for species inclusion to be
conservative (Horn et al., 2018). The growth and/or survival models for 71 of the 94 species
analyzed met this criterion. Although not utilized in model assessments 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, collect climate data and apply
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).
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survival of one species, black locust (Robinia pseudoacacia),10 which was also among those 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., 2003) and insect infestation
(Eshleman et al., 1998, 2004). Further, the influence of soil characteristics on growth or survival
was also not analyzed. However, so long as these other factors do not spatially and temporally
correlate with N or S deposition, the omission of these factors would not affect the reported
relationships. 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). This uncertainty in the west primarily had to do with the
sulfur relationships, which were based on often shorter S deposition gradients that were often
highly correlated with N despite lower VIF scores overall. 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,
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).
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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],pitch pine [0.66]) (Horn et al., 2018, supplemental information). Differences in
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'Vr"1. Focusing first on association for growth,
the median S deposition metric values for the species for which growth was negatively
associated with S dep (excluding the two species with samples only in the west) ranged from 4 to
12 kg S ha'Vr"1, with values below 5 kg S ha'Vr"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"1 yr"1
for two species, black locust and sweet birch, which have 70% to more than 90% of their sites in
the Eastern Temperate Forests ecoregion13 (Figure 5B-3; distribution of measurement sites
shown in Attachment 2A).
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 Minnesota (https://www.epa.gov/eco-research/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-research/ecoregions-north-america).
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CO
O)
(/)
o
Q_
. CO
£ S 8
1 a s
~]—I—T"
| 1 IS
Q_ Q. o
-?r *co =?
.3?
8. 5
£ M 8
CO ^ .if
£
s S
a s
CD
(g o
g; ! S
"gf 5L " E
^ ^ dp
E
<
-g £ 05 E
.1
? " s" ir
§ ® 9- to
o I g m
-II
ID ™ 2 J Q. CO
E
<
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).
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With regard to N deposition, of the 39 species with significant associations of growth
with N deposition, the association was negative across the full deposition range of their sites for
two species, pitch pine and bur oak. These species' sites were predominantly in the Atlantic
coastal pine barrens and northern plains and forests, respectively. The median deposition across
all sites of these species were 9 and 10 kg N ha ha'Vi""1 (Figure 5B-5). The median deposition
values for the two other species, with hump shaped functions that were negative at the median,14
were 7 and 8 kg N ha ha^yr"1, respectively (Figure 5B-5).
05
CD
in
o
CL
o
25 -
20 -
15 -
10 -
5 -
0 -
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"'yr~'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
14 Given its role as a measure of central tendency of a dataset, the nature of the association for hump shape models at
the median is what is described in the groupings here.
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.
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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"1 yr"1 (Figure 5B-6).
60 -
50 -
40 -
O
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1
3
fill
I- 1 I 1
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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 ah, 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 hcf'yr1 .(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 Mtns 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).
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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). Blue asterisks indicate species with hump shape associations.
Turning to positive associations of survival with N, there was 1 species (black locust)
with a positive associations of survival with N across the full deposition range with a median
deposition of 11 kg N ha ha~'yf' (Figure 5B-8). The median deposition values for the 4 species
with hump-shaped associations that were positive at the median ranged from 7 to 12 kg N ha ha"
'yr"'. The two values below 10 were for paper birch, for which nearly 80% of the measurement
sites were in the Northern Forests ecoregion, and American beech with more than 50% of sites in
Northern Forests (and N/S correlation coefficient of 0.76).
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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 variable 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).
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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. The reason for this is that many of these soil factors are not available
nationally in the FIA database.
• 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 SO4 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'Vi""1-
o Median average S deposition (2000-13) estimated at sites of
nonwestern species with neg associations with growth or survival
ranged from 5 to 12 kg S ha~'yr~', 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 assessed by Dietze and Moorcroft (2011) for the eastern
U.S. study area was 6 to 16 kg N ha'Vi""1-
o Median average N deposition (2000-13) estimated 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_1yr"1 (Horn et al., 2018).
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o Median average N deposition (2000-13) estimated 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_1yr"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' 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. An example of this is
illustrated by the patterns of wet deposition of SO4 and NO3 in Figures 5B-9 and 5B-10,
respectively, and patterns of total S andN deposition in Figures 5B-11 and 5B-12. 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 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 different
time periods assessed. Positive effects in Thomas could translate to negative effects in Horn if
the N deposition effects accumulate through time, as hypothesized in Aber et al. 1998. 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 positive association of survival with N, includes quaking aspen for which
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1 Thomas et al. (2010) reported negative survival association. The study area of Thomas et al.
2 (2010) was limited to the Northeast, however, while aspen is prevalent in the Northern Forests
3 ecoregion, which is included in Dietze and Moorcroft (2011) study area.
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1 Table 5B-6. Significant associations in the three studies using USFS tree measurements.
Species
S Deposition
N Deposition
Dietz and
Moorcroft
(2011)
(SO4, 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,
FIA data, 1970s-90s)
Horn et al.
(2018)
(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
USuUG
Prunus serotina, black cherry
|Su |G
USuUG
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
USuUG
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
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S Deposition
N Deposition
Dietz and
Moorcroft
Horn et al.
(2018)
Dietze and
Moorcroft
Thomas et al.
(2010)
(total N, 2000-2004,
FIA data, 1970s-90s)
Horn et al.
(2018)
Species
(2011)
(SO4, wet,
1994-2005)
(total S,
-2000-
2013)
(2011)
(NO3, wet,
1994-2005)
(total N,
-2000-
2013)
Positive (|) or negative (j)association for growth (G) or survival (Su)
Southern Midsuccessional Hardwood
iSu
|Su
Carya alba, mockernut hickory
|Su
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
USuUG
Quercus falcata, southern red oak
ISu
Quercus laurifolia, laurel oak
|Su
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
|Su
|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).
May 2023
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1
2
3
4 Figure 5B-9. Annual mean wet SO-i deposition in the U.S. for 1989-1991 (top panel) and
5 2014-2016 (bottom panel) (U.S. EPA, 2023; NADP, 2018).
6
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1
Wet N03 deposition {kilograms per hectare):
3
4 Figure 5B-10. Annual mean wet NO3 deposition in the U.S. for 1989-1991 (top panel) and
5 2014-2016 (bottom panel) (U.S. EPA, 2023; NADP, 2018).
6
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Source: CASTNET/CMAQ/NADP
Total deposition of sulfur 0002
USEPA 09/12/18
1
2
3
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.
Source: CASTNET/CMAQ/NADP
Total deposition of sulfur 1618
USEPA 10/21/19
Total S
(kg-S/ha)
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Total deposition of nitrogen 0002
USEPA 02/19/19
Source: CASTNET/CMAQ/NADP
Total N
(kg-N/ha)
1
-8
-10
12
14
16
18
>20
1
2
3
Total N
(kg-N/ha)
Source: CASTNET/CMAQ/NADP
Tota] deposition of nitrogen 1618
USEPA 10/21/19
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.
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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). 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 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 due to losses of species in
controls.
Dry sedge
meadow in
Rocky Mountain
National Park,
CO
Bowman et al
(2012)
Five replicate plots (20
total) in a dry meadow
community. Study
assessed plant species
richness, cover of
vascular plants, above
ground biomass, and soil
chemistry.
5,10 and 30 kg N ha-1yr1
(ammonium nitrate
addition) over 4 years
starting in 2006.
Background deposition
was estimated to be 4 kg
N ha-1yr1
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
N/ha-yr as deposition associated with
an increase in C rupestris cover and
9 -14 kg N/ha-yr with NO3" leaching
in soil solution.
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Location
Description
Additions
Findings
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-1yr1over 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-y1 for 7
years starting in 2006. A
wildfire burned the plots
after the second year.
Ambient deposition was
approximately 3 kg N ha-
1yr-1A
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-1yr-1
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.
1 5B.3.2. Gradient or Observational Studies
2 Recent gradient studies have included analyses investigating the potential of N
3 enrichment in southern California to alter plant community composition through increases in the
4 presence of invasive annual species (ISA, Appendix 6, section 6.3.6). A recent study by Cox et
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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'Vi""1)
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).
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Open-canopy
Figure 5B-13. Sites included in analysis by Simkin et al. (2016). Based on dataset available at
https ://datadry ad. org/ stash/dataset/doi: 10.5 061 /dry ad. 7kn5 3
When sites were grouped as closed-canopy (forested) sites vs 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
values above an average of 8.7 kg N ha_1yf1 (Simkin et al., 2016). In closed-canopy ecosystems,
the variation in forest understory species richness with variation in N deposition was highly
dependent on soil pH. At sites with low pH (4.5) and N deposition above 11.6 kg N ha"'yr"'. a
negative relationship was observed for species richness with N deposition (higher N deposition
sites had lower species richness). At sites with basic soils (pH >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).
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
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associated with the point at which species losses begin given the site conditions.. For the forested
(closed-canopy sites), these N deposition values ranged from 7.9 to 19.6 kg N ha'Vr"1, with a
mean of 13.4 kg N ha'Vr"1. Across the open-canopy sites, these N deposition values ranged from
7.4 to 10.3 kg N ha^yr"1, with a mean of 8.7 kg N ha_1yr"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'Vr"1. Overall, a negative association of species richness w N deposition estimates was
more common for gradients involving soil that was acidic, 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'Vr"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). With N deposition above 8.7 kg N ha"1 yr"1, on average across site conditions,
there were lower levels of species richness. 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 there was a reduction in
species richness. At forested sites with basic soil, no value of N deposition was negatively
associated with species richness up to 20 kg N ha"1 yr"1. At both the national and gradient
analyses, sites with N deposition estimates at or below 3 kg N ha'Vr"1 showed little or no
reduction in species richness with N deposition (Simkin et al., 2016). It is important to note that
species richness is merely the count of the number of species at a site. The national results show
that at lower N deposition levels, there are more species gained than lost as N deposition
increases up to point (e.g., 8.7 kg N ha"1 yr"1 for open canopy on average), above that level there
are more species lost than gained. Thus, there could be species lost at all levels of N deposition
(Clark et al. (2008), only a species-level analysis would show whether there were individual
species lost at these lower levels that may have been masked in the total count of species.
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
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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 vs present, or their role in the
community, across the varying species richness values. Additionally, site distribution was
heterogeneous across parts of the U.S. 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 an 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.,
199616;). The relative influences of airborne versus deposited air pollutants in such impacts 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).
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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 2-21). For example the 95th percentile of U.S. sites ranged from just over 50 ppb to just
over 60 ppb during the 1980s (Figure 2-21). During the 1980s, and earlier, the Los Angeles
metropolitan statistical area (MSA) 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 is 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.
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 Pacific NW to vary with estimates of N deposition (and N-PM2.5) across sample sites ranging
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from approximately 8.2 to <1 kg N ha"1 yr"1 and 10 to <1 kg dissolved inorganic N ha"1 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.
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1 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
NOr 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 NO3 +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 HIS 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.
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1 Attachments
2
May 2023
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1
2 Attachment 1
3 Species by Plant Functional Group
4 Drawn from Dietze and Moorcroft (2011) "Tree mortality in the eastern and
5 central United States: patterns and drivers"
6
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
moniiifera
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 SCU
Catalpa
catalpa
Prunus nigra
Canada plum
deposition on
Catalpa bignoniodes
southern catalpa
Prunus pensylvanica
pin cherry
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
Uquidambar styraciflua
sweetgum
Salix nigra
black willow
Madura pomlfera
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
Avicennia germinans
Black-mangrove
Magnolia grandifolia
southern magnolia
Evergreen Hardwoods
Large positive
influence of SO4
deposition on
mortality
- negative influence
Casuarina lepidophloia
belah
Magnolia virginiana
sweetbay
Clnnamomum camphora
camphor tree
Melaleuca quinquenervia
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 aquatica
water hickory
Planera aquatica
water elm
Citrus
Citrus
Populus heterophylla
swamp cottonwood
May 2023 5B-Attachment 1-1 Draft - Do Not Quote or Cite
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Plant Functional Group
Genus Species
Common Name
Genus Species
Common Name
Large positive
influence of SCU
deposition on
mortality
- negative influence
of NO3 deposition
on mortality
Eugenia rhombea
red stopper
Quercus lyrata
overcup oak
Gleditsia aquatica
waterlocust
Sabal palmetto
cabbage palmetto
Metopium toxiferum
Florida poisontree
Salix amygdaioides
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 SO4
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 scopuiorum
Rocky Mountain juniper
Tsuga caroliniana
Carolina hemlock
Juniperus virginiana
eastern redcedar
Late Successional
Hardwood
Large positive
influence of SO4
deposition on
mortality
- negative influence
of NO3 deposition
on mortality
Acer
Maple
Carpinus caroliniana
hornbeam
Acer barbatum
Florida maple
Castanea dentata
American chestnut
Acer ieucoderme
chalk maple
Cornus florida
Flowering dogwood
Acernegundo
boxelder
Diospyros
persimmon
Acer nigrum
black maple
Diospyros virginiana
common persimmon
Acer pensyivanicum
striped maple
Fagus grandifolia
beech
Acer piatanoides
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
Aescuius
buckeye
Sapindus saponaria var
drummondii
western soapberry
Aescuius fiava
yellow buckeye
Tilia
basswood
Aescuius glabra
Ohio buckeye
Tilia americana
american basswood
Aescuius 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
Amelanchier
serviceberry
Morus alba
white mulberry
Amelanchier arborea
Downy serviceberry
Morus rubra
red mulberry
May 2023
5B-Attachment 1-2
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Plant Functional Group
Genus Species
Common Name
Genus Species
Common Name
Northern
Midsuccessional
Hardwood
- positive influence
of NO3 deposition
on mortality
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
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 pennsyivanica
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 SCUon
mortality
- negative influence
of NCbon 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 SO4
deposition on
mortality
- negative influence
of NO3 deposition
on mortality
Asimina triloba
pawpaw
Morus
mulberry
Carya 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
smoke tree
Quercus oglethorpensis
Oglethorpe oak
May 2023
5B-Attachment 1-3
Draft - Do Not Quote or Cite
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Plant Functional Group
Genus Species
Common Name
Genus Species
Common Name
Fraxinus
ash
Quercus pagoda
cherrybark oak
Fraxinus caroliniana
Carolina ash
Quercus phellos
willow oak
Fraxinus quadranqulata
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
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 SCU
deposition on
mortality
- negative influence
of NO3 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
1
2
3
4
5
May 2023
5B-Attachment 1-4
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1
2
Attachment 2A
3
4
Species-specific Sample Distribution across Ecoregions
5 for Species with Statistically Significant Associations of Growth with N/S
6
from Horn et al 2018 Supplemental Information Dataset
7
8 Key:
9 NA_L2 = North American Ecoregion, code for level 2
10 NA_L3 = North American Ecoregion, code for level 3
11 US_L3NAME = Name of Ecoregion at level 3
12 See: https://www.epa.gov/eco-research/ecoregions
13 Median = Tree-specific median S and/or N deposition for the species samples
14 Assoc = U= unimodal, t=positive, j=negative
15 N/S = elation coefficient forN and S deposition values for the species samples
16 Count = number of species' tree samples assessed in all plots in that ecoregion
17 % = percent of species' tree samples in that ecoregion
18
19
May 2023
5B-Attachment 2A
Draft - Do Not Quote or Cite
-------
NA
L2
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-f, 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%
May 2023
5B-Attachment 2A-1
Draft - Do Not Quote or Cite
-------
NA
L2
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-f, 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%
May 2023
5B-Attachment 2A-2
Draft - Do Not Quote or Cite
-------
NA
L2
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-f, 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
May 2023
5B-Attachment 2A-3
Draft - Do Not Quote or Cite
-------
NA
L2
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
May 2023
5B-Attachment 2A-4
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
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%
May 2023
5B-Attachment 2A-5
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
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%
May 2023
5B-Attachment 2A-6
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
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
May 2023
5B-Attachment 2A-7
Draft - Do Not Quote or Cite
-------
NA
L2
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
May 2023
5B-Attachment 2A-8
Draft - Do Not Quote or Cite
-------
honeylocust
Median S=6
black walnut
Median
N=12,S=9
Assoc N-f, S-J,
N/S = 0.08
Utah juniper
Median N=3, S=1
eastern
redcedar
Median S=7
Assoc S-J,
N/S = 0.3
sweetgum
Median N=9, S=7
yellow-poplar
Median N=10
NA
L2
NA L3
CODE
Assoc S-J,
N/S = 0.27
Assoc N-|, S-J,
N/S =0.71
Assoc N-f, S-J,
N/S =0.37
Assoc N-|
N/S = 0.41
US L3NAME
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%
May 2023
5B-Attachment 2A-9
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
honeylocust
Median S=6
Assoc S-J,
N/S = 0.27
black walnut
Median
N=12,S=9
Assoc N-f, 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
US_L3NAME
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%
May 2023
5B-Attachment 2A-10
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
honeylocust
Median S=6
Assoc S-J,
N/S = 0.27
black walnut
Median
N=12,S=9
Assoc N-f, 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
US_L3NAME
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
May 2023
5B-Attachment 2A-11
Draft - Do Not Quote or Cite
-------
NA
L2
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
May 2023
5B-Attachment 2A-12
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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 =
US_L3NAME
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%
May 2023
5B-Attachment 2A-13
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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 =
US L3NAME
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
May 2023
5B-Attachment 2A-14
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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 =
US L3NAME
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
May 2023
5B-Attachment 2A-15
Draft - Do Not Quote or Cite
-------
NA
L2
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
May 2023
5B-Attachment 2A-16
Draft - Do Not Quote or Cite
-------
shortleaf pine
Median S=6
slash pine
Median S=5
singleleaf
pinyon
Median N=3
Assoc N-|
N/S =0.58
longleaf pine
Median N=8
red pine
Median N=8, S=5
pitch pine
Median N=10
NA
L2
NA L3
CODE
Assoc S-J,
N/S =0.16
Assoc S-J,
N/S = 0.46
Assoc N-U
N/S =0.45
Assoc N-f, S-J,
N/S =0.53
Assoc N-U
N/S = 0.66
US L3NAME
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%
May 2023
5B-Attachment 2A-17
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
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%
May 2023
5B-Attachment 2A-18
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
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
May 2023
5B-Attachment 2A-19
Draft - Do Not Quote or Cite
-------
NA
L2
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
May 2023
5B-Attachment 2A-20
Draft - Do Not Quote or Cite
-------
eastern white
pine
Median N=8, S=6
Assoc N-f, S-J,
N/S = 0.59
loblolly pine
Median S=7
bigtooth aspen
Median S=6
quaking
aspen
Median N=7
Assoc N-U
N/S = 0.6
black cherry
Median N=11
Douglas-fir
Median N=3, S=1
NA
L2
NA L3
CODE
Assoc S-J,
N/S = 0.32
Assoc S-J,
N/S =0.57
Assoc N-U
N/S =0.33
Assoc N-f, S-J,
N/S = 0.65
US L3NAME
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%
May 2023
5B-Attachment 2A-21
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
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%
May 2023
5B-Attachment 2A-22
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
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
May 2023
5B-Attachment 2A-23
Draft - Do Not Quote or Cite
-------
NA
L2
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
May 2023
5B-Attachment 2A-24
Draft - Do Not Quote or Cite
-------
NA
L2
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%
May 2023
5B-Attachment 2A-25
Draft - Do Not Quote or Cite
-------
NA
L2
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%
May 2023
5B-Attachment 2A-26
Draft - Do Not Quote or Cite
-------
NA
L2
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%
May 2023
5B-Attachment 2A-27
Draft - Do Not Quote or Cite
-------
NA
L2
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
May 2023
5B-Attachment 2A-28
Draft - Do Not Quote or Cite
-------
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
L2
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%
May 2023
5B-Attachment 2A-29
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
northern red
oak
Median N=10
Assoc N-f
N/S = 0.42
black oak
Median N=11
Assoc N-f
N/S =0.13
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
Assoc N-|
N/S =0.28
pondcypress
Median N=7
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%
May 2023
5B-Attachment 2A-30
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
northern red
oak
Median N=10
Assoc N-f
N/S = 0.42
black oak
Median N=11
Assoc N-f
N/S =0.13
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
Assoc N-|
N/S =0.28
pondcypress
Median N=7
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
May 2023
5B-Attachment 2A-31
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
US_L3NAME
northern red
oak
Median N=10
Assoc N-|
N/S = 0.42
black oak
Median N=11
Assoc N-|
N/S =0.13
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
Assoc N-|
N/S =0.28
pondcypress
Median N=7
Assoc N-|
N/S = 0.71
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
May 2023
5B-Attachment 2A-32
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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-f, S-J,
N/S =0.25
slippery elm
Median S=8
Assoc S-J,
N/S = 0.09
US L3NAME
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%
May 2023
5B-Attachment 2A-33
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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-f, S-J,
N/S =0.25
slippery elm
Median S=8
Assoc S-J,
N/S = 0.09
US_L3NAME
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%
May 2023
5B-Attachment 2A-34
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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-f, S-J,
N/S =0.25
slippery elm
Median S=8
Assoc S-J,
N/S = 0.09
US_L3NAME
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
May 2023
5B-Attachment 2A-35
Draft - Do Not Quote or Cite
-------
NA
L2
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-f, 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
May 2023
5B-Attachment 2A-36
Draft - Do Not Quote or Cite
-------
1 Attachment 2B
2
3 Species-specific Sample Distribution across Ecoregions
4 for Species with Statistically Significant Associations of Survival with N/S
5 from Horn et al 2018 Supplemental Information Dataset
6 Key:
7 NA_L2 = North American Ecoregion, code for level II
8 NA L3 = North American Ecoregion, code for level III
9 US L3NAME = Name of Ecoregion at level III
10 See: https://www.epa.gov/eco-research/ecoregions
11 Median = Tree-specific median S and/or N deposition for the species samples
12 Assoc = U= unimodal, t=positive, j=negative
13 N/S = correlation coefficient for N and S deposition values for the species samples
14 Count = number of species' tree samples assessed in all plots in that ecoregion
15 % = percent of species' tree samples in that ecoregion
16
17
May 2023
5B-Attachment 2B
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
5.2
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
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
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
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
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%
May 2023
5B-Attachment 2B-1
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
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
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
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
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%
May 2023
5B-Attachment 2B-2
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
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
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
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
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
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
May 2023
5B-Attachment 2B-3
Draft - Do Not Quote or Cite
-------
NA
L2
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
9.5.1
Western Gulf Coastal Plain
11
0.0%
9.6
9.6.1
Southern Texas Plains
10.1
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
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1
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
12.1.1
Madrean Archipelago
13.1
13.1.1
Arizona/New Mexico Mountains
1
0.0%
15.4
15.4.1
Southern Florida Coastal Plain
65
0.1%
Total Tree Count
7513
121288
74760
16273
10215
24815
1
2
May 2023
5B-Attachment 2B-4
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
5.2
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
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
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
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1
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%
May 2023
5B-Attachment 2B-5
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
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
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
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
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%
May 2023
5B-Attachment 2B-6
Draft - Do Not Quote or Cite
-------
NA
L2
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
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
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
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
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%
May 2023
5B-Attachment 2B-7
Draft - Do Not Quote or Cite
-------
NA
L2
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
9.5.1
Western Gulf Coastal Plain
13
0.4%
2
0.0%
1
0.0%
9.6
9.6.1
Southern Texas Plains
10.1
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
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1
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
12.1.1
Madrean Archipelago
13.1
13.1.1
Arizona/New Mexico Mountains
15.4
15.4.1
Southern Florida Coastal Plain
Total Tree Count
3214
11392
12185
5565
24397
20266
1
2
May 2023
5B-Attachment 2B-8
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
5.2
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
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
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
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1
8.1.1
Eastern Great Lakes Lowlands
546
2.9%
40
0.6%
48
0.3%
9
0.0%
May 2023
5B-Attachment 2B-9
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
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
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
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
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%
May 2023
5B-Attachment 2B-10
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
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
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
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
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
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%
May 2023
5B-Attachment 2B-11
Draft - Do Not Quote or Cite
-------
NA
L2
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
9.5.1
Western Gulf Coastal Plain
39
0.2%
11
0.1%
118
0.3%
9.6
9.6.1
Southern Texas Plains
10.1
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
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
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
12.1.1
Madrean Archipelago
2
0.0%
13.1
13.1.1
Arizona/New Mexico Mountains
1457
7.8%
15.4
15.4.1
Southern Florida Coastal Plain
7
0.0%
Total Tree Count
18854
6591
18681
17249
37211
27701
May 2023
5B-Attachment 2B-12
Draft - Do Not Quote or Cite
-------
NA
L2
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
5.2.1
Northern Lakes and Forests
2
0.0%
1051
17.8%
5.2.2
Northern Minnesota Wetlands
5
0.1%
5.3
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
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
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1
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%
May 2023
5B-Attachment 2B-13
Draft - Do Not Quote or Cite
-------
NA
L2
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
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
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
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%
May 2023
5B-Attachment 2B-14
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
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
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
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
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
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
May 2023
5B-Attachment 2B-15
Draft - Do Not Quote or Cite
-------
NA
L2
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
9.5.1
Western Gulf Coastal Plain
1
0.0%
21
0.2%
1
0.0%
9.6
9.6.1
Southern Texas Plains
10.1
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
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1
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
12.1.1
Madrean Archipelago
13.1
13.1.1
Arizona/New Mexico Mountains
15.4
15.4.1
Southern Florida Coastal Plain
6
0.1%
1
0.0%
Total Tree Count
4199
11110
13464
5912
8946
17028
May 2023
5B-Attachment 2B-16
Draft - Do Not Quote or Cite
-------
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
L2
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
%
5.2
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
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
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
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1
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%
May 2023
5B-Attachment 2B-17
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
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
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
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
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%
May 2023
5B-Attachment 2B-18
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
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
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
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
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
9.4
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
May 2023
5B-Attachment 2B-19
Draft - Do Not Quote or Cite
-------
NA
L2
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
9.5.1
Western Gulf Coastal Plain
9
0.2%
306
0.4%
9.6
9.6.1
Southern Texas Plains
10.1
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
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1
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
12.1.1
Madrean Archipelago
13.1
13.1.1
Arizona/New Mexico Mountains
15.4
15.4.1
Southern Florida Coastal Plain
283
2.4%
Total Tree Count
11902
5373
10139
3163
23562
69321
May 2023
5B-Attachment 2B-20
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
5.2
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
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
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
7.1.7
Puget Lowland
409
0.9%
7.1.8
Coast Range
4823
10.2%
7.1.9
Willamette Valley
184
0.4%
8.1
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%
May 2023
5B-Attachment 2B-21
Draft - Do Not Quote or Cite
-------
NA
L2
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
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
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
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%
May 2023
5B-Attachment 2B-22
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
8.4.4
Blue Ridge
946
10.1%
1
0.0%
417
1.7%
1265
2.7%
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
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
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
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
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%
May 2023
5B-Attachment 2B-23
Draft - Do Not Quote or Cite
-------
NA
L2
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
%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
9.5
9.5.1
Western Gulf Coastal Plain
5
0.0%
9.6
9.6.1
Southern Texas Plains
10.1
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
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1
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
12.1.1
Madrean Archipelago
38
0.1%
13.1
13.1.1
Arizona/New Mexico Mountains
218
0.4%
657
1.4%
15.4
15.4.1
Southern Florida Coastal Plain
Total Tree Count
9324
11547
51946
24493
47417
46927
May 2023
5B-Attachment 2B-24
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
5.2
5.2.1
Northern Lakes and Forests
5.2.2
Northern Minnesota Wetlands
5.3
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
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
7.1.7
Puget Lowland
7.1.8
Coast Range
2
0.1%
7.1.9
Willamette Valley
8.1
8.1.1
Eastern Great Lakes Lowlands
3
0.0%
8.1.3
Northern Allegheny Plateau
21
0.2%
229
1.0%
May 2023
5B-Attachment 2B-25
Draft - Do Not Quote or Cite
-------
NA
L2
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
%
8.1.4
North Central Hardwood Forests
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
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
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
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%
May 2023
5B-Attachment 2B-26
Draft - Do Not Quote or Cite
-------
NA
L2
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
%
8.4.4
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%
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
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
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
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
9.4
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%
May 2023
5B-Attachment 2B-27
Draft - Do Not Quote or Cite
-------
NA
L2
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
%
9.4.6
Edwards Plateau
9.4.7
Texas Blackland Prairies
1
0.0%
3
0.0%
9.5
9.5.1
Western Gulf Coastal Plain
21
0.2%
29
0.5%
139
1.0%
9.6
9.6.1
Southern Texas Plains
10.1
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
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1
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
12.1.1
Madrean Archipelago
13.1
13.1.1
Arizona/New Mexico Mountains
1
0.0%
15.4
15.4.1
Southern Florida Coastal Plain
44
0.8%
Total Tree Count
10640
8855
5813
3089
14566
23425
May 2023
5B-Attachment 2B-28
Draft - Do Not Quote or Cite
-------
northern red
oak
Median N=10
Assoc N-U
N/S = 0.41
post oak
Median 10
black oak
Median S=8
black locust
Median
N=11,S=12
Assoc N-f.S-J,
N/S =0.19
sassafras
Median S=12
baldcypress
Median S=6
NA
L2
NA L3
CODE
Assoc N-U
N/S =0.14
Assoc S-J,
N/S =0.15
Assoc S-J,
N/S = 0.3
Assoc S-J,
N/S = 0.55
US L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
5.2
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
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
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
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1
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%
May 2023
5B-Attachment 2B-29
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
8.1.4
North Central Hardwood Forests
1604
5.1%
988
4.5%
143
2.6%
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
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
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
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%
May 2023
5B-Attachment 2B-30
Draft - Do Not Quote or Cite
-------
NA
L2
NA L3
CODE
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
US_L3NAME
count
%
count
%
count
%
count
%
count
%
count
%
8.4.5
Ozark Highlands
1437
4.5%
6909
34.1%
7233
33.0%
31
0.6%
334
5.3%
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
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
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
9.3.1
Northwestern Glaciated Plains
9.3.3
Northwestern Great Plains
9.3.4
Nebraska Sand Hills
9.4
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%
May 2023
5B-Attachment 2B-31
Draft - Do Not Quote or Cite
-------
NA
L2
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
%
9.4.7
Texas Blackland Prairies
18
0.1%
9.5
9.5.1
Western Gulf Coastal Plain
12
0.1%
1
0.0%
1
0.0%
29
0.7%
9.6
9.6.1
Southern Texas Plains
10.1
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
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1
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
12.1.1
Madrean Archipelago
13.1
13.1.1
Arizona/New Mexico Mountains
15.4
15.4.1
Southern Florida Coastal Plain
187
4.5%
Total Tree Count
31689
20277
21914
5533
6278
4162
May 2023
5B-Attachment 2B-32
Draft - Do Not Quote or Cite
-------
NA
L2
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
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
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
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
7.1.7
Puget Lowland
7.1.8
Coast Range
7.1.9
Willamette Valley
8.1
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%
May 2023
5B-Attachment 2B-33
Draft - Do Not Quote or Cite
-------
NA
L2
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
%
8.1.4
North Central Hardwood Forests
2072
13.6%
598
2.3%
1420
7.4%
171
3.1%
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
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
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
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%
May 2023
5B-Attachment 2B-34
Draft - Do Not Quote or Cite
-------
NA
L2
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
%
8.4.5
Ozark Highlands
33
0.2%
457
6.8%
821
4.3%
468
8.5%
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
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
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
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
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
May 2023
5B-Attachment 2B-35
Draft - Do Not Quote or Cite
-------
NA
L2
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
%
9.4.7
Texas Blackland Prairies
5
0.1%
3
0.0%
9.5
9.5.1
Western Gulf Coastal Plain
20
0.3%
29
0.2%
6
0.1%
9.6
9.6.1
Southern Texas Plains
10.1
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
10.2.1
Mojave Basin and Range
10.2.2
Sonoran Basin and Range
10.2.10
Chihuahuan Deserts
11.1
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
12.1.1
Madrean Archipelago
13.1
13.1.1
Arizona/New Mexico Mountains
15.4
15.4.1
Southern Florida Coastal Plain
Total Tree Count
15225
25676
6760
19107
5497
May 2023
5B-Attachment 2B-36
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
APPENDIX 6.A
DERIVATION OF THE ECOREGION AIR QUALITY
METRICS (EAQM)
In order to better understand the relationship between past and present air quality
concentrations and nitrogen (N) and sulfur (S) deposition in various downwind locations of
significance, the EPA conducted HYSPLIT air parcel trajectory modeling to identify the
meteorological patterns that determine the transport of pollutant material from source to receptor.
Using actual air quality monitoring sites as forward trajectory starting points, the EPA was able
to estimate the potential regions of influence for the 84 Ecoregion III areas. After identifying the
upwind geographic areas from which emissions potentially contribute to N and S deposition in
the ecoregion, the EPA analyzed air quality design values within each ecoregion's zone of
influence to estimate an Ecoregion Air Quality Metric (EAQM). EAQM values were estimated
for each ecoregion and for four separate pollutants: NO2, SO2, and PM2.5 and are intended to
provide a perspective of air quality levels in the upwind regions that potentially contribute to
downwind deposition levels. For pollutants with multiple forms of the standard, the EPA
estimated EAQM values for each form of the standard. This Appendix describes the
methodology used to calculate the air parcel trajectories that led to the zones of influence
identification, as well as the methodologies used to estimate the EAQM values for each
ecoregion/pollutant pair using historical air quality design value (DV) data.
6A.1. HYSPLIT TRAJECTORY METHODOLOGY:
The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model1 is
commonly used to compute simple air parcel trajectories using historical meteorological data.
HYSPLIT simulates the trajectory of air parcels as they are 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 from this location. In this exercise, HYSPLIT was
used to estimate the frequency at which air transport patterns indicated that air pollutant
1 Stem, A.F., Draxler, R.R, Rolph, G.D., Stunder, 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-14-00110.1.
May 2023
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
concentrations at individual monitoring sites plausibly could have contributed to deposition
within an ecoregion. The EPA was interested in assessing the air quality DVs for multiple
pollutants (and multiple forms of the standard, where relevant) in all areas that potentially
contribute to a downwind ecoregion. As explained in more detail below, multiple HYSPLIT
trajectories were generated and analyzed to determine a potential zone of influence for each
region, and then the all of the valid DVs from monitors within that area were assessed to
generate a composite "ecoregion air quality metric" (EAQM) for multiple ecoregion-pollutant
pairs.
The analysis used 48-hour forward trajectories with an initial plume height of 500 m and
a single year (2016) of meteorological data from the 32-km resolution North American Regional
Reanalysis (NARR-32)2. 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 to a weakly negative one by the end
of the year. Trajectories were calculated for each monitoring site with a valid DV in the 2000-
2018 time period. The set of sites differed by pollutant and the specific form of the standard for
that pollutant. In all, 568,398 individual trajectories were generated. Each 48-hour trajectory was
divided into 288 sequential segments corresponding to 10 minutes of the trajectory length to help
ensure that we did not miss an impacting trajectory. Using geospatial tools, the EPA assessed
whether a trajectory segment from an individual monitoring site was located in an ecoregion. If
so, this was counted as a "hit". The analysis evaluated the frequency of "hits" for each
monitoring site. 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 air quality
concentrations that lead to the deposition estimated in that ecoregion. Figure 6A-1 depicts the
outcome for one ecoregion pollutant pair. For this ecoregion in central Kentucky, given the
prevailing winds, the trajectory analysis indicates that PM2.5 data from sites within the ecoregion
itself, along with 22 other sites in surrounding upwind areas (e.g., Southwest IN, Central TN)
may be representative of air quality levels that contribute to N and S deposition within the
ecoregion given the analysis parameters.
2 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.
May 2023
6A-2
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
6A.2. ESTIMATION OF ECOREGION AIR QUALITY METRICS
(EAQMS)
After the trajectories were generated and sets of air quality monitoring sites potentially
within the zone of influence were identified for each ecoregion-pollutant pair, the EPA then
assessed the DVs at the sites within the contributing zone for each ecoregion-pollutant pair.
These EAQMs were generated to enable an assessment of the relationship between air quality
levels in the upwind contributing region to the deposition levels within the ecoregion. For each
pollutant, two types of EAQMs were derived for each ecoregion based on the pollutant DVs for
that ecoregion's contributing monitors:
• EAQM-max: the highest DV from any monitor within the zone of influence, and
• EAQM-weighted: a weighted average DV where each monitor's value is weighted by the
percentage of the ecoregion's HYSPLIT hits.
Both versions of EAQMs have value. EAQM-max represents the highest DV within the
upwind region potentially contributing to deposition in an ecoregion, and as such it enables one
to determine a relationship between deposition levels (and associated adverse effects) and worst-
case 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
the general-case upwind air quality that is associated with downwind deposition. Both types of
EAQMs have inherent uncertainties related to the trajectories themselves, the methodology used
to link upwind regions to downwind receptors (e.g., the 1% hit assumption), and the density of
the existing monitoring network.
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, for a single year
• SO2: annual average of 24-hour averages, for a single year
• NO2: annual average of hourly data, for a single year
• PM2.5: annual average of hourly data, averaged over 3-year periods
For the three combinations that are based on data averaged over three years, EAQMs
were generated for the following periods: 2001-2003, 2006-2008, 2010-2012, 2014-2016, and
2018-2020. For the four combinations that are based on a single year of data, EAQMs were
calculated for each of the individual 15 years between 2001 and 2020 within the identified 3-year
periods (e.g., 2001, 2002, 2003, 2006, 2007, 2008, 2010, ..., 2020). Tables 6A-1 and 6A-2 show
example EAQM outputs for a three-year metric and a single-year metric. In Table 6A-1, which
displays the EAQM-weighted annual average PM2.5 data, it can be noted that most historical
EAQM-weighted values have been below the current secondary PM2.5 standard of 15 |ig/m3 and
May 2023
6A-3
Draft - Do Not Quote or Cite
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
that a downward trend has been observed over the past two decades. In Table 6A-2, which
displays the EAQM-max data for the annual 2nd high 3-hour SO2 average (i.e., the current
secondary SO2 NAAQS), it can be seen that even the worst-case maximum DVs for SO2 are all
well below the 500 ppb (0.5 ppm) standard across these years and that an improving trend has
been observed between 2001 and 2020. Table 6A-3 is provided to show a sample difference
between EAQM-max and EAQM-weighted for an example case (annual average PM2.5) and
suggests, as expected, that there can be significant differences between the two types of EAQM
in some situations.
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
each 3-year period) within the ecoregions.
• • • * • x ' • <
> 1 v • 1 •
* jf • •• . V
•
•V
f-
* 3
%• *
: * ••
^ • •
• • •
• » ®
__ ——-— < •
• ^
. * /. *• •
-*?- if . r •• *
• /r
^
• I # * \* • •
y • • t
• • •
. .<•
> X
Figure 6A-1. Map of PM2.5 monitoring sites of influence (red circles) determined in the
trajectory analysis to impact Ecoregion 8.3.3 (purple shaded region).
Other PM monitoring sites determined not to impact the ecoregion are
shown as gray circles.
May 2023
6A-4
Draft - Do Not Quote or Cite
-------
1 Table 6A-1. Example EAQM output table for a three-year average metric. These data are
2 the EAQM-weighted for the annual average of hourly PM2.5 data, averaged
3 over 3-year periods (jig/m3).
2001-
2006-
2010-
2014-
2018-
Ecoregion
2003
2008
2012
2016
2020
5.2.1
8.7
8.0
7.4
6.1
5.6
5.2.2
8.6
8.4
8.1
5.9
5.6
5.3.1
10.4
8.2
7.1
5.9
5.4
5.3.3
13.2
13.1
10.7
9.1
7.9
6.2.10
9.4
8.9
8.9
8.0
6.0
6.2.11
8.8
8.0
6.6
7.2
10.5
6.2.12
13.2
12.0
10.3
10.0
11.4
6.2.13
10.6
9.4
7.8
7.4
6.8
6.2.14
7.3
7.9
6.3
6.3
6.7
6.2.15
8.9
9.1
8.7
8.6
8.6
6.2.3
9.4
9.8
8.5
8.5
9.2
6.2.4
9.4
9.7
9.2
8.8
8.9
6.2.5
8.8
8.9
7.1
6.3
7.7
6.2.7
9.0
8.9
7.1
7.3
10.0
6.2.8
9.4
9.3
7.5
7.9
11.6
6.2.9
8.6
8.8
7.7
7.9
10.4
7.1.7
9.3
9.1
7.0
6.3
7.6
7.1.8
8.7
8.1
6.8
6.8
9.0
7.1.9
8.4
8.1
7.0
6.9
9.3
8.1.1
12.0
8.6
8.6
6.9
6.3
8.1.10
15.4
13.4
11.3
9.7
8.0
8.1.3
12.4
9.3
8.2
6.6
6.3
8.1.4
10.2
9.8
8.7
6.8
6.7
8.1.5
11.4
11.1
9.9
7.9
7.8
8.1.6
13.6
11.3
9.5
9.0
8.0
8.1.7
13.1
10.6
8.4
7.0
6.5
8.1.8
11.0
8.5
7.3
6.2
5.1
8.2.1
12.5
12.4
10.1
8.3
8.1
8.2.2
14.7
12.4
9.8
9.2
8.0
8.2.3
13.9
12.3
11.1
9.3
8.8
8.2.4
15.5
13.9
11.9
9.5
8.8
8.3.1
14.6
13.0
10.6
9.1
7.6
8.3.2
14.9
13.1
11.3
9.7
8.7
8.3.3
14.0
13.4
11.2
8.9
8.0
8.3.4
14.1
13.5
9.9
8.8
7.9
8.3.5
13.0
13.0
11.2
8.7
8.4
8.3.6
12.5
11.7
9.9
8.3
8.3
8.3.7
11.8
11.4
10.3
8.7
8.8
8.3.8
11.8
11.5
10.3
9.1
9.1
8.4.1
15.8
12.7
10.4
8.8
7.0
8.4.2
13.8
13.6
10.4
8.8
7.1
8.4.3
15.1
14.4
11.2
9.1
7.9
8.4.4
13.6
12.4
9.7
8.7
7.0
8.4.5
12.9
11.4
10.5
8.6
7.9
8.4.6
12.4
11.8
10.7
8.7
8.5
8.4.7
12.3
11.8
10.7
8.8
8.7
8.4.8
12.4
11.9
10.9
8.9
8.8
May 2023
6A-5
Draft - Do Not Quote or Cite
-------
2001-
2006-
2010-
2014-
2018-
Ecoregion
2003
2008
2012
2016
2020
8.4.9
14.6
13.8
11.2
9.0
7.9
8.5.1
11.2
11.0
8.7
7.4
6.0
8.5.2
12.2
11.3
9.8
8.2
8.1
8.5.3
9.6
8.6
9.8
6.8
7.3
8.5.4
14.5
12.6
9.9
9.2
7.9
9.2.1
7.9
8.0
8.0
5.4
5.5
9.2.2
8.0
8.2
8.1
5.5
5.6
9.2.3
10.9
9.8
9.4
7.7
7.2
9.2.4
11.8
10.6
9.9
8.1
8.0
9.3.1
8.1
8.3
8.1
6.6
6.3
9.3.3
7.8
7.2
6.9
5.5
5.1
9.3.4
7.9
7.2
6.1
5.5
6.1
9.4.1
7.8
7.3
6.5
6.3
6.9
9.4.2
10.1
9.4
8.9
8.2
8.6
9.4.3
7.2
7.0
6.4
6.3
6.7
9.4.4
11.0
10.0
9.5
8.2
8.6
9.4.5
11.5
10.8
10.1
8.9
9.0
9.4.6
11.0
10.5
9.5
8.9
8.8
9.4.7
12.2
11.2
10.2
9.0
9.2
9.5.1
11.1
11.2
10.0
8.9
9.0
9.6.1
10.9
10.8
9.8
9.2
9.0
10.1.2
8.6
9.1
7.6
7.3
9.2
10.1.3
9.5
9.0
7.8
8.0
10.5
10.1.4
10.6
8.9
7.4
6.0
5.7
10.1.5
12.6
11.2
9.1
8.8
8.2
10.1.6
9.2
9.2
6.9
7.1
6.5
10.1.7
7.4
8.0
6.5
7.1
7.0
10.1.8
9.5
9.1
8.2
8.1
8.5
10.2.1
16.7
13.9
10.7
10.2
10.1
10.2.2
14.4
10.9
8.8
7.7
8.3
10.2.4
8.1
8.6
7.8
7.2
7.2
11.1.1
14.5
11.4
9.1
8.1
9.0
11.1.2
14.1
12.5
10.6
10.1
12.0
11.1.3
18.6
13.7
10.4
9.6
9.4
12.1.1
8.8
8.7
7.5
7.1
7.3
13.1.1
8.4
8.6
7.0
7.1
7.4
15.4.1
8.4
7.8
7.4
6.4
7.1
1
2
May 2023
6A-6
Draft - Do Not Quote or Cite
-------
Table 6A-2. Example EAQM output table for a single-year metric. These data are the
EAQM-max for the annual 2nd high of individual 3-hour SO2 averages (ppb).
Ecoregion
2001
2002
2003
2006
2007
2008
2010
2011
2012
2014
2015
2016
2018
2019
2020
5.2.1
138
139
128
186
182
229
145
199
176
153
126
106
83
65
62
5.2.2
96
95
107
55
82
106
55
63
76
47
49
66
41
65
45
5.3.1
96
51
69
42
45
204
221
193
59
64
14
12
80
77
75
5.3.3
249
319
264
231
188
152
155
332
117
116
111
81
78
70
51
6.2.10
112
246
200
62
91
60
118
40
55
57
68
38
34
33
57
6.2.11
84
78
78
74
55
48
19
20
14
14
13
9
17
13
10
6.2.12
84
78
78
74
55
48
12
15
44
19
13
9
17
13
10
6.2.13
102
246
200
62
91
42
118
35
55
50
68
38
34
33
57
6.2.14
219
246
200
162
174
169
538
116
216
178
147
257
132
150
57
6.2.15
112
246
200
74
91
57
118
39
55
57
68
27
82
65
57
6.2.3
112
91
99
62
64
57
65
39
55
57
68
27
82
65
54
6.2.4
112
91
99
62
64
57
118
39
55
57
68
27
82
65
54
6.2.5
28
41
23
20
24
24
19
20
14
10
13
9
82
65
54
6.2.7
34
44
23
20
24
48
19
20
14
14
13
9
82
65
54
6.2.8
34
44
23
20
24
48
19
20
14
14
13
9
82
65
54
6.2.9
112
246
200
62
91
48
118
35
55
50
68
27
34
33
57
7.1.7
28
41
23
18
24
24
19
20
14
4
13
3
82
65
54
7.1.8
34
44
23
20
24
48
19
20
14
14
13
9
82
65
54
7.1.9
34
44
23
18
24
24
19
20
14
11
8
9
82
65
54
8.1.1
245
212
264
200
158
125
118
132
169
116
131
361
83
77
75
8.1.10
121
123
174
231
178
126
118
132
169
116
131
361
83
49
56
8.1.3
245
273
264
200
129
125
93
102
60
116
85
34
50
47
35
8.1.4
138
139
128
186
182
229
145
199
176
153
126
106
83
65
62
8.1.5
100
103
240
236
312
229
202
199
219
182
124
38
26
65
45
8.1.6
107
85
122
109
193
234
149
153
89
92
101
96
83
58
62
8.1.7
135
105
108
126
134
204
221
193
59
64
25
12
12
20
19
8.1.8
135
105
108
126
134
204
221
193
59
27
25
12
80
77
75
8.2.1
331
179
240
236
312
234
202
199
219
182
126
106
83
58
62
8.2.2
113
118
176
109
106
123
121
132
169
76
131
361
83
58
58
8.2.3
331
179
240
236
312
311
202
199
219
182
124
96
78
58
56
8.2.4
158
178
146
165
193
234
149
153
134
142
101
96
91
151
60
8.3.1
91
91
87
75
123
77
61
332
117
77
69
46
182
129
51
8.3.2
254
226
291
235
318
311
202
199
219
182
124
96
361
386
357
8.3.3
182
225
196
165
164
162
107
153
134
142
101
96
361
386
59
8.3.4
170
235
145
168
194
188
158
138
126
46
41
38
191
87
184
8.3.5
114
123
137
149
237
78
235
293
178
77
67
63
67
60
54
8.3.6
254
210
238
121
237
311
235
293
181
77
67
63
361
386
357
8.3.7
142
105
236
151
68
98
65
99
94
44
52
57
73
92
70
8.3.8
142
105
236
151
73
84
65
99
94
44
52
57
73
92
70
8.4.1
170
134
145
168
194
188
158
138
126
77
111
81
191
129
184
8.4.2
197
244
175
168
194
188
158
332
126
77
111
81
191
129
184
8.4.3
249
319
237
231
188
203
155
332
117
108
111
81
78
70
55
8.4.4
170
235
145
168
194
188
158
138
126
46
41
38
191
87
184
8.4.5
254
226
238
224
318
311
140
146
181
110
117
38
361
386
357
8.4.6
254
226
238
151
127
143
140
146
181
110
117
57
73
92
70
May 2023
6A-7
Draft - Do Not Quote or Cite
-------
Ecoregion
2001
2002
2003
2006
2007
2008
2010
2011
2012
2014
2015
2016
2018
2019
2020
8.4.7
142
210
236
151
127
143
94
146
144
110
117
57
73
92
70
8.4.8
142
105
236
151
87
92
94
99
94
44
52
57
73
92
70
8.4.9
170
235
145
168
194
188
107
77
126
115
53
38
191
60
31
8.5.1
93
141
139
119
83
77
80
58
82
60
34
35
61
50
47
8.5.2
254
226
238
151
237
98
235
293
181
77
67
63
361
386
357
8.5.3
135
123
137
149
120
135
160
66
110
60
75
57
35
34
54
8.5.4
91
141
139
256
141
77
42
91
59
64
21
46
11
10
9
9.2.1
112
95
107
57
82
106
65
63
76
57
49
66
173
133
137
9.2.2
96
95
107
57
82
106
55
63
76
57
49
66
41
65
45
9.2.3
126
103
113
121
182
143
94
199
176
153
124
66
41
65
45
9.2.4
128
94
87
114
127
143
94
146
144
110
117
66
41
92
70
9.3.1
112
95
107
62
91
60
118
63
55
57
68
27
34
33
57
9.3.3
112
246
200
62
91
60
118
63
55
57
68
38
34
33
57
9.3.4
112
246
200
62
91
60
118
63
55
57
68
46
173
133
137
9.4.1
219
246
200
162
174
169
538
116
216
178
147
257
283
150
137
9.4.2
128
86
87
99
73
84
55
146
76
42
46
46
283
133
137
9.4.3
219
179
165
162
174
169
538
116
216
178
147
257
283
150
137
9.4.4
128
86
87
114
127
143
94
146
144
110
117
66
283
133
137
9.4.5
142
86
93
99
73
84
55
99
47
44
34
38
283
92
87
9.4.6
142
105
236
151
73
84
65
66
47
44
34
20
283
92
87
9.4.7
142
105
236
151
73
84
65
99
94
44
52
57
73
92
70
9.5.1
142
105
236
151
237
98
235
293
178
77
52
63
73
92
70
9.6.1
142
105
236
151
73
84
65
66
47
44
52
57
283
92
87
10.1.2
112
91
87
62
51
48
19
20
55
35
68
27
82
65
54
10.1.3
102
246
200
74
91
48
118
35
55
50
68
27
34
33
57
10.1.4
102
246
200
62
91
57
118
39
55
57
68
38
34
33
57
10.1.5
84
78
78
74
55
48
26
30
55
35
68
27
34
13
42
10.1.6
102
66
200
99
98
82
118
84
71
143
147
257
132
88
57
10.1.7
219
179
165
162
174
169
538
116
216
178
147
257
132
150
46
10.1.8
102
246
200
62
91
48
118
35
55
50
68
27
34
33
57
10.2.1
84
78
78
74
55
37
26
30
44
19
13
11
17
13
10
10.2.2
219
179
165
162
174
169
538
116
216
178
147
257
132
150
46
10.2.4
219
179
165
162
174
169
538
116
216
178
147
257
283
150
137
11.1.1
84
78
78
74
55
48
26
30
44
19
13
11
17
13
10
11.1.2
84
78
78
74
55
48
12
15
44
19
13
9
17
13
10
11.1.3
84
78
78
74
55
37
26
30
44
19
13
11
6
13
10
12.1.1
219
179
165
162
174
169
538
116
216
178
147
257
132
150
46
13.1.1
219
179
165
162
174
169
538
116
216
178
147
257
132
150
46
15.4.1
135
119
129
93
120
135
96
66
110
51
75
57
35
16
31
1
2
May 2023
6A-8
Draft - Do Not Quote or Cite
-------
1 Table 6A-3. Example table showing differences between an EAQM-max and EAQM-
2 weighted values. These sample data are for the annual average of hourly PM2.5
3 metric, averaged over 3-year periods (jig/m3). The data are EAQM-max"
4 minus EAQM-weighted.
Ecoregion
2003
2008
2012
2016
2020
5.2.1
3.3
3.0
2.3
2.6
2.7
5.2.2
3.4
2.6
3.4
2.8
2.7
5.3.1
1.4
2.8
2.5
2.2
2.1
5.3.3
2.7
5.2
4.1
3.7
3.2
6.2.10
4.6
2.8
2.3
4.4
4.9
6.2.11
4.6
5.4
3.7
3.2
5.8
6.2.12
8.6
9.5
8.9
8.4
6.2
6.2.13
3.4
1.7
1.4
2.9
2.9
6.2.14
3.6
3.2
4.0
2.9
3.2
6.2.15
6.2
4.3
2.8
3.8
7.7
6.2.3
6.6
3.9
3.0
3.9
4.1
6.2.4
6.6
4.0
2.3
3.6
4.4
6.2.5
2.3
2.8
1.6
2.5
4.2
6.2.7
4.4
4.5
3.2
3.1
6.3
6.2.8
5.7
4.1
4.0
7.1
4.7
6.2.9
6.5
4.6
3.8
7.1
5.9
7.1.7
1.8
2.5
1.8
2.5
3.1
7.1.8
4.7
5.3
3.5
2.7
4.9
7.1.9
5.0
3.5
3.3
2.6
4.6
8.1.1
1.9
2.2
4.6
4.5
1.2
8.1.10
2.9
1.7
1.9
2.4
1.3
8.1.3
1.5
1.5
1.4
1.6
0.9
8.1.4
1.8
2.3
1.7
1.9
1.6
8.1.5
1.6
2.8
2.3
1.5
1.0
8.1.6
1.6
0.9
2.7
1.4
1.6
8.1.7
4.4
3.7
1.5
3.2
2.0
8.1.8
3.2
2.5
2.3
1.9
2.5
8.2.1
1.7
2.1
2.1
1.1
2.5
8.2.2
4.8
3.0
1.7
2.1
2.9
8.2.3
2.2
2.3
1.1
0.8
1.8
8.2.4
2.6
1.8
1.5
1.9
2.8
8.3.1
2.4
1.9
1.7
3.7
1.7
8.3.2
4.2
2.6
1.1
0.4
1.8
8.3.3
2.9
1.9
2.0
1.5
2.1
8.3.4
1.5
1.7
2.4
1.6
1.6
8.3.5
0.6
1.6
1.9
1.4
0.7
8.3.6
1.5
2.3
2.0
1.5
1.3
8.3.7
2.4
3.8
1.8
2.1
1.8
8.3.8
2.4
3.7
1.8
1.7
1.5
8.4.1
0.0
0.0
1.7
1.6
1.1
8.4.2
3.3
1.8
2.1
1.9
1.6
8.4.3
2.1
1.0
3.6
3.7
3.2
8.4.4
2.8
2.7
2.4
1.7
2.5
May 2023
6A-9
Draft - Do Not Quote or Cite
-------
Ecoregion
2003
2008
2012
2016
2020
8.4.5
3.8
1.8
1.4
1.5
2.4
8.4.6
1.7
0.8
1.4
2.1
2.1
8.4.7
1.8
3.4
1.4
2.0
1.9
8.4.8
1.8
3.3
1.2
1.9
1.8
8.4.9
3.4
3.5
1.8
2.2
2.1
8.5.1
0.3
1.7
0.7
0.7
1.0
8.5.2
1.9
1.6
2.1
1.6
1.5
8.5.3
4.2
4.1
3.3
2.5
1.8
8.5.4
1.7
1.9
3.2
3.6
1.5
9.2.1
2.8
2.8
3.5
3.3
2.7
9.2.2
2.7
2.6
3.4
3.2
2.6
9.2.3
3.0
2.2
2.1
1.8
2.4
9.2.4
2.1
1.4
1.6
1.4
2.0
9.3.1
7.9
5.4
3.4
5.8
7.0
9.3.3
5.0
4.0
4.3
6.9
4.4
9.3.4
4.9
3.9
3.2
3.7
3.8
9.4.1
3.7
4.6
4.0
3.1
3.0
9.4.2
3.8
2.4
1.9
1.7
1.4
9.4.3
4.3
4.9
4.1
3.1
3.2
9.4.4
2.9
2.0
2.0
1.4
1.4
9.4.5
2.7
4.4
2.0
1.9
1.6
9.4.6
3.2
4.7
2.6
1.9
1.8
9.4.7
2.0
4.0
1.9
1.8
1.4
9.5.1
3.1
4.1
2.1
1.9
1.6
9.6.1
3.3
4.4
2.3
1.6
1.6
10.1.2
4.8
4.6
3.9
4.6
7.1
10.1.3
5.6
4.4
3.7
7.0
5.8
10.1.4
3.4
2.2
3.8
1.8
2.6
10.1.5
12.6
10.3
6.9
9.6
8.4
10.1.6
4.8
1.9
6.7
5.5
3.2
10.1.7
8.4
5.0
7.1
5.5
5.8
10.1.8
5.6
4.3
3.3
6.9
7.8
10.2.1
11.1
7.6
5.3
8.2
7.6
10.2.2
13.4
9.1
6.4
6.8
4.5
10.2.4
3.6
5.1
2.7
2.9
5.6
11.1.1
13.4
8.6
10.1
6.4
4.0
11.1.2
7.7
9.0
8.6
8.3
5.6
11.1.3
9.2
7.8
5.2
8.8
4.8
12.1.1
2.9
5.0
3.0
2.4
5.5
13.1.1
7.4
5.1
6.6
5.5
5.4
15.4.1
2.6
1.7
5.7
2.0
2.0
May 2023
6 A-10
Draft - Do Not Quote or Cite
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1 Table 6A-4. Table of the median TDEP S deposition estimated for each Ecoregion III area
2 compared to the median TDEP deposition estimate for each of the water body
3 locations used in Ecoregion III areas in the aquatic critical load analysis in
4 Chapter 5
Region
Median S
Deposition at
Aquatic CL
Sites from
TDEP
Median S
Deposition
from TDEP
Difference
Percent
Difference
Year
8.1.1
8.04
10.97
2.94
30.92%
2001-2003
8.1.1
6.50
8.82
2.32
30.29%
2006-2008
5.3.3
3.22
4.09
0.87
23.91%
2014-2016
5.3.3
5.83
7.24
1.41
21.52%
2010-2012
8.1.1
3.26
4.04
0.78
21.30%
2010-2012
8.1.1
2.22
2.71
0.48
19.64%
2014-2016
5.3.3
15.73
18.08
2.36
13.95%
2001-2003
8.1.1
1.44
1.64
0.20
12.69%
2018-2020
8.3.3
3.67
4.16
0.49
12.58%
2014-2016
5.3.3
13.37
15.05
1.68
11.81%
2006-2008
8.3.3
5.58
6.24
0.66
11.16%
2010-2012
8.3.3
9.84
10.96
1.12
10.74%
2006-2008
5.3.3
2.17
2.40
0.23
10.04%
2018-2020
8.4.1
1.94
2.14
0.20
9.61%
2018-2020
8.3.5
2.41
2.63
0.23
8.94%
2018-2020
5.2.1
4.01
4.29
0.28
6.73%
2001-2003
5.2.1
3.10
3.24
0.14
4.41%
2006-2008
5.2.1
2.34
2.44
0.10
4.09%
2010-2012
8.1.7
2.32
2.40
0.07
3.18%
2014-2016
8.3.3
13.11
13.52
0.41
3.10%
2001-2003
8.1.7
9.29
9.57
0.28
3.00%
2001-2003
8.1.7
3.71
3.82
0.11
2.97%
2010-2012
8.4.2
4.00
4.12
0.11
2.83%
2014-2016
8.1.3
2.71
2.79
0.08
2.73%
2014-2016
8.3.7
4.58
4.70
0.12
2.66%
2014-2016
8.1.3
4.69
4.81
0.12
2.52%
2010-2012
8.3.4
4.24
4.34
0.10
2.25%
2010-2012
8.1.3
11.69
11.92
0.24
2.02%
2001-2003
8.1.7
8.28
8.42
0.14
1.71%
2006-2008
5.2.1
1.31
1.33
0.01
0.99%
2018-2020
8.3.5
3.44
3.48
0.03
0.97%
2014-2016
5.2.1
1.88
1.89
0.02
0.81%
2014-2016
8.4.4
4.41
4.41
-0.01
-0.11%
2010-2012
8.3.1
3.34
3.32
-0.01
-0.35%
2014-2016
May 2023
6 A-11
Draft - Do Not Quote or Cite
-------
Region
Median S
Deposition at
Aquatic CL
Sites from
TDEP
Median S
Deposition
from TDEP
Difference
Percent
Difference
Year
8.3.3
2.74
2.73
-0.01
-0.37%
2018-2020
8.4.1
14.18
14.10
-0.08
-0.60%
2001-2003
8.4.1
11.93
11.86
-0.08
-0.66%
2006-2008
8.4.4
11.29
11.12
-0.17
-1.50%
2001-2003
8.3.1
15.18
14.94
-0.24
-1.61%
2001-2003
8.4.4
2.65
2.61
-0.05
-1.81%
2014-2016
8.1.7
1.91
1.87
-0.03
-1.85%
2018-2020
8.1.3
10.45
10.24
-0.20
-1.98%
2006-2008
8.4.9
5.59
5.47
-0.12
-2.20%
2010-2012
8.3.7
5.03
4.91
-0.11
-2.29%
2010-2012
8.4.5
3.27
3.19
-0.08
-2.45%
2014-2016
8.1.3
1.73
1.68
-0.04
-2.60%
2018-2020
8.4.5
2.66
2.59
-0.07
-2.69%
2018-2020
8.3.1
12.94
12.58
-0.36
-2.82%
2006-2008
8.4.2
7.25
7.05
-0.21
-2.91%
2010-2012
8.4.4
9.58
9.26
-0.32
-3.39%
2006-2008
5.3.1
3.12
3.01
-0.11
-3.49%
2010-2012
8.3.4
2.72
2.62
-0.10
-3.72%
2014-2016
8.3.1
2.21
2.12
-0.08
-3.87%
2018-2020
8.4.5
4.87
4.65
-0.22
-4.59%
2010-2012
8.3.4
12.26
11.71
-0.55
-4.61%
2001-2003
8.4.2
2.43
2.32
-0.11
-4.80%
2018-2020
8.4.2
17.03
16.20
-0.82
-4.96%
2001-2003
8.4.1
3.40
3.23
-0.17
-5.09%
2014-2016
8.4.2
13.98
13.28
-0.71
-5.19%
2006-2008
8.3.7
7.15
6.78
-0.36
-5.22%
2006-2008
8.4.4
2.06
1.95
-0.11
-5.33%
2018-2020
8.3.7
7.77
7.34
-0.43
-5.63%
2001-2003
8.3.4
10.14
9.58
-0.56
-5.67%
2006-2008
8.4.5
6.18
5.84
-0.35
-5.75%
2006-2008
5.3.1
6.12
5.78
-0.34
-5.79%
2006-2008
8.3.1
5.63
5.30
-0.33
-6.07%
2010-2012
8.1.4
1.48
1.39
-0.09
-6.40%
2018-2020
8.3.7
3.88
3.64
-0.24
-6.44%
2018-2020
8.3.4
2.03
1.89
-0.14
-7.22%
2018-2020
8.4.1
5.71
5.31
-0.41
-7.40%
2010-2012
8.1.4
3.72
3.42
-0.30
-8.46%
2006-2008
8.1.4
2.86
2.63
-0.23
-8.49%
2010-2012
May 2023
6A-12
Draft - Do Not Quote or Cite
-------
Region
Median S
Deposition at
Aquatic CL
Sites from
TDEP
Median S
Deposition
from TDEP
Difference
Percent
Difference
Year
8.1.4
2.19
2.01
-0.18
-8.53%
2014-2016
8.4.5
6.95
6.31
-0.64
-9.68%
2001-2003
5.3.1
1.48
1.34
-0.14
-10.06%
2018-2020
8.3.5
4.83
4.34
-0.48
-10.53%
2010-2012
5.3.1
2.23
1.99
-0.23
-11.07%
2014-2016
8.1.8
4.98
4.46
-0.52
-11.12%
2001-2003
8.4.9
2.93
2.61
-0.32
-11.45%
2018-2020
8.3.5
10.88
9.68
-1.20
-11.65%
2001-2003
5.3.1
7.29
6.46
-0.84
-12.15%
2001-2003
8.3.5
9.14
8.05
-1.09
-12.68%
2006-2008
8.1.4
5.30
4.57
-0.73
-14.70%
2001-2003
8.4.9
17.27
14.71
-2.56
-16.03%
2001-2003
8.1.8
5.42
4.61
-0.81
-16.14%
2006-2008
8.1.8
1.94
1.65
-0.29
-16.15%
2014-2016
8.1.8
1.44
1.22
-0.22
-16.49%
2018-2020
8.1.8
2.83
2.38
-0.45
-17.27%
2010-2012
8.4.9
4.17
3.46
-0.72
-18.83%
2014-2016
8.4.9
14.44
11.56
-2.89
-22.19%
2006-2008
May 2023
6A-13
Draft - Do Not Quote or Cite
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-452/D-23-002
Environmental Protection Health and Environmental Impacts Division May 2023
Agency Research Triangle Park, NC
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