Policy Assessment for the Review of 

the Secondary National Ambient Air 

Quality Standards for Oxides of 

Nitrogen and Oxides of Sulfur

January 14, 2011 Version - Appendices
EPA-452/R-11-004b

January 2011

Policy Assessment for the Review of the Secondary 

National Ambient Air Quality Standards for Oxides of 

Nitrogen and Oxides of Sulfur

January 14, 2011 Version - Appendices

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, North Carolina
January 14, 2011 A-1

Appendix A

Analysis of Critical Loads, Comparing Aquatic and Terrestrial Acidification

Critical load is defined as, “a quantitative estimate of ecosystem exposure to one or more 

pollutants below which significant harmful effects on specified sensitive elements of the 

environment do not occur, according to present knowledge” (McNulty et al., 2007), and critical 

loads can be estimated for aquatic and terrestrial ecosystems. Within the Risk and Exposure 

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

Nitrogen and Sulfur (hereafter referred to as REA Report) (US EPA, 2009), critical loads of 

acidification for aquatic systems were determined by relating specific amounts of acidifying 

nitrogen and sulfur deposition to selected Acid Neutralizing Capacities (ANC) within freshwater 

lakes or streams. The presence and abundance of fish species served as the biological indicator 

of the impacts of the exceedance of critical acid loads by nitrogen and sulfur deposition. 

Estimation of critical acid loads for terrestrial systems within the REA Report (US EPA, 2009) 

related acidifying nitrogen and sulfur deposition to the base cation to aluminum (Bc/Al) ratio in 

the soil solution, and the health of sugar maple and red spruce in forest ecosystems served as the 

biological indicator of the impacts of critical acid load exceedance. A main distinction between 

these two critical loads is that aquatic critical loads are largely an integrated function of the 

chemistry of run-off waters that feed the lake or stream within a watershed, while terrestrial 

critical acid loads are determined by the rooting zone section of the soil profile in a forest 

ecosystem. Therefore, it is possible to have different critical load values for aquatic and 

terrestrial ecosystems within the same watershed. 

Background

The goal of this Task was to determine the relative degree of protection offered by 

aquatic versus terrestrial critical acid loads within a landscape. Critical acid loads for lakes and 

streams within watersheds of the Adirondacks and Shenandoah Valley were compared against 

terrestrial critical loads calculated for same watersheds to determine which estimate had the 

lowest, most protective critical load for acidifying nitrogen and sulfur deposition.
January 14, 2011 A-2

For the REA Report (US EPA, 2009), critical acid loads were determined for 169 lakes 

and 60 streams in the Adirondacks and Shenandoah Case Study Areas, respectively. These 

critical loads were calculated using four different ANCs, 0, 20, 50 and 100 μeq/L, that ranged in 

the level of protection offered to fish species abundance and diversity, and the resulting critical 

acid loads were classified into four “current condition of acidity and sensitivity to acidification” 

categories. “Highly Sensitive” water bodies had critical loads less than or equal to 50 

meq/m2

/yr, “Moderately Sensitive” systems had critical loads ranging from 51 to 100 meq/m2/yr, 

“Low Sensitivity” lakes and streams had critical loads that ranged from 101 to 200 meq/m2/yr, 

and “Not Sensitive” systems had critical acid loads greater than 201 meq/m2

/yr. 

Methods

For the purposes of this Task, aquatic critical acid loads corresponding to an ANC of 50 

meq/m2

/yr were selected, and the locations of the lakes and streams in the Adirondacks and 

Shenandoah Case Study Areas were mapped by HUC12 watersheds. Availability of data for 

terrestrial acidification estimates was determined for each HUC, and only HUCs that had 

sufficient data were mapped. Data from the U.S. Department of Agriculture- Natural Resources 

Conservation Service (USDA-NRCS) SSURGO soils database (USDA-NRCS, 2008) had the 

poorest coverage. This data restriction limited the number of water bodies that could be included 

in the analysis to 62 and 35 for the Adirondacks and Shenandoah Case Study Areas, respectively.

To examine a representative selection of water bodies in each Case Study Area, four

watersheds containing lakes or streams from each of the four “current condition of acidity and 

sensitivity to acidification” categories were randomly selected. Therefore, a total of 16 

watersheds were chosen for each Case Study Area. All four “current condition of acidity and 

sensitivity to acidification” categories were evenly represented for the Adirondacks Case Study 

Area (four watersheds for each of the four categories). However, due to the limited number of 

watersheds in the Shenandoah Area and a lower proportion of lakes with low sensitivities to 

acidifying nitrogen and sulfur deposition (“Low Sensitivity” and “Not Sensitive”), it was not 

possible to have equal representation of all “current condition of acidity and sensitivity to 

acidification” categories. Therefore, there was a larger representation of streams that were more 

sensitive to acidification (“Highly Sensitive” and “Moderately Sensitive”). All water bodies that 

were located in each of the selected HUCs were included in the analyses. In many cases, these 
January 14, 2011 A-3

water bodies ranged in sensitivity to acidification. In total, 29 lakes and 20 streams were 

analyzed in the Adirondacks and Shenandoah Case Study Areas, respectively (Table A-1 and A2).
September, 2010 A-4 Draft – Do Not Quote or Cite

Table A-1. Watersheds (HUC 12) and fresh water lakes in the Adirondacks Case Study Area that were used in the 

comparison of aquatic and terrestrial critical acid loads. Lake IDs and associated aquatic critical acid loads (CL) in meq/m2/yr,

based on an ANC of 50 μeq/L, are indicated in each cell and are from the REA REPORT (US EPA, 2009).

HUC

CURRENT CONDITION OF ACIDITY AND

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive (CL <

50 meq/m2/yr)

Moderately Sensitive (CL = 

51-100 meq/m2/yr)

Low Sensitivity

(CL = 101-200 meq/m2/yr)

Not Sensitive 

(CL > 201 meq/m2/yr)

020100010103 NY534L (CL = 1043)

020100040203 1A2-028O (CL = 106) 

NY310L (CL = 147) NY308L (CL = 485)

020100080304 NY312L (CL = 588) NY313L 

(CL = 598)

020100081602 NY500L (CL = 610)

020200020101 NY013L (CL = 64)

020200020704 NY536L (CL = 69)

020200040805 1A2-078O (CL = 33) NY292L (CL = 117)

041501011001 NY029L (CL = 39)

041503020801 NY783L (CL = 455)

041503040102 NY284L (CL = 23) 

NY285L (CL = 42)

041503040204 NY278L (CL = 57)

041503050103 1A1-089O (CL = 43) 050215AO (CL = 74)

NY793L (CL = 97)

041503050104 NY290L (CL = 30) 

NY289L (CL = 50)

041503050302 NY008L (CL = 146) 

NY007L (CL = 165)
September, 2010 A-5 Draft – Do Not Quote or Cite

HUC

CURRENT CONDITION OF ACIDITY AND

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive (CL <

50 meq/m2/yr)

Moderately Sensitive (CL = 

51-100 meq/m2/yr)

Low Sensitivity

(CL = 101-200 meq/m2/yr)

Not Sensitive 

(CL > 201 meq/m2/yr)

041503050407

NY767L (CL = 51) NY529L 

(CL = 73) NY528L (CL = 82) 

NY769L (CL = 99)

NY768L (CL = 114)

041503050601 NY004L (CL = 168)
September, 2010 A-6 Draft – Do Not Quote or Cite

Table A-2. Watersheds (HUC 12) and streams in the Shenandoah Case Study Area that were used in the comparison of 

aquatic and terrestrial critical acid loads. Stream IDs and associated aquatic critical acid loads (CL) in meq/m2/yr, based on an 

ANC of 50 μeq/L, are indicated in each cell and are from the REA REPORT (US EPA, 2009).

HUC

CURRENT CONDITION OF ACIDITY AND SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive (CL 

< 50 meq/m2/yr)

Moderately Sensitive (CL 

= 51-100 meq/m2/yr)

Low Sensitivity (CL 

= 101-200 meq/m2/yr)

Not Sensitive 

(CL > 201 meq/m2/yr)

020700050401 VT37 (CL = 26)

020700050502 VT57 (CL = 39)

020700050703 VT40 (CL = 13)

020700050705 VT35 (CL = 37) 

VT36 (CL = 24)

020700050801 DR01 (CL = 33) 

WOR1 (CL = 43)

020700050803 VT53 (CL = 40)

020700060101 VT54 (CL = 69)

020801030301 VT60 (CL = 198) VT61 (CL = 231)

020801030402 VT62 (CL = 68)

020802010702 VT10 (CL = 15)

020802010703 VT11 (CL = 14) VT12 (C = 75)

020802010801 VT14 (CL = 14) 

VT15 (CL = 13)

020802010803 VT16 (CL = 20)

020802020102 VT38 (CL = 66)

020802020401 VT41 (CL = 15)

020802030601 VT46 (CL = 52)
January 14, 2011 A-7

Terrestrial critical acid loads were calculated for each of the 16 watersheds using the 

simple mass balance method (UNECE, 2004) and data sources outlined in the REA Report (US 

EPA, 2009), and Bc/Al soil solution indicator values of 1.2 and 10.0. Briefly, average values for 

base cation deposition (calcium, potassium, magnesium and sodium), chloride deposition, and 

annual runoff (m3/ha/yr) were determined for each watershed (Table A-3). The Kgibb constant 

(m6eq

2

) was determined by the average percent organic matter in the soil (Table A-4), and N 

immobilization in the soil was set to the constant value of 42.86 eq/ha/yr (McNulty et al., 2007). 

It was assumed that active harvesting did not occur in each of the watersheds. Therefore base 

cation (calcium, magnesium and potassium) and nitrogen uptake were 0 eq/ha/yr (UNECE, 

2004). Similarly, it was assumed that the majority of each watershed consisted of upland sites. 

Therefore, denitrification losses were assumed to be 0 eq/ha/yr (McNulty et al., 2007). Base 

cation weathering was estimated using the clay substrate model (equations 1-3) (McNulty et al., 

2007). 

Acid Substrate: ( ) ( ( ) ) 2

e BC = 56.7 × %clay − 0.32× %clay (1)

Intermediate Substrate: ( ) ( ( ) ) 2

e BC = 500 + 53.6× %clay − 0.18× %clay (2)

Basic Substrate: BC 500 (59.2 %clay) e = + × (3)

where

BCe = empirical soil base cation (Ca2+ + K+ + Mg2+ + Na+) weathering rate 

(eq/ha/yr)

% clay = the percentage of clay within the top 50cm of the soil.

The U.S. Department of Agriculture- Natural Resources Conservation Service (USDA-NRCS) 

SSURGO soils database (USDA-NRCS, 2008)) and state-level geology (U.S. Geological Survey 

(USGS) state-level integrated map database for the United States (USGS, 2009)) were used to 

determine parent material acidity classification. Parent material acidity was determined for each 

SSURGO polygon within each watershed using the criteria outlined in the REA Report (US 

EPA, 2009), and the contributions of base cations from the weathering of acid, intermediate and 

basic substrates (eq/ha/yr) were determined by a weighted average based on the proportion of 

area occupied by each parent material acidity class. Rooting depth was assumed to be 50 cm and 

masses of calcium, magnesium, potassium, sodium and nitrogen were converted to eq/ha/yr units 

based on molar charge equivalents. Unless indicated otherwise, the units used in the calculation 
January 14, 2011 A-8

of critical acid loads were eq/ha/yr. The estimated terrestrial critical loads for the 16 watersheds 

in the Adirondacks and Shenandoah Case Study Areas are presented in Table A-5.

Table A-3. Name, type and source of data used in the simple mass balance estimates of 

terrestrial critical acid loads for the watersheds in the Adirondacks and Shenandoah Case 

Study Areas.

DATA NAME TYPE SOURCE 

Base cation (Ca2+, 

Mg2+, Na+

, K+) 

deposition— wet 

CMAQ/ 

NADP

GIS 

datalayers

Provided by U.S. Environmental 

Protection Agency (EPA)/NADP, 

2003a,c, d, e

Chloride (Cl-) 

deposition— wet

NADP GIS 

datalayer

NADP, 2003b

Runoff Annual run-off 

(1: 7,500,000 

scale)

GIS 

datalayer

Gebert et al., 1987

Soil horizon depth SSURGO GIS 

datalayer

USDA-NRCS, 2008

Percentage of clay 

by soil horizon

SSURGO GIS 

datalayer

USDA-NRCS, 2008

Percentage of 

organic matter by 

soil horizon

SSURGO GIS 

datalayer

USDA-NRCS, 2008

Soil parent 

material

SSURGO GIS 

datalayer

USDA-NRCS, 2008

State-level 

bedrock geology

State 

Geological 

Map 

Compilation

GIS 

datalayer

USGS, 2009

Note: CMAQ = Community Multiscale Air Quality Model; NADP = National Atmospheric 

Deposition Program; GIS = Geographic Information System; SSURGO = Soil Survey 

Geographic Database
January 14, 2011 A-9

Table A-4. Gibbsite equilibrium (Kgibb) constant determined by percentage of soil organic 

matter (modified from McNulty et al. 2007).

Soil Type Layer

Organic 

Matter % Kgibb (m6

/eq2)

Mineral soils: C layer <5 950

Soils with low organic matter: B/C layers 5 to15 300

Soils with some organic material: A/E 

layers 15 to 70 100

Peaty and organic soils: organic layers >70 9.5
January 14, 2011 A-10

Table A-5. Terrestrial critical acid loads (in eq/ha/yr) for the watersheds in the 

Adirondacks and Shenandoah Case Study Areas.

Case Study 

Area

HUC12

Terrestrial Critical Acid Load 

(eq/ha/yr)

Bc/Al = 1.2 Bc/Al = 10.0

Adirondacks 020100010103 2045 1134

Adirondacks 020100040203 1316 712

Adirondacks 020100080304 1329 731

Adirondacks 020100081602 1670 922

Adirondacks 020200020101 1484 819

Adirondacks 020200020704 1707 935

Adirondacks 020200040805 1770 951

Adirondacks 041501011001 1770 955

Adirondacks 041503020801 1664 912

Adirondacks 041503040102 1627 880

Adirondacks 041503040204 1436 786

Adirondacks 041503050103 1774 957

Adirondacks 041503050104 1794 968

Adirondacks 041503050302 1754 947

Adirondacks 041503050407 1447 789

Adirondacks 041503050601 1203 656

Shenandoah 020700050401 1440 802

Shenandoah 020700050502 1560 871

Shenandoah 020700050703 1762 979

Shenandoah 020700050705 1852 1032

Shenandoah 020700050801 1799 1003

Shenandoah 020700050803 1975 1102

Shenandoah 020700060101 1638 914

Shenandoah 020801030301 1511 843

Shenandoah 020801030402 1393 776

Shenandoah 020802010702 1603 890

Shenandoah 020802010703 1642 912

Shenandoah 020802010801 1635 909

Shenandoah 020802010803 1573 876

Shenandoah 020802020102 1519 845

Shenandoah 020802020401 1264 703

Shenandoah 020802030601 1660 918
January 14, 2011 A-11

Maps were generated to compare the aquatic and terrestrial critical acid loads in each 

watershed to determine which estimate provided the greatest protection against acidifying 

nitrogen and sulfur deposition. In each watershed, the terrestrial critical load estimate was 

compared against each aquatic critical load, and the load with the lowest value was set to 

represent the most sensitive component in the watershed. All critical load estimates were 

converted to eq/ha/yr for the comparisons.

Maps indicating and comparing the sensitivities of the terrestrial and aquatic critical loads 

to nitrogen and sulfur deposition in each watershed of the Adirondacks and Shenandoah Case 

Study Areas are presented in Figures A-1 to A-4 and Tables A-6 to A-9. 

Results

In the Adirondacks Case Study Area, 7 of the 16 watersheds had terrestrial critical acid 

loads (based on a Bc/Al of 10.0) that were lower and therefore more sensitive to acidification 

than all the lakes in the watershed. However, when the terrestrial critical loads were calculated 

with a Bc/Al soil solution ratio of 1.2, only 5 of the 16 watersheds were protected by a terrestrial 

critical load that was lower than the aquatic critical loads of the lakes. Three watersheds in the 

Adriondacks Case Study Area had terrestrial critical loads (based on a Bc/Al of 10.0) that were 

lower and higher than the critical loads for the lakes in the watershed, and one watershed had a 

similar mixture of aquatic versus terrestrial acid load protections for terrestrial critical loads 

estimated with a Bc/Al of 1.2. In general, a main trend in the Adirondacks Case Study Area was 

that watersheds with “Highly Sensitive” and “Moderately Sensitive” lakes were more protected 

by aquatic than terrestrial critical acid loads, while the watersheds with “Low Sensitivity” and 

“Not Sensitive” lakes were more protected by terrestrial critical acid loads.

Similar trends were found in the Shenandoah Case Study Area. However, there was little 

distinction between terrestrial acid loads that were calculated with a Bc/Al of 10.0 versus 1.2. 

Terrestrial critical acid loads offered a higher level of protection than did the stream aquatic 

critical loads in only one watershed. The two streams in this watershed had “Low Sensitivity” or 

were “Not Sensitive” to acidifying nitrogen and sulfur deposition. The 15 watersheds that had 

streams with aquatic critical loads lower and more protective than the terrestrial critical loads

were all “Highly Sensitive” or “Moderately Sensitive” to acidifying nitrogen and sulfur 

deposition. 

 
January 14, 2011 A-12

In summary, a comparison of the terrestrial and aquatic critical acid loads for watersheds 

in the Adirondacks and Shenandoah Case Study Areas indicated that, in general, the aquatic 

critical acid loads offered greater protection to the watersheds than did the terrestrial critical 

loads. In situations where the terrestrial loads were more protective, the lakes or streams in the 

watershed were rated as having “Low Sensitivity” or “Not Sensitive” to acidifying nitrogen and 

sulfur deposition. Conversely, when the water bodies were more sensitive to deposition 

(“Highly Sensitive” or “Moderately Sensitive”), the aquatic critical acid loads consistently 

provided a greater level of protection against acidifying nitrogen and sulfur deposition in the 

watershed.
January 14, 2011 A-13

Figure A-1. Comparison of aquatic and terrestrial critical loads of acidification (in 

eq/ha/yr) in the 16 watersheds of the Adirondacks Case Study Area, based on an ANC of 50 

eq/L for the aquatic loads and a Bc/Al of 10.0 for the terrestrial loads. Colored circles 

indicate the locations of the waters bodies within each watershed. Green circles indicate lakes 

with critical load values less than the terrestrial critical load for the same watershed. Red circles 

indicate a condition where the terrestrial critical load is lower than the lake critical load.
January 14, 2011 A-14

Figure A-2. Comparison of aquatic and terrestrial critical loads of acidification (in 

eq/ha/yr) in the 16 watersheds of the Adirondacks Case Study Area, based on an ANC of 50 

eq/L for the aquatic loads and a Bc/Al of 1.2 for the terrestrial loads. Colored circles 

indicate the locations of the waters bodies within each watershed. Green circles indicate lakes 

with critical load values less than the terrestrial critical load for the same watershed. Red circles 

indicate a condition where the terrestrial critical load is lower than the lake critical load.
January 14, 2011 A-15

Figure A-3. Comparison of aquatic and terrestrial critical loads of acidification (in 

eq/ha/yr) in the 16 watersheds of the Shenandoah Case Study Area, based on an ANC of 50 

eq/L for the aquatic loads and a Bc/Al of 10.0 for the terrestrial loads. Colored circles 

indicate the locations of the waters bodies within each watershed. Green circles indicate streams 

with critical load values less than the terrestrial critical load for the same watershed. Red circles 

indicate a condition where the terrestrial critical load is lower than the stream critical load.
January 14, 2011 A-16

Figure A-4. Comparison of aquatic and terrestrial critical loads of acidification (in 

eq/ha/yr) in the 16 watersheds of the Shenandoah Case Study Area, based on an ANC of 50 

eq/L for the aquatic loads and a Bc/Al of 1.2 for the terrestrial loads. Colored circles 

indicate the locations of the waters bodies within each watershed. Green circles indicate streams 

with critical load values less than the terrestrial critical load for the same watershed. Red circles 

indicate a condition where the terrestrial critical load is lower than the stream critical load.
January 14, 2011 A-17

Table A-6. Relative sensitivities of aquatic versus terrestrial critical loads in the 29 lakes and 16 watersheds of the Adirondack 

Case Study Area (based on an ANC of 50 μeq/L for the aquatic loads and a Bc/Al of 10.0 for the terrestrial critical loads and 

common unit of eq/ha/yr) to acidifying nitrogen and sulfur deposition. Lake IDs are indicated in each cell and are from the REA 

REPORT (US EPA, 2009). Green text indicates lakes where the aquatic critical load was less than the terrestrial critical load value for 

the watershed. Red text indicates a condition where the terrestrial critical load for the watershed was lower than the aquatic critical 

load for the lake within the same watershed.

HUC

CURRENT CONDITION OF ACIDITY AND 

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive 

(CL < 50 

meq/m2/yr)

Moderately Sensitive 

(CL = 51-100 

meq/m2/yr)

Low Sensitivity 

(CL = 101-200 

meq/m2/yr)

Not Sensitive 

(CL > 201 

meq/m2/yr)

020100010103 NY534L 

020100040203 1A2-028O 

NY310L NY308L 

020100080304 NY312L 

NY313L

020100081602 NY500L

020200020101 NY013L

020200020704 NY536L 

020200040805 1A2-078O NY292L

041501011001 NY029L

041503020801 NY783L 

041503040102 NY284L 

NY285L

041503040204 NY278L

041503050103 1A1-089O 050215AO 

NY793L

041503050104 NY290L 
January 14, 2011 A-18

HUC

CURRENT CONDITION OF ACIDITY AND 

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive 

(CL < 50 

meq/m2/yr)

Moderately Sensitive 

(CL = 51-100 

meq/m2/yr)

Low Sensitivity 

(CL = 101-200 

meq/m2/yr)

Not Sensitive 

(CL > 201 

meq/m2/yr)

NY289L

041503050302 NY008L 

NY007L 

041503050407

NY767L 

NY529L 

NY528L 

NY769L

NY768L 

041503050601 NY004L 
January 14, 2011 A-19

Table A-7. Relative sensitivities of aquatic versus terrestrial critical loads in the 29 lakes and 16 watersheds of the Adirondack 

Case Study Area (based on an ANC of 50 μeq/L for the aquatic loads and a Bc/Al of 1.2 for the terrestrial critical loads and 

common unit of eq/ha/yr) to acidifying nitrogen and sulfur deposition. Lake IDs are indicated in each cell and are from the REA 

REPORT (US EPA, 2009). Green text indicates lakes where the aquatic critical load was less than the terrestrial critical load value for 

the watershed. Red text indicates a condition where the terrestrial critical load for the watershed was lower than the aquatic critical 

load for the lake within the same watershed.

HUC

CURRENT CONDITION OF ACIDITY AND 

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive 

(CL < 50 

meq/m2/yr)

Moderately Sensitive 

(CL = 51-100 

meq/m2/yr)

Low Sensitivity 

(CL = 101-200 

meq/m2/yr)

Not Sensitive 

(CL > 201 

meq/m2/yr)

020100010103 NY534L 

020100040203 1A2-028O 

NY310L NY308L 

020100080304 NY312L 

NY313L 

020100081602 NY500L 

020200020101 NY013L 

020200020704 NY536L 

020200040805 1A2-078O NY292L 

041501011001 NY029L 

041503020801 NY783L 

041503040102 NY284L 

NY285L 

041503040204 NY278L 

041503050103 1A1-089O 050215AO 

NY793L 
January 14, 2011 A-20

HUC

CURRENT CONDITION OF ACIDITY AND 

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive 

(CL < 50 

meq/m2/yr)

Moderately Sensitive 

(CL = 51-100 

meq/m2/yr)

Low Sensitivity 

(CL = 101-200 

meq/m2/yr)

Not Sensitive 

(CL > 201 

meq/m2/yr)

041503050104 NY290L 

NY289L 

041503050302 NY008L 

NY007L 

041503050407

NY767L 

NY529L 

NY528L 

NY769L 

NY768L 

041503050601 NY004L 
January 14, 2011 A-21

Table A-8. Relative sensitivities of aquatic versus terrestrial critical loads in the 20 streams and 16 watersheds of the 

Shenandoah Case Study Area (based on an ANC of 50 μeq/L for the aquatic loads and a Bc/Al of 10.0 for the terrestrial 

critical loads and common unit of eq/ha/yr) to acidifying nitrogen and sulfur deposition. Stream IDs are indicated in each cell 

and are from the REA REPORT (US EPA, 2009). Green text indicates streams where the aquatic critical load was less than the 

terrestrial critical load value for the watershed. Red text indicates a condition where the terrestrial critical load for the watershed was 

lower than the aquatic critical load for the stream within the same watershed.

HUC

CURRENT CONDITION OF ACIDITY AND 

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive 

(CL < 50 

meq/m2/yr)

Moderately Sensitive 

(CL = 51-100 

meq/m2/yr)

Low Sensitivity 

(CL = 101-200 

meq/m2/yr)

Not Sensitive 

(CL > 201 

meq/m2/yr)

020700050401 VT37

020700050502 VT57 

020700050703 VT40 

020700050705 VT35 

VT36 

020700050801 DR01 

WOR1 

020700050803 VT53 

020700060101 VT54 

020801030301 VT60 VT61

020801030402 VT62 

020802010702 VT10 

020802010703 VT11 VT12 

020802010801 VT14 

VT15 
January 14, 2011 A-22

HUC

CURRENT CONDITION OF ACIDITY AND 

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive 

(CL < 50 

meq/m2/yr)

Moderately Sensitive 

(CL = 51-100 

meq/m2/yr)

Low Sensitivity 

(CL = 101-200 

meq/m2/yr)

Not Sensitive 

(CL > 201 

meq/m2/yr)

020802010803 VT16

020802020102 VT38 

020802020401 VT41 

020802030601 VT46 
January 14, 2011 A-23

Table A-9. Relative sensitivities of aquatic versus terrestrial critical loads in the 20 streams and 16 watersheds of the 

Shenandoah Case Study Area (based on an ANC of 50 μeq/L for the aquatic loads and a Bc/Al of 1.2 for the terrestrial critical 

loads and common unit of eq/ha/yr) to acidifying nitrogen and sulfur deposition. Stream IDs are indicated in each cell and are 

from the REA REPORT (US EPA, 2009). Green text indicates streams where aquatic critical loads were less than the terrestrial 

critical load value for the watershed. Red text indicates a condition where the terrestrial critical load for the watershed was lower than 

the aquatic critical load for the stream within the same watershed.

HUC

CURRENT CONDITION OF ACIDITY AND 

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive 

(CL < 50 

meq/m2/yr)

Moderately Sensitive 

(CL = 51-100 

meq/m2/yr)

Low Sensitivity 

(CL = 101-200 

meq/m2/yr)

Not Sensitive 

(CL > 201 

meq/m2/yr)

020700050401 VT37

020700050502 VT57 

020700050703 VT40 

020700050705 VT35 

VT36 

020700050801 DR01 

WOR1 

020700050803 VT53 

020700060101 VT54 

020801030301 VT60 VT61

020801030402 VT62 

020802010702 VT10 

020802010703 VT11 VT12 

020802010801 VT14 

VT15 
January 14, 2011 A-24

HUC

CURRENT CONDITION OF ACIDITY AND 

SENSITIVITY TO ACIDIFICATION CATEGORY

Highly Sensitive 

(CL < 50 

meq/m2/yr)

Moderately Sensitive 

(CL = 51-100 

meq/m2/yr)

Low Sensitivity 

(CL = 101-200 

meq/m2/yr)

Not Sensitive 

(CL > 201 

meq/m2/yr)

020802010803 VT16

020802020102 VT38 

020802020401 VT41 

020802030601 VT46 
January 14, 2011 A-25

REFERENCES 

Gebert, W.A., D.J. Graczyk, and W.R. Krug, 1987. Average Annual Runoff in the United States, 

1951-80: U.S. Geological Survey Hydrologic Investigations Atlas HA-710, Scale 

1:7,500,000. GIS datalayer. U.S. Department of Interior, U.S. Geological Survey, 

Madison, WI. Available at: http://water.usgs.gov/GIS/dsdl/runoff.e00.gz (accessed 

September 9, 2009).

McNulty, S.G., E.C. Cohen, H. Li, and J.A. Moore-Myers. 2007. Estimates of critical acid loads 

and exceedences for forest soils across the conterminous United States. Environmental 

Pollution 149:281–292.

NADP (National Atmospheric Deposition Program). 2003a. Annual Calcium Wet Deposition, 

2002. GIS datalayer. National Atmospheric Deposition Program, Illinois State Water 

Survey, Champaign, IL. Available at http://nadp.sws.uiuc.edu/maps/2002.

NADP (National Atmospheric Deposition Program). 2003b. Annual Chloride Wet Deposition, 

2002. GIS datalayer. National Atmospheric Deposition Program, Illinois State Water 

Survey, Champaign, IL. Available at http://nadp.sws.uiuc.edu/maps/2002.

NADP (National Atmospheric Deposition Program). 2003c. Annual Magnesium Wet Deposition, 

2002. GIS datalayer. National Atmospheric Deposition Program, Illinois State Water 

Survey, Champaign, IL. Available at http://nadp.sws.uiuc.edu/maps/2002.

NADP (National Atmospheric Deposition Program). 2003d. Annual Potassium Wet Deposition, 

2002. GIS datalayer. National Atmospheric Deposition Program, Illinois State Water 

Survey, Champaign, IL. Available at http://nadp.sws.uiuc.edu/maps/2002.

NADP (National Atmospheric Deposition Program). 2003e. Annual Sodium Wet Deposition, 

2002. GIS datalayer. National Atmospheric Deposition Program, Illinois State Water

UNECE (United Nations Economic Commission for Europe). 2004. Manual on Methodologies 

and Criteria for Modeling and Mapping Critical Loads and Levels and Air Pollution 

Effects, Risks, and Trends. Convention on Long-Range Transboundary Air Pollution, 

Geneva Switzerland. Available at http://www.icpmapping.org (accessed August 16, 

2006).

USDA-NRCS (United States Department of Agriculture-Natural Resources Conservation 

Service). 2008. Soil Survey Geographic (SSURGO) Database. GIS datalayer. U.S. 

Department of Agriculture, Natural Resources Conservation Service, Washington, DC. 

Available at http://datagateway.nrcs.usda.gov. 

US EPA (United States Environmental Protection Agency). 2009. Risk and Exposure Assessment 

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

Nitrogen and Sulfur. Final. U.S. Environmental Protection Agency, Office of Research 

and Development, National Center for Environmental Assessment, Research Triangle 

Park, NC. September.

USGS (U.S. Geological Survey). 2009. State Geological Map Compilation. U.S. Department of 

the Interior, U.S. Geological Survey, Reston, VA. Available at: 

http://tin.er.usgs.gov/geology/state (accessed January 28, 2009).
January 14, 2011 B-1

Appendix B

Methodologies and assumptions used in steady state ecosystem modeling

The critical load of acidity for lakes or streams was derived from present-day water 

chemistry using a combination of steady-state models. Both the Steady-State Water Chemistry 

(SSWC) model and First-order Acidity Balance model (FAB) are based on the principle that 

excess base-cation production within a catchment area should be equal to or greater than the acid 

anion input, thereby maintaining the ANC above a preselected level (Reynolds and Norris, 2001; 

Posch et al. 1997). These models assume steady-state conditions and assume that all SO4

2– in 

runoff originates from sea salt spray and anthropogenic deposition. Given a critical ANC 

protection level, the critical load of acidity is simply the input flux of acid anions from 

atmospheric deposition (i.e., natural and anthropogenic) subtracted from the natural (i.e., 

preindustrial) inputs of base cations in the surface water. (REA 2009 Appendix 4)

Technical summary of critical loads modeling in the REA 

Atmospheric deposition of NOx and SOx contributes to acidification in aquatic 

ecosystems through the input of acid anions, such as NO3- and SO4

2–

. The acid balance of 

headwater lakes and streams is controlled by the level of this acidifying deposition of NO3- and 

SO4

2– and a series of biogeochemical processes that produce and consume acidity in watersheds. 

The biotic integrity of freshwater ecosystems is then a function of the acid-base balance, and the 

resulting acidity-related stress on the biota that occupy the water. The calculated ANC of the 

surface waters is a measure of the acid-base balance:

ANC = [BC]* - [AN]* (1)

where [BC]* and [AN]* are the sum of base cations and acid anions (NO3- and SO4

2–

), 

respectively. Equation (1) forms the basis of the linkage between deposition and surface water 

acidic condition and the modeling approach used. Given some “target” ANC concentration 

[ANClimit] that protects biological integrity, the amount of deposition of acid anions [AN] or

depositional load of acidity CL(A) is simply the input flux of acid anions from atmospheric 

deposition that result in a surface water ANC concentration equal to the [ANClimit] when 

balanced by the sustainable flux of base cations input and the sinks of nitrogen and sulfur in the 

lake and watershed catchment.
January 14, 2011 B-2

Critical loads for nitrogen and sulfur (CL(N) + CL(S) ) or critical load of acidity CL(A) were 

calculated for each waterbody from the principle that the acid load should not exceed the 

nonmarine, nonanthropogenic base cation input and sources and sinks in the catchment minus a 

neutralizing to protect selected biota from being damaged:

CL(N) + CL(S) or CL(A) = BC*dep + BCw – Bcu – AN - ANClimit (2)

Where,

BC*dep = (BC*=Ca*+Mg*+K*+Na*), nonanthropogenic deposition flux of base cations, 

BCw = the average weathering flux producing base cations, 

Bcu (Bc=Ca*+Mg*+K*) = the net long-term average uptake flux of base cations in the biomass 

(i.e., the annual average removal of base cations due to harvesting), 

AN = the net long-term average uptake, denitrification, and immobilization of nitrogen anions 

(e.g. NO3-

) and uptake of SO4

2–

, and

ANClimit = the lowest ANC-flux that protects the biological communities.

Since the average flux of base cations weathered in a catchment and reaching the lake or 

streams is difficult to measure or compute from available information, the average flux of base 

cations and the resulting critical load estimation were derived from water quality data (Henriksen 

and Posch, 2001; Henriksen et al., 1992; Sverdrup et al., 1990). Weighted annual mean water 

chemistry values were used to estimate average base cation fluxes, which were calculated from 

water chemistry data collected from the Temporally Integrated Monitoring of Ecosystems 

(TIME)/Long-Term Monitoring (LTM) monitoring networks, that include Adirondack Longterm 

Monitoring (ALTM), Virginia Trout Stream Sensitivity Study (VTSSS), and the Shenandoah 

Watershed Study (SWAS), and Environmental Monitoring and Assessment Program (EMAP) 

(see REA Section 4.1.2.1 of Chapter 4).

The preacidification nonmarine flux of base cations for each lake or stream, BC*0, is 

BC*0 = BC*dep + BCw - Bcu (3)
January 14, 2011 B-3

Thus, critical load for acidity can be rewritten as

CL(N) + CL(S) = BC*0 - AN - ANClimit = Q([BC*]0 - [AN] - [ANC]limit), (4)

where the second identity expresses the critical load for acidity in terms of catchment runoff (Q) 

m/yr and concentration ([x] = X/Q). The sink of nitrogen in the watershed is equal to the uptake 

(Nupt), immobilization (Nimm), and denitrification (Nden) of nitrogen in the catchment. Thus, 

critical load for acidity can be rewritten as 

CL(N) + CL(S) = {fNupt + (1 − r)(Nimm + Nden)} + ( [BC]0* − [ANClimit])Q (5)

where f and r are dimensionless parameters that define the fraction of forest cover in the 

catchment and the lake/catchment ratio, respectively. The in-lake retention of nitrogen and sulfur 

was assumed to be negligible. 

Equation (5) described the FAB model that was applied when sufficient data was 

available to estimate the uptake, immobilization, and denitrification of nitrogen and the 

neutralization of acid anions (e.g. NO3-) in the catchment. In the case were data were not 

available, the contribution of nitrogen anions to acidification was assumed to be equal to the 

nitrogen leaching rate (Nleach) into the surface water. The flux of acid anions in the surface 

water is assumed to represent the amount of nitrogen that is not retained by the catchment, which 

is determined from the sum of measured concentration of NO3- and ammonia in the stream 

chemistry. This case describes the SSWC model and the critical load for acidity is

CL(A) = Q([BC*]0 – [ANC]limit) (6)

where the contribution of acid anions is considered as part of the exceedances calculation (see 

REA App 4 Section 1.2.5). For the assessment of current condition in both case study areas in 

the REA, the critical load calculation described in Equation (6) was used for most lakes and 

streams. The lack of sufficient data for quantifying nitrogen denitrification and immobilization 

prohibited the wide use of the FAB model. In addition, given the uncertainty in quantifying 
January 14, 2011 B-4

nitrogen denitrification and immobilization, the flux of nitrogen anions in the surface water was 

assumed to more accurately reflect the contribution of NO3- to acidification. Several major 

assumptions are made: (1) steady-state conditions exist, (2) the effect of nutrient cycling between 

plants and soil is negligible, (3) there are no significant nitrogen inputs from sources other than 

atmospheric deposition, (4) ammonium leaching is negligible because any inputs are either taken 

up by biota or adsorbed onto soils or nitrate compounds, and (5) longterm sinks of sulfate in the 

catchment soils are negligible.

The pre-industrial concentration of base cations [BC]0

*

effectively set the long term 

capacity of the catchment to neutralize acidic deposition, because it represents the only source of 

base cation input that is sustainable over the long-term. Input of cations from weathering is 

assumed to be a relatively constant process driven largely by the reaction of CO2 with primary 

minerals in the soils and bedrock. Base cations are removed by leaching from the soil solution 

through surface water runoff. At a steady-state, the leaching rate of base cation occurs at lesser 

or greater rates than the weathering supply. However, base cation leaching is not at steady-state 

today because anthropogenic acid deposition actually increases the leaching of base cations 

through ion-exchange within catchment soils. Soils contain a store of adsorbed base cations, as 

measured as base saturation, which are derived from weathering, but have accumulated in the 

soil over millennia, until eventually a steady-state is achieved, whereby the supply of base 

cations from weathering was in approximate equilibrium with the removal of base cations by 

rainwater, itself in equilibrium with the atmosphere. For this reason, [BC]0

* cannot be derived 

from measured data in runoff, but derived from a empirical relationships (i.e., pre-industrial base 

cation concentration).

Pre-industrial Base Cation Concentration

The pre-industrial base cation concentration is the sum of weathering ([BC*

w]) supply 

plus base cation deposition ([BC*

dep]), if it is assumed that base cation deposition has not 

significantly changed since pre-industrial times, minus long-term average uptake of base cations 

in the biomass (i.e., the annual average removal of base cations due to harvesting):

[BC]0

* = [BCw] + [BC*

dep] – [BCu] (7)
January 14, 2011 B-5

The first step is to quantify the proportion of measured base cation leaching which is 

derived from transient ion-exchange processes (BCex) and is proportional to the load of acid 

anions. This proposition is represented by the term ‘F’, calculated according to the methodology 

of Brakke et al. (1990):

F = sin (π/2 . [BC*]t/[S] (8)

where [BC*]t is the measured non-marine base cation concentration and S is a constant which 

varies regionally according to geology, but from empirical studies is taken as 400 μeq/L 

(Harriman and Christie, 1995). This constant determines the measured non-marine base cation 

concentration which represents a catchment likely to be unaffected by acid deposition; [BC*

]t = 

S, F =1 and base cation leaching is increased by exactly the value of the acid anion load, 

resulting in no change in the ANC of runoff. For values of [BC*]t greater then S, F is 1. 

This F value is then used to calculate the pre-industrial base cation concentration 

according to the following equation: 

[BC*]0

* = [BC*

]t – F([AA]t

* - [AA]0* ) (9)

where [AA]0

* is the pre-acidification concentration of non-marine acid anions from weathering 

and natural atmospheric sources and the measured leaching rate of non-marine base cations 

[BC*]t represent the sum of weathering, non-marine deposition and leaching sources. We 

assumed the steady-state concentration of nitrate ([AA]0) was zero ([AA]0

*

= 0). Since [BC*]0

* is 

now known, the maximum depositional load can be calculated. 

An F-factor was used to correct the concentrations and estimate preindustrial base concentrations 

for lakes in the Adirondack Case Study Area (REA 2009). In the case of streams in the 

Shenandoah Case Study Area, the preindustrial base concentrations were derived from the 

MAGIC model as the base cation supply in 1860 (hindcast) because the F-factor approach is 

untested in this region. An F-factor is a ratio of the change in nonmarine base cation 

F-factor
January 14, 2011 B-6

concentration due to changes in strong acid anion concentrations (Henriksen, 1984; Brakke et al., 

1990):

Table B-1 Illustrates SSWC Approach – Environmental Variables

CL(A) = BC*

dep + BCw – Bcu – ANClimit

CL(A) = Q.([BC*]0 – [ANC]limit)

Variable 

Code

Description Source

1 BC*

dep Sum (Ca*+Mg*+K*+Na*), nonanthropogenic 

deposition flux of base cations

Wet NADP and Dry 

CASTNET

2 BCw Average weathering flux of base cations Calculated (5-17)

3 Bcu Sum (Ca+Mg+K), the net long-term average 

uptake flux of base cations in the biomass

USFS-FIA data

4 ANClimit Lowest ANC-flux that protects the biological 

communities

Set

5 Ca* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Ca – (CL × 

0.0213))

Water quality data

6 Mg* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Mg – (CL 

× 0.0669))

Water quality data

7 Na* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Na – (CL × 

0.557))

Water quality data

8 K* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (K – (CL × 

0.0.0206))

Water quality data

9 SO4

* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (SO4 – (CL × 

0.14))

Water quality data

10 CL Surface water concentration (μeq/L) growing 

season average. 

Water quality data

11 SO4

* Surface water concentration (μeq/L) growing

season average. 

Water quality data

12 NO3

* Surface water concentration (μeq/L) growing 

season average. 

Water quality data

13 Q The annual runoff (m/yr) USGS

14 [BC*]0 Preindustrial flux of base cations in surface 

water, corrected for sea salts

Calculated from water 

quality data

15 [SO4

*

]0 Preindustrial flux of sulfate in surface water, 

corrected for sea salts

Estimated 

16 [NO3

*

]0 Preindustrial flux of nitrate, corrected for sea 

salts

Equal to 0
January 14, 2011 B-7

F

=([BC*]t - [BC*]0)/([SO4*]t - [SO4*]0 + [NO3*]t - [NO3*]0), (10)

where the subscripts t and 0 refer to present and preacidification conditions, respectively. If F=1, 

all incoming protons are neutralized in the catchment (only soil acidification); at F=0, none of 

the incoming protons are neutralized in the catchment (only water acidification). The F-factor 

was estimated empirically to be in the range 0.2 to 0.4, based on the analysis of historical data 

from Norway, Sweden, the United States, and Canada (Henriksen, 1984). Brakke et al. (1990) 

later suggested that the F-factor should be a function of the base cation concentration:

F = sin (π/2 Q[BC*]t/[S]) (11)

Where

Q = the annual runoff (m/yr),

[S] = the base cation concentration at which F=1; and 

for [BC*]t>[S] F is set to 1. 

For Norway [S] has been set to 400 milliequivalents per cubic meter (meq/m3)(circa.8 

mg Ca/L) (Brakke et al., 1990). The preacidification SO42- concentration in lakes, [SO4*]0, is 

assumed to consist of a constant atmospheric contribution and a geologic contribution 

proportional to the concentration of base cations (Brakke et al., 1989). The preacidification 

SO42- concentration in lakes, [SO4*]0 was estimated from the relationship between [SO42-]o* 

and [BC]t* based on work completed by Henriksen et al., 2002 as described by the following 

equation:

[SO42-]o* = 15 + 0.16 * [BC]t* (12)

Tables B-1 and B-2 list the factors used in the SSWC and FAB approaches.

17 F Calculated factor Fix values
January 14, 2011 B-8

Table B-2 FAB Approach – Environmental Variables

DL(N) + DL(S) = {fNupt + (1 − r)(Nimm + Nden) + (Nret + Sret)} + ( [BC]0

* − [ANClimit])Q

Variable 

Code

Description Source

1 Ndepo Total N deposition NADP/CMAQ

2 ANClimit Lowest ANC-flux that protects the biological 

communities

Set

3 [BC*]0 Preindustrial flux of base cations in surface 

water, corrected for sea salt

Calculated from 

water quality data

4 Ca* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Ca – (CL × 

0.0213))

Water quality data

5 Mg* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Mg – (CL × 

0.0669))

Water quality data

6 Na* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (Na – (CL × 

0.557))

Water quality data

7 K* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (K – (CL × 

0.0.0206))

Water quality data

8 SO4

* Sea Salt corrected Surface water concentration 

(μeq/L) growing season average. (SO4 – (CL × 

0.14))

Water quality data

9 CL Surface water concentration (μeq/L) growing 

season average. 

Water quality data

10 SO4

* Surface water concentration (μeq/L) growing 

season average. 

Water quality data

11 NO3

* Surface water concentration (μeq/L) growing 

season average. 

Water quality data

12 Q The annual runoff (m/yr) USGS

13 f f is a dimensionless parameter that define the 

fraction of forest cover in the catchment 

14 r r is a dimensionless parameter that define the 

lake/catchment ratio

14 Nret The in-lake retention of nitrogen Estimated 

15 Sret The in-lake retention of sulfur Estimated

16 Nupt The net long-term average uptake flux of N in 

the biomass

USFS-FIA data

17 Nimm Immobilization of N in the soils Estimated fix value

18 Nden Denitrification Estimated fix value

19 Lake Size Lake size (ha) DLMs

20 WSH Watershed area (ha) Calculated 
January 14, 2011 B-9

The MAGIC model (Cosby et al., 1985a; 1985b; 1985c) is a mathematical model (a 

lumped-parameter model) of soil and surface water acidification in response to atmospheric 

deposition based on process-level information about acidification. A process model, such as 

MAGIC, characterizes acidification into (1) a section in which the concentrations of major ions 

are assumed to be governed by simultaneous reactions involving SO4

2- adsorption, cation 

exchange, dissolution-precipitation- speciation of aluminum, and dissolution-speciation of 

inorganic carbon; and (2) a mass balance section in which the flux of major ions to and from the 

soil is assumed to be controlled by atmospheric inputs, chemical weathering, net uptake and loss 

in biomass and losses to runoff. At the heart of MAGIC is the size of the pool of exchangeable 

base cations in the soil. As the fluxes to and from this pool change over time owing to changes in 

atmospheric deposition, the chemical equilibria between soil and soil solution shift to give 

changes in surface water chemistry. The degree and rate of change of surface water acidity thus 

depend both on flux factors and the inherent characteristics of the affected soils.

Data requirements for MAGIC

There are numerous input data required to run MAGIC making it rather data intensive. 

Atmospheric deposition fluxes for the base cations and strong acid anions are required as inputs 

to the model. These inputs are generally assumed to be uniform over the catchment. The volume 

discharge for the catchment must also be provided to the model. In general, the model is 

implemented using average hydrologic conditions and meteorological conditions in annual 

simulations, i.e., mean annual deposition, precipitation and lake discharge are used to drive the 

model. Values for soil and surface water temperature, partial pressure of carbon dioxide and 

organic acid concentrations must also be provided at the appropriate temporal resolution. 

The aggregated nature of the model requires that it be calibrated to observed data from a 

system before it can be used to examine potential system response. Calibrations are based on 

volume weighted mean annual or seasonal fluxes for a given period of observation. The length of 

the period of observation used for calibration is not arbitrary. Model output will be more reliable 

if the annual flux estimates used in calibration are based on a number of years rather than just 

one year. There is a lot of year-to-year variability in atmospheric deposition and catchment 

runoff. Averaging over a number of years reduces the likelihood that an “outlier” year (very dry, 

etc.) is used to specify the primary data on which model forecasts are based. On the other hand, 
January 14, 2011 B-10

averaging over too long a period may remove important trends in the data that need to be 

simulated by the model. 

The calibration procedure requires that stream water quality, soil chemical and physical 

characteristics, and atmospheric deposition data be available for each catchment. The water 

quality data needed for calibration are the concentrations of the individual base cations (Ca, Mg, 

Na, and K) and acid anions (Cl, SO4

2-

, and NO3

-

) and the pH. The soil data used in the model 

include soil depth and bulk density, soil pH, soil cation-exchange capacity, and exchangeable 

bases in the soil (Ca, Mg, Na, and K). The atmospheric deposition inputs to the model must be 

estimates of total deposition, not just wet deposition. In some instances, direct measurements of 

either atmospheric deposition or soil properties may not be available for a given site with stream 

water data. In these cases, the required data can often be estimated by: (a) assigning soil 

properties based on some landscape classification of the catchment; and (b) assigning deposition 

using model extrapolations from some national or regional atmospheric deposition monitoring 

network. Soil data for model calibration are usually derived as aerially averaged values of soil 

parameters within a catchment. If soils data for a given location are vertically stratified, the soils 

data for the individual soil horizons at that sampling site can be aggregated based on horizon, 

depth, and bulk density to obtain single vertically aggregated values for the site, or the stratified 

data can be used directly in the model.

The steady-state critical load model suggested for use in the NAAQS by the PA could be 

constrained by a quantity of N which would be taken up, immobilized or denitrified by 

ecosystems and used to adjust the quantity of deposition required to meet a specified critical 

load. This term is abbreviated by Neco, and could be derived multiple ways. The first is by taking 

the mean value calculated to represent the long-term amount of N an ecosystem can immobilize 

and denitrify before leaching (i.e., N saturation) that is derived from the FAB model. This 

approach requires the input of multiple ecosystem parameters. Its components are expressed by 

equation (13).

Example of the two ways to calculate NECO

( )( ) eco ret Nimm Nden N = fNupt + N + 1− r + (13)

Where,

Nupt= nitirogen uptake by the catchment,
January 14, 2011 B-11

Nimm= nitrogen immobilization by the catchment soil,

Nden=denitrification of nitrogen in the catchment, 

Nret = in-lake retention of nitrogen,

f =forest cover in the catchment (dimensionless parameter),

r = fraction lake/catchment ratio (dimensionless parameter),

The second approach for estimating Neco is to take the difference between N deposition 

and measured N leaching in a catchment as expressed by equation (14).

Neco = DL N − Nleach ( ) (14)

The site specific values of critical loads can be used to derive such a deposition loading, 

here called the deposition metric, which represents a group or percentage of water bodies that 

reach a specified ANC (or higher) in a given spatial area. For example, if it is desired that all 

water bodies reach a specified ANC, the allowable amount of deposition for all water bodies is 

equal to the lowest critical load of the population of water bodies. Because the deposition metric 

represents a percentage of individual catchments from a population of water bodies, and not an 

individual catchment, the deposition metric is noted by the follow abbreviation DL%ECO. 

The preindustrial concentration of base cations ([BC]0

*

) is calculated to represent 

conditions prior to industrialization (~about 1860). It incorporates the main source of base 

cations to an ecosystem including preindustrial weathering from soil and pre-industrial base 

cation deposition. It is therefore considered one of the governing factors of critical loads. [BC]0

* 

is commonly calculated using one of two approaches: dynamic modeling (i.e. MAGIC) and 

calculation by the F-factor approach (Henriksen and Posch 2001). 

Two methods to calculate pre-industrial base cation weathering: F factor and MAGIC

In this section, critical load estimates obtained from two steady-state approaches were 

compared. The exercise is not intended to provide an assessment of the accuracy of the two 

models, but rather to provide a means for evaluating the relative performance of the two different 

models. The EPA conducted analysis compared steady-state CL values based on Henriksen and 

Posch (2001) F-factor approach and output from the MAGIC model. The primary purpose of the 

F-factor is to obtain estimates of preindustrial surface water base cation concentrations ([BC]0

* ) 

for equation (3). The MAGIC (Cosby et al., 1985) model can also be used to derive preindustrial 
January 14, 2011 B-12

surface water base cation concentrations. MAGIC is a process-based model designed to mimic 

the geochemical effects of mineral weathering, soil cation exchange, and other watershed 

processes. Once the model has been calibrated for a watershed, it can be run to simulate how 

surface water chemistry changes with time and to predict preindustrial cation concentrations 

([BC]0

* ) to be used in the steady-state SSWC CL model in equation (6). 

The comparison of CLs between the F-factor and MAGIC approached was done for two 

regions, streams in Southern Appalachia and lakes in the Adirondack Mountains. For 67 streams 

and 99 lakes, [BC]0

* were determined for both approaches and CLs were calculated using the 

same value of Q. The results are show in Figure B-1. For this analysis, the steady-state MAGIC 

model yielded critical load values that show the same trend for both regions, but were on average 

16 meq S /m2

-yr for the Adirondack Lakes and 5 meq S/m2-yr for Southern Appalachia streams 

higher than those from the SSWC F-factor approach. The two models converge at low critical

loads, but diverge as the buffering potential for watersheds increase. This is particularly the case 

for CL values above 80 meq/m2

-yr for lakes in the Adirondack Mountains. These results are 

consistent with similar comparison of critical loads done by Holdren et al. 1992. Holdren et al 

1992 found that the MAGIC model yielded CL values that were on average 29 meq S /m2-yr 

higher than those from the SSWC model. Holdren et al 1992 also found that as the buffering 

potential for watersheds increased, as indicated by increasing CLs, the results from the two 

models gradually diverge. 
January 14, 2011 B-13

Figure B-1 Relationship between CLs using pre-industrial base cation weathering 

([BC]0

* =BCo) calculated by MAGIC versus the F-factor methods: A) 67 

streams in the Southern Appalachian and B) 99 lakes in the Adirondacks 

Mountains. 

y = 0.9976x - 4.591

R2

 = 0.8453

-100

0

100

200

300

400

-100 0 100 200 300 400

SSWC CL

MAGIC - BCo

meq/m2

/yr

SSWC CL 

F-Factor

meq/m 2/yr

1:1

y = 0.4996x + 19.541

R2

 = 0.7787

-100

0

100

200

300

400

-100 0 100 200 300 400

MAGIC CL

MAGIC BCo

meq/m2

/yr

SSWC CL 

F-Factor BCo

meq/m 2/yr

1:1

A

B
January 14, 2011 B-14

References

Brakke, D.F., A. Henriksen, and S.A. Norton. 1989. Estimated background concentrations of 

sulfate in dilute lakes. Water Resources Bulletin 25(2):247–253.

Brakke, D.F., A. Henriksen, and S.A. Norton. 1990. A variable F-factor to explain changes in 

base cation concentrations as a function of strong acid deposition. International 

Association of Theoretical and Applied Limnology, Proceedings 24:146–149.

Cosby, B.J., R.F. Wright, G.M. Hornberger, and J.N. Galloway. 1985a. Modelling the effects of 

acid deposition: Assessment of a lumped parameter model of soil water and streamwater 

chemistry. Water Resources Research 21:51–63.

Cosby, B.J., R.F. Wright, G.M. Hornberger, and J.N. Galloway. 1985b. Modelling the effects of 

acid deposition: Estimation of long-term water quality responses in a small forested 

catchment. Water Resources Research 21:1591–1601.

Cosby B.J., G.M. Hornberger, J.N. Galloway, and R.F. Wright. 1985c. Time scales of catchment 

acidification: a quantitative model for estimating freshwater acidification. Environ Sci 

Technol, 19, 1144-1149.

Harriman, R. and Christie, A. E. G.: 1995, ‘Estimating Critical Loads for Biota: the Steady-state 

Water Chemistry (Henriksen) Model’, in Critical Loads of Acid Deposition for United 

Kingdom Freshwaters, Critical Loads Advisory Group, Sub-group on Freshwaters, 

Institute of Terrestrial Ecology: Edinburgh, pp. 7–8.

Henriksen, A. 1984. Changes in base cation concentrations due to freshwater acidification. 

International Association of Theoretical and Applied Limnology, Proceedings 22:692–

698.

Henriksen A., P.J. Dillon, and J. Aherne. 2002. Critical loads of acidity to surface waters in 

south-central Ontario, Canada: Regional application of the steady-state water chemistry 

model. Can J Fish Aquat Sci, 59, 1287-1295.

Henriksen, A., and M. Posch. 2001. Steady-state models for calculating critical loads of acidity 

for surface waters. Water, Air, and Soil Pollution: Focus 1:375–398.

Henriksen, A., J. Kämäri, M. Posch, and A. Wilander. 1992. Critical loads of acidity: Nordic 

surface waters. Ambio 21:356–363.

Holdren, G., T. Strickland, P. Shaffer, P. Ryan, P. Ringold, and R. Turner. 1992. Sensitivity of 

Critical Load Estimates for Surface Waters to Model Selection and Regionalization 

Schemes. Journal of Environmental Quality, 22: 279-289.

Posch, M., J. Kämäri, M. Forsius, A. Henriksen, and A. Wilander. 1997. Exceedance of critical 

loads for lakes in Finland, Norway and Sweden: Reduction requirements for acidifying 

nitrogen and sulfur deposition. Environmental Management 21: 291–304.

Reynolds, B., and D.A. Norris. 2001. Freshwater critical loads in Wales. Water, Air, and Soil 

Pollution: Focus 1:495–505.

Sverdrup, H., W. de Vries, and A. Henriksen. 1990. Mapping Critical Loads. Miljörapport 14. 

Nordic Council of Ministers, Copenhagen, Denmark.

US 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. U.S. Environmental Protection Agency, Washington, D.C., EPA-452/R-09-008a.
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Appendix C

Ecoregions, Level III: Description and Summary of Environmental Conditions

This appendix provides descriptions of each of the level III ecoregions as described by Omernik

(U.S. EPA 2010) and used in this Policy Assessment. The level III ecoregions are presented 

grouped by the level II ecoregion in which they are located. Figure C-1 illustrates the location of 

the level II ecoregions across the U.S. The subsequent figures (Figures C-2 through C-21) 

illustrate the level III ecoregions within each level II ecoregion. These maps also indicate the 

extent of ANC and critical load values available for each ecoregion. The general regional 

descriptions are taken from Omernik (U.S. EPA 2010).

Introduction

Figure C-1: Omernik Ecoregions, Levels II and III
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Region 5.2 Mixed Wood Shield

Figure C-2: Region 5.2

Region 5.2.1 Northern Lakes and Forests

The Northern Lakes and Forests is a region of relatively nutrient-poor glacial soils, coniferous 

and northern hardwood forests, undulating till plains, morainal hills, broad lacustrine basins, and 

extensive sandy outwash plains. Soils in this ecoregion are thicker than in those to the north and 

generally lack the arability of soils in adjacent ecoregions to the south. The numerous lakes that 

dot the landscape are clearer and less productive than those in ecoregions to the south.

Region 5.2.2 Northern Minnesota Wetlands

Much of the Northern Minnesota Wetlands is a vast and nearly level marsh that is sparsely 

inhabited by humans and covered by swamp and boreal forest vegetation. Formerly occupied by 

broad glacial lakes, most of the flat terrain in this ecoregion is still covered by standing water.
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Region 5.3 Atlantic Highlands

Figure C-3: Region 5.3

Region 5.3.1 Northeastern Highlands

The Northeastern Highlands cover most of the northern and mountainous parts of New England 

as well as the Adirondacks and higher Catskills in New York. It is a relatively sparsely populated 

region characterized by hills and mountains, a mostly forested land cover, nutrient-poor soils, 

and numerous high-gradient streams and glacial lakes. Forest vegetation is somewhat transitional 

between the boreal regions to the north in Canada and the broadleaf deciduous forests to the 

south. Typical forest types include northern hardwoods (maple-beech-birch), northern 

hardwoods/spruce, and northeastern spruce-fir forests. Recreation, tourism, and forestry are 

primary land uses. Farm-to-forest conversion began in the 19th century and continues today. In 

spite of this trend, alluvial valleys, glacial lake basins, and areas of limestone-derived soils are 

still farmed for dairy products, forage crops, apples, and potatoes. Many of the lakes and streams 

in this region have been acidified by sulfur depositions originating in industrialized areas upwind 

from the ecoregion to the west.
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Region 5.3.3 North Central Appalachians

More forest-covered than most adjacent ecoregions, the North Central Appalachians ecoregion is 

part of a vast, elevated plateau composed of horizontally bedded sandstone, shale, siltstone, 

conglomerate, and coal. It is made up of plateau surfaces, high hills, and low mountains, which, 

unlike the ecoregions to the north and west, were largely unaffected by continental glaciation. 

Only a portion of the Poconos section in the east has been glaciated. Land use activities are 

generally tied to forestry and recreation, but some coal and natural gas extraction occurs in the 

west.
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Region 6.2 Western Cordillera

Figure C-4: Region 6.2

Region 6.2.3 Northern Rockies

The Northern Rockies ecoregion is mountainous and rugged. Despite its inland position, climate 

and vegetation are, typically, marine-influenced. Douglas-fir, subalpine fir, Englemann spruce, 

and ponderosa pine and Pacific indicators such as western red cedar, western hemlock, and grand 

fir are found in the ecoregion. The vegetation mosaic is different from that of the Idaho Batholith 

(6.2.15) and Middle Rockies (6.2.10) which are not dominated by maritime species. The 

Northern Rockies ecoregion is not as high nor as snow- and ice-covered as the Canadian Rockies 

(6.2.4) although alpine characteristics occur at highest elevations and include numerous glacial 

lakes. Granitics and associated management problems are less extensive than in the Idaho 

Batholith.
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Region 6.2.4 Canadian Rockies

As its name indicates, most of this region is located in Canada. It straddles the border between 

Alberta and British Columbia in Canada and extends southeastward into northwestern Montana. 

The region is generally higher and more ice-covered than the Northern Rockies, and portions are 

strongly influenced by moist maritime air masses. Vegetation is mostly Douglas-fir, Engelmann 

spruce, subalpine fir, and lodgepole pine in the forested elevations, with treeless alpine 

conditions at higher elevations. A large part of the region is in national parks where tourism is 

the major land use. Forestry and mining occur on the non-park lands.

Region 6.2.5 North Cascades

The terrain of the North Cascades is composed of high, rugged mountains. It contains the 

greatest concentration of active alpine glaciers in the conterminous United States and has a 

variety of climatic zones. A dry continental climate occurs in the east and mild, maritime, 

rainforest conditions are found in the west. It is underlain by sedimentary and metamorphic rock 

in contrast to the adjoining Cascades (6.2.7) which are composed of volcanics.

Region 6.2.7 Cascades

This mountainous ecoregion is underlain by Cenozoic volcanics and much of the region has been 

affected by alpine glaciation. It is characterized by steep ridges and river valleys in the west, a 

high plateau in the east, and both active and dormant volcanoes. Elevations range upwards to 

14,411 feet. Its moist, temperate climate supports an extensive and highly productive coniferous 

forest that is intensively managed for logging. Subalpine meadows and rocky alpine zones occur 

at high elevations.

Region 6.2.8 Eastern Cascades Slopes and Foothills 

The Eastern Cascade Slopes and Foothills ecoregion is in the rainshadow of the Cascade Range. 

It experiences greater temperature extremes and receives less precipitation than ecoregions to the 

west. Open forests of ponderosa pine and some lodgepole pine distinguish this region from the 

higher ecoregions to the west where fir and hemlock forests are common, and the lower dryer 

ecoregions to the east where shrubs and grasslands are predominant. The vegetation is adapted to 

the prevailing dry continental climate and is highly susceptible to wildfire. Historically, creeping 

ground fires consumed accumulated fuel, and devastating crown fires were less common in dry 

forests. Volcanic cones and buttes are common in much of the region.

Region 6.2.9 Blue Mountains

The Blue Mountains ecoregion is a complex of mountain ranges that are generally lower and 

more open than the neighboring Cascades (6.2.7), Northern Rockies (6.2.3), and the Idaho 

Batholith (6.2.15) ecoregions. Like the Cascades, but unlike the Northern Rockies, the region is 

mostly volcanic in origin. Only the few higher ranges, particularly the Wallowa and Elkhorn 

Mountains, consist of granitic intrusive and metamorphic rocks that rise above the dissected lava 

surface of the region. Unlike the bulk of the Cascades, Idaho Batholith, and Northern Rockies, 

much of this ecoregion is grazed by cattle.
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Region 6.2.10 Middle Rockies

The climate of the Middle Rockies lacks the strong maritime influence of the Northern Rockies 

(6.2.3). Mountains have Douglas-fir, subalpine fir, and Engelmann spruce forests, as well as 

some large alpine areas. Pacific tree species are never dominant and forests can have open 

canopies. Foothills are partly wooded or shrub- and grass-covered. Intermontane valleys are 

grass- and/or shrub-covered and contain a mosaic of terrestrial and aquatic fauna that is distinct 

from the nearby mountains. Many mountain-fed, perennial streams occur and differentiate the 

intermontane valleys from the Northwestern Great Plains (9.3.3). Granitics and associated 

management problems are less extensive than in the Idaho Batholith (6.2.15). Recreation, 

logging, mining, and summer livestock grazing are common land uses.

Region 6.2.11 Klamath Mountains

This physically and biologically diverse ecoregion covers the highly dissected ridges, foothills, 

and valleys of the Klamath and Siskiyou mountains. It also extends south in California to include 

the mixed conifer and montane hardwood forests that occur in the North Coast Range mountains. 

The region’s mix of granitic, sedimentary, metamorphic, and extrusive rocks contrasts with the 

predominantly volcanic rocks of the Cascades (6.2.7) to the east. It was unglaciated during the 

Pleistocene epoch, when it served as a refuge for northern plant species. The regions diverse 

flora, a mosaic of both northern Californian and Pacific Northwestern conifers and hardwoods, is 

rich in endemic and relic species. The mild, subhumid climate of the Klamath Mountains is 

characterized by a lengthy summer drought.

Region 6.2.12 Sierra Nevada

The Sierra Nevada is a deeply dissected fault-block mountain range that rises sharply from the 

arid basin and range ecoregions on the east and slopes gently toward the Central California 

Valley to the west. The eastern portion has been strongly glaciated and generally contains higher 

mountains than are found in the Klamath Mountains (6.2.11) to the northwest. Much of the 

central and southern parts of the region is underlain by granite as compared to the mostly 

sedimentary and metamorphic formations of the Klamath Mountains and the volcanic rocks of 

the Cascades (6.2.7). The higher elevations of this region are largely federally owned and include 

several national parks. The vegetation grades from mostly ponderosa pine and Douglas-fir at the 

lower elevations on the west side, pines and Sierra juniper on the east side, to fir and other 

conifers at the higher elevations. Alpine conditions exist at the highest elevations.

Region 6.2.13 Wasatch and Uinta Mountains

This ecoregion is composed of a core area of high, precipitous mountains with narrow crests and 

valleys flanked in some areas by dissected plateaus and open high mountains. The elevational 

banding pattern of vegetation is similar to that of the Southern Rockies (6.2.14) except that areas 

of aspen, interior chaparral, and juniper-pinyon and scrub oak are more common at middle 

elevations. This characteristic, along with a far lesser extent of lodgepole pine and greater use of 
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the region for grazing livestock in the summer months, distinguish the Wasatch and Uinta 

Mountains ecoregion from the more northerly Middle Rockies (6.2.10).

Region 6.2.14 Southern Rockies

The Southern Rockies are composed of steep, rugged mountains with high elevations. Although 

coniferous forests cover much of the region, as in most of the mountainous regions in the 

western United States, vegetation, as well as soil and land use, follows a pattern of elevational 

banding. The lowest elevations are generally grass or shrub covered and heavily grazed. Low to 

middle elevations are also grazed and covered by a variety of vegetation types including 

Douglas-fir, ponderosa pine, aspen, and juniper-oak woodlands. Middle to high elevations are 

largely covered by coniferous forests and have little grazing activity. The highest elevations have 

alpine characteristics.

Region 6.2.15 Idaho Batholith

This ecoregion is a dissected, partially glaciated, mountainous plateau. Many perennial streams 

originate here and water quality can be high if basins are undisturbed. Deeply weathered, acidic, 

intrusive igneous rock is common and is far more extensive than in the Northern Rockies (6.2.3) 

or the Middle Rockies (6.2.10). Soils are sensitive to disturbance especially when stabilizing 

vegetation is removed. Land uses include logging, grazing, and recreation. Mining and related 

damage to aquatic habitat was widespread. Grand fir, Douglas-fir, and, at higher elevations, 

Engelmann spruce and subalpine fir occur. Ponderosa pine, shrubs, and grasses grow in very 

deep canyons. Maritime influence lessens toward the south and is never as strong as in the 

Northern Rockies.
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Region 7.1 Marine West Coast Forest

Figure C-5: Region 7.1

Region 7.1.7 Puget Lowland

This broad rolling lowland is characterized by a mild maritime climate. It occupies a continental 

glacial trough and is composed of many islands, peninsulas, and bays in the Puget Sound area. 

Coniferous forests originally grew on the ecoregion’s ground moraines, outwash plains, 

floodplains, and terraces. The distribution of forest species is affected by the rainshadow from 

the Olympic Mountains.

Region 7.1.8 Coast Range

The low mountains of the Coast Range are covered by highly productive, rain-drenched 

coniferous forests. Sitka spruce forests originally dominated the fog-shrouded coast, while a 

mosaic of western redcedar, western hemlock, and seral Douglas-fir blanketed inland areas. 

Today, Douglas-fir plantations are prevalent on the intensively logged and managed landscape. 

In California, redwood forests are a dominant component in much of the region.
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Region 7.1.9 Willamette Valley

This ecoregion contains terraces and floodplains of the Willamette River system, along with 

scattered hills, buttes, and adjacent foothills. Originally, it was covered by prairies, oak savannas, 

coniferous forests, extensive wetlands, and deciduous riparian forests. Elevation and relief are 

lower and the vegetation mosaic differs from the coniferous forests of the surrounding Coast 

Range (7.1.8), Cascades (6.2.7), and Klamath Mountains (6.2.11). Mean annual rainfall is 37 to 

60 inches and summers are generally dry; overall, precipitation is lower than in the surrounding 

mountains. Today, the Willamette Valley contains the bulk of Oregon’s population, industry, 

commerce, and cropland. Productive soils and a temperate climate make it one of the most 

important agricultural areas in Oregon.
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Region 8.1 Mixed Wood Plains

Figure C-6: Region 8.1

Region 8.1.1 Eastern Great Lakes Lowlands

This glaciated region of irregular plains bordered by hills generally contains less surface 

irregularity and more agricultural activity and population density than the adjacent Northeastern 

Highlands (5.3.1) and Northern Allegheny Plateau (8.1.3). Although orchards, vineyards, and 

vegetable farming are important locally, a large percentage of the agriculture is associated with 

dairy operations. The portion of this ecoregion that is in close proximity to the Great Lakes 

experiences an increased growing season, more winter cloudiness, and greater snowfall.

Region 8.1.3 Northern Allegheny Plateau

The Northern Allegheny Plateau is made up of horizontally bedded, erodible shales and 

siltstones, and moderately resistant sandstones of Devonian age. It is generally lower and less 

forested than the adjacent unglaciated North Central Appalachians (5.3.3). Its rolling hills, open 

valleys, and low mountains are covered by till from Wisconsinan Age glaciation and the 
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landscape is a mosaic of cropland, pastureland, and woodland. Historically, the natural 

vegetation was primarily Appalachian oak forest dominated by white oak and red oak, with some 

northern hardwood forest at higher elevations. The Northern Allegheny Plateau has more level 

topography and more fertile, arable land than the more rugged and forested North Central 

Appalachians (5.3.3).

Region 8.1.4 North Central Hardwood Forests

The North Central Hardwood Forests ecoregion is transitional between the predominantly 

forested Northern Lakes and Forests (5.2.1) to the north and the agricultural ecoregions to the 

south. Land use/land cover in this ecoregion consists of a mosaic forests, wetlands and lakes, 

cropland agriculture, pasture, and dairy operations. The growing season is generally longer and 

warmer than that of the Northern Lakes and Forest and the soils are more arable and fertile, 

contributing to the greater agricultural component of land use. Lake trophic states tend to be 

higher here than in the Northern Lakes and Forests, with higher percentages in eutrophic and 

hypereutrophic classes.

Region 8.1.5 Driftless Area

The hilly uplands of the Driftless Area easily distinguish it from surrounding ecoregions. Much 

of the area consists of a deeply dissected, loess-capped, bedrock dominated plateau. The region 

is also called the Paleozoic Plateau because the landscape’s appearance is a result of erosion 

through rock strata of Paleozoic age. Although there is evidence of glacial drift in the region, its 

influence on the landscape has been minor compared to adjacent ecoregions. In contrast to 

adjacent ecoregions, the Driftless Area has few lakes, most of which are reservoirs with 

generally high trophic states. Livestock and dairy farming are major land uses and have had a 

major impact on stream quality.

Region 8.1.6 Southern Michigan/Northern Indiana

Bordered by Lake Michigan on the west, this ecoregion is less agricultural than the Central Corn 

Belt (8.2.3) and Eastern Corn Belt (8.2.4) to the south, it is better drained and contains more 

lakes than the flat agricultural Huron/Erie Lake Plains (8.2.2) to the east, and its soils are not as 

nutrient poor as Northern Lakes and Forests (5.2.1) to the north. The region is characterized by 

many lakes and marshes as well as an assortment of landforms, soil types, soil textures, and land 

uses. Broad till plains with thick and complex deposits of drift, paleobeach ridges, relict dunes, 

morainal hills, kames, drumlins, meltwater channels, and kettles occur. Oak-hickory forests, 

northern swamp forests, and beech forests were typical. Feed grain, soybean, and livestock 

farming as well as woodlots, quarries, recreational development, and urban-industrial areas are 

now common.

Region 8.1.7 Northeastern Coastal Zone

Similar to the Northeastern Highlands (5.3.1), the Northeastern Coastal Zone contains relatively 

nutrient poor soils and concentrations of continental glacial lakes, some of which are sensitive to 

acidification; however, this ecoregion contains considerably less surface irregularity and much 
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greater concentrations of human population. Landforms in the region include irregular plains, 

and plains with high hills. Appalachian oak forests and northeastern oak-pine forests are the 

natural vegetation types. Although attempts were made to farm much of the Northeastern Coastal 

Zone after the region was settled by Europeans, land use now mainly consists of forests, 

woodlands, and urban and suburban development, with only some minor areas of pasture and 

cropland.

Region 8.1.8 Acadian Plains and Hills

This mostly forested region, with dense concentrations of continental glacial lakes, is less rugged 

than the Northeastern Highlands (5.3.1) to the west and considerably less populated than 

Northeastern Coastal Zone (8.1.7) to the south. Vegetation here is mostly spruce-fir on the 

lowlands with some patches of maple, beech, and birch on the hills. Soils are predominantly 

frigid Spodosols. By contrast, the forests in the Northeastern Coastal Zone (8.1.7) to the south 

are mostly Appalacian oak or northeastern oak-pine and the soils are generally mesic Inceptisols 

and Entisols.

Region 8.1.10 Erie Drift Plain

Once largely covered by a maple-beech-birch forest in the west and northern hardwoods in the 

east, much of the Erie Drift Plain is now in farms, many associated with dairy operations. The 

Eastern Corn Belt Plains (8.2.4), which border the region on the west, are flatter, more fertile, 

and therefore more agricultural. The glaciated Erie Drift Plain is characterized by low rounded 

hills, scattered end moraines, kettles, and areas of wetlands, in contrast to the adjacent 

unglaciated ecoregions (5.3.3, 8.4.3) to the south and east that are more hilly and less 

agricultural. Areas of urban development and industrial activity occur locally. Lake Erie’s 

influence substantially increases the growing season, winter cloudiness, and snowfall in the 

northernmost areas bordering the strip of the Eastern Great Lakes Lowland (8.1.1) which fringes 

the lake.
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Region 8.2 Central USA Plains

Figure C-7: Region 8.2

Region 8.2.1 Southeastern Wisconsin Till Plains

The Southeastern Wisconsin Till Plains support a mosaic of vegetation types, representing a 

transition between the hardwood forests and oak savannas of the ecoregions to the west and the 

tallgrass prairies of the Central Corn Belt Plains (8.2.3) to the south. Like Ecoregion 54, land use 

in the Southeastern Wisconsin Till Plains is mostly cropland, but the crops are largely forage and 

feed grains to support dairy operations, rather than corn and soybeans for cash crops. The 

ecoregion has a higher plant hardiness value and a different mosaic of soils than ecoregions to 

the north and west.

Region 8.2.2 Huron/Erie Lake Plains

The Huron/Erie Lake Plains ecoregion is a broad, fertile, nearly flat plain punctuated by relic 

sand dunes, beach ridges, and end moraines. Originally, soil drainage was typically poorer than 

in the adjacent Eastern Corn Belt Plains (8.2.4), and elm-ash swamp and beech forests were 
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dominant. Oak savanna was typically restricted to sandy, well-drained dunes and beach ridges. 

Today, most of the area has been cleared and artificially drained and contains highly productive 

farms producing corn, soybeans, livestock, and vegetables; urban and industrial areas are also 

extensive. Stream habitat and quality have been degraded by channelization, ditching, and 

agricultural activities.

Region 8.2.3 Central Corn Belt Plains

Extensive prairie communities intermixed with oak-hickory forests were native to the glaciated 

plains of the Central Corn Belt Plains; they were a stark contrast to the hardwood forests that 

grew on the drift plains of Ecoregions 8.2.4 and 8.1.6 to the east. Ecoregions 9.2.4 and 9.2.3 to 

the west were mostly treeless except along larger streams. Beginning in the nineteenth century, 

the natural vegetation was gradually replaced by agriculture. Farms are now extensive on the 

dark, fertile soils of the Central Corn Belt Plains and mainly produce corn and soybeans; cattle, 

sheep, poultry, and, especially hogs, are also raised, but they are not as dominant as in the drier 

Western Corn Belt Plains (9.2.3) to the west. Agriculture has affected stream chemistry, 

turbidity, and habitat.

Region 8.2.4 Eastern Corn Belt Plains

The Eastern Corn Belt Plains ecoregion is primarily a rolling till plain with local end moraines; it 

had more natural tree cover and has lighter colored soils than the Central Corn Belt Plains 

(8.2.3). The region has loamier and better drained soils than the Huron/Erie Lake Plain (8.2.2), 

and richer soils than the Erie Drift Plain (8.1.10). Glacial deposits of Wisconsinan age are 

extensive. They are not as dissected nor as leached as the pre-Wisconsinan till which is restricted 

to the southern part of the region. Originally, beech forests were common on Wisconsinan soils 

while beech forests and elm-ash swamp forests dominated the wetter pre-Wisconsinan soils. 

Today, extensive corn, soybean, and livestock production occurs and has affected stream 

chemistry and turbidity.
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Region 8.3 Southeastern USA Plains

Figure C-8: Region 8.3

Region 8.3.1 Northern Piedmont

The Northern Piedmont is a transitional region of low rounded hills, irregular plains, and open 

valleys in contrast to the low mountains of Ecoregions 5.3.1, 8.4.4, and 8.4.1 to the north and 

west and the flatter coastal plains of Ecoregions 8.3.5 and 8.5.1 to the east. It is underlain by a 

mix of metamorphic, igneous, and sedimentary rocks, with soils that are mostly Alfisols and 

some Ultisols. Potential natural vegetation here was predominantly Appalachian oak forest as 

compared to the mostly oak-hickory-pine forests of the Piedmont (8.3.4) ecoregion to the 

southwest. The region now contains a higher proportion of cropland compared to the Piedmont.

Region 8.3.2 Interior River Valleys and Hills

The Interior River Lowland is made up of many wide, flat-bottomed terraced valleys, forested 

valley slopes, and dissected glacial till plains. In contrast to the generally rolling to slightly 

irregular plains in adjacent ecological regions to the north (8.2.3), east (8.2.4) and west (9.2.4, 
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9.2.3), where most of the land is cultivated for corn and soybeans, a little less than half of this 

area is in cropland, about 30 percent is in pasture, and the remainder is in forest. Bottomland 

deciduous forests and swamp forests were common on wet lowland sites, with mixed oak and 

oak-hickory forests on uplands. Paleozoic sedimentary rock is typical and coal mining occurs in 

several areas.

Region 8.3.3 Interior Plateau

The Interior Plateau is a diverse ecoregion extending from southern Indiana and Ohio to northern 

Alabama. Rock types are distinctly different from the coastal plain sediments and alluvial 

deposits of ecoregions to the west, and elevations are lower than the Appalachian ecoregions 

(8.4.4, 8.4.1, 8.4.9) to the east. Mississippian to Ordovician-age limestone, chert, sandstone, 

siltstone and shale compose the landforms of open hills, irregular plains, and tablelands. The 

natural vegetation is primarily oak-hickory forest, with some areas of bluestem prairie and cedar 

glades. The region has a diverse fish fauna.

Region 8.3.4 Piedmont

Considered the non-mountainous portion of the old Appalachians Highland by physiographers, 

the northeast-southwest trending Piedmont ecoregion comprises a transitional area between the 

mostly mountainous ecoregions of the Appalachians to the northwest and the relatively flat 

coastal plain to the southeast. It is a complex mosaic of Precambrian and Paleozoic metamorphic 

and igneous rocks, with moderately dissected irregular plains and some hills. The soils tend to be 

finer-textured than in coastal plain regions (8.5.1, 8.3.5). Once largely cultivated, much of this 

region has reverted to successional pine and hardwood woodlands, with an increasing conversion 

to an urban and suburban land cover.

Region 8.3.5 Southeastern Plains

These irregular plains have a mosaic of cropland, pasture, woodland, and forest. Natural 

vegetation was predominantly longleaf pine, with smaller areas of oak-hickory-pine and 

Southern mixed forest. The Cretaceous or Tertiary-age sands, silts, and clays of the region 

contrast geologically with the older metamorphic and igneous rocks of the Piedmont (8.3.4), and 

with the Paleozoic limestone, chert, and shale found in the Interior Plateau (8.3.3). Elevations 

and relief are greater than in the Southern Coastal Plain (8.5.3), but generally less than in much 

of the Piedmont. Streams in this area are relatively low-gradient and sandy-bottomed.

Region 8.3.6 Mississippi Valley Loess Plains

This ecoregion stretches from near the Ohio River in western Kentucky to Louisiana. It consists 

primarily of irregular plains, some gently rolling hills, and near the Mississippi River, bluffs. 

Thick loess is one of the distinguishing characteristics. The bluff hills in the western portion 

contain soils that are deep, steep, silty, and erosive. Flatter topography is found to the east, and 

streams tend to have less gradient and siltier substrates than in the Southeastern Plains ecoregion 

(8.3.5). To the east, upland forests dominated by oak, hickory, and both loblolly and shortleaf 

pine, and to the west on bluffs some mixed and southern mesophytic forests, were the dominant 
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natural vegetation. Agriculture is now the typical land cover in the Kentucky and Tennessee 

portion of the region, while in Mississippi there is a mosaic of forest and cropland.

Region 8.3.7 South Central Texas Plains

Locally termed the “piney woods”, this region of mostly irregular plains represents the western 

edge of the southern coniferous forest belt. Once blanketed by a mix of pine and hardwood 

forests, much of the region is now in loblolly and shortleaf pine plantations. Only about one sixth 

of the region is in cropland, primarily within the Red River floodplain, while about two thirds of 

the region is in forests and woodland. Lumber, pulpwood, oil and gas production are major 

economic activities.

Region 8.3.8 East Central Texas Plains

Also called the Post Oak Savanna or the Claypan Area, this region of irregular plains was 

originally covered by post oak savanna vegetation, in contrast to the more open prairie-type 

regions to the north, south, and west and the pine forests to the east. The boundary with 

Ecoregion 8.3.7 is a subtle transition of soils and vegetation. Many areas have a dense, 

underlying clay pan affecting water movement and available moisture for plant growth. The 

bulk of this region is now used for pasture and range.
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Region 8.4 Ozark/Ouchita-Appalachian Forests

Figure C-9: Region 8.4

Region 8.4.1 Ridge and Valley

This northeast-southwest trending, relatively low-lying, but diverse ecoregion is sandwiched 

between generally higher, more rugged mountainous regions with greater forest cover. As a 

result of extreme folding and faulting events, the region’s roughly parallel ridges and valleys 

have a variety of widths, heights, and geologic materials, including limestone, dolomite, shale, 

siltstone, sandstone, chert, mudstone, and marble. Springs and caves are relatively numerous. 

Present-day forests cover about 50% of the region. The ecoregion has a great diversity of aquatic 

habitats and species of fish.

Region 8.4.2 Central Appalachians

The Central Appalachian ecoregion, stretching from central Pennsylvania to northern Tennessee, 

is primarily a high, dissected, rugged plateau composed of sandstone, shale, conglomerate, and 

coal. The rugged terrain, cool climate, and infertile soils limit agriculture, resulting in a mostly 
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forested land cover. The high hills and low mountains are covered by a mixed mesophytic forest 

with areas of Appalachian oak and northern hardwood forest. Bituminous coal mines are 

common, and have caused the siltation and acidification of streams.

Region 8.4.3 Western Allegheny Plateau

The hilly and wooded terrain of the Western Allegheny Plateau was not muted by glaciation and 

is more rugged than the agricultural till plains of Ecoregions 8.1.10 and 8.2.4 to the north and 

west, but is less rugged and not as forested as Ecoregion 8.4.2 to the east and south. Extensive 

mixed mesophytic forests and mixed oak forests originally grew in the Western Allegheny 

Plateau and, today, most of its rounded hills remain in forest; dairy, livestock, and general farms 

as well as residential developments are concentrated in the valleys. Horizontally-bedded 

sedimentary rock underlying the region has been mined for bituminous coal.

Region 8.4.4 Blue Ridge

The Blue Ridge extends from southern Pennsylvania to northern Georgia, varying from narrow 

ridges to hilly plateaus to more massive mountainous areas, with high peaks reaching over 6600 

feet. The mostly forested slopes, high-gradient, cool, clear streams, and rugged terrain occur 

primarily on metamorphic rocks, with minor areas of igneous and sedimentary geology. Annual 

precipitation of over 100 inches can occur in the wettest areas, while dry basins can average as 

little as 40 inches. The southern Blue Ridge is one of the richest centers of biodiversity in the 

eastern U.S. It is one of the most floristically diverse ecoregions, and includes Appalachian oak 

forests, northern hardwoods, and, at the highest elevations, Southeastern spruce-fir forests. 

Shrub, grass, and heath balds, hemlock, cove hardwoods, and oak-pine communities are also 

significant.

Region 8.4.5 Ozark Highlands

The Ozark Highlands ecoregion has a more irregular physiography and is generally more 

forested than adjacent regions, with the exception of the Boston Mountains (8.4.6) to the south. 

Soils are mostly derived from cherty carbonate rocks. Cambrian and Ordovician dolomite and 

sandstone comprise the dominant bedrock in the interior of the region with Mississippian 

limestone underlying the western outer regions. Karst features, including caves, springs, and 

spring-fed streams are found throughout most of the Ozark Highlands. The majority of the region 

is forested; oak is the predominant forest type but mixed stands of oak and pine are also 

common, with pine concentrations greatest to the southeast. Less than one fourth of the core of 

this region has been cleared for pasture and cropland, but half or more of the periphery, while not 

as agricultural as bordering ecological regions, is in cropland and pasture.

Region 8.4.6 Boston Mountains

In contrast to the nearby Ouachita Mountains (8.4.8) region which comprises folded and faulted 

linear ridges mostly covered by pine forests, the Boston Mountains ecological region consists of 

a deeply dissected sandstone and shale plateau, originally covered by oak-hickory forests. Red 

oak, white oak, and hickory remain the dominant vegetation types in this region, although 
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shortleaf pine and eastern red cedar are found in many of the lower areas and on some south- and 

west-facing slopes. The region is sparsely populated and recreation is a principal land use.

Region 8.4.7 Arkansas Valley

A region of mostly forested valleys and ridges, the physiography of the Arkansas Valley is much 

less irregular than that of the Boston Mountains (8.4.6) to the north and the Ouachita Mountains 

(8.4.8) to the south, but is more irregular than the ecological regions to the west and east. About 

one fourth of the region is grazed and roughly one tenth is cropland. In the Arkansas Valley, 

even streams that have been relatively unimpacted by human activities have considerably lower 

dissolved oxygen levels, and hence support different biological communities, than those of most 

of the adjacent regions.

Region 8.4.8 Ouchita Mountains

The Ouachita Mountains ecological region is made up of sharply defined east-west trending 

ridges, formed through erosion of compressed sedimentary rock formations. The Ouachitas are 

structurally different from the Boston Mountains (8.4.6), more folded and rugged than the 

lithologically distinct Ozark Highlands (8.4.5), and physiographically unlike the Arkansas Valley 

(8.4.7), South Central Plains (8.3.7), and Mississippi Alluvial Plain (8.5.2). Potential natural 

vegetation is oak-hickory-pine forest, which contrasts with the oak-hickory forest that dominates 

Ecoregion 8.4.5 and the northern part of the Boston Mountains (8.4.6). Most of this region is 

now in loblolly and shortleaf pine. Commercial logging is the major land use in the region.

Region 8.4.9 Southwestern Appalachians

Stretching from Kentucky to Alabama, these open low mountains contain a mosaic of forest and 

woodland with some cropland and pasture. The eastern boundary of the ecoregion, along the 

more abrupt escarpment where it meets the Ridge and Valley (8.4.1), is relatively smooth and 

only slightly notched by small, eastward flowing streams. Much of the western boundary, next to 

the Interior Plateau (8.3.3), is more crenulated, with a rougher escarpment that is more deeply 

incised. The mixed mesophytic forest is restricted mostly to the deeper ravines and escarpment 

slopes, and the upland forests are dominated by mixed oaks with shortleaf pine. Ecoregion 8.4.9 

has less agriculture than the adjacent Ecoregion 8.3.3. Coal mining occurs in several parts of the 

region.
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January 14, 2011

Region 8.5 Mississippi Alluvial and Southeastern USA Coastal Plains

Figure C-10: Region 8.5

Region 8.5.1 Middle Atlantic Coastal Plain

The Middle Atlantic Coastal Plain ecoregion stretches from Delaware to the South 

Carolina/Georgia border and consists of low elevation flat plains, with many swamps, marshes, 

and estuaries. Forest cover in the region, once dominated by longleaf pine in the Carolinas, is 

now mostly loblolly and some shortleaf pine, with patches of oak, gum, and cypress near major 

streams, as compared to the mainly longleaf-slash pine forests of the warmer Southern Coastal 

Plain (8.5.3). Its low terraces, marshes, dunes, barrier islands, and beaches are underlain by 

unconsolidated sediments. Poorly drained soils are common, and the region has a mix of coarse 

and finer textured soils compared to the mostly coarse soils in the majority of Ecoregion 8.5.3. 

The Middle Atlantic Coastal Plain is typically lower, flatter, more poorly drained, and more 

marshy than Ecoregion 8.3.5. Less cropland occurs in the southern portion of the region than in 

the central and northern parts.
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Region 8.5.2 Mississippi Alluvial Plain

This riverine ecoregion extends from southern Illinois, at the confluence of the Ohio River with 

the Mississippi River, south to the Gulf of Mexico. It is mostly a broad, flat alluvial plain with 

river terraces, swales, and levees providing the main elements of relief. Soils are typically finertextured and more poorly drained than the upland soils of adjacent Ecoregions 8.3.7 and 8.3.6, 

although there are some areas of coarser, better-drained soils. Winters are mild and summers are 

hot, with temperatures and precipitation increasing from north to south. Bottomland deciduous 

forest vegetation covered the region before much of it was cleared for cultivation. Presently, 

most of the northern and central parts of the region are in cropland and receive heavy treatments 

of insecticides and herbicides. Soybeans, cotton, and rice are the major crops.

Region 8.5.3 Southern Coastal Plains

The Southern Coastal Plain consists of mostly flat plains, but it is a heterogeneous region 

containing barrier islands, coastal lagoons, marshes, and swampy lowlands along the Gulf and 

Atlantic coasts. In Florida, an area of discontinuous highlands contains numerous lakes. This 

ecoregion is lower in elevation with less relief and wetter soils than the Southeastern Plains 

(8.3.5). It is warmer, more heterogeneous, and has a longer growing season and coarser textured 

soils than the Middle Atlantic Coastal Plain (8.5.1). Once covered by a variety of forest 

communities that included trees of longleaf pine, slash pine, pond pine, beech, sweetgum, 

southern magnolia, white oak, and laurel oak, land cover in the region is now mostly slash and 

loblolly pine with oak-gum-cypress forest in some low lying areas, citrus groves in Florida, 

pasture for beef cattle, and urban.

Region 8.5.4 Atlantic Coastal Pine Barrens

This is a transitional ecoregion, distinguished from the coastal ecoregion (8.5.1) to the south by 

its coarser-grained soils, cooler climate, and Northeastern oak-pine potential natural vegetation. 

The climate is milder than the coastal ecoregion (8.1.7) to the north that contains Appalachian 

oak forests and some northern hardwoods forests. The physiography of this ecoregion is not as 

flat as that of the Middle Atlantic Coastal Plain (8.5.1), but it is not as irregular as that of the 

Northeastern Coastal Zone (8.1.7). The shore characteristics of sandy beaches, grassy dunes, 

bays, marshes, and scrubby oak-pine forests are more like those to the south, in contrast to the 

more rocky, jagged, forested coastline found to the north.
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Region 9.2 Temperate Prairies

Figure C-11: Region 9.2

Region 9.2.1 Northern Glaciated Plains

The Northern Glaciated Plains ecoregion is characterized by a flat to gently rolling landscape 

composed of glacial drift. The subhumid conditions foster a grassland transitional between tall 

and shortgrass prairie. High concentrations of temporary and seasonal wetlands create favorable 

conditions for waterfowl nesting and migration. Although the till soils are very fertile, 

agricultural success is subject to annual climatic fluctuations.

Region 9.2.2 Lake Agassiz Plains

Glacial Lake Agassiz was the last in a series of proglacial lakes to fill the Red River valley in the 

three million years since the beginning of the Pleistocene. Thick beds of lake sediments on top of 

glacial till create the extremely flat floor of the Lake Agassiz Plain. The historic tallgrass prairie 

has been replaced by intensive row crop agriculture. The preferred crops in the northern half of 
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the region are potatoes, beans, sugar beets, and wheat; soybeans, sugar beets, and corn

predominate in the south.

Region 9.2.3 Western Corn Belt Plains

Once mostly covered with tallgrass prairie, over 80 percent of the Western Corn Belt Plains is 

now used for cropland agriculture and much of the remainder is in forage for livestock. A 

combination of nearly level to gently rolling glaciated till plains and hilly loess plains, an 

average annual precipitation of 26 to 37 inches, which occurs mainly in the growing season, and 

fertile, warm, moist soils make this one of the most productive areas of corn and soybeans in the 

world. Agricultural practices have contributed to environmental issues, including surface and 

groundwater contamination from fertilizer and pesticide applications as well as concentrated 

livestock production.

Region 9.2.4 Central Irregular Plains

The Central Irregular Plains have a mix of land use and are topographically more irregular than 

the Western Corn Belt Plains (9.2.3) to the north, where most of the land is in crops. The region, 

however, is less irregular and less forest covered than the ecoregions to the south and east. The 

potential natural vegetation of this ecological region is a grassland/forest mosaic with wider 

forested strips along the streams compared to Ecoregion 9.2.3 to the north. The mix of land use 

activities in the Central Irregular Plains includes mining operations of high-sulfur bituminous 

coal. The disturbance of these coal strata in southern Iowa and northern Missouri has degraded 

water quality and affected aquatic biota.
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January 14, 2011

Region 9.3 West-Central Semi-Arid Prairies

Figure C-12: Region 9.3

Region 9.3.1 Northwestern Glaciated Plains

The Northwestern Glaciated Plains ecoregion is a transitional region between the generally more 

level, moister, more agricultural Northern Glaciated Plains (9.2.1) to the east and the generally 

more irregular, dryer, Northwestern Great Plains (9.3.3) to the west and southwest. The western 

and southwestern boundary roughly coincides with the limits of continental glaciation. Pocking 

this ecoregion is a moderately high concentration of semi-permanent and seasonal wetlands, 

locally referred to as Prairie Potholes.

Region 9.3.3 Northwestern Great Plains

The Northwestern Great Plains ecoregion encompasses the Missouri Plateau section of the Great 

Plains that is mostly unglaciated. It is a semiarid rolling plain of shale, siltstone, and sandstone 

punctuated by occasional buttes and badlands. Rangeland is common, but spring wheat and 
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January 14, 2011

alfalfa farming also occur; native grasslands, persist in areas of steep or broken topography. 

Agriculture is restricted by the erratic precipitation and limited opportunities for irrigation.

Region 9.3.4 Nebraska San Hills

The Nebraska Sandhills comprise one of the most distinct and homogenous ecoregions in North 

America. One of the largest areas of grass stabilized sand dunes in the world, this region is 

generally devoid of cropland agriculture and except for some riparian areas in the north and east, 

and the region is treeless. Large portions of this ecoregion contain numerous lakes and wetlands 

and have a lack of streams. The area is sparsely populated; however, large cattle ranches are 

found throughout the region.
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January 14, 2011

Region 9.4 South Central Semi-Arid Prairies

Figure C-13: Region 9.4

Region 9.4.1 High Plains

Higher and drier than the Central Great Plains (9.4.2) to the east, and in contrast to the irregular, 

mostly grassland or grazing land of the Northwestern Great Plains (9.3.3) to the north, much of 

the High Plains is characterized by smooth to slightly irregular plains having a high percentage 

of cropland. Grama-buffalo grass is the potential natural vegetation in this region as compared to 

mostly wheatgrass-needlegrass to the north, Trans-Pecos shrub savanna to the south, and taller 

grasses to the east. The northern boundary of this ecological region is also the approximate 

northern limit of winter wheat and sorghum and the southern limit of spring wheat.

Region 9.4.2 Central Great Plains

The Central Great Plains are slightly lower, receive more precipitation, and are somewhat more 

irregular than the High Plains (9.4.1) to the west. Once grassland, with scattered low trees and 

shrubs in the south, much of this ecological region is now cropland, the eastern boundary of the 
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region marking the eastern limits of the major winter wheat growing area of the United States. 

Subsurface salt deposits and leaching contribute to high salinity found in some streams.

Region 9.4.3 Southwestern Tablelands

The southwestern Tablelands flank the High Plains (9.4.1) with red hued canyons, mesas, 

badlands, and dissected river breaks. Unlike most adjacent Great Plains ecological regions, little 

of the Southwestern Tablelands is in cropland. Much of this region is in sub-humid grassland and 

semiarid range land. The potential natural vegetation is grama-buffalo grass with some mesquitebuffalo grass in the southeast, juniper-scrub oak-midgrass savanna on escarpment bluffs, and 

shinnery (midgrass prairie with open low and shrubs) along the Canadian River.

Region 9.4.4 Flint Hills

The Flint Hills is a region of rolling hills with relatively narrow steep valleys, and is composed 

of shale and cherty limestone with rocky soils. In contrast to surrounding ecological regions that 

are mostly in cropland, most of the Flint Hills region is grazed by beef cattle. The Flint Hills 

mark the western edge of the tallgrass prairie, and contain the largest remaining intact tallgrass 

prairie in the Great Plains.

Region 9.4.5 Cross Timbers

The Cross Timbers ecoregion is a transition area between the once prairie, now winter wheat 

growing regions to the west, and the forested low mountains or hills of eastern Oklahoma and 

Texas. The region does not possess the arability and suitability for crops such as corn and 

soybeans that are common in the Central Irregular Plains (9.2.4) to the northeast. Transitional 

“cross-timbers” (little bluestem grassland with scattered blackjack oak and post oak trees) is the 

native vegetation, and presently rangeland and pastureland comprise the predominant land cover, 

with some areas of woodland. Oil extraction has been a major activity in this region for over 

eighty years.

Region 9.4.6 Edwards Plateau

This ecoregion is largely a dissected limestone plateau that is hillier in the south and east where 

it is easily distinguished from bordering ecological regions by a sharp fault line. The region 

contains a sparse network of perennial streams, but due to karst topography and resultant 

underground drainage they are relatively clear and cool compared to those of surrounding areas. 

Originally covered by juniper-oak savanna and mesquite-oak savanna, most of the region is used 

for grazing beef cattle, sheep, goats, and wildlife. Hunting leases are a major source of income.

Region 9.4.7 Texas Blackland Prairies

The Texas Blackland Prairies form a disjunct ecological region, distinguished from surrounding 

regions by its fine-textured, clayey soils and predominantly prairie potential natural vegetation. 

This region now contains a higher percentage of cropland than adjacent regions, and pasture and 

forage production for livestock is common. Large areas of the region are being converted to 

urban and industrial uses.
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January 14, 2011

Region 9.5 Texas-Louisiana Coastal Plain

Figure C-14: Region 9.5

Region 9.5.1 Western Gulf Coastal Plain

The principal distinguishing characteristics of the Western Gulf Coastal Plain are its relatively 

flat coastal plain topography and mainly grassland potential natural vegetation. Inland from this 

region the plains are older, more irregular, and have mostly forest or savanna-type vegetation 

potentials. Largely because of these characteristics, a higher percentage of the land is in cropland 

than in bordering ecological regions. Urban and industrial land uses have expanded greatly in 

recent decades, and oil and gas production is common.
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January 14, 2011

Region 9.6 Tamaulipas-Texas Semi-Arid Plain

Figure C-15: Region 9.6

Region 9.6.1 Southern Texas Plains

This rolling to moderately dissected plain was once covered with grassland and savanna 

vegetation that varied during wet and dry cycles. Following long continued grazing and fire 

suppression, thorny brush, such as mesquite, is now the predominant vegetation type. Also 

known as the Tamualipan Thornscrub, or the “brush country”, as it is called locally, the 

subhumid to dry region has its greatest extent in Mexico. It is generally lower in elevation with 

warmer winters than the Chihuahuan Deserts (10.2.10) to the northwest, and it contains a high 

and distinct diversity of plant and animal life. Oil and natural gas production activities are 

widespread.
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January 14, 2011

Region 10.1 Cold Deserts

Figure C-16: Region 10.1

Region 10.1.2 Columbia Plateau

The Columbia Plateau is an arid sagebrush steppe and grassland, surrounded on all sides by 

moister, predominantly forested, mountainous ecological regions. This region is underlain by 

basalt up to two miles thick. It is covered in some places by loess soils that have been 

extensively cultivated for wheat, particularly in the eastern portions of the region where 

precipitation amounts are greater. During the glaciation of the Pleistocene era, parts of the area 

were scoured to bedrock by huge floods from breached ice dams.

Region 10.1.3 Northern Basin and Range

This ecoregion contains tablelands, dissected lava plains, valleys, alluvial fans, and scattered 

mountains. Overall, it is higher and cooler than the Snake River Plain (10.1.8) to the east and has 

more available moisture and a cooler climate than the Central Basin and Range (10.1.5) to the 

south. The region has more extensive basins and fewer mountain ranges than the Central Basin 
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January 14, 2011

and Range. Non-mountain areas have sagebrush steppe vegetation; cool season grasses and 

Mollisols are more common than in the hotter-drier basins of the Central Basin and Range where 

Aridisols are dominated by sagebrush, shadscale, and greasewood. Ranges are covered in 

mountain sagebrush, mountain brush, and Idaho fescue at lower and mid-elevations; Douglas-fir, 

and aspen are common at higher elevations. Soils are less suitable for agriculture than those in 

the Columbia Plateau (10.1.2) and the Snake River Plain. Rangeland is common and dryland and 

irrigated agriculture occur in eastern basins.

Region 10.1.4 Wyoming Basin

This ecoregion is a broad intermontane basin interrupted by hills and low mountains and 

dominated by arid grasslands and shrublands. Nearly surrounded by forest covered mountains, 

the region is somewhat drier than the Northwestern Great Plains (9.3.3) to the northeast and does 

not have the extensive cover of pinyon-juniper woodland found in the Colorado Plateaus (10.16) 

to the south. Much of the region is used for livestock grazing, although many areas lack 

sufficient vegetation to support this activity. The region contains major producing natural gas 

and petroleum fields. The Wyoming Basin also has extensive coal deposits along with areas of 

trona, bentonite, clay, and uranium mining.

Region 10.1.5 Central Basin and Range

The Central Basin and Range ecoregion is internally drained and is characterized by a mosaic of 

xeric basins, scattered low and high mountains, and salt flats. It has a hotter and drier climate, 

more shrubland, and more mountain ranges than the Northern Basin and Range (10.1.3) 

ecoregion to the north. Basins are covered by Great Basin sagebrush or saltbush-greasewood 

vegetation that grow in Aridisols; cool season grasses are less common than in the Mollisols of 

the Snake River Plain (10.1.8) and Northern Basin and Range. The region is not as hot as the 

Mojave Basin and Range (10.2.1) ecoregion to the south and it has a greater percent of land that 

is grazed.

Region 10.1.6 Colorado Plateaus

Ecoregion 10.1.6 is an uplifted, eroded, and deeply dissected tableland. Its benches, mesas, 

buttes, salt valleys, cliffs, and canyons are formed in and underlain by thick layers of 

sedimentary rock. Precipitous side-walls mark abrupt changes in local relief, often from 1,000 to 

2,000 feet. The region contains a greater extent of pinyon-juniper and Gambel oak woodlands 

than the Wyoming Basin (10.1.4) to the north. There are also large low lying areas containing 

saltbrush-greasewood (typical of hotter drier areas), which are generally not found in the higher 

Arizona/New Mexico Plateau (10.1.7) to the south where grasslands are common. Summer 

moisture from thunderstorms supports warm season grasses not found in the Central Basin and 

Range (10.1.5) to the west. Many endemic plants occur and species diversity is greater than in 

Ecoregion 10.1.5. Several national parks are located in this ecoregion and attract many visitors to 

view their arches, spires, and canyons.
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January 14, 2011

Region 10.1.7 Arizona/New Mexico Plateau

The Arizona/New Mexico Plateau represents a large transitional region between the semiarid

grasslands and low relief tablelands of the Southwestern Tablelands (9.4.3) in the east, the drier 

shrublands and woodland covered higher relief tablelands of the Colorado Plateau (10.1.6) in the 

north, and the lower, hotter, less vegetated Mojave Basin and Range (10.2.1) in the west and 

Chihuahuan Deserts (10.2.10) in the southeast. Higher, forest-covered, mountainous ecoregions 

border the region on the northeast (6.2.14) and south (13.1.1). Local relief in the region varies 

from a few feet on plains and mesa tops to well over 1000 feet along tableland side slopes.

Region 10.1.8 Snake River Plain

This portion of the xeric intermontane western United States is considerably lower and more 

gently sloping than the surrounding ecoregions. Mostly because of the available water for 

irrigation, a large percent of the alluvial valleys bordering the Snake River are in agriculture, 

with sugar beets, potatoes, alfalfa, and vegetables being the principal crops. Cattle feedlots and 

dairy operations are also common in the river plain. Except for the scattered barren lava fields, 

most of the plains and low hills in the ecoregion have sagebrush-grassland vegetation, now used 

mostly for cattle grazing.
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January 14, 2011

Region 10.2 Warm Deserts

Figure C-17: Region 10.2

Region 10.2.1 Mojave Basin and Range

This ecoregion contains broad basins and scattered mountains that are generally lower, warmer, 

and drier, than those of the Central Basin and Range (10.1.5). Its creosote bush-dominated shrub 

community is distinct from the saltbush–greasewood and sagebrush–grass associations that occur 

to the north in the Central Basin and Range (10.1.5) and Northern Basin and Range (10.1.3); it is 

also differs from the palo verde–cactus shrub and saguaro cactus that occur in the Sonoran Basin 

and Range (10.2.2) to the south. Most of this region is federally owned and grazing is 

constrained by the lack of water and forage for livestock. Heavy use of off-road vehicles and 

motorcycles in some areas has made the soils susceptible to wind and water erosion.

Region 10.2.2 Sonoran Basin and Range

Similar in topography to the Mojave Basin and Range (10.2.1) to the north, this ecoregion 

contains scattered low mountains and has large tracts of federally owned land, a large portion of 
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January 14, 2011

which is used for military training. However, the Sonoran Basin and Range is slightly hotter than 

the Mojave and contains large areas of palo verde-cactus shrub and giant saguaro cactus, 

whereas the potential natural vegetation in the Mojave is largely creosote bush. Winter rainfall 

decreases from west to east, while summer rainfall decreases from east to west.

Region 10.2.10 Chihuahuan Deserts

This desert ecoregion extends from the Madrean Archipelago (12.1.1) in southeastern Arizona to 

the Edwards Plateau (9.4.6) in south-central Texas. The physiography is generally a continuation 

of basin and range terrain that is typical of the Mojave Basin and Range (10.2.1) and the Central 

Basin and Range (10.1.5) to the west and northwest, although the patterns of alternating 

mountains and valleys is not as pronounced as in Ecoregions 10.1.5 and 10.2.1. Vegetative cover 

is predominantly desert grassland and shrubland, except on the higher mountains where oak, 

juniper, and pinyon woodlands occur. The extent of desert shrubland is increasing across 

lowlands and mountain foothills due to the gradual desertification caused in part by historical 

grazing pressure.
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January 14, 2011

Region 11.1 Mediterranean California

Figure C-18: Region 11.1

Region 11.1.1 Southern and Central California Chaparral and Oak Woodlands

The primary distinguishing characteristic of this ecoregion is its Mediterranean climate of hot 

dry summers and cool moist winters, and associated vegetative cover comprising mainly 

chaparral and oak woodlands; grasslands occur in some lower elevations and patches of pine are 

found at higher elevations. Most of the region consists of open low mountains or foothills, but 

there are areas of irregular plains in the south and near the border of the adjacent Central 

California Valley ecoregion. Large parts of the region are grazed by domestic livestock; 

relatively little land has been cultivated, although some valleys are or were important agricultural 

centers.

Region 11.1.2 Central California Valley

This region is characterized by flat, intensively farmed plains having long, hot dry summers and 

mild winters, distinguish the Central California Valley from its neighboring ecoregions that are 
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January 14, 2011

either hilly or mountainous, forest or shrub covered, and generally nonagricultural. Nearly half of 

the region is in cropland, about three fourths of which is irrigated. Environmental concerns in the 

region include salinity due to evaporation of irrigation water, groundwater contamination from 

heavy use of agricultural chemicals, wildlife habitat loss, and urban sprawl.

Region 11.1.3 Southern California Mountains

Like the other ecoregions in central and southern California, the Southern California Mountains 

has a Mediterranean climate of hot dry summers and moist cool winters. Although 

Mediterranean types of vegetation such as chaparral and oak woodlands predominate in this 

region, the elevations are considerably higher, the summers are slightly cooler, and precipitation 

amounts are greater than in adjacent ecoregions, resulting in more dense vegetation and some 

large areas of coniferous woodlands. Severe erosion problems are common where the vegetation 

cover has been destroyed by fire or overgrazing.
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January 14, 2011

Region 12.1 Western Sierra Madre Piedmont

Figure C-19: Region 12.1

Region 12.1.1 Madrean Archipelago

Also known as the Sky Islands in the United States, this is a region of basins and ranges with 

medium to high local relief, typically 3,000 to 5,000 feet. Native vegetation in the region is 

mostly grama-tobosa shrubsteppe in the basins and oak-juniper woodlands on the ranges, except 

at higher elevations where ponderosa pine is predominant. The region has ecological significance 

as both a barrier and bridge between two major cordilleras of North America, the Rocky 

Mountains and the Sierra Madre Occidental.
C-40

January 14, 2011

Region 13.1 Upper Gila Mountains

Figure C-20: Region 13.1

Region 13.1.1 Arizona/New Mexico Mountains

The Arizona/New Mexico Mountains are distinguished from neighboring mountainous 

ecoregions by their lower elevations and associated vegetation indicative of drier, warmer 

environments, which is due in part to the region’s more southerly location. Forests of spruce, fir, 

and Douglas-fir, that are common in the Southern Rockies (6.2.14) and the Uinta and Wasatch 

Mountains (6.2.13), are only found in a few high elevation parts of this region. Chaparral is 

common on the lower elevations, pinyon-juniper and oak woodlands are found on lower and 

middle elevations, and the higher elevations are mostly covered with open to dense ponderosa 

pine forests. These mountains are the northern extent of some Mexican plant and animal species.
C-41

January 14, 2011

Region 15.4 Everglades

Figure C-21: Region 15.4

Region 15.4.1 Southern Florida Coastal Plain

The frost free climate of the Southern Florida Coastal Plain makes it distinct from other 

ecoregions in the conterminous United States. This region is characterized by flat plains with wet 

soils, marsh and swamp land cover with everglades and palmetto prairie vegetation types. 

Relatively slight differences in elevation and landform have important consequences for 

vegetation and the diversity of habitat types. Although portions of this region are in parks, game 

refuges, and Indian reservations, a large part of the region has undergone extensive hydrological 

and biological alteration.
C-42

January 14, 2011

References

US EPA. 2010. Primary Distinguishing Characteristics Of Level III Ecoregions Of The 

Continental United States, July, 2010. U.S. Environmental Protection Agency, 

Washington, DC. Available at 

http://www.epa.gov/wed/pages/ecoregions/level_iii_iv.htm.
D-1

January 14, 2011

Appendix D

Maps and calculation procedures for alternative standards

This appendix includes supplemental information that is referred to in section 7.5, 

Considerations associated with alternative standards. Section D.1 provides the calculation 

procedures used in calculating the AAI values; D.2 provides maps of ecoregions not likely

meeting alternative standards and summary tables of all related calculations; D.3 provides the 

critical loads for all acid sensitive ecoregions, calculated AAI values and critical loads for 

relatively non-acid sensitive regions, and CMAQ deposition values and transference ratios for 

each region; and D.4 provides further explanation of calculations in the context of combinations

of NOy and SOx, concentrations and associated deposition, as well as illustrating the effect of 

Neco on calculations.

D.1 Analytical approach and data sources

Results of this assessment are based on calculating AAIs for alternative standards using

the range of levels (20, 35, 59, 75) and nth percentiles (70, 75, 80, 85, 90) discussed in section 

7.4. Because we have modeled data only as a source of deposition and concentration fields, 

CMAQ output for concentration fields, transference ratios, and deposition values enables 

applications for calculating AAI values using equation 7-11. 

ANCcalc = {ANClim + CLr/Qr } – NHx/Qr - TNOy [NOy]/Qr - TSOx[SOx]/Qr (7-11)

Calculations procedures:

1. Assemble current deposition and concentration fields using the annual average value of 

each grid cell and then averaged over the grid cells of the ecoregion of interest.

 Ndep = NHxdep + NOydep

All depositions are in values of meq/m2-yr

2. Calculate CLr(ANClim,%); 

CLr(ANC,%) = ([BC*

0]anclim,% - [ANClim])QANClim,% + Neco

Where the ANClim,% refers to the specified target ANC level and the specific water 

body representing the nth percentile critical load in the ecoregion.
D-2

January 14, 2011

([BC]0,%* is calculated with water quality data (major cations, NO3 and SO4) as 

described in appendix .B)

Neco = ∑ (Ndep – Nleachi)/n

3. Determine if Neco > Ndep

4. Calculate deposition exceedance, and check for Neco conditions:

DEPex = Ndep + Sdep – (([BC]0,%* - [ANClim])Q% + Neco%),, Ndep > Neco

DEPex = Sdep - ([BC]0,%* - [ANClim])Q%, Ndep < Neco

5. Calculate an AAI value, which essentially is a design value at the ecoregion level:

Calculated AAI = Level (as ANClim) - DEPex/Qr

As described in section 7.2, this calculation procedure using deposition exceedances is 

the basis for deriving equation 7-11, and the AAI calculations using this approach is identical, 

algebraically, to using equation 7-11 directly.

Data sources used in the exceedance analyses.

The AAI calculations require estimates of current time frame N and S deposition, 

observed water quality major cations (CA, Mg, K) and strong anions (NO3, SO4) and runoff 

rates, summarized in Table D-1. The 2005 and emissions sensitivity CMAQ simulations were 

applied at 12 km horizontal grid cell resolution and provided all deposition and concentration 

estimates. Water quality data were based on TIME/LTM and other data bases as described in 

chapter 2. The representative critical loads for each ecoregion are provided in Table D.2.
D-3

January 14, 2011

Table D.1 Data sources for calculating AAIs.

Parameter Description Data source Notes

Ndep Sum of wet and dry NOy and NHx 

deposition

2005 12 CMAQ 

base case

meq/(m2

-yr), average of all 12 

km grid cells within ecoregion

NHx Sum of wet and dry NHx 

deposition

2005 12 CMAQ 

base case

meq/(m2

-yr), average of all 12 

km grid cells within ecoregion

Sdep Sum of wet and dry SO2 and SO4 

deposition

2005 12 CMAQ 

base case

meq/(m2

-yr), average of all 12 

km grid cells within ecoregion

Nleach Outflow water column 

concentration of NO3

TIME/LTM and 

STORET data bases 

(Table 2-4)

μeq/l; average of all water bodies 

in an ecoregion

BC*

0,% Preindustrial base cation levels TIME/LTM and 

STORET data bases 

(Table 2-4)

μeq/l; based on the % water body

Q Annual runoff rate USGS (Table 2-4) m/yr; based on the % water body

Qr Ecoregion representative

Annual runoff rate

USGS (Table 2-4) m/yr; based on the 50 % water 

body
D-4

January 14, 2011

D.2 Maps and tables of exceedances relative to different alternative standards

Figure D-1. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 35 μeq/L and 

the form is based on the 70th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-5

January 14, 2011

Figure D-2. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 35 μeq/L and 

the form is based on the 80th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-6

January 14, 2011

Figure D-3. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 35 μeq/L and 

the form is based on the 90th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-7

January 14, 2011

Figure D-4. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 50 μeq/L and 

the form is based on the 70th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-8

January 14, 2011

 

Figure D-5. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 50 μeq/L and 

the form is based on the 80th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-9

January 14, 2011

Figure D-6. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 50 μeq/L and 

the form is based on the 90th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-10

January 14, 2011

Figure D-7. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 75 μeq/L and 

the form is based on the 70th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-11

January 14, 2011

Figure D-8. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 75 μeq/L and 

the form is based on the 80th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-12

January 14, 2011

Figure D-9. Acid sensitive ecoregion level III areas likely not meeting the standard when the level = 75 μeq/L and 

the form is based on the 90th percentile water body; based on CMAQ 2005 (top) and emissions sensitivity(bottom) 

simulations. The legend reflects the magnitude of the exceedance relative to the specified level of the standard.
D-13

January 14, 2011

D.3 Data summary tables

Tables D-3a-d include all AAI calculations for 2005 and emissions sensitivity 

simulations for acid sensitive regions; Tables D-4a-b includes critical load values for all acid 

sensitive areas; Table D-5 includes 2005 calculated AAI values and critical loads for relatively 

non-acid sensitive areas; Table D-6 includes CMAQ deposition values and transference ratios for 

each ecoregion.
D-14

January 14, 2011

Table D-3a. Calculated AAI values for sensitive ecoregions across the range of nth percentiles for a Level 

of 20 μeq/L: pairs of 2005 base and emissions sensitivity (42% and 48% NOx and SOx reduction). 

Highlighted cell pairs: Yellow-Green (likely not meeting 2005; likely meeting with emissions reductions); YellowRed (likely not meeting 2005 and after emissions reductions)

70th 75th 80th 85th 90th

6.2.4 Canadian Rockies 933.4 935.8 740.3 742.7 685.6 688 551.0 553.4 84.5 86.9

6.2.3 Columbia 

Mountains/Northern 

Rockies

353.5 359.5 267.3 273.2 190.3 196.3 136.5 142.5 106.3 112.2

6.2.7 Cascades 90.2 92.7 72.2 74.7 46.2 48.6 31.3 33.8 19.1 21.6

8.5.4 Atlantic Coastal Pine 

Barrens

-154.6 -62.5 -172.7 -80.6 -174.6 -82.5 -182.4 -90.2 -193.6 -101.5

5.3.1 Northern Appalachian 

and Atlantic Maritime 

Highlands

58.3 102.9 49.0 93.6 33.6 78.2 21.3 65.9 6.4 51

6.2.10 Middle Rockies 180.0 187.9 122.1 130 99.0 106.9 81.6 89.5 69.4 77.3

8.1.3 Northern Appalachian 

Plateau and Uplands

227.0 342.3 173.4 288.7 165.7 281 120.4 235.7 85.6 200.9

8.1.7 Northeastern Coastal 

Zone

42.0 117.5 22.6 98.1 9.3 84.8 -4.4 71.1 -23.6 51.9

5.3.3 North Central 

Appalachians

-60.8 79.5 -74.4 66 -87.4 52.9 -97.8 42.6 -112.5 27.9

8.1.8 Maine/New Brunswick 

Plains and Hills

89.7 110.1 84.4 104.7 71.0 91.4 65.5 85.9 48.5 68.9

6.2.5 North Cascades 138.4 139.8 130.7 132.1 112.9 114.2 93.8 95.2 65.8 67.2

5.2.1 Northern Lakes and 

Forests

51.4 97.9 38.7 85.2 25.9 72.4 14.1 60.6 3.8 50.3

6.2.15 Idaho Batholith 66.8 70.3 62.0 65.4 59.3 62.8 48.0 51.5 41.6 45.1

8.4.1 Ridge and Valley -72.3 76.2 -95.1 53.4 -117.6 30.9 -143.7 4.8 -177.8 -29.3

8.4.2 Central Appalachians -78.3 109.2 -109.0 78.4 -147.0 40.5 -169.5 17.9 -186.2 1.3

8.4.3 Western Allegheny 

Plateau

412.4 694.3 280.7 562.6 47.4 329.3 -20.8 261.1 -97.9 184

6.2.13 Wasatch and Uinta 

Mountains

297.8 323.8 255.3 281.4 230.6 256.7 174.6 200.6 136.6 162.6

8.5.1 Middle Atlantic Coastal 

Plain

-17.2 75.4 -29.5 63.1 -64.0 28.5 -131.6 -39.1 -169.4 -76.9

6.2.12 Sierra Nevada 49.1 59.2 38.2 48.2 28.1 38.2 22.2 32.2 12.6 22.6

6.2.14 Southern Rockies 120.6 129.4 98.5 107.4 85.7 94.6 67.6 76.4 50.8 59.7

8.4.4 Blue Ridge -65.5 68.8 -73.5 60.8 -83.3 51 -93.1 41.3 -104.9 29.4

8.3.5 Southeastern Plains -51.2 21 -59.2 13 -73.1 -0.9 -91.0 -18.9 -106.7 -34.5

8.3.4 Piedmont 131.6 266.5 102.7 237.5 72.7 207.6 45.7 180.6 11.8 146.7

8.4.9 Southwestern 

Appalachians

35.3 131.9 -18.5 78.1 -29.4 67.2 -69.8 26.7 -121.5 -25

8.4.6 Boston Mountains 65.1 119.3 65.1 119.3 27.7 81.9 8.7 62.8 -24.4 29.7

8.4.7 Arkansas Valley 90.1 129.6 82.5 122 66.2 105.7 50.7 90.2 -1.0 38.5

8.5.3 Southern Coastal Plain -31.2 102.6 -61.8 71.9 -105.1 28.6 -143.2 -9.4 -154.9 -21.2

8.4.8 Ouachita Mountains 89.3 140.5 74.6 125.9 64.6 115.8 51.3 102.6 -3.3 47.9

8.3.7 South Central Plains 287.1 346.3 279.6 338.8 213.3 272.6 136.4 195.7 47.3 106.6
D-15

January 14, 2011

Table D-3b. Calculated AAI values for sensitive ecoregions across the range of nth percentiles for a 

Level of 35 μeq/L: pairs of 2005 base and emissions sensitivity (42% and 48% NOx and SOx 

reduction). Highlighted cell pairs: Yellow-Green (likely not meeting 2005; likely meeting with emissions 

reductions); Yellow-Red (likely not meeting 2005 and after emissions reductions)

70% 75% 80% 85% 90%

6.2.4 Canadian Rockies 934.4 936.8 736.5 738.9 692.4 694.8 556.9 559.2 80.9 83.2

6.2.3 Columbia 

Mountains/Northern 

Rockies

342.8 348.8 257.3 263.2 188.0 194 122.5 128.5 106.8 112.8

6.2.7 Cascades 92.1 94.6 74.7 77.1 47.8 50.3 30.7 33.2 21.4 23.9

8.5.4 Atlantic Coastal Pine 

Barrens

-160.7 -68.5 -173.7 -81.5 -174.3 -82.1 -180.7 -88.5 -192.9 -100.7

5.3.1 Northern Appalachian 

and Atlantic Maritime 

Highlands

58.8 103.5 46.2 90.8 32.1 76.7 20.1 64.7 3.4 48

6.2.10 Middle Rockies 179.1 187 121.7 129.6 99.2 107.1 81.0 88.9 63.3 71.2

8.1.3 Northern Appalachian 

Plateau and Uplands

225.5 340.7 173.7 289 163.9 279.2 121.4 236.7 87.3 202.6

8.1.7 Northeastern Coastal 

Zone

42.3 117.8 23.2 98.8 8.9 84.4 -6.1 69.4 -24.2 51.3

5.3.3 North Central 

Appalachians

-59.8 80.6 -71.8 68.6 -88.7 51.7 -95.3 45.1 -114.3 26.1

8.1.8 Maine/New Brunswick 

Plains and Hills

89.5 109.9 84.6 105 72.1 92.5 65.9 86.3 47.1 67.5

6.2.5 North Cascades 144.4 145.8 125.1 126.4 110.3 111.7 86.5 87.8 71.6 73

5.2.1 Northern Lakes and 

Forests

52.7 99.2 39.6 86.1 25.7 72.2 14.3 60.8 1.0 47.5

6.2.15 Idaho Batholith 60.2 63.6 56.7 60.1 53.7 57.2 49.6 53.1 39.8 43.2

8.4.1 Ridge and Valley -72.3 76.2 -94.0 54.5 -116.3 32.1 -144.8 3.7 -175.4 -26.9

8.4.2 Central Appalachians -80.3 107.1 -107.3 80.1 -143.2 44.2 -173.0 14.4 -182.0 5.4

8.4.3 Western Allegheny 

Plateau

415.2 697.1 281.4 563.3 46.2 328.1 -22.1 259.8 -94.1 187.8

6.2.13 Wasatch and Uinta 

Mountains

287.3 313.4 243.0 269 221.8 247.9 184.4 210.5 126.3 152.4

8.5.1 Middle Atlantic Coastal 

Plain

-16.2 76.4 -32.0 60.5 -61.4 31.1 -133.2 -40.7 -163.4 -70.9

6.2.12 Sierra Nevada 47.4 57.4 40.5 50.5 30.6 40.7 21.8 31.8 13.8 23.8

6.2.14 Southern Rockies 120.2 129 102.3 111.2 87.3 96.2 66.4 75.3 53.1 62

8.4.4 Blue Ridge -65.4 69 -71.6 62.7 -82.4 52 -93.6 40.7 -104.6 29.7

8.3.5 Southeastern Plains -55.5 16.6 -63.4 8.8 -72.1 0.1 -96.2 -24 -107.9 -35.7

8.3.4 Piedmont 131.2 266 96.1 231 72.6 207.4 43.7 178.6 14.0 148.9

8.4.9 Southwestern 

Appalachians

31.1 127.7 -12.2 84.3 -29.1 67.5 -71.4 25.1 -121.3 -24.8

8.4.6 Boston Mountains 65.1 119.3 65.1 119.3 28.9 83 8.7 62.9 -21.2 33

8.4.7 Arkansas Valley 89.3 128.8 85.7 125.2 71.4 110.9 50.6 90.1 -2.0 37.5

8.5.3 Southern Coastal Plain -29.1 104.6 -61.7 72 -106.5 27.3 -138.6 -4.9 -150.8 -17

8.4.8 Ouachita Mountains 89.5 140.8 78.9 130.1 67.4 118.7 49.3 100.5 -4.8 46.5

8.3.7 South Central Plains 291.9 351.1 275.4 334.6 210.4 269.7 133.1 192.4 47.3 106.6
D-16

January 14, 2011

Table D-3c. Calculated AAI values for sensitive ecoregions across the range of nth percentiles for a Level 

of 50 μeq/L: pairs of 2005 base and emissions sensitivity (42% and 48% NOx and SOx reduction).

Highlighted cell pairs: Yellow-Green (likely not meeting 2005; likely meeting with emissions reductions); YellowRed (likely not meeting 2005 and after emissions reductions)

70% 75% 80% 85% 90%

6.2.4 Canadian Rockies 935.5 937.8 732.7 735 699.2 701.6 562.7 565.1 79.4 81.8

6.2.3 Columbia 

Mountains/Northern 

Rockies

327.0 333 262.1 268.1 192.3 198.2 132.6 138.5 106.4 112.4

6.2.7 Cascades 93.0 95.4 72.5 74.9 51.7 54.2 29.2 31.7 23.9 26.4

8.5.4 Atlantic Coastal Pine 

Barrens

-166.7 -74.5 -174.0 -81.8 -174.7 -82.5 -176.5 -84.3 -192.7 -100.5

5.3.1 Northern Appalachian and 

Atlantic Maritime Highlands

57.0 101.6 44.0 88.7 30.8 75.4 17.0 61.7 1.1 45.7

6.2.10 Middle Rockies 178.2 186.1 119.5 127.4 97.5 105.4 81.6 89.5 56.9 64.8

8.1.3 Northern Appalachian 

Plateau and Uplands

223.9 339.2 174.1 289.4 162.2 277.5 122.4 237.7 89.0 204.3

8.1.7 Northeastern Coastal Zone 42.1 117.6 23.9 99.4 8.7 84.2 -7.2 68.3 -24.8 50.7

5.3.3 North Central Appalachians -58.7 81.7 -69.2 71.1 -87.5 52.9 -95.5 44.9 -115.8 24.6

8.1.8 Maine/New Brunswick 

Plains and Hills

90.1 110.5 84.8 105.2 73.4 93.8 65.1 85.5 45.8 66.2

6.2.5 North Cascades 148.3 149.7 123.8 125.2 116.8 118.2 93.0 94.4 69.7 71.1

5.2.1 Northern Lakes and Forests 54.0 100.5 39.8 86.3 24.7 71.1 14.1 60.6 -0.2 46.3

6.2.15 Idaho Batholith 59.3 62.8 53.6 57.1 43.9 47.4 40.6 44 37.5 40.9

8.4.1 Ridge and Valley -75.2 73.3 -94.4 54.1 -116.8 31.6 -144.0 4.5 -172.8 -24.3

8.4.2 Central Appalachians -82.0 105.5 -105.5 82 -140.5 46.9 -172.1 15.3 -182.1 5.3

8.4.3 Western Allegheny Plateau 418.0 699.9 282.2 564.1 43.2 325.1 -23.4 258.5 -90.3 191.6

6.2.13 Wasatch and Uinta 

Mountains

276.9 303 230.6 256.7 199.4 225.5 194.2 220.3 109.5 135.6

8.5.1 Middle Atlantic Coastal 

Plain

-15.2 77.4 -34.6 58 -58.8 33.8 -134.8 -42.3 -157.4 -64.9

6.2.12 Sierra Nevada 44.9 55 39.0 49 32.0 42.1 24.3 34.3 13.7 23.8

6.2.14 Southern Rockies 124.1 133 112.6 121.5 85.2 94.1 67.9 76.7 58.1 66.9

8.4.4 Blue Ridge -65.3 69 -72.9 61.5 -82.7 51.6 -92.5 41.8 -102.8 31.5

8.3.5 Southeastern Plains -57.5 14.7 -65.2 7 -77.2 -5.1 -102.6 -30.4 -112.7 -40.6

8.3.4 Piedmont 125.8 260.6 99.6 234.5 74.2 209.1 40.1 175 14.8 149.7

8.4.9 Southwestern Appalachians 26.9 123.4 -6.0 90.5 -28.8 67.7 -73.1 23.5 -121.1 -24.6

8.4.6 Boston Mountains 65.1 119.2 65.1 119.2 30.0 84.2 8.7 62.9 -17.9 36.2

8.4.7 Arkansas Valley 88.8 128.3 88.4 127.9 76.6 116.1 50.5 90 0.7 40.2

8.5.3 Southern Coastal Plain -27.5 106.2 -55.8 77.9 -101.9 31.8 -134.7 -1 -151.7 -18

8.4.8 Ouachita Mountains 89.8 141 83.1 134.4 70.3 121.5 47.2 98.5 -6.2 45

8.3.7 South Central Plains 296.7 355.9 271.2 330.5 209.4 268.7 129.8 189.1 47.3 106.6
D-17

January 14, 2011

Table D-3d. Calculated AAI values for sensitive ecoregions across the range of nth percentiles for a Level 

of 75 μeq/L: pairs of 2005 base and emissions sensitivity (42% and 48% NOx and SOx reduction).

Highlighted cell pairs: Yellow-Green (likely not meeting 2005; likely meeting with emissions reductions); YellowRed (likely not meeting 2005 and after emissions reductions)

70% 75% 80% 85% 90%

6.2.4 Canadian Rockies

937.2 939.5 726.3 728.7 710.5 712.9 572.5 574.9 63.5 77.1

6.2.3 Columbia 

Mountains/Northern 

Rockies 316.6 322.6 268.7 274.7 201.8 207.7 130.5 136.5 40.3 103.6

6.2.7 Cascades

89.4 91.9 70.1 72.6 49.9 52.4 36.2 38.7 11.3 26.8

8.5.4 Atlantic Coastal Pine 

Barrens -166.7 -74.6 -173.5 -81.3 -176.4 -84.2 -176.7 -84.6 -205.6 -201.3

5.3.1 Northern Appalachian and 

Atlantic Maritime 

Highlands 56.3 100.9 43.4 88 27.6 72.2 14.6 59.3 -25.3 -3

6.2.10 Middle Rockies

176.6 184.5 125.2 133.1 98.0 105.9 83.1 91 32.7 52.8

8.1.3 Northern Appalachian 

Plateau and Uplands 225.3 340.6 174.6 289.9 159.3 274.5 128.5 243.7 -35.2 86.6

8.1.7 Northeastern Coastal Zone

43.0 118.5 25.0 100.5 8.1 83.6 -5.2 70.3 -47.4 -25.2

5.3.3 North Central Appalachians -56.9 83.5 -73.1 67.3 -85.3 55 -92.8 47.6 -128.1 -112.5

8.1.8 Maine/New Brunswick 

Plains and Hills 87.9 108.2 83.2 103.6 71.7 92.1 62.5 82.9 15.2 43.3

6.2.5 North Cascades

145.6 147 125.1 126.5 116.0 117.4 95.4 96.7 24.4 73.2

5.2.1 Northern Lakes and Forests 54.9 101.4 41.0 87.5 26.2 72.7 12.8 59.3 -17.4 -2.9

6.2.15 Idaho Batholith

55.7 59.2 44.9 48.3 38.8 42.3 30.1 33.5 13.6 27.5

8.4.1 Ridge and Valley -73.1 75.4 -95.5 53 -116.5 32 -142.2 6.3 -203.2 -169.4

8.4.2 Central Appalachians -81.7 105.7 -106.4 81.1 -137.9 49.6 -169.5 18 -263.2 -180.3

8.4.3 Western Allegheny Plateau 422.7 704.6 283.4 565.3 41.2 323.1 -25.6 256.3 -183.9 -84

6.2.13 Wasatch and Uinta 

Mountains 259.6 285.6 210.6 236.7 186.2 212.3 168.0 194 71.9 102.8

8.5.1 Middle Atlantic Coastal 

Plain -13.5 79 -38.8 53.7 -54.4 38.1 -137.4 -44.9 -218.4 -148.5

6.2.12 Sierra Nevada 45.9 55.9 39.6 49.6 30.8 40.8 23.9 33.9 -6.9 12.5

6.2.14 Southern Rockies

127.1 135.9 112.6 121.4 92.2 101.1 78.3 87.2 46.6 64.8

8.4.4 Blue Ridge -64.8 69.5 -74.7 59.6 -83.3 51 -92.9 41.4 -116.5 -104.8

8.3.5 Southeastern Plains -59.8 12.3 -68.9 3.2 -89.3 -17.1 -109.0 -36.8 -142.1 -126.5

8.3.4 Piedmont

126.1 261 98.6 233.5 70.2 205 37.6 172.5 -50.6 13

8.4.9 Southwestern 

Appalachians 19.9 116.4 4.4 100.9 -28.4 68.2 -75.8 20.7

-

3741.4 -120.8

8.4.6 Boston Mountains

65.0 119.2 65.0 119.2 31.9 86.1 8.8 62.9 -12.5 -12.5

8.4.7 Arkansas Valley 93.8 133.3 87.0 126.5 85.3 124.8 50.4 89.9 -7.4 5.2

8.5.3 Southern Coastal Plain -31.0 102.7 -60.7 73 -94.4 39.3 -132.0 1.7 -182.4 -156.6

8.4.8 Ouachita Mountains 90.2 141.5 90.2 141.5 75.1 126.3 43.8 95 -44.9 -8.7

8.3.7 South Central Plains

304.7 363.9 264.3 323.5 220.8 280.1 124.3 183.6 20.9 47.3
D-18

January 14, 2011

Table D-4a. Representative critical loads for acid sensitive ecoregions - Level values of 35 and 20 μeq/L.

EcoRegion Level III 70% 75% 80% 85% 90% 70% 75% 80% 85% 90%

Numbe

r Description

ANC 

35

ANC 

35

ANC 

35

ANC 

35

ANC 

35

ANC 

20

ANC 

20

ANC 

20

ANC 

20

ANC 

20

5.2.1

No. Lakes and

Forests 79.1 74.7 70.1 66.3 61.9 83.6 79.4 75.2 71.3 67.8

5.3.1

No. Appalachian/

Atlantic Maritime

Highlands 120.4 112.3 103.4 95.8 85.1 129.6 123.6 113.9 106.0 96.6

5.3.3

No. Central

Appalachians 132.4 125.8 116.5 112.9 102.4 140.1 132.6 125.4 119.7 111.7

6.2.10 Middle Rockies 82.0 60.8 52.5 45.7 39.1 87.9 66.5 57.9 51.5 47.0

6.2.12 Sierra Nevada 55.0 50.5 44.1 38.3 33.1 66.0 58.8 52.3 48.4 42.1

6.2.13

Wasatch and

Uinta Mountains 90.5 81.3 76.9 69.0 56.9 95.9 87.0 81.8 70.1 62.2

6.2.14 Southern Rockies 75.4 66.2 58.4 47.7 40.8 83.3 71.9 65.3 56.0 47.3

6.2.15 Idaho Batholith 41.2 38.9 36.9 34.1 27.5 55.8 52.5 50.7 43.1 38.8

6.2.3

Columbia Mtns/

No. Rockies 172.9 131.7 98.4 66.9 59.4 185.2 143.8 106.8 80.9 66.3

6.2.4 Canadian Rockies 893.1 703.5 661.2 531.3 75.2 906.5 721.5 669.1 540.1 93.1

6.2.5 No. Cascades 227.7 194.4 169.0 128.0 102.4 243.2 230.0 199.3 166.5 118.3

6.2.7 Cascades 131.0 104.1 62.8 36.6 22.3 151.0 123.5 83.4 60.6 41.8

8.1.3

No. Appalachian

Plateau and 

Uplands 256.8 230.1 225.1 203.2 185.6 265.3 237.7 233.7 210.4 192.5

8.1.7 NE Coastal Zone 168.7 156.7 147.6 138.2 126.8 177.9 165.7 157.3 148.7 136.6

8.1.8

Maine/NBrunswic

k

Plains and Hills 112.8 109.5 101.0 96.7 84.0 123.2 119.5 110.4 106.7 95.1

8.3.4 Piedmont 208.4 193.1 182.8 170.3 157.3 215.1 202.5 189.4 177.7 162.9

8.3.5

Southeastern 

Plains 87.6 83.7 79.4 67.3 61.5 97.3 93.3 86.4 77.4 69.6

8.3.7

South Central 

Plains 213.4 207.2 182.7 153.6 121.2 217.2 214.4 189.4 160.5 126.9

8.4.1 Ridge and Valley 133.0 122.9 112.6 99.3 85.1 140.0 129.4 119.0 106.9 91.0

8.4.2

Central

Appalachians 161.6 146.9 127.4 111.3 106.4 170.8 154.1 133.5 121.3 112.3

8.4.3

Western 

Allegheny

Plateau 445.6 384.9 278.0 247.0 214.3 451.2 391.3 285.4 254.4 219.3

8.4.4 Blue Ridge 122.9 119.9 114.8 109.3 104.0 130.1 126.2 121.5 116.8 111.1

8.4.6 Boston Mountains 137.3 137.3 121.4 112.6 99.5 143.8 143.8 127.5 119.1 104.7

8.4.7 Arkansas Valley 139.0 137.3 130.4 120.4 95.0 146.7 143.0 135.1 127.6 102.7

8.4.8

Ouachita 

Mountains 138.3 133.3 127.9 119.3 93.8 145.3 138.4 133.6 127.4 101.6

8.4.9

Southwestern

Appalachians 177.6 149.3 138.4 110.8 78.3 190.1 155.0 148.0 121.6 88.0

8.5.1

Middle Atlantic

Coastal Plain 145.9 138.6 125.0 91.8 77.8 152.4 146.7 130.7 99.5 82.0

8.5.3

Southern Coastal

Plain 100.9 92.5 81.0 72.7 69.6 104.2 96.4 85.2 75.4 72.4

8.5.4

Atlantic Coastal 

Pine

Barrens 85.4 78.4 78.1 74.7 68.1 96.6 86.9 85.9 81.8 75.8
D-19

January 14, 2011

Table D-4b. Representative critical loads for acid sensitive ecoregions - Level values of 75 and 50 μeq/L.

EcoRegion Level III 70% 75% 80% 85% 90% 70% 75% 80% 85% 90%

Numbe

r Description

ANC 

75

ANC 

75

ANC 

75

ANC 

75

ANC 

75

ANC 

50

ANC 

50

ANC 

50

ANC 

50 ANC 50

5.2.1

No. Lakes and 

Forests 66.5 61.9 57.0 52.6 47.4 74.5 69.8 64.8 61.3 56.5

5.3.1

No. Appalachian/

Atlantic Maritime

Highlands 93.4 85.1 75.1 66.9 55.7 109.7 101.4 93.0 84.3 74.1

5.3.3

No. Central

Appalachians 112.0 103.1 96.3 92.3 81.4 124.8 119.0 108.9 104.5 93.3

6.2.10 Middle Rockies 66.3 47.3 37.2 31.7 20.4 76.1 54.4 46.3 40.4 31.2

6.2.12 Sierra Nevada 27.9 23.8 18.0 13.5 6.1 43.6 39.8 35.2 30.2 23.2

6.2.13

Wasatch and 

Uinta 

Mountains 76.4 66.2 61.1 57.2 43.6 85.2 75.6 69.0 68.0 50.2

6.2.14

Southern 

Rockies 58.3 50.8 40.3 33.1 26.2 69.7 63.7 49.6 40.6 35.6

6.2.15 Idaho Batholith 11.4 4.1 0.0 -5.9 -7.6 30.6 26.7 20.2 18.0 15.9

6.2.3

Columbia 

Mtns/

No. Rockies 141.0 118.0 85.8 51.5 38.6 158.1 126.8 93.2 64.5 52.0

6.2.4

Canadian 

Rockies 857.4 655.4 640.2 508.0 33.3 879.7 685.4 653.3 522.6 59.5

6.2.5 No. Cascades 161.0 125.6 110.0 74.4 36.2 208.6 166.4 154.3 113.4 73.3

6.2.7 Cascades 65.3 35.7 4.6 -16.4 -30.9 109.2 77.7 45.8 11.2 3.1

8.1.3

No. 

Appalachian

Plateau and 

Uplands 236.1 210.0 202.1 186.2 164.6 248.2 222.6 216.5 196.0 178.7

8.1.7

NE Coastal 

Zone 143.9 132.6 121.9 113.5 100.9 159.1 147.6 138.1 128.0 117.0

8.1.8 Maine/NBrunswick

Plains and Hills 84.5 81.3 73.5 67.2 54.1 103.0 99.4 91.7 86.0 72.9

8.3.4 Piedmont 188.7 176.7 164.4 150.2 139.5 199.5 188.1 177.0 162.2 151.1

8.3.5

Southeastern 

Plains 65.5 60.9 50.8 41.0 32.2 79.2 75.3 69.3 56.6 51.6

8.3.7

South Central 

Plains 203.1 187.9 171.5 135.2 106.2 209.5 199.9 176.7 146.7 115.6

8.4.1

Ridge and 

Valley 114.1 103.7 93.9 82.0 69.3 124.7 115.8 105.4 92.8 79.4

8.4.2

Central

Appalachians 139.1 125.7 108.6 91.5 85.6 152.5 139.8 120.7 103.6 98.2

8.4.3

Western 

Allegheny

Plateau 430.9 367.6 257.5 227.2 200.7 440.1 378.4 269.8 239.6 209.2

8.4.4 Blue Ridge 103.9 99.2 95.1 90.4 84.7 115.7 112.1 107.4 102.7 97.7

8.4.6

Boston 

Mountains 119.7 119.7 105.2 95.1 85.8 130.7 130.7 115.3 106.0 94.4

8.4.7

Arkansas 

Valley 121.9 118.6 117.8 101.0 79.1 131.6 131.4 125.7 113.1 89.0

8.4.8

Ouachita 

Mountains 119.8 119.8 112.6 97.9 73.1 131.4 128.2 122.2 111.3 86.1

8.4.9

Southwestern

Appalachians 144.2 134.1 112.8 81.9 52.6 165.1 143.6 128.8 100.0 68.7

8.5.1

Middle Atlantic

Coastal Plain 128.7 116.9 109.7 71.4 66.3 139.4 130.5 119.3 84.1 73.7

8.5.3

Southern 

Coastal

Plain 90.1 82.5 73.8 64.1 57.8 97.4 90.2 78.3 69.9 65.5

8.5.4

Atlantic 

Coastal Pine

Barrens 60.8 57.2 55.6 55.4 42.3 74.1 70.2 69.9 68.9 60.3
D-20

January 14, 2011

Table D-5. Critical Loads (meq/(m2-yr) and calculated AAI values for relatively less non-acid sensitive ecoregions 

(note all calculations based on nth %percentile = 50).

Region Name CL75 CL50 CL35 CL20 AAI75 AAI50 AAI35 AAI20

10.1.2 Columbia Plateau 112.8 113.5 113.8 114.2 3618.4 3618.4 3618.4 3618.4

10.1.3 Northern Basin and Range 60.2 64.4 66.1 66.9 877.6 932.5 950.5 951.3

10.1.4 Wyoming Basin 54.0 70.3 77.2 87.1 133.6 140.3 139.0 143.3

10.1.5 Central Basin and Range 79.3 86.1 89.6 92.2 1814.0 1989.3 2077.6 2141.6

10.1.6 Colorado Plateaus 147.2 149.2 150.4 151.6 2083.9 2093.0 2098.5 2104.0

10.1.7 Arizona/New Mexico Plateau 90.2 90.6 90.9 91.1 969.1 950.0 938.6 927.1

10.1.8 Snake River Plain 113.9 116.1 117.4 118.8 1133.5 1140.1 1144.0 1147.9

10.2.2 Sonoran Desert 100.0 100.6 101.0 101.3 3284.1 3287.5 3289.5 3291.6

11.1.1 California Coastal Sage, Chaparral, and Oak 

Woodlands 281.6 283.0 283.8 284.6 2174.8 2162.5 2155.1 2147.7

11.1.2 Central California Valley 174.3 184.0 189.9 195.7 361.8 366.0 368.5 371.1

11.1.3 Southern and Baja California Pine-Oak 

Mountains 386.4 392.0 393.9 395.9 3253.8 3285.5 3290.0 3294.5

12.1.1 Madrean Archipelago 71.0 71.3 71.4 71.6 3076.7 3073.1 3071.0 3068.8

13.1.1 Arizona/New Mexico Mountains 152.6 153.0 153.3 153.6 2032.3 2015.0 2004.6 1994.2

6.2.11 Klamath Mountains 755.3 766.3 772.9 782.4 1045.5 1035.2 1029.0 1026.7

6.2.8 Eastern Cascades Slopes and Foothills 184.9 196.7 201.6 230.2 335.6 329.3 322.2 352.7

6.2.9 Blue Mountains 209.5 212.7 222.3 223.1 867.3 855.6 880.7 869.0

7.1.7 Strait of Georgia/Puget Lowland 271.0 282.7 301.0 325.1 232.6 217.0 216.7 221.2

7.1.8 Coast Range 881.0 947.7 957.6 992.1 715.8 741.9 734.5 746.0

8.1.1 Eastern Great Lakes and Hudson Lowlands 637.2 653.3 662.9 672.6 974.0 978.8 981.8 984.7

8.1.10 Erie Drift Plain 888.1 896.3 901.2 906.1 1314.6 1305.5 1300.1 1294.6

8.1.4 North Central Hardwood Forests 396.2 401.2 402.6 404.0 1428.7 1426.9 1418.5 1410.1

8.1.5 Driftless Area 943.6 948.7 951.8 954.9 3951.7 3950.9 3950.4 3950.0

8.1.6 S. Michigan/N. Indiana Drift Plains 919.3 926.3 930.4 934.6 2620.3 2618.9 2618.1 2617.2

8.2.1 Southeastern Wisconsin Till Plains 878.9 884.5 887.8 891.1 3705.1 3707.2 3708.4 3709.6

8.2.3 Central Corn Belt Plains 1050.1 1058.0 1062.7 1067.5 3880.7 3889.9 3895.4 3900.9

8.2.4 Eastern Corn Belt Plains 931.2 939.8 944.9 950.1 2295.2 2297.0 2298.2 2299.3

8.3.1 Northern Piedmont 386.2 403.8 414.4 425.0 401.3 410.7 416.4 422.0

8.3.2 Interior River Valleys and Hills 574.0 581.0 585.1 589.3 1799.0 1804.5 1807.7 1811.0

8.3.3 Interior Plateau 803.4 815.5 822.7 829.9 1357.2 1357.7 1357.9 1358.2

8.4.5 Ozark Highlands 719.4 725.8 729.6 733.4 2202.4 2200.1 2198.7 2197.3

8.5.2 Mississippi Alluvial Plain 1039.4 1052.2 1059.9 1067.7 2069.5 2072.4 2074.2 2075.9

9.2.1 Aspen Parkland/Northern Glaciated Plains 154.1 154.8 155.3 155.7 6663.8 6690.8 6707.0 6723.1

9.2.2 Lake Manitoba and Lake Agassiz Plain 261.0 261.7 262.1 262.5 7748.4 7750.1 7751.1 7752.1

9.2.3 Western Corn Belt Plains 492.9 495.5 497.0 498.6 3006.8 3002.3 2999.6 2996.9

9.2.4 Central Irregular Plains 495.7 500.4 503.2 505.9 1800.5 1796.5 1794.0 1791.6

9.3.1 Northwestern Glaciated Plains 145.4 145.6 145.7 145.8 9116.2 9105.5 9099.0 9092.6

9.3.3 Northwestern Great Plains 173.9 174.5 174.9 175.3 5810.6 5812.9 5814.2 5815.6

9.3.4 Nebraska Sand Hills 354.4 355.8 356.6 357.5 4103.2 4096.8 4093.0 4089.2
D-21

January 14, 2011

Table D-5. Critical Loads (meq/(m2-yr) and calculated AAI values for relatively less non-acid sensitive ecoregions 

(note all calculations based on nth %percentile = 50).

Region Name CL75 CL50 CL35 CL20 AAI75 AAI50 AAI35 AAI20

9.4.1 High Plains 101.7 102.0 102.2 102.4 1632.4 1618.2 1609.7 1601.3

9.4.2 Central Great Plains 197.5 198.9 199.7 200.5 2340.2 2341.2 2341.8 2342.4

9.4.3 Southwestern Tablelands 86.5 86.7 86.9 87.0 5535.8 5538.1 5539.5 5540.8

9.4.5 Cross Timbers 309.4 312.6 314.5 316.5 1916.1 1918.2 1919.4 1920.7

9.5.1 Western Gulf Coastal Plain 738.9 750.9 758.0 765.1 1416.0 1416.0 1416.0 1416.0

15.4.11 Southern Florida Coastal Plain 281.6 283.0 301.0 316.5 931.9 913.4 986.0 1046.1

5.2.21 Northern Minnesota Wetlands 281.6 283.0 301.0 316.5 1166.4 1147.9 1220.5 1280.6

8.2.21 Huron/Erie Lake Plains 281.6 283.0 301.0 316.5 547.1 528.6 601.3 661.4

9.6.1 Southern Texas Plains/Interior Plains and Hills 

with Xerophytic Shrub and Oak Forest 281.6 283.0 301.0 316.5 1086.2 1067.7 1140.3 1200.4

10.2.11 Mojave Basin and Range 281.6 283.0 301.0 316.5 1291.0 1272.5 1345.1 1405.2

10.2.41 Chihuahuan Desert 281.6 283.0 301.0 316.5 1265.2 1246.7 1319.3 1379.4

7.1.91 Willamette Valley 281.6 283.0 301.0 316.5 1109.3 1090.9 1163.5 1223.6

8.3.61 Mississippi Valley Loess Plains 281.6 283.0 301.0 316.5 783.1 764.6 837.2 897.3

9.4.61 Edwards Plateau 281.6 283.0 301.0 316.5 1087.3 1068.8 1141.4 1201.5

9.4.41 Flint Hills 281.6 283.0 301.0 316.5 939.4 920.9 993.5 1053.6

9.4.71 Texas Blackland Prairies 281.6 283.0 301.0 316.5 835.5 817.0 889.7 949.7

8.3.81 East Central Texas Plains 281.6 283.0 301.0 316.5 881.2 862.7 935.3 995.4

1 – indicates relatively non-acidless- sensitive regions with data from less than 10 water bodies; CL and Qr values are based on median values of 

all the relatively non-acid sensitive regions with data from ≥ 10 water bodies with data.
D-22

January 14, 2011

Table D-6. CMAQ 2005 annual average depositions, transference ratios, and Neco vales. All depositions and 

Neco in meq/(m2

-yr); T ratios in m/yr; Ndep = NOydep + NHxdep. 

EcoRgnIII

3III

Name Total

NDep

NHx

Dep

SOx

Dep

TSOx TNOy Neco1

10.1.2 Columbia Plateau 19.2 8.9 3.6 8.9 7.1 17.1

10.1.3 Northern Basin and Range 14.2 5.6 4.3 15.3 14.9 13.1

10.1.4 Wyoming Basin 15.7 4.7 8.3 13.3 11.9 14.8

10.1.5 Central Basin and Range 15.6 4.4 4.7 14.3 13.4 15.1

10.1.6 Colorado Plateaus 21.3 5.8 9.0 19.3 16.4 19.8

10.1.7 Arizona/New Mexico Plateau 19.3 4.7 7.5 14.1 12.2 18.7

10.1.8 Snake River Plain 33.1 21.6 6.2 14.8 7.6 27.3

10.2.1 Mojave Basin and Range 26.1 4.9 5.4 8.6 11.2 NA

10.2.2 Sonoran Desert 23.6 7.1 5.7 6.7 8.5 NA

10.2.4 Chihuahuan Desert 24.5 7.8 12.4 11.6 15.3 24.4

11.1.1 California Coastal Sage, Chaparral, and Oak Woodlands 45.8 11.4 14.6 9.8 7.5 32.7

11.1.2 Central California Valley 68.8 34.4 9.8 7.0 5.5 67.9

11.1.3 Southern and Baja California Pine-Oak Mountains 59.6 12.1 10.9 11.8 13.5 57.7

12.1.1 Madrean Archipelago 23.4 8.6 9.1 12.2 13.9 23.2

13.1.1 Arizona/New Mexico Mountains 24.6 6.5 8.6 18.1 18.1 24.4

15.4.1 Southern Florida Coastal Plain 64.6 10.1 40.8 17.6 12.9 NA

5.2.2 Northern Minnesota Wetlands 39.8 19.5 17.4 21.5 13.9 NA

6.2.11 Klamath Mountains 20.9 6.1 9.7 30.8 17.6 18.9

6.2.8 Eastern Cascades Slopes and Foothills 16.3 4.9 4.5 16.6 15.2 15.7

6.2.9 Blue Mountains 15.9 6.0 4.1 15.5 13.5 15.5

7.1.7 Strait of Georgia/Puget Lowland 50.3 22.7 25.4 15.5 5.0 42.7

7.1.8 Coast Range 25.2 7.5 20.9 39.6 14.4 17.2

7.1.9 Willamette Valley 51.8 27.8 17.0 15.7 4.6 NA

8.1.1 Eastern Great Lakes and Hudson Lowlands 82.1 35.3 70.4 20.1 10.8 78.7

8.1.10 Erie Drift Plain 111.6 37.6 140.1 20.6 10.9 90.0

8.1.4 North Central Hardwood Forests 70.8 38.5 34.2 17.4 7.9 69.2

8.1.5 Driftless Area 80.8 46.5 39.5 16.1 8.3 0.9

8.1.6 S. Michigan/N. Indiana Drift Plains 90.0 32.7 78.5 16.5 8.3 86.7

8.2.1 Southeastern Wisconsin Till Plains 81.0 37.6 51.2 14.6 6.9 51.7

8.2.2 Huron/Erie Lake Plains 95.0 38.1 89.5 16.7 7.9 NA

8.2.3 Central Corn Belt Plains 89.2 33.8 79.6 14.2 7.2 -36.1

8.2.4 Eastern Corn Belt Plains 104.0 40.0 116.8 17.3 8.5 87.3

8.3.1 Northern Piedmont 110.0 37.9 108.6 13.4 6.8 39.1

8.3.2 Interior River Valleys and Hills 87.1 32.3 91.6 15.3 9.7 81.5

8.3.3 Interior Plateau 90.5 27.4 106.5 16.8 11.5 80.2

8.3.6 Mississippi Valley Loess Plains 80.6 27.7 55.4 15.5 11.5 NA

8.3.8 East Central Texas Plains 73.7 30.4 42.1 13.9 11.8 NA

8.4.5 Ozark Highlands 76.4 32.2 47.0 16.0 14.0 75.6
D-23

January 14, 2011

Table D-6. CMAQ 2005 annual average depositions, transference ratios, and Neco vales. All depositions and 

Neco in meq/(m2

-yr); T ratios in m/yr; Ndep = NOydep + NHxdep. 

EcoRgnIII

3III

Name Total

NDep

NHx

Dep

SOx

Dep

TSOx TNOy Neco1

8.5.2 Mississippi Alluvial Plain 73.4 26.4 48.9 14.2 9.2 70.4

9.2.1 Aspen Parkland/Northern Glaciated Plains 46.3 29.2 15.9 17.1 10.1 46.0

9.2.2 Lake Manitoba and Lake Agassiz Plain 50.7 33.6 15.0 18.5 9.0 50.1

9.2.3 Western Corn Belt Plains 86.9 53.9 34.5 15.4 9.0 40.1

9.2.4 Central Irregular Plains 73.8 33.4 40.2 14.4 10.0 71.0

9.3.1 Northwestern Glaciated Plains 29.1 15.7 11.2 15.5 9.1 28.8

9.3.3 Northwestern Great Plains 23.4 10.2 10.3 17.4 12.5 22.9

9.3.4 Nebraska Sand Hills 43.6 23.4 12.4 18.7 13.7 43.4

9.4.1 High Plains 45.1 23.3 12.1 14.1 10.1 41.8

9.4.2 Central Great Plains 61.6 30.2 19.4 15.0 12.0 60.2

9.4.3 Southwestern Tablelands 32.6 12.1 10.9 13.7 12.7 32.5

9.4.4 Flint Hills 70.6 31.5 33.3 15.1 11.8 NA

9.4.5 Cross Timbers 66.3 24.2 25.0 11.2 10.5 65.9

9.4.6 Edwards Plateau 50.6 14.7 22.8 17.0 16.6 NA

9.4.7 Texas Blackland Prairies 81.7 33.1 43.5 11.6 8.7 NA

9.5.1 Western Gulf Coastal Plain 62.9 20.4 37.0 14.9 9.0 60.8

9.6.1 Southern Texas Plains/Interior Plains and Hills with 

Xerophytic Shrub and Oak Forest

46.2 13.3 27.4 15.7 15.5 NA

5.2.1 Northern Lakes and Forests 46.1 18.4 27.1 20.2 14.3 45.6

5.3.1 Northern Appalachian and Atlantic Maritime Highlands 56.7 17.9 48.5 25.1 15.9 52.2

5.3.3 North Central Appalachians 80.4 18.8 104.2 20.9 15.4 71.1

6.2.10 Middle Rockies 20.9 8.6 7.8 25.4 20.5 19.9

6.2.12 Sierra Nevada 35.9 11.3 11.1 30.5 25.0 35.2

6.2.13 Wasatch and Uinta Mountains 28.4 9.1 9.4 22.6 17.6 26.8

6.2.14 Southern Rockies 22.6 6.0 8.9 27.0 22.3 21.3

6.2.15 Idaho Batholith 18.5 7.4 5.8 26.7 21.4 17.8

6.2.3 Columbia Mountains/Northern Rockies 18.5 6.8 6.4 19.6 13.6 17.9

6.2.4 Canadian Rockies 19.6 7.1 11.7 17.3 17.4 15.6

6.2.5 North Cascades 27.2 8.0 12.2 35.8 22.0 25.0

6.2.7 Cascades 30.0 10.1 13.2 36.1 17.3 27.9

8.1.3 Northern Appalachian Plateau and Uplands 76.2 22.7 82.4 19.2 14.3 59.3

8.1.7 Northeastern Coastal Zone 82.4 21.5 81.7 17.9 7.1 77.7

8.1.8 Maine/New Brunswick Plains and Hills 36.4 10.9 39.3 29.7 18.3 35.8

8.3.4 Piedmont 84.5 27.6 82.0 15.3 10.4 80.1

8.3.5 Southeastern Plains 73.2 25.8 59.7 15.6 12.6 60.9

8.3.7 South Central Plains 72.1 24.4 44.5 16.7 13.4 70.4

8.4.1 Ridge and Valley 86.0 27.0 96.9 16.3 10.9 61.8

8.4.2 Central Appalachians 90.9 20.6 133.3 23.1 18.1 74.1

8.4.3 Western Allegheny Plateau 98.1 23.1 174.8 20.5 12.5 78.7
D-24

January 14, 2011

Table D-6. CMAQ 2005 annual average depositions, transference ratios, and Neco vales. All depositions and 

Neco in meq/(m2

-yr); T ratios in m/yr; Ndep = NOydep + NHxdep. 

EcoRgnIII

3III

Name Total

NDep

NHx

Dep

SOx

Dep

TSOx TNOy Neco1

8.4.4 Blue Ridge 84.2 24.4 87.1 21.3 17.8 79.5

8.4.6 Boston Mountains 81.9 36.0 42.2 17.9 17.7 79.0

8.4.7 Arkansas Valley 75.5 35.2 37.3 14.5 12.2 66.0

8.4.8 Ouachita Mountains 72.0 27.1 40.6 16.8 16.8 71.0

8.4.9 Southwestern Appalachians 88.9 30.4 91.2 18.1 14.1 72.5

8.5.1 Middle Atlantic Coastal Plain 87.0 33.9 82.6 17.9 11.7 4.4

8.5.3 Southern Coastal Plain 66.2 15.3 51.2 18.4 13.6 62.9

8.5.4 Atlantic Coastal Pine Barrens 91.5 22.1 98.3 16.1 6.8 68.1

1- NA indicates a lessrelatively non-acid sensitive area with less than 10o water bodies with water chemistry data. 
D-25

January 14, 2011

D.4 Relating exceedances to ambient air indicators: introducing trade-off curves 

The exceedance calculations based on deposition reflect a straightforward approach to 

determine if the AAI is met, and serves the purpose of explaining current conditions with respect 

various combinations of alternative levels and forms of the standard. In practice, a state agency 

would use observed ambient concentrations of NOy, SO2 and SO4 and calculate the AAI using 

equation 

AAI = F1 –F2 –F3[NOy] – F4[SOx]

An alternative standard would be met if the calculated AAI is equal to or above the alternative 

standard level. An infinite amount of combinations of NOy and SOx concentrations can satisfy 

the condition that the calculated AAI be greater than or equal to the level of the standard. 

NOx/SOx tradeoff curves, using nitrogen and sulfur axes are used to illustrate the possible 

combinations of nitrogen and sulfur that could meet a target AAI. The following discussion on 

tradeoff curves illustrates how NHx and Neco affect the AAI calculations, and demonstrate the 

range of tradeoff options between NOy and SOx for attaining a hypothetical standard. 

NOx and SOx tradeoff curves are introduced using deposition axes to illustrate (figure 7-

trade1) the basic concepts of combinations of NOx and SOx deposition and the role of Neco and 

NHx deposition. The tradeoff curve is based on the critical load equation described earlier,

CLANClim(N + S) = ([BC]0

* - [ANClim])Q + Neco (D-1)

expressed in terms of nitrogen (zero sulfur deposition) and sulfur (zero nitrogen deposition) 

deposition only;

CLANClim(Nmax) = ([BC]0

* - [ANClim])Q + Neco (D-2a)

CLANClim(Smax) = ([BC]0

* - [ANClim])Q (D-2b)

Equations D-1a and D-1b provide the x and y intercepts of the basic tradeoff curve (Figure 7-

trade1). As the two equations imply, Neco can only affect nitrogen deposition, and Neco 
D-26

January 14, 2011

reflects how much nitrogen deposition can be neutralized before it contributes to acidification. 

The larger the Neco term, the greater the allowable levels of nitrogen deposition. 

Figure D-4.1. Hypothetical tradeoff diagram for acidifying nitrogen and sulfur deposition. The solid horizontal 

and diagonal lines represent the combinations of nitrogen and sulfur deposition to meet a target ANC. Deposition

values within the area bounded by these lines do not exceed the target critical load and values outside the borders 

exceed a critical load. When all units are transformed to equivalent charge, there is a 1:1 slope in the region where 

nitrogen deposition exceeds Neco which illustrates the area of tradeoffs between nitrogen and sulfur deposition.

Because, NHx is not treated as an ambient air indicator, NHx deposition is extracted from the 

tradeoff curve by reducing the available pool of Neco and changing the total nitrogen to a an 

NOy deposition axis (Figure D-4.2);

CLANClim(NOymax) = ([BC]0

* - [ANClim])Q + Neco-NHx
D-27

January 14, 2011

Figure D-4.1. Deposition tradeoff diagram for sulfur and the oxidized fraction of total nitrogen deposition with 1 

equivalent unit of NHx deposition removed. The removal of one equivalent unit of NHx from figure 1 shifts the 

original diagram to the left and defines the amount of allowable maximum NOy deposition, which is simply the 

difference between Nmax and NHx in Figure 7-trade1.

The deposition axes can be directly converted to concentration axes using the transference ratios, 

TSOx and TNOy;

[SOx]max = Dep(SOx)max/ TSOx and

[NOy]max = Dep(NOy)max/ TSOx .

With tradeoff curves expressed in NOx and SOx concentrations (Figure 7-trade3), a variety of 

scenarios with respect to exccedances can be explained. More thorough explanations covering 

the development of tradeoff curves follow.
D-28

January 14, 2011

Additional Explanation of Developing Tradeoff Curves

The “basic” acid-base charge balance equation,

CL(Acidify) = BCdep + BCw + Nup + Nde + Ni – BCup – ANClimit (1)

Where CL(Acidity) represents the deposition flux of nitrogen and sulfur beyond which a 

prescribed ANClimit is not met. The BC terms represent the contribution of major cations due 

to weathering (BCw) and atmospheric deposition (BCdep) and loss through plant uptake (BCup). 

We assume that BCup is negligible and combine cation contributions due to weathering and 

deposition to define BCo;

BCo = BCdep + BCw

BCo integrates at the watershed level of deposition of BC from the air and weathering 

processes.

The N terms represent the ecosystems capacity for neutralizing nitrogen deposition through plant 

uptake, denitrification and immobilization, largely through adsorption, which collectively is 

represented by the term, Neco. 

Neco = Nup + Nde + Ni

Neco is estimated by subtracting measured surface water outflow nitrate (leached nitrogen) from 

total nitrogen deposition (oxidized and reduced). Substituting the expressions for BCo and 

Neco into equation (1) and accounting for catchment runoff, Q to retain mass consistency 

between deposition fluxes and concentrations yields the basic critical load expression for a 

specific ANC target at the catchment level:

( ) [ ] [ ] ANC BC O ANC Q Neco CL N + S = ( − lim ) + *

lim (2)

Equation (2) defines the maximum amount of acidifying deposition a watershed can handle to 

meet a prescribed ANC. Recognizing that Neco only affects depositing nitrogen, 

The max S deposition when N deposition ≤ Neco is defined as:

CLANC (S ) ([BC]O [ANClim ])Q *

lim = − (3)

The max N deposition when S deposition = 0

( ) [ ] [ ] CLANC N = BC O − ANC Q + Neco ( ) lim

*

lim (4)
D-29

January 14, 2011

Tradeoff Curves

Equations (3) and (4) establish the y and x intercepts, respectively, of a hypothetical 

Nitrogen/Sulfur tradeoff curve (Figure 1). When all units are transformed to equivalent charge, 

there is a 1:1 slope in the region where nitrogen deposition exceeds Neco which illustrates the 

possibilities of combined nitrogen and sulfur deposition to meet a target ANC. Note that the 

concept of tradeoff between S and N is best realized where nitrogen deposition exceeds Neco. 

As long as nitrogen deposition is less than Neco, the amount of allowable sulfur deposition 

remains constant at Smax. Any deposition value above the horizontal and diagonal lines reflect 

levels greater than the targeted critical load. The terms Ndep and Sdep reflect deposition of total 

nitrogen and SOx, respectively. 

The N/S tradeoff diagram is based on the following conditions:

1. CL(S) = Smax, when Ndep < Neco; 

2. For a given nitrogen deposition , Ndep; the allowable sulfur deposition, CL(S) = Smax 

– (Ndep – Neco), when Nmax> Ndep > Neco

The following conditions must be met in order to not exceed a CL;

Sdep is always ≤ ([BC]O [ANClim ])Q * − , 

and, 

Sdep + Ndep ≤ ([BC]O [ANClim ])Q * − + Neco 

Note that Nmax – Neco = Smax

Figure 2. Hypothetical tradeoff diagram for acidifying nitrogen and sulfur deposition. The solid 

horizontal and diagonal lines represent the combinations of nitrogen and sulfur deposition to meet 

a target ANC. Deposition values within the area bounded by these lines do not exceed the target 

critical load and values outside the borders exceed a critical load. When all units are transformed 

to equivalent charge, there is a 1:1 slope in the region where nitrogen deposition exceeds Neco 

which illustrates the area of tradeoffs between nitrogen and sulfur deposition.
D-30

January 14, 2011

Because the standard addresses oxidized forms of nitrogen, it is useful to illustrate how reduced 

nitrogen deposition conceptually is incorporated in the deposition tradeoff diagram. Figure 2 

describes the tradeoff diagram in terms of reactive oxidized nitrogen (NOy) deposition and sulfur 

deposition. The NHx deposition displayed in Figure 1 effectively is “neutralized” by Neco, 

and therefore the remaining “pool” of Neco is available to neutralize NOy deposition. The 

impact of NHx deposition impacts the “allowable” NOy deposition to meet a target critical load. 

Since Nmax incorporates reduced and oxidized forms of nitrogen, the level of NHx directly 

impacts the amount of allowable NOy deposition where;

Delineating reduced nitrogen, NHx, deposition from total.

Maximum NOy deposition = Nmax - NHx

Figure 3. Deposition tradeoff diagram for sulfur and the oxidized fraction of total nitrogen 

deposition with 1 equivalent unit of NHx deposition removed. The removal of one equivalent unit 

of NHx from figure 1 shifts the original diagram to the left and defines the amount of allowable 

maximum NOy deposition, which is simply the difference between Nmax and NHx in Figure 1.

In equation form, the removal of NHx to develop oxidized nitrogen and SOx deposition tradeoff 

curves is represented as:

The max S deposition when N deposition < Neco 

S max CLANC (S max) ([BC]O [ANClim ])Q * = lim = − (3)

The max NOy deposition when S deposition is zero

NOy CL (NOy ) [BC] [ANC ] Q N NHx max = ANC max = ( O − lim ) + eco − *

lim (5)
D-31

January 14, 2011

Or, NOy max = Nmax – NHx

In cases where NHx is greater than Neco, the amount of maximum NOy deposition is decreased 

by a quantity equal to the difference between NHx dep and Neco as described by equation (5) 

(e.g., consider two cases of NHx = 1 and 4 with Neco a constant at 3). Equation (3) by 

definition remains satisfied in that Smax is defined as the allowable amount of sulfur deposition 

in the absence of any nitrogen deposition. However, because there always remains some 

residual atmospheric nitrogen burden in the form of NHx when all NOy is removed, the 

allowable sulfur deposition in concept also is reduced an amount equal to the difference between 

NHx dep and Neco, which is consistent with the condition that:

Case where NHx > Neco

CLANClim(S + NOy) ≤ ([BC]0

* - [ANClim])Q +Neco – NHx

Smaxα ≤ ([BC]0

* - [ANClim])Q +Neco – NHx

NOymax ≤ ([BC] 0

* - [ANClim])Q +Neco – NHx

Where Smaxα is the amount of allowable sulfur deposition in the presence of excess NHx

 

Figure 4. NOy and SOx deposition tradeoff curve for hypothetical case where NHx deposition exceeds Neco. The effect 

can be visualized as the system retaining a residual one equivalent unit of NHx that can not be neutralized by Neco with 

the consequent reduction on both allowable maximum amounts of SOx and NOy deposition.

Starting with:

Relating Concentrations of NOy and SOx to critical loads

1. CLANClim(N+S) = (BC*

0 – ANClim)·Q + Neco;

2. Substituting CLANClim(N+S) = Ndep + Sdep = NOydep + NHx + Sdep (conceived as any 

combinations NOy, S, NHx deposition that = CLANClim(N+S)

3. NOydep + NHx + Sdep = (BC*

0 – ANClim)·Q + Neco;

4. NOydep + Sdep = (BC*

0 – ANClim)·Q + (Neco – NHx);
D-32

January 14, 2011

5. Converting deposition to concentration using Sdep =[SOx]·TSOx; NOydep = [NOy]·TNOy;

6. [SOx]·TSOx + [NOy]·TNOy = (BC*

0 – ANClim)·Q + Neco – NHx;

7. [NOy]CL = [((BC*

0 – ANClim)·Q + Neco – NHx) – Sdep]/TNOy = CLANClim – NHx – Sdep)/

TNOy ; 

defining the maximum allowable NOy concentration for any value of Sdep and a fixed value of 

NHx.

8. [SOx]CL = [((BC*

0 – ANClim)·Q + Neco – NHx) – NOydep]/TSOx = CLANClim –NHx –

Sdep)/ TSOx ;; 

defining the maximum allowable SOx concentration for any value of NOydep and a fixed value 

of NHx.

With the condition that when Ndep < Neco;

9. [SOx]CL = [((BC*

0 – ANClim)·Q ) /TSOx, a constant value as all nitrogen deposition is 

neutralized

Maximum allowable NOy and SOx concentrations defined as in the absence of the other 

component are defined as:

[NOy]max =( Nmax – NHx)/ TNOy = NOymax/ TNOy in the absence of any SOx deposition

[SOx]max = Smax/ TSOx ; in the absence of any N deposition 

We also can define a maximum SOx concentration in cases where NHx exceeds Neco;

[SOx]αmax = [Smax – (NHx-Neco)]/ TSOx ; in the absence of NOy deposition and excess NHx 

beyond Neco.
E-1

January 14, 2011

Appendix E

Derivation to use measured total nitrate as a surrogate for NOy

Our recommended measurement approach is to use NOy monitors to judge compliance 

with a NOx/SOx secondary standard. This derivation illustrates how the AAI equation would 

be modified to use total nitrate data from the CASTNET filter packs.

Two general approaches enable the use of measured total nitrate to estimate the level of 

NOy consistent with the AAI formulation. The first approach introduces an aggregated dry

oxidized nitrogen deposition transference ratio to separate total nitrate and NOy and yield NOy

as a function of measured nitrate, based on CMAQ attributes consistent with the AAPI 

formulation. The second approach would be to adjust measured nitrate upward to represent 

NOy. This adjustment would be performed by multiplying measured total nitrate by the CMAQ 

derived ratio of (ambient NOy/ambient total nitrate) for the acid sensitive area of interest.

1. Rearranging AAPI expression to show NOy as a function of measured total nitrate

[ ] [ ] amb

SOx SOx

amb

NOy NOy

Total

NHx

eco

eco O T C T C

Q Dep Q

N BC

Q

AAI − − ⋅ + ⋅ 



 



 = ⋅ + α α α

1 1 1

%

*

amb T NOy CNOy ⋅ α TdryαNOyamb CNOy TdryαNOy

amb CTNO3

amb C(NOy−TNO3)

amb CNOy TdryαNOy

( )

3

amb measured CTNO

( )

( 3)

amb CMAQ C NOy−TNO TαNOy

Where the new terms,

TdryαNOy is a CMAQ derived parameter that excludes wet deposited NOy = [dry NOy dep/ amb CNOy ]

( )

( 3)

amb CMAQ C NOy−TNO is a CMAQ derived parameter calculated by subtracting nitric acid and p-NO3 from NOy
E-2

January 14, 2011

2. Adjust measured total nitrate upward to estimate ambient NOy. This adjustment could 

be performed multiplying by the CMAQ NOy/TNO3 ratio, or by using the ratio of 

collocated NOy and nitrate measurements. 

Conclusions: Moreover, nitrate does not account for a significant portion of the ambient 

air oxidized nitrogen budget, and can also miss a significant part of oxidized nitrogen 

deposition. These two concerns combined with the dynamic response of nitrate to 

changes in emissions, as discussed in chapter 2, override any benefit to be derived from 

using an available, partial indicator like total nitrate.
F-1 

January 14, 2011 

1 Appendix F 

2 Evaluation of Variability, Sensitivity and Uncertainty in the Acidification Index

3

4 F.1 INTRODUCTION AND PURPOSE 

5 This appendix provides analyses and discussion of the relative uncertainty in the AAI 

6 equation. This includes analyses of the individual and combined components of the linked 

7 atmospheric-ecological effects system described in chapter 7, as well as important uncertainties 

8 in the scientific evidence that should be considered in developing options for the standard. This 

9 appendix is intended to integrate a variety of analyses related to the sensitivity of the models and 

10 model components to uncertainty and variability, and place the results of those analyses within 

11 the context of the conclusions that can be drawn regarding the components of the AAI. These 

12 components include ecosystem effects; dose-response relationships; underlying ecosystem 

13 sensitivity to acid deposition, biogeochemical, atmospheric and deposition processes; and 

14 characterization of ecosystem services. While several processes are imbedded in the AAI 

15 equation introduced in chapter 7, the level of the AAI, as in all NAAQS, is to include 

16 consideration of information on uncertainty and variability. Consequently, knowledge of the 

17 relative confidence and natural variability in the structural components of the AAI are considered 

18 in staff conclusions on options for ranges of the level of the standard. These analyses are not 

19 intended to be a comprehensive treatment of all uncertainties that exist relative to the overall 

20 review of the standards, instead, it focused on those that are most relevant in evaluating choices 

21 regarding the AAI form of the standard and options regarding the indicator, averaging time, and 

22 ranges of levels of the AAI-based NOx and SOx standard. 

23 Uncertainty and sensitivity analyses are used to inform the relative confidence in the 

24 components and models that are used in defining the standard. Assessments of variability in the 

25 data used to determine parameters of the standard increases the level of understanding about the 

26 likelihood that alternative parameterizations of the standard will achieve targeted levels of 

27 protection when applied to sensitive ecosystems across the U.S. Assessments of the sensitivity of 

28 the overall AAI to the components of the equation proposed to calculate the AAI can help 

29 demonstrate how important uncertainty and variability in those components are in assessing the 

30 protection of ecosystems provided by an AAI standard. To evaluate the potential interactions 
F-2 

January 14, 2011 

1 between uncertain and/or variable AAI components, a multifactor sensitivity analysis is also 

2 conducted. The ranges of component values evaluated in the multifactor sensitivity assessment 

3 are guided by individual variability and uncertainty analyses of specific components. In addition 

4 to informing considerations of the AAI level, an additional objective of these “confidence” 

5 related analyses and discussions is to help guide research and data collection efforts intended to 

6 reduce uncertainty for future NAAQS reviews and implementation efforts. Spatial and 

7 temporal variability analyses of AAI components are especially useful to inform monitoring 

8 network design, the spatial boundaries of acid sensitive regions, and averaging periods relevant 

9 to NAAQS implementation. 

10 Significant emphasis is placed on evaluations of CMAQ due to the unique role that 

11 atmospheric models hold in the linked AAI system. The AAI as currently formulated relies on 

12 CMAQ for both the initial characterization of reduced nitrogen deposition, and the deposition 

13 transformation ratios (TNOx and TSOx) which characterize the relationships between atmospheric 

14 concentrations of NOx and SOx and deposition of N and S. Included are interpretations of 

15 model evaluation results from the REA (EPA, 2009) as well as more recent results related to wet 

16 deposition and the treatment of ammonia deposition. Comparison of model results to 

17 observations provides a general sense of the confidence we have that the models capture the 

18 spatial, temporal and compositional texture of the relevant atmospheric and deposition species 

19 that drive the linked atmospheric-ecosystem processes. Both model evaluation results and 

20 assessments of spatial and temporal variability guide implementation strategies for monitoring 

21 network design and emission inventory improvement. Sensitivity of CMAQ derived deposition 

22 transformation ratios to changes in emissions, and treatment of chemistry and variability over 

23 time provide insight into the stability of these parameters that are used in a relatively static 

24 manner in the AAI, and into how well proposed averaging times capture the overall spatial and 

25 temporal trends in the parameters. 

26 We evaluate the sensitivity of critical load modeling components by comparing dynamic 

27 (MAGIC) and hybrid steady state model results, looking at terminal results of MAGIC. This 

28 approach was viewed as a test of the more reduced form approximations used in steady state 

29 modeling relative to more sophisticated treatment in MAGIC. The MAGIC critical load 

30 simulations also provide information on the temporal trajectory of ANC, including the expected 

31 time necessary to reach a desired ANC, which can help inform the level of the AAI, recognizing 
F-3 

January 14, 2011 

1 that there may be additional consideration given to reaching a target ANC within a specific 

2 timeframe, e.g. by 2030 or 2040. 

3 For the purposes of this discussion, we characterize uncertainty regarding models and 

4 their outputs as referring to the lack of knowledge regarding both the actual values of model 

5 input variables (parameter uncertainty) and the model characterization of physical systems or 

6 relationships (model uncertainty). In any application, uncertainty is, ideally, reduced to the 

7 maximum extent possible, but significant uncertainty often remains. It can be reduced by 

8 improved measurement and improved model formulation. Model evaluation results provide 

9 some insight into the relative uncertainty associated with the ability of models to capture key 

10 environmental state characteristics. Confidence regarding the fundamental science supporting 

11 causal determinations about the effects of acid deposition, and the translation of those effects 

12 into ecosystem services and values is less amenable to quantification. As a result, these 

13 uncertainties are more difficult to explicitly account for in development of the standards. In the 

14 case of the equation describing the AAI, while the degree of uncertainty in some elements can be 

15 characterized, sometimes quantitatively, a formal uncertainty analysis using statistical sampling 

16 techniques (e.g., Monte Carlo simulation) to identify the relative and combined influences of 

17 parameter uncertainty was not performed. However, we did evaluate the sensitivity of the AAI 

18 to its components using two related assessments: elasticity calculations and a bootstrapping, 

19 Monte Carlo type analysis which is included in Appendix G. The results of these assessments 

20 are addressed in section 7.6. 

21 Sensitivity refers to the influence on modeled results due to perturbations in input 

22 variables or change of process formulations. Sensitivity analysis can provide a sense of how 

23 important different parameters and inputs might be to the outcomes of interest, e.g. the AAI 

24 level, but cannot by themselves indicate how important specific parameters actually are, because 

25 they do not incorporate information on the range of parameter values or the likelihood associated 

26 with any specific parameter value. Sensitivity results in this PAD are intended to provide 

27 insight into the relative stability of the AAI and associated NOx and SOx tradeoff curves and 

28 confidence in modeled parameterizations. Sensitivity analyses are especially useful in the 

29 absence of observed data to challenge models. For example, the NOy and SOx transference 

30 ratios are a model construct that is difficult, if not impossible, to compare to observations. The 

31 sensitivity of these ratios to changing meteorology, emissions and chemical mechanism 
F-4 

January 14, 2011 

1 treatments is evaluated in reference to the stability of these ratios under changing conditions. 

2 Low sensitivity here implies that the choice to use long-term averages of modeled ratios is 

3 justified. Sensitivity analyses also are used to discern the relative influence (on AAI results) of 

4 AAI parameters. Toward that end, elasticity analyses were applied to determine the relative 

5 sensitivity of AAI results associated with individual and combined AAI parameters. 

6 Variability refers to the heterogeneity in a population or variable of interest that is 

7 inherent and cannot be reduced through further data collection and research. In the context of 

8 the AAI and trade-off curves, variability is considered in guiding the design of monitoring and 

9 modeling analyses supporting implementation activities. 

10

11 F.2 Uncertainty associated with ecosystem effects and dose – response relationships. 

12 This section provides a brief summary of uncertainties based on the REA and is 

13 reproduced here to centralize all uncertainty discussions. There are different levels of 

14 uncertainty associated with relationships between deposition, ecological effects and ecological 

15 indicators. In Chapter 7 of the REA, the case study analyses associated with each targeted effect 

16 area were synthesized by identifying the strengths, limitations, and uncertainties associated with 

17 the available data, modeling approach, and relationship between the ANC and atmospheric 

18 deposition. The key uncertainties were characterized as follows to evaluate the strength of the 

19 scientific basis for setting a national standard to protect against a given effect (REA 7.0): 

20  Data Availability: high, medium or low quality. This criterion is based on the 

21 availability and robustness of data sets, monitoring networks, availability of data that 

22 allows for extrapolation to larger assessment areas, and input parameters for modeling 

23 and developing the ecological effect function. The scientific basis for the ecological 

24 indicator selected is also incorporated into this criterion. 

25  Modeling Approach: high, fairly high, intermediate, or low confidence. This value is 

26 based on the strengths and limitations of the models used in the analysis and how 

27 accepted they are by the scientific community for their application in this analysis. 

28  Ecological Effect Function: high, fairly high, intermediate, or low confidence. This 

29 ranking is based on how well the ecological effect function describes the relationship 

30 between atmospheric deposition and the ecological indicator of an effect. 

31
F-5 

January 14, 2011 

1 The REA concludes that the available data are robust and considered high quality. There is 

2 high confidence about the use of these data and their value for extrapolating to a larger regional 

3 population of lakes. The EPA TIME/LTM network represents a source of long-term, 

4 representative sampling. Data on sulfate concentrations, nitrate concentrations and ANC from 

5 1990 to 2006 used for this analysis as well as EPA EMAP and REMAP surveys, provide 

6 considerable data on surface water trends. 

7 There is fairly high confidence associated with modeling and input parameters. Uncertainty in 

8 water quality estimates (i.e., ANC) from MAGIC was derived from multiple site calibrations. 

9 The 95% confidence interval for pre-acidification of lakes was an average of 15 μeq/L difference 

10 in ANC concentrations or 10% and 8 μeq/L or 5% for streams (REA 7.1.2). The use of the 

11 critical load model used to estimate aquatic critical loads is limited by the uncertainties 

12 associated with runoff and surface water measurements and in estimating the catchment supply 

13 of base cations from the weathering of bedrock and soils (McNulty et al., 2007). To propagate 

14 uncertainty in the model parameters, Monte Carlo methods were employed to develop an inverse 

15 function of exceedances. There is high confidence associated with the ecological effect function 

16 developed for aquatic acidification. In calculating the ANC function, the depositional load for N 

17 or S is fixed by the deposition of the other, so deposition for either will never be zero (Figure 

18 F.1-6 REA). 

19 Chapter 3 also reviews the basic evidence underlying effects on fish mortality, aquatic species 

20 diversity and more extended food web disruptions leading to adverse impacts on birds associated 

21 with aquatic acidification. There is high confidence associated with correlation between 

22 acidification and these ecological effects. Also, there is high confidence in the relationship 

23 between the ecological indicator, ANC, and the more direct chemical properties (lower pH and 

24 increased Al) associated with acidification. 

25 F.3 Uncertainty in benefits estimates 

26 Descriptions of the current provision of ecosystem services presented for each of the 

27 effect areas analyzed for this review followed by estimations of the damages incurred to selected 

28 services due to nitrogen and sulfur deposition. The current services are presented to give the 

29 reader a sense of the magnitude of the benefit the public receives from these ecosystems under 

30 current conditions. The data used in these descriptive passages is generally derived from 

31 government (either federal or state) sources we are reasonably certain to be of the highest 
F-6 

January 14, 2011 

1 quality. Where monetary values are placed on the these services we have generally used widely 

2 cited studies, particularly meta analyses that provide an average value that smoothes the variation 

3 in WTP estimates. These estimates underestimate the total value of these services as they use 

4 benefit estimates for a marginal increase in these services. It is likely that the total benefits of 

5 these services are greater because their marginal value likely is lower than the average value. 

6 While reductions in sulfur and nitrogen emissions would increase the size of the benefits from 

7 these services, for many of them it is unknown how significant the increase will be. 

8 The analyses of damages incurred are more uncertain and are limited to those areas where 

9 data and tools were available. Only some services were analyzed which in some cases meant 

10 that the results were limited to one or two services and in the case of terrestrial nutrient 

11 enrichment no services had sufficient data available to attempt an estimate of damage. This 

12 means that the estimates presented are a very small part of the total damage incurred due to 

13 deposition. 

14 Aquatic Acidification 

15 Recreational Fishing Model 

16 The analysis of recreational fishing damages presented in Chapter 4 is subject to the 

17 assumptions necessary to perform the analysis. The original analysis performed for the REA 

18 was based on projecting future benefits of increased recreational fishing based on a complete 

19 cessation of all nitrogen and sulfur emissions. These decisions under or over estimate the current 

20 damages to public welfare incurred from nitrogen and sulfur deposition. The magnitude of the 

21 bias in results is unknown in either direction however the majority of the assumptions influence 

22 the estimates downward. These include the use of emissions estimates that include projected 

23 decreases due to implementation of Title IV regulations in 2020. These emissions estimates are 

24 lower than current emissions and therefore lead to underestimation of damages. Because the 

25 models only value this improvement for New York residents (without accounting for out-of-state 

26 visitors) the damages are underestimates of the benefits of these improvements in the 

27 Adirondacks region. 

28 The use of projected population in the REA analyses contributes to an overestimate of 

29 current damages since current population is smaller than future population. Further, these 

30 estimates are extrapolated from a 44 lake subset and applied to all Adirondack lakes. The 
F-7 

January 14, 2011 

1 representativeness of this sample is unknown. This analysis also does not account for any 

2 change in fishing demand (possible overestimate) and income (possible underestimate). 

3 Benefits Transfer 

4 The approach using the WTP estimates from the Banzhaf et al. (2006) study is subject to 

5 the same uncertainties described above and some additional considerations. Specifically there is 

6 some uncertainty regarding which types of ecosystem services are reflected in the study’s 

7 estimates of the improvements in ecosystem services of reducing acidification, particularly 

8 provisioning and regulating services. The values likely include recreational fishing services, 

9 which mean they cannot be added to the recreational fishing model results, and other cultural 

10 services including other recreation and nonuse services. The inclusion in the survey of other 

11 ecosystem changes (birds, trees, etc.) leads to an overestimation of WTP for remediation of lake 

12 acidification alone. Finally, assumptions were required to align the Banzhaf survey scenarios to 

13 the likely results of complete removal of all nitrogen and sulfur emissions. These are reasonably 

14 close but not exact and may not be applicable to another baseline. 

15 Conclusion 

16 While these estimates are subject to uncertainty we are reasonably confident that they 

17 represent a good first-order approximation of the damages to recreational fishing due to nitrogen 

18 and sulfur deposition. Additionally it should be noted that the Banzhaf survey results represent a 

19 broader picture (though by no means complete) of the damages to ecosystem services in the 

20 Adirondacks. Finally, we would again like to emphasize that these estimates represent only a 

21 small sample of the damages incurred to a broad range of ecosystem services affected and the 

22 areas of the nation where acidic deposition is an ongoing issue. 

23 F.4 Uncertainty in the AAI related to component parameters 

24 Uncertainty within the AAI is divided into four components of analysis. First, an 

25 analysis of elasticity; second and third, analyses of uncertainty within individual components for 

26 atmospheric and ecosystem modeling, respectively; fourth, a Monte Carlo style analysis 

27 incorporating the uncertainty derived into the preceding section to assess the cumulative effect of 

28 the uncertainty of the input parameters. This appendix concludes with a summary discussion 

29 that includes more qualitative conclusions. 

30 F.4.1 Elasticity Analyses 
F-8 

January 14, 2011 

1 An elasticity analysis was applied to investigate sensitivity of the AAI to its components. 

2 The means, medians and quartiles of the AAI component variables were based on the range 

3 variable values across ecoregions that overlapped with the CMAQ domains. Elasticities 

4 measure the percent change in the AAI for a 1% change in the AAI parameters: Q, Neco, NHx, 

* BC0 5 , TNOy, TSOx, NOy, and (SO2 + SO4). We note that an earlier form of the expression is used 

6 where Q is at the catchment level.

 ( ) 1 1 1

4 2

*

0 T NOy T SO SO

Q

NHx

Q

N BC

Q

AAI 7  eco    NOy   SOx  

8 In general, the formula for elasticity is: 

AAI

X

X

AAI E j

j

AAPI

X j  



9 

Where AAPI

X j 10 E is the elasticity of AAI with respect to component Xj, and j is the number of 

11 components. So, for AAI defined as 

12

T NOy T SOx Q

NHx

Q

N BC

Q

AAI eco NOy SOx        1 * 1 1

13 0

14 The set of relevant elasticities are: 

15 For runoff, Q: 

  AAI

Q N NHx T NOy T SOx

Q

E eco NOy SOx

AAPI

Q       2

1

16 , which can be rewritten as 

  AAI

Q AAPI BC

Q

EAAPI

Q     *0

1

17 , or 

AAI

BC EAAPI

Q

*

0 18  1 

For * BC0 19 , 

20

AAI

BC EAAPI

Q

*

0 21 

22 For Neco, 

AAI

N

Q

EAAPI eco

Neco   1

23

24 For NHx, 

AAI

NHx

Q

EAAPI

NHx    1

25
F-9 

January 14, 2011 

1 For TNOy, 

AAI

T

NOy QE AAPI NOy

TNOy     1

2

3 For TSOx, 

AAI

T SOx

Q

EAAPI SOx

TSOx     1

4

5 For NOy, 

AAI

NOy T

Q

E NOy

AAPI

NOy     1

6

7 For SOx, 

AAI

SOx T

Q

E SOx

AAI

SOx     1

8

9 These elasticities can be evaluated at various points along the ranges of each component, as well 

10 as along ranges of the AAI. We evaluate the elasticities at the sample means, medians, first 

11 quartiles, and third quartiles. Elasticities are evaluated only for ecoregions that overlap the 

12 CMAQ modeling domain which provides values for reduced nitrogen and the transformation 

13 ratios (TNOx and TSOx). This will provide a reasonable assessment of the sensitivity of the AAI to 

14 input components. Table F-4.1 provides the estimated elasticities. Elasticities are summarized 

15 across ecoregions using means, medians, minimums, and maximums. 

16 Note that elasticities can be either positive or negative. A negative elasticity means that the 

17 calculated AAI will decrease as a component increases. The magnitude of the elasticity depends 

18 on the values of the components and the starting value of AAI. 

19 Based on the calculated elasticities, AAI is most responsive to changes in Q, BC0, and Neco 

20 with some responsiveness to reduced N. Note that for some components, such as Q, the 

21 elasticities switch signs depending on the values of the variables for which the elasticity is 

22 evaluated. This suggests potentially important interactions. AAI is not responsive to the 

23 transformation ratios, TNOx and TSOx at mean values of the AAI components. However, when 

24 the elasticities for TNOx and TSOx are evaluated at the first quartiles of the data, some locations 

25 in the Eastern U.S. show higher responsiveness to changes in TNOx and TSOx, with elasticities 

26 as high as 2. 

27 
F-10 

January 14, 2011 

1 Table F-4.1 Summary of Elasticity Results 

AAI 

Component 

Metric for 

Which 

Elasticities 

are Evaluated 

Mean 

Elasticity 

Across 

Ecoregions 

Median 

Elasticity 

Across 

Ecoregions 

Minimum 

Elasticity 

Across 

Ecoregions 

Maximum 

Elasticity 

Across 

Ecoregions 

Runoff (Q) 

Mean -0.1047 0.1221 -20.4572 1.6005

Median 0.2561 0.1426 -6.2481 2.4578

1st Quartile -0.9283 0.1303 -135.2544 88.1063

3rd Quartile 0.2988 0.1110 -0.1810 7.5684

Base Cation Weathering (BC0) 

Mean 0.8953 1.1221 -19.4572 2.6005

Median 1.2561 1.1426 -5.2481 3.4578

1st Quartile 0.0717 1.1303 -134.2544 89.1063

3rd Quartile 1.2988 1.1110 0.8190 8.5684

Neco 

Mean 0.0179 0.1464 -13.8545 1.6051

Median 0.3376 0.2563 -4.7203 2.5044

1st Quartile 0.3543 0.2596 -115.1112 137.0526

3rd Quartile 0.3016 0.1440 0.0137 6.5565

Reduced Nitrogen 

Mean -0.0409 -0.0702 -0.4708 3.9332

Median -0.1407 -0.1031 -0.7957 1.0061

1st Quartile -0.0308 -0.1190 -33.9063 35.3783

3rd Quartile -0.1128 -0.0615 -1.9167 -0.0050

NOx Transformation Ratio (TNOx) 

Mean 0.0089 -0.0053 -0.0597 1.0598

Median 

-0.0061 -0.0064 -0.0506 0.2751

1st Quartile -0.0191 -0.0071 -2.7061 1.7749

 3rd Quartile 

-0.0154 -0.0044 -0.5028 -0.0002

SOx Transformation Ratio (TSOx) 

Mean 

0.0019 -0.0024 -0.0185 0.3608

Median -0.0045 -0.0036 -0.0413 0.1091

1st Quartile 0.0040 -0.0032 -1.8023 1.9860

3rd Quartile -0.0058 -0.0021 -0.1534 -0.0002

2 * Elasticity is the percent change in AAI for a one percent change in the component variable. 

3 For example, when evaluated at the means of all component variables, the mean elasticity of 

4 AAI to the runoff variable Q is -0.1047, which means that for each 1 percent increase in Q, the 

5 AAI is reduced by 0.1047 percent. 

6 

7 
F-11 

January 14, 2011 

1 Conclusions 

Base cation weathering, * BC0 2 , and hydraulic flow rate, Q, exerted strong influence on 

3 AAI, an expected result given the explicit dependency evident in the AAI expression. The 

4 transference rations for NOy (TNOy) and SOx (TSOx) exhibited relatively less influence on AAI 

5 calculations than all other parameters when evaluated at means of the variables. However, in 

6 some locations, when evaluated at other values of the variables, AAI can be more sensitive to the 

7 deposition transformation ratios. 

8 These results suggest focusing on the uncertainties in the non-atmospheric inputs, 

9 including base cation weathering and runoff rates, and the implications of those uncertainties in 

10 setting an AAI that will have a high likelihood of providing the targeted level of protection. 

11 F.4.2 Individual Components of the AAI 

12 F.4.2.1 Overview of CMAQ model application 

13 The CMAQ model is a comprehensive, peer-reviewed (Aiyyer et al., 2007), three14 dimensional grid-based Eulerian air quality model designed to simulate the formation and fate of 

15 gaseous and particle (i.e., particulate matter or PM) species, including ozone, oxidant precursors, 

16 and primary and secondary PM concentrations and deposition over urban, regional, and larger 

17 spatial scales (Dennis et al., 1996; U.S. EPA, 1999; Bryun and Schere, 2006). CMAQ is run for 

18 user-defined input sets of meteorological conditions and emissions. For this analysis, we are 

19 using predictions from several existing CMAQ runs. These runs include annual simulations for 

20 2002 using CMAQv4.6 and annual simulations for each of the years 2002 through 2005 using 

21 CMAQv4.7 (Foley et al., 2010). CMAQv4.6 was released by the U.S. Environmental Protection 

22 Agency’s (EPA’s) Office of Research and Development (ORD) in October 2007. CMAQv4.7 

23 along with an updated version of CMAQ’s meteorological preprocessor (MCIPv3.4, Otte and 

24 Pleim, 2010)1 were released in October 20082. The 2002 simulation with CMAQv4.6 was 

25 performed for both the Eastern and Western domains. The horizontal spatial resolution of the 

26 CMAQ grid cells in these domains is 12 x 12 km. The 2002 through 2005 simulations with 

27 CMAQv4.7 were performed for the eastern 12-km domain and for the continental United States 

                                                           

1 The scientific updates in CMAQ v4.7 and MCIP v3.4 can be found at the following web links: 

 http://www.cmascenter.org/help/model_docs/cmaq/4.7/RELEASE_NOTES.txt 

 http://www.cmascenter.org/help/model_docs/mcip/3.4/ReleaseNotes 

2 The differences in nitrogen and sulfur deposition in the case study areas between CMAQ v4.6 and v4.7 for 2002 

are small, as described in Chapter 3. 
F-12 

January 14, 2011 

1 domain, which has a grid resolution of 36 x 36 km. The CMAQv4.6 and v4.7 annual simulations 

2 feature year-specific meteorology, as well as year-specific emissions inventories for key source 

3 sectors, such as utilities, on-road vehicles, nonroad vehicles, wild fires, and natural biogenic 

4 sources. Emissions for other sectors of the inventory for each of the years modeled rely on 

5 inventories for 2002. Details on the development of emissions, meteorology, and other inputs to 

6 the 2002 CMAQv4.6 runs can be found in a separate report (U.S. EPA, 2008). Inputs for the 

7 CMAQv4.7 runs for 2002 through 2005 were derived using procedures similar to those for the 

8 CMAQv4.6 2002 runs. 

9 Additional details of the modeling domain, emissions and meteorological inputs are 

10 provided in EPA (2009; REA Appendices). 

11

12 F.4.2.2 CMAQ Evaluation, Sensitivity and Variability Analyses

13 Past results. A variety of comparisons of modeled estimates to observations were 

14 included in the REA (EPA, 2009), and some of the highlights are summarized here in addition to 

15 new work on ammonia characterization and wet deposition. Readers are encouraged to review 

16 the earlier report. Ambient air concentrations and wet deposition observations are paired against 

17 modeled estimates. In contrast, dry deposition is always a modeled value, either derived from 

18 ambient or modeled ambient concentrations. Given the interest in relevant nitrogen and sulfur 

19 species, CASTNET observations were used extensively. Comparisons of modeled annual 

20 average total nitrate (sum of nitric acid and particulate nitrate), ammonium, sulfate, and sulfur 

21 dioxide to observations for the 2002 base year are provided in Figures F-1 through F-4. 

22 Normalized mean bias statistics for 2002-2005 base years are provided in Table F-4.2. 

23 CMAQ overpredicts SO2 and underpredicts SO4. Although model performance is good 

24 for total SOx, the inclusion of co-located SO2 and sulfate measurements required for future 

25 secondary NOx/SOx NAAQS comparisons will help diagnose issues with the model’s ability to 

26 partition these two species. CMAQ generally overpredicts total nitrate and slightly 

27 underpredicts ammonium and the model captures the monthly temporal patterns of sulfate, total 

28 nitrate and ammonium when all sites are aggregated (Figures F-5 to F-7). There are some basic 

29 incommensurabilities between model estimates and observations that complicate interpretation 

30 of model to observation comparisons, most notably the representation of space as a model 

represents a volume average of roughly 144 km2 31 , which depends on the time varying vertical 
F-13 

January 14, 2011 

1 depth of the lowest modeled layer. Most surface based observations rely on point sampling and 

2 the extent to which a point is representative of broader volume space varies with meteorology, 

3 distribution of emissions and surface characteristics. 

4

5 Table F-4.2. Normalized Mean Bias Statistics for Predicted and Observed Pollutant 

6 Concentration

Pollutant 

Concentrations 

2002 2003 2004 2005 

SO2 45% 39% 47% 41% 

SO4

2- -13% -9% -13% -17% 

TNO3 22% 26% 22% 24% 

NH4

+

 4% 11% 7% 2% 

7 
F-14 

January 14, 2011 

1

2 Figure F-1 2002 CMAQv4.6 annual average SO2 predicted concentrations versus 

3 observations at CASTNet sites in the eastern domain (note, units are in actual mass for SO2, 

4 including oxygen). 

5 
F-15 

January 14, 2011 

1

Figure F-2 2002 CMAQv4.6 annual average SO2- 2 4 predicted concentrations versus 

3 observations at CASTNet sites in the eastern domain (note, units are in actual mass for SO4, 

4 including oxygen).

5 
F-16 

January 14, 2011 

1

2 Figure F-3 2002 CMAQv4.6 annual average TNO3 predicted concentrations versus 

3 observations at CASTNet sites in the eastern domain (note, units are in actual mass for NO3, 

4 including oxygen). 

5
F-17 

January 14, 2011 

1

Figure F-4. 2002 CMAQv4.6 annual average NH4

+ 2 predicted concentrations versus 

3 observations at CASTNet sites in the eastern domain (note, units are in actual mass for NH4, 

4 including hydrogen). 

5
F-18 

January 14, 2011 

1

Figure F-5. 2002–2005 Domain-wide average SO4

2- 2 predicted concentrations and 

3 observations by month at CASTNet Sites in the eastern domain(note, units are in actual mass for 

4 SO4, including oxygen) . 

5 
F-19 

January 14, 2011 

1

2 Figure F-6. 2002–2005 Domain-wide average TNO3 predicted concentrations and 

3 observations by month at CASTNet sites in the eastern domain (note, units are in actual mass for 

4 NO3, including oxygen).

5 
F-20 

January 14, 2011 

1

Figure F-7 2002–2005 Domain-wide average NH4

+ 2 predicted concentrations and observations 

3 by month at CASTNet sites in the eastern domain (note, units are in actual mass for NH4, 

4 including hydrogen). 

5 
F-21 

January 14, 2011 

1

2

3 Figure F-8. Comparison of CMAQ predictions and measurements for 12-hour (6am-6pm) 

4 average NH3 concentrations, with a monitoring cycle of 4 days on and 4days off, at a high 

5 emission site (Kenansville) and a low emission urban site (Raleigh) in North Carolina compared 

6 to CMAQ for July 2004 (top) and August 2004 (bottom), from Dennis et al., 2010 (note, units 

7 are in actual mass for NH3, including hydrogen). 

8

9

Kenansville Ammonia July 2004

12-hour Averages: 6am-6pm

0

5

10

15

20

25

30

35

40

45

184 186 188 190 192 194 196 198 200 202 204 206 208 210 212 214

Julian Day: TickMark at Midnight (July 2004)

NH3 (ug/m3)

Observations CMAQ

Millbrook (Raleigh) Ammonia July 2004

12-hour Averages: 6am-6pm

0

1

2

3

4

5

6

183.5 185.5 187.5 189.5 191.5 193.5 195.5 197.5 199.5 201.5 203.5 205.5 207.5 209.5 211.5 213.5

Julian Day: TickMark at Midnight (July 2004)

NH3 (ug/m3)

Observations CMAQ

Kenansville Ammonia August 2004

12 hour averages: 6am-6pm

0

5

10

15

20

25

30

214 216 218 220 222 224 226 228 230 232 234 236 238 240 242 244

Julian Day: TickMark at Midnight (August 2004)

NH3 (ug/m3)

Observations CMAQ

Millbrook (Raleigh) Ammonia August 2004

12 hour averages: 6am-6pm

0

1

2

3

4

5

6

7

8

214 216 218 220 222 224 226 228 230 232 234 236 238 240 242 244

Julian Day: TickMark at Midnight (August 2004)

NH3 (ug/m3)

Observations CMAQ
F-22 

January 14, 2011 

1 Comparison with SEARCH Data

2

3 The SEARCH network (chapter 2) includes sites instrumented with continuously 

4 operating SO2 monitors. Comparison of CMAQ estimated SO2 and SERACH data showed 

5 similar over-predictions to CASTNET data sets. The SEARCH data is affords the ability to 

6 diagnose the diurnal aspects of SO2 patterns, which is not possible with the weekly averaged 

7 CASTNET data. Example model to observation plots follow for the SEARCH sites reflecting 

8 regional air quality, located away from major sources.. 

9 
F-23 

January 14, 2011 

1

2 Figure F-9. 2006 hour by hour comparisons of CMAQ and SEARCH SO2 data at the Ook Grove, MS site (source, 

3 K. Foley, U.S. EPA-ORD) 

4

Figure F- 10. 2006 hour by hour comparisons of CMAQ and CSEARCH SO2 data at the Centerville, AL site 

(source, K. Foley, U.S. EPA-ORD)
F-24 

January 14, 2011 

1

2 Wet deposition. 

3 Modeled wet deposition in CMAQ is a function of the volume of predicted precipitation 

4 within a grid cell and the pollutant concentrations scavenged from the atmosphere during 

5 precipitation events. As a result, errors in modeled precipitation and in emission inputs can lead 

6 to significant bias and error in the wet deposition predictions compared to observed values. EPA 

7 (Dennis and Foley, 2010) has corrected CMAQ wet deposition predictions by scaling the model 

8 output based on observation-based gridded precipitation data generated by the Parameter9 elevation Regressions on Independent Slopes Model (PRISM, 2004). The precipitation adjusted 

10 deposition fields are more highly correlated with observed values for all wet deposited nitrogen 

11 and sulfur species compared to the base model output (Figures F-12, F-13). In addition, the 

12 adjusted fields are better able to capture the spatial heterogeneity of accumulated wet deposition 

13 due to orographic effects on precipitation amounts. 

14 Adjusting the wet deposition values to account for over-predictions in the model 

15 precipitation inputs revealed compensating errors for nitrate and ammonium. The negative bias 

16 seen in these species after the precipitation adjustment is believed to be due to missing emissions 

Figure F- 11. 2006 hour by hour comparisons of CMAQ and CSEARCH SO2 data at the Yorkville, GA site 

(source, K. Foley, U.S. EPA-ORD)
F-25 

January 14, 2011 

1 sources. A second bias adjustment was performed for nitrate and ammonium based on observed 

2 levels at the NADP/NTN sites (Figure F-13). The final adjusted spatial fields of annual total wet 

3 deposition values are more consistent with observed wet deposition values. Ongoing studies 

4 suggest that much of this bias can be reduced in the Eastern half of the US by including nitrogen 

5 oxide produced by lightning and accounting for the bi-directional flux of ammonia. Once these 

6 model improvements are incorporated in CMAQ a second bias adjustment may not be needed in 

7 the East. 

8 In 2011, the EPA will deploy a prototype flux measurement package over a grass field at 

9 the Duke Forest Blackwood Division. Dry deposition fluxes will include NOy, NO2, HNO3, 

10 HONO, NO3 aerosol, NH3, NH4 aerosol, SO2, SO4 aerosol and ozone. Concentrations of 

11 organic N in aerosol, but not fluxes, also will be measured. The availability of these flux 

12 measurements will enable a more direct assessment of CMAQ treatment of sulfur and nitrogen 

13 deposition processes. 

14 

Figure F-12 Unadjusted (left) and PRISM (right) adjusted CMAQ annual wet deposited 

sulfate for 2002 (note, units are in actual mass for SO4, including oxygen). 
F-26 

January 14, 2011 

1

2 Figure F-13 Unadjusted (left) and PRISM and bias (right) adjusted CMAQ annual wet 

3 deposition of nitrate (top) and ammonium (bottom) (note, units are in actual mass for NH4 and 

4 NH3, including hydrogen). 

5

6 
F-27 

January 14, 2011 

1

2 Ammonia. 

3 The role of NHx deposition is incorporated in the AAPI expression as a parameter that 

4 influences the level of allowable concentrations of NOy and SOx, due to its role as part of the 

5 total reactive nitrogen budget which affects acidification. Characterizing ammonia deposition is 

6 challenging due to the variety of surface and vegetation types that influence ammonia dry 

7 deposition velocities as well the potential for bi-directional flux of ammonia. In addition, 

8 ammonia emission estimates remain relatively more uncertain than emissions of NOx and SO2

9 given the complexity of meteorology and agricultural practices that influence the spatial and 

10 temporal patterns of ammonia releases. An exploration of the sensitivity of ammonia to three 

11 different treatments of deposition processes in CMAQ was performed by EPA (Dennis et al., 

12 2010) to test the inclusion of a bi-directional NH3 flux algorithm and elucidate the relative 

13 importance associated with advection, deposition and chemical transformation on ammonia 

14 patterns. These treatments included a (1) base case of current CMAQ treatment using existing 

15 ammonia deposition velocity schemes and uni-directional deposition, (2) modified the base case 

16 by replacing ammonia deposition velocity calculations with SO2 deposition velocities (SO2

17 interacts with surfaces and vegetation similarly to NH3, but with reduced velocity) as a lower 

18 bound and (3) introducing a bi-directional flux algorithm to the base case (retaining NH3 

19 deposition velocities). Based on modeled process analysis that delineates the effects of 

20 deposition, chemical transformation and advection (horizontal and vertical) on emitted ammonia, 

21 the results (Figure 7-14) suggest that ammonia patterns, especially when a bi-directional flux 

22 process is incorporated, are more indicative of a transported pollutant where emissions influence 

23 can span hundreds of kilometers, markedly different from some earlier perspectives where 

24 ammonia often was thought of as near source phenomenon due to high deposition velocities. 

25 The process analysis illustrates the importance of vertical advection which enables the movement 

26 of ammonia into traditional mesoscale flow patterns. The effect is enhanced by the 

27 reintroduction of deposited ammonia through bi-directional flux into the ambient environment. 

28 From a monitoring perspective, a design that addresses the regional characterization of 

29 NOy and SOx would be consistent with characterizing NHx. Not only would ammonia and 

30 ammonium measurements be useful for estimating dry deposition through deposition modeling 

31 approaches such as those used in CASTNET, but they would serve as important diagnostic data 
F-28 

January 14, 2011 

1 to continually assess the effectiveness of NHx deposition processes in models like CMAQ. This 

2 is especially important as we recognize a large uncertainty in the bi-directional formulation 

3 associated with the estimation of Γ, the emissions potential due to the existence of compensation 

4 points. Nonetheless, we can learn much about the NH3 budget in spite of these uncertainties. 

5 High priority research is ongoing to improve the bi-directional parameterization and the 

6 estimates of the leaf and soil gammas across different cropping regions and throughout the year. 

7 We are developing a software tool to estimate the soil Γ associated with fertilizer application. 

8 When we have a spatially and temporally varying Γg, we will investigate the emissions budgets 

9 for fertilized fields, as well as reexamine the animal operation emission budgets, as this will be 

10 of interest. Work to examine the seasonality of single cell budgets and their range of influence is 

11 continuing. Current and future CMAQ applications to ecosystem deposition will incorporate bi12 directional flux treatment of ammonia. 

13 
F-29 

January 14, 2011 

1

2

3 Figure F-14. Cumulative regional NH3 budget of advection, wet- and dry deposition, 

4 calculated for an expanding box starting at the high-emitting Sampson County NC cell (from 

5 Dennis et al, 2010) 

6

7 F.4.2.3 Variability and sensitivity of CMAQ generated components. 

8 Ambient Concentration to Deposition Transformation ratios. 

9 Derivation 

10 Atmospheric pollutants deposit onto land and water surfaces through at least two major 

11 mechanisms: direct contact with the surface (dry deposition), and transfer into liquid 
F-30 

January 14, 2011 

1 precipitation (wet deposition). The magnitude of each deposition process is related to the 

2 ambient concentration through the time-, location-, process- and species-specific deposition 

3 velocity (Seinfeld and Pandis,1998): 

 Amb

i

Dry

i

Dry 4 Depi  v C (1) 

 Wet

i

Wet

i

Wet 5 Depi  v C (2) 

where v

Dry

i and v

Wet

i are the dry and wet deposition velocities, DepDryi and DepWeti 6 are the dry and 

wet deposition fluxes, CAmb

i 7 is the ambient concentration, and the i subscript indicates the 

8 pollutant species under study. The total deposition of each pollutant is 

 Wet

i

Dry

i

Tot 9 Depi  Dep  Dep (3) 

10 Substituting Equations 1 and 2 into Equation 3 yields 

 Amb

i

Wet

i

Amb

i

Dry

i

Tot 11 Depi  v C  v C (4) 

12 The total deposition of sulfur or nitrogen would therefore be: 

      i

Amb

i i

Wet

i

Dry

i

Tot 13 DepS|N (v v ) m C (5) 

where mi 14 is the molar ratio of the atom (sulfur or nitrogen) of interest to the ith pollutant. 

Ambient sulfur- and nitrogen-containing pollutants include gases such as sulfur dioxide (SO2 15 ), 

ammonia (NH3), various nitrogen oxides (NO, NO2, HONO, N2O5), nitric acid (HNO3 16 ), and 

17 organic nitrates such as peroxyacetyl nitrates (PAN); as well as particulate species such as sulfate 

(SO4

2-), nitrate (NO3-

), and ammonium (NH4

+ 18 ). The species regulated by the SOx/NOx standard 

19 will include the sulfur-containing species above and the above oxidized forms of nitrogen 

20 (NOy); ammonia and ammonium are not currently included as regulated pollutants. 

21 Aggregation Issues 

22 Equation 5 provides a relationship for converting a sulfur or nitrogen deposition to an 

23 “equivalent” airborne concentration. This is useful for setting the NAAQS, where we expect to 

24 convert deposition critical loads developed by the ecosystem models (Section XXX) to ambient 

25 concentrations, which will then be the air quality standard. A major issue to consider during 

26 such conversion is the spatial, temporal and chemical resolutions of the deposition data and the 

27 resulting standards. Since the objective is to regulate total oxidized sulfur and nitrogen, and this 
F-31 

January 14, 2011 

1 is also the chemical resolution provided by the ecosystem models, it is convenient to collect the 

2 deposition velocities and apply the mi values to the Ci’s in Equation 5 and re-arrange it as 

 Amb

S N S N

Tot CS|N V / Dep / 3   (6) 

4 where VS/N is a constant that relates total deposition of sulfur or nitrogen to the total ambient 

5 concentration. Since the deposition critical loads are annual total depositions, it is proposed that 

6 the standard be an annual average concentration. Data used to derive annual VS/N values will 

7 need to have the same spatial representativeness as the critical loads. 

8 Air Quality Simulation Models 

9 Ideally, VS/N values would be derived for each area of interest from concurrently collected 

10 sulfur and nitrogen deposition and concentration measurements. However, no monitoring 

11 network currently exists that can provide such information. We therefore propose using output 

12 of the Community Multi-scale Air Quality (CMAQ) model (EPA, 1999) for initial calculation of 

13 VS/N values. 

14 CMAQ provides both concentrations and depositions of a large suite of pollutant species 

15 on an hourly basis for 12 km grids across the continental U.S. Its comprehensive structure is 

16 ideal for providing VS/N values that appropriately address the chemical and temporal aggregation 

17 issues discussed above, and weighted spatial averages of the gridded data can be used for areas 

18 that span multiple grid cells. The major potential drawback to using CMAQ output is that the 

19 data is simulated rather than measured, which calls its accuracy into question (discussed further 

20 below). 

21 CMAQ does not directly calculate or use VS/N values; instead the following procedures 

22 are used in the code to model deposition: 

1) vdry 23 values of gaseous pollutants are calculated in the CMAQ weather module called 

24 the Meteorology-Chemistry Interface Processor (MCIP) through a complex function of 

25 meteorological parameters (e.g. temperature, relative humidity) and properties of the geographic 

26 surface (e.g. leaf area index, surface wetness) 

2) vdry 27 values for particulate pollutants are calculated in the aerosol module of CMAQ, 

28 which, in addition to the parameters needed for the gaseous calculations, also accounts for 

29 properties of the aerosol size distribution 

3) vwet 30 values are not explicitly calculated. Wet deposition is derived from the cloud 

31 processing module of CMAQ, which performs simulations of mass transfer into cloud droplets 
F-32 

January 14, 2011 

1 and aqueous chemistry to incorporate pollutants into rainwater, all of which is conceptually 

contained in the vwet 2 parameter in Equation 2. 

3 The deposition transference ratios3 introduced in Chapter 5 are referenced as TSOx and 

4 TNOy, to distinguish these parameters from an exact linkage to deposition velocity, which is 

5 uniquely associated with individual atmospheric species. Deposition transference ratios are 

6 defined as the annual wet and dry deposition of all oxidized species (NOy for TNOy, SO2 plus SO4

7 for TSOx) divided by the average annual concentration of NOy, for TNOy, or SO4 plus SO2, for 

8 TSOx. The units for TNOy and TSOx are distance/time. Deposition transference ratios provide a 

9 mechanism to associate ambient concentrations to deposition loads and to determine if an area’s 

10 air concentrations of NOy and SOx meet a NAAQS level using the AAI form. A deposition 

11 transformation ratio is an aggregate representation of the deposition process generated through 

12 modeling which does not lend itself to a traditional analysis relating observations and 

13 predictions. Furthermore, there is an implicit assumption that the response of deposition 

14 transformation ratios to changes in meteorology and emissions is relatively stiff, as these ratios 

15 are an attribute of the system that channels ambient air response associated with decreases in 

16 emissions of NOx and SOx to changes in deposition. The stiffness of the deposition transference 

17 ratios would suggest that the relationship between ambient concentrations and deposition is 

18 strictly a constant proportion, not impacted by the mixture and level of emissions or by changes 

19 in meteorology. To better understand the implications of this assumption, we investigated the 

20 relative variability of the modeled deposition transformation ratios across time and space, and the 

21 stability of the ratios relative to emissions and meteorological inputs was conducted to guide 

22 EPA in determining how uncertainties in this parameter may eventually impact AAI related 

23 calculations. 

24 Spatial and Interannual Variation of TS and TN. 

25 Generally small spatial and inter-annual variability exist in the deposition transformation 

26 ratios for the 2002 -2005 model years (Figure F-15). The inter-annual variability, calculated at 

27 the grid cell level, as measured by the median coefficient of variation is around 10% and the 

28 absolute values of the ratios remain stable, suggesting that year to year changes in meteorology 

                                                           

3 In the first draft of the Policy Assessment, the deposition transformation ratios were labeled VNOx and VSOx. For 

this draft, based on recommendations from CASAC, we have renamed these ratios TNOx and TSOx. 
F-33 

January 14, 2011 

1 have minimal impact on the ratios. Spatial homogeneity of deposition transformation ratios 

2 within the two acid sensitive areas we evaluated in the REA (Adirondacks and Shenendoah) 

3 (Figure F-15) is consistent with a relatively homogeneous ambient concentration environment 

4 overlaid upon a landscape of similar vegetation and surface conditions. Such spatial 

5 homogeneity within case study areas provides confidence that an area wide application AAI will 

6 not be strongly dependent on the exact boundaries chosen to define an acid sensitive areF. 

7 TSOx and TNOy Sensitivity to emission changes. 

8 The response of TSOx and TNOy to emission changes was explored by analyzing available 

9 base case 2005 and a CMAQ simulation emissions reduction scenario referred as the 2030 case. 

10 The 2030 case represents Eastern U.S. domain wide NOx and SOx emission reductions of 48% 

11 and 40%, respectively. Median changes in deposition transference ratios tended to be around 

12 zero (Figure F-16), with the Adirondack region exhibiting slightly higher response than the 

13 Shenandoah region and remainder of the Eastern U.S. domain. 

14 
F-34 

January 14, 2011 

1

2 Figure F-15 Spatial and interannual variability of inverse deposition transference ratios, 1/TSOx

3 and 1/TNOy, for Adirondack (top) and Shenandoah case study areas. 
F-35 

January 14, 2011 

1 Figure F-16. Summary of inter-annual and emissions sensitivity variability of sulfur and 

2 nitrogen deposition transference ratios. 

3

4 
F-36 

January 14, 2011 

1 Comparisons of CMAQ and CASTNET and NADP generated TSOx and TNOy

2 Transference ratios based on CASTNET sulfur and nitrogen measurements, CASTNET 

3 derived dry deposition and NADP wet deposition were constructed to approximate ratios 

4 developed using measurements where possible. These ratios were developed to compare with 

5 CMAQ derived ratio. The limitations of the ratios constructed from observations include 

6 avaialability of only part of the NOy species mix, total nitrate, as well as the use of a model to 

7 calculate dry deposition velocities. Nevertheless, any comparison to measurements not only 

8 can provide some assurance that CMAQ constructed ratios relate to observed data as well as 

9 serving as a potential diagnostic metric for modeled deposition processes. Modeled and 

10 observed ratios (Figure F-17) indicate closer agreement for TSOx relative to TNOy, an expected 

11 result given the missing nitrogen species in the CASTNET data set. It is difficult to diagnose 

12 the cause of differences as the dry deposition estimates from CASTNET are based on different 

13 modeling approach relative to that used in CMAQ. However, as observation data sets become 

14 more complete, these types of analyses can help elucidate what aspects of deposition 

15 characterization require improvement. The next logical steps in developing comparisons 

16 between observed and modeled ratios will be to replace CMAQ wet deposition with PRISM 

17 based wet deposition results to allow for a better spatial pairing of observed and modeled data 

18 sets. This would partially constrain the problem to differences in dry deposition calculation 

19 methods between CASTNET and CMAQ. EPA’s ORD will be conducting sulfur and nitrogen 

20 dry deposition flux experiments in 2011. Those data will provide be extremely valuable 

21 diagnostic information to improve CMAQ deposition processes, which in turn will lead to 

22 improved confidence in CMAQ derived deposition ratios. 
F-37 

January 14, 2011 

1 Figure F-17. Comparison of observation based and CMAQ derived transference ratios. Each 

2 point represents an annual average at the grid cell of the CASNET station. The Observed values 

3 used NADP wet precipitation data, CASTNET air quality data and modeled dry deposition 

4 based on the CASTNET observations and the dry deposition model specific to CASTNET. 

5 
F-38 

January 14, 2011 

1 Comparsons between models 

2 The variability in T ratios of SOx and NOx was explored by comparing their values from 

3 a number of different modeling scenarios. Model runs were procured for the purpose of 

4 analyzing variability from the following sources: 

5 The variability in T ratios due to each of these sources is shown through boxplots specific 

6 to pollutants (i.e. SOx and NOx) and selected study areas (the Adirondacks, Shenandoahs) in 

7 Figures F-18- F-21. The data in the boxplots are the T values of all gridcells whose center lies 

8 within the specified study area. The center line of each box are the population medians, the box 

9 edges are the 25th and 75th percentiles, the ends of the whiskers are the 10th and 90th 

10 percentiles, and the points are data that lie outside the 10th and 90th percentiles. The notches in 

11 the boxes indicate the significance differences in the medians: non-overlapping notches strongly 

12 suggest that the populations are significantly different. The model scenario that produced each 

13 box's data is indicated on the x-axis. 

14 The boxplots show that, in general, there is not a large degree of inter-annual variability 

15 in T ratios. The notches of T ratios for nitrogen appear to overlap for all 4 years of CDC 12km 

16 and 36 km CMAQ runs in both study areas. The S values appear to be slightly high in the 

17 Shenandoahs in 2003 and lower in 2005 in the 12 km run. The 36 km result show what appears 

18 to be a decline in S values in both areas, especially when comparing the first 2 years to the latter 

19 2 years. Results from the AURAMS 2005 runs appear to generally be similar to those of the 

20 2005 CMAQ runs, which is surprising given the different mix of chemical species available for 

21 calculations and the much larger (45 km) grid size of the AURAMS model. 

22

23
F-39 

January 14, 2011 

1

2

3

4

5 Figure F-19. Modeled generated TSOx ratios in the Shenadoah area showing CMAQ 12 and 36 km values 2002 – 

6 2005 and AURAMS 2005. 

Figure F-18. Modeled generated TNOy ratios in the Shenadoah area showing CMAQ 12 and 

36 km values 2002 – 2005 and AURAMS 2005.
F-40 

January 14, 2011 

1 Figure F-20. Modeled generated TNOy ratios in the Adirondacks area showing CMAQ 12 and 36 km values 2002 – 

2 2005 and AURAMS 2005. 

3

4

Figure F-21. Modeled generated TSOx ratios in the Adirondacks area showing CMAQ 12 and 36 

km values 2002 – 2005 and AURAMS 2005.
F-41 

January 14, 2011 

1 CMAQ uncertainties and the AAI 

2 The AAI relies on CMAQ for the sulfur and nitrogen transference ratios and NHx

3 deposition. The model evaluation results, including the ammonia and wet precipitation 

4 treatments, reflect a continual process of model improvement designed to ingest the latest 

5 science within a framework links a myriad of atmospheric and surface processes across multiple 

6 pollutant species. While this document focuses in on the more direct processes affecting N and 

7 S deposition, these modifications are incorporated with the philosophy that the best science is 

8 being adopted and they in turn support the overall improvement of the models’ treatment of all 

9 processes. The inclusion of better chemistry and physics of a particular process acts as an 

10 internal diagnostic tool for other processes that are linked throughout the model framework 

11 through basic conservation of mass principles. With respect to the AAI, the CMAQ model 

12 must be relied on to provide the spatial flexibility attendant with a national standard. As the 

13 model continually adopts the best science, confidence in relevant CMAQ generated AAI 

14 parameters is raised for both near term and future scenarios. 

15

16 F4.3 Ecosystem Modeling 

17 F.4.3.1 MAGIC modeling 

18 An extensive uncertainty analysis of the MAGIC model was conducted as part of the 

19 REA, and documented in Appendix 4 of the REF. This uncertainty analysis included 

20 comparison of MAGIC outputs with observed water chemistry and ANC values. The 

21 uncertainty analysis also included an approach for generating confidence intervals for predicted 

22 ANC, using ensembles of model results based on alternative model calibration methods. 

23 The model performance comparisons documented in Appendix 4 of the REA show close 

24 correspondence between simulated and observed annual average surface water SO4, NO3, and 

25 ANC during the model calibration period for 44 lakes in the Adirondacks Case Study Area and 

26 60 streams in the Shenendoah Case Study Area. These comparisons are reproduced in Figures F27 22 and F-23. Comparisons in the ability of MAGIC to reproduce the temporal pattern of ANC 

28 for individual lakes was also assessed, and the model does reasonably well at matching the 

29 pattern of ANC, although the fit is not as good as during the model calibration period. 

30 The estimated confidence bounds on predicted ANC suggest that the 95 percent upper 

31 confidence bound is on average 10 percent higher in lakes and 5 percent higher in streams. This 

32 suggests relatively low uncertainty introduced by the MAGIC modeling assumptions. MAGIC 
F-42 

January 14, 2011 

1 modeling is used in developing the estimates of base cation weathering for comparison to the F2 factor approach described in Chapter 5. 

3 F.4.3.2 SSWC modeling 

4 As stated in Appendix 4 of the REA, uncertainties in some elements of the SSWC 

5 modeling are not well understood. The version of the SSWC model used here uses the F-factor 

6 approach to estimate the preindustrial base cation supply for a given catchment. While this 

7 approach has been widely applied in Canada and Europe, it has only been used in a few cases 

8 within the United States and its assumptions and parameters have not been fully evaluated for 

9 aquatic systems. The natural or preindustrial catchment supply of base cations (i.e. weathering 

10 rates) has the most influence on the critical load calculation and also has the largest uncertainty 

11 (Li and McNulty, 2007). The uncertainty and ability to accurately estimate this parameter has 

12 not fully been evaluated and its uncertainty is unknown. It is important to note that for the 

13 United States, there is only one study for surface waters critical loads that compared steady-state 

14 and dynamic models and different steady-state approaches (MAGIC and F-factor) (Holdren et al. 

15 1992) other than what is presented in Chapter 5. Holdren et al. 1992 compared critical loads 

16 calculated by the steady-state MAGIC and the SSWC F-factor model for lakes in the Northeast. 

17 In this study, steady-state MAGIC model yielded critical load values that show the same general 

18 trend and on average were 14 kg/(ha-yr) SO4 higher than those from the SSWC F-factor 

19 approach, which is consistent with results, presented in Chapter 5. The two models converge at 

20 low critical, but diverge as the buffering potential for watersheds increase, as indicated by 

21 increasing critical loads. 

22 The REA conducted an uncertainty assessment using Monte Carlo simulation methods to 

23 characterize the uncertainty in estimated critical loads using the SSWC, varying a number of 

24 important inputs including runoff rates, water chemistry variables, and acid deposition. The 

25 coefficients of variation (CV) for the estimated critical loads (standard deviation divided by the 

26 mean) were calculated for each lake in the study as a measure of relative uncertainty. 

27 The results of this uncertainty analysis show that the coefficients of variation are on 

28 average very low for target ANC values within the range we are recommending (20 to 50 μeq/L). 

29 The CVs for critical loads are only 5% and 9% for critical load limits of 20 and 50 μeq/L, 

30 respectively. Although the average CV is relatively small for the population of sites modeled, 
F-43 

January 14, 2011 

1 individual site CV can vary from 1% to 45%. This difference is due to the high degree of 

2 uncertainty in site specific parameters for particular sites. 

3 These analyses suggest that uncertainties introduced in the AAI directly by the SSWC 

4 Factor model are likely to be moderate. Additional uncertainties are introduced by the 

5 generalization of the F-factor approach to estimate critical loads in locations where F-factors 

6 have not been developed. 

7

8

9 Figure F-22 Simulated versus observed annual average surface water SO42-, NO3-, ANC, and 

10 pH during the model calibration period for each of the 44 lakes in the Adirondacks Case Study 

11 AreF. The black line is the 1:1 line. (Source: reproduced from REA, Appendix 4, Figure 1.1-1). 
F-44 

January 14, 2011 

1

2 Figure F-23. Simulated versus observed annual average surface water SO42-, NO3-, ANC, and 

3 pH during the model calibration period for each of the 60 streams in the Shenandoah Case Study 

4 AreF. The black line is the 1:1 line. (Source: reproduced from REA, Appendix 4, Figure 1.1-2) . 

5 
F-45 

January 14, 2011 

1 F4.4 Cumulative uncertainty analysis

2

3 An analysis of the cumulative effects of uncertainty on the AAI was conducted and is 

4 described in Appendix G. In summary, this included bootstrapping analyses of the parameters in 

5 the AAI equation to translate error in individual measurements to the regional values used in the 

6 equation. The parameters that are averages of grid-level CMAQ modeled values, Ndep and NHx, 

7 had bootstrapped uncertainty values of approximately +20% (Figures G-1 and G-5). The 

8 transference ratios include two different grid-level CMAQ modeled values and had much higher 

9 uncertainty, exceeding 100% (Figures G-3 and G-4). The calculation of Neco also includes two 

10 different input values, the CMAQ derived Ndep values and lake specific nitrogen leaching 

11 values. The Neco results also had high uncertainty values, ranging from -65% to approximately 

12 200% (Figure G-2). The critical load value for the region is affected by the Q and BCo values at 

13 the individual lakes within a region. Uncertainty in these parameters gave a regional uncertainty 

14 range for the critical load of +35% (Figure G-6). 

15 The results of the bootstrapping analyses were used to complete a cumulative analysis of 

16 uncertainty in a subsequent Monte Carlo style analysis. This analysis was illustrated in the form 

17 of the tradeoff curve for the concentrations of NOy and SOx (Figure G-7). The results in the two 

18 regions analyzed were similar. There was a range of uncertainty, with 50% of the distribution 

19 within +20% of the observed value. Most importantly, the mean value of the results was very 

20 close to the observed valuein both regions. This indicates that there is no systematic bias in the 

21 results despite what can be relatively high levels of uncertainty in the input parameters.

22 
F-46 

January 14, 2011 

1 F.5 Modeling and Data Gaps 

2 Deposition processes. Currently, there are efforts to improve a number of nitrogen and 

3 sulfur deposition processes in CMAQ. Active areas of model process improvement are in the 

4 treatment of lightning generated NOX and the transference of nitrogen between atmospheric and 

5 terrestrial and aquatic media, often referred to as bi-directional flux. Lightning NOx potentially 

6 provides a significant contribution to wet deposition as the resulting NOX is rapidly entrained 

7 into aqueous cloud processes. Both the thermodynamics of soil processes and mass transfer of 

8 nitrogen species across the surface-atmosphere interface is governed by an assortment of 

9 temperature, moisture, advection and concentration patterns. These processes and mass transfer 

10 relationships are coupled within the emissions, meteorological, and chemical simulation 

11 processes and associated surface/vegetation and terrain information incorporated in or accessed 

12 by the CMAQ. In addition to research activities to improve the characterization of nitrogen13 related processes in CMAQ, efforts are also underway to improve the general characterization of 

14 ammonia emissions which remains as an area of large uncertainty due to limited source data and 

15 the ubiquitous nature of these emissions. Another challenge for regional/national air quality 

16 modeling is properly representing the effects on pollutant concentrations, precipitation and 

17 therefore deposition of variable terrain features, particularly steep mountain-valley gradients and 

18 the interfaces to wide open basins encountered in the Western United States. 

19 Two important enhancements regarding the treatment of wet precipitation and the bi20 directional flux of ammonia were discussed in Appendix F. In 2011, the EPA will deploy a 

21 prototype flux measurement package over a grass field at the Duke Forest Blackwood Division. 

22 Dry deposition fluxes will include NOy, NO2, HNO3, HONO, NO3 aerosol, NH3, NH4 aerosol, 

23 SO2, SO4 aerosol and ozone. Concentrations of organic N in aerosol, but not fluxes, also will be 

24 measured. The availability of these flux measurements will enable a more direct assessment of 

25 CMAQ treatment of sulfur and nitrogen deposition processes. 

26 The interest in deposition of sulfur and nitrogen raises the potential importance of occult 

27 (cloud and fog related processes) deposition associated with mists and clouds, which may be 

28 particularly relevant for aquatic acidification of high elevation watersheds. Occult deposition 

29 currently is in the early stages of development within the CMAQ framework. 

30 Lightning generated NOx emissions have been an active area of research over the last 

31 decade and approaches that incorporate lightning count data and estimated NOx generation based 
F-47 

January 14, 2011 

1 on satellite measurements and aircraft campaigns have been tested in modern air quality models, 

2 including CMAQ. Lightning NOx is hypothesized to increase upper tropospheric ozone levels 

3 and wet nitrogen precipitation, with relatively negligible impact on near surface ambient nitrogen 

4 patterns. It is anticipated that CMAQ will incorporate lightning NOx for EPA assessments in the 

5 2012 timeframe. 

6 Interest in organic bound nitrogen has increased based on NADP measurements 

7 suggesting that organic nitrogen contributes as much as 30% of the total nitrogen in precipitation 

8 samples. Significant uncertainties regarding the origin and composition of organic nitrogen 

9 (Altieri et al., 2009) suggest a need for research to improve our understanding of organic 

10 nitrogen prior to developing parameterizations in air quality models. Concentartions of organic –

11 N will be measured as part of EPA’s flux studies in 2011. Questions regarding the the relative 

12 contribution of anthropogenic or natural sources as well as the effects of re-entrainment from the 

13 surface require attention. 

14 Atmospheric Observations. Chapter 2 addresses the current state of atmospheric 

15 observations relative to the NOx/SOx secondary standard and includes suggestions for enriching 

16 the observational data base used to evaluate models. This new standard poses measurement 

17 resource challenges as the current networks, with the exception of CASTNET and some National 

18 Park Service (NPS) efforts, there is sparse spatial coverage relevant to anticipated acid sensitive 

19 areas and the specific measurement needs related to NOy, speciated NOy and ammonia and 

20 ammonium. Temporally resolved data based on co-located measurements of continuously 

21 operating SO2 and sulfate would help diagnose the partitioning of these species and support 

22 evaluation of SOx deposition. As discussed above, deposition flux studies should be 

23 considered in a wide variety of ecoregions given the influence surface, vegetation type and 

24 meteorology imparts on deposition velocities. 

25 Source emissions. Anthropogenic emissions of nitrogen oxides (NO and NO2) and 

26 sulfur dioxide generally are believed to be well characterized as the major contributors of NOx 

27 and SO2 from energy generation and transportation sectors have a history of continuous 

28 improvements of emissions modeling as well as direct emission measurements for major power 

29 generating units. Greater uncertainty resides in natural emissions of NOx from lightning 

30 processes (discussed above) and soil and agricultural related phenomena. Both NOx and 

31 ammonia emissions are subject to re-emission after deposition as part of the complex cycling of 
F-48 

January 14, 2011 

1 nitrogen in soils and biotF. Characterizing the variety of agricultural practices that impact both 

2 ammonia and NOx is complicated by the dispersed nature of agriculture processes as well as the 

3 influence of various meteorological factors on relevant biogeochemical processes controlling 

4 transformation and removal of nitrogen species. 

5

6 Ecosystem processes and surface water observations. The critical load modeling 

7 approaches underlying the standard require a variety of input data depending on the approach 

8 chosen. In general terms, the availability of watershed related deposition, soil and vegetation 

9 characteristics and surface water chemistry determine the approach taken. There is a relatively 

10 extensive source of data for critical load modeling using the steady state model for most of the 

11 contiguous U.S. However, several ecoregions included relatively small sample sizes of critical 

12 load estimates and an increased pool of data to increase the representativeness of water quality 

13 variables should be pursued at the national level. 

14 Data to support dynamic modeling like MAGIC has focused on the Adirondack and 

15 Shenandoah regions. A more thorough characterization of nitrogen retention, dissolved organic 

16 carbon, soil chemistry in all acid sensitive areas would lead to reduced uncertainties in applying 

17 the AAI as well as future considerations for standards that incorporate terrestrial acidification 

18 and nutrient enrichment effects. In looking forward, it may not be practical to use dynamic 

19 modeling everywhere, but periodic application of dynamic models in different ecoregions 

20 gradually will result in an information system to revisit and evaluate many of the assumptions 

21 applied in national scale steady state modeling. This extension to previously undersampled 

22 areas is especially important in the mountainous West where there is evidence of aquatic 

23 acidification, yet many of the atmospheric attributes are markedly different than in Eastern 

24 systems, starting with a nitrogen dominated atmosphere, the strong influence of mountain-valley 

25 terrain on local meteorology and the closer proximity to transport of hemispheric pollution 

26 across the Pacific Ocean. 

27

28 F.6 Summary and Conclusions 

29 Uncertainty and natural variability exist in all of the components of the structure of the 

30 NOx and SOx standard introduced in this PA, and should be considered in establishing the level 

31 of the AAI. A summary of the relative uncertainties of these components is provided in Table 
F-49 

January 14, 2011 

1 F-6.1. On balance, the confidence level in the information and processes associated with the 

2 linkages from ecological effects to atmospheric conditions through deposition and ecosystem 

3 modeling is very high. The considerable body of evidence is conclusive with regard to 

4 causality between aquatic acidification and biological and ecological effects. Confidence in the 

5 linkage associating aquatic acidification and ANC is extremely high, as the aquatic chemistry 

6 describing this relationship, while nonlinear, is relatively simple with regard to chemical species 

7 and reactions. The relationships between deposition and ANC, while complicated by a variety 

8 of biogeochemical and hydrological processes and data requirements within watersheds, are well 

9 established and the critical load models have been thoroughly vetted through the scientific 

10 community with a demonstrated level of successful evaluation. The linkages between ambient 

11 concentrations of relevant species and deposition are best handled through air quality modeling 

12 systems like CMAQ. The relationship between concentrations and deposition loads is well 

13 characterized by these models, which are constrained by mass balance principles. While much 

14 of the physical and chemical processing that determine concentrations and consequent deposition 

15 is interwoven with numerous fundamental processes characterizing mass transport and 

16 atmospheric chemical oxidation, the science is relatively mature with years of applications and 

17 continued evolution of the models. The specific processes guiding nitrogen and sulfur 

18 chemistry and deposition are relatively simple. More challenging is the ability to parameterize 

19 processes at the air-surface interface which guide the estimation of deposition velocities and the 

20 re-emission of certain species, as well as many of the area wide natural processes and 

21 agricultural practices which influence emissions of oxidized and reduced forms of nitrogen. 

22 The variety of uncertainty, variability and sensitivity analyses included in this chapter 

23 have been conducted under the assumption that the basic model construct is solid, as discussed 

24 immediately above, and are used to inform conclusions regarding the level of the AAI that 

25 incorporate consideration of uncertainty. These analyses are also useful in guiding 

26 implementation efforts related to future monitoring, emissions and model process improvements. 

27 The influence of uncertainty on the level of the AAI can be thought of as reducing or increasing 

28 relative stringency of the level to increase the likelihood that requisite protection of public 

29 welfare is provided. Throughout these discussions there is no apparent directional bias in the 

30 uncertainty regarding the biological, chemical and physical processes incorporated in the AAI. 

31 From the perspective of valuation of ecosystem services, the estimates generally are believed to 
F-50 

January 14, 2011 

1 be biased low, meaning the values of reaching a target level of protection are underestimated. 

2 However, quantification of these values is perhaps the most uncertain of all aspects considered. 

3 Consequently, the level of the AAI should be relatively high in a buffering context to account 

4 for the existence of uncertainties in several components. In addition to, but related to these 

5 uncertainties discussions, are considerations of time lag to reach a target level ANC due to 

6 ecosystem response dynamics, as well the uncertainties in the severity and prevalence of episodic 

7 events. Both of these considerations suggest support for an AAI that is somewhat higher than 

8 the target ANC supported by the specific evidence and risk information. 

9
51

 Table F-6-1. Summary of Qualitative Uncertainty Analysis of Key Elements Affecting the AAI form of the NOx

Source Description 

Potential influence of 

uncertainty in element 

KnowledgeBase 

uncertainty Comme

Direction 

(negative 

implies 

less 

relative 

protection) Magnitude 

Major elements (and sub-models) of the ecological effects to ambient concentration framework 

Biological/ecosystem response to acidification 

Clear associations between aquatic acidification (pH, elevated Al) and adverse ecosystem effects (fish mortality, decreased species diversity) 

Both Low Low (regionally) 

The ecosystem level responses alevels. The uncertainty increaseincreasing number of factors inflatitudinal species gradient, spec

Linkage between direct acidification species and ecological indicator (ANC) 

The relationships across ANC, pH and dissolved Al are controlled by well defined aquatic equilibrium chemistry 

Both Low Low 

ANC is the preferred ecosystem relationship with pH and the depthe NOx/SOx standard. 

Linkage between ecological indictor and adverse ecological effects 

Direct nonlinear associations between ANC and fish mortality and species diversity 

Both Low- medium Low 

Although the pH dependency on always directionally consistent. high ANC environments the relavalue as catchments are in relatiregimes due to natural conditionanthropogenic influence (i.e., acsensitive areas of concern the resimilar to the relationships betwspecies and adverse effects. 

Deposition to ANC linkage through Critical Load approach 

Mass-balance Steady State critical load model is applied to determine critical load values. MAGIC 

Both Low Low 

The model formulation is well csubstantial amount of research ain the peer reviewed literature. uncertainty associated with the a
52

Source Description 

Potential influence of 

uncertainty in element 

KnowledgeBase 

uncertainty Comme

Direction 

(negative 

implies 

less 

relative 

protection) Magnitude 

model is used to validate steady State model. The Steady State critical load model formulation is used as the foundation for deriving the AAI equation. 

support certain model componen

Atmospheric concentrations to deposition 

Deposition is a direct function of ambient concentration, influenced by several processes, and handled in the AAI through air quality modeling. 

Both Low Low 

The model design is appropriate temporal complexities that influas well as the variety of atmosphare not measured. Greater uncerinformation (e,g,, ammonia emiscalculations and availability of omodel behavior. 

Ecological indicator to changes in the value of ecosystem services 

Definitions of public welfare may include economic considerations, based on the tradeoffs people would make to avoid the negative impacts of acidification, through effects on the values of ecosystem services. Empirical estimates of valuation for limited ecosystem service categories are used to 

Negative Medium- high Low-medium 

There are many studies that estimincreasing services that may be aacidification and eutrophicationstudies focus on the particular imeutrophication on the quality of preferences for avoiding these imThose studies that do are often limpacts on a narrow population environmental quality. The monand to New York residents for ethe Adirondacks associated with ecological indicator are significa
53

Source Description 

Potential influence of 

uncertainty in element 

KnowledgeBase 

uncertainty Comme

Direction 

(negative 

implies 

less 

relative 

protection) Magnitude 

inform the discussions of adversity associated with alternative ANC levels. 

total benefits in the U.S. This is outside New York would value iAdirondacks and similar natural The methodologies used in the sestimates of the value of changethe Adirondacks region are sounpeer review. The method of aligvalued in the Banzhaf et al. studeliminating current damages leaor underestimate of the benefits. difference is difficult to know a improvements in the share of lakANC threshold of 20 μeq/L are 

Sub-components and data of individual models 

Atmospheric Components 

DepSOx

Annual deposition of sulfur mass from dry deposition of (SO2 and SO4) and wet SO4

derived from CMAQ 12km horizontal grid resolution averaged over 5 years 

both low low 

The treatment of SOx deposition models has evolved over the last general consensus that the overatreated well with difficulties in sobservations and modeled resultspatial pairing has improved witadjustments. 

DepNOy

Annual deposition of oxidized nitrogen mass from dry deposition of (all NOy species) and wet NO3

both low low-medium 

The treatment of oxidized nitrogquality models has evolved over There is general consensus that tof oxidized N is treated well. H
54

Source Description 

Potential influence of 

uncertainty in element 

KnowledgeBase 

uncertainty Comme

Direction 

(negative 

implies 

less 

relative 

protection) Magnitude 

derived from CMAQ 12 km horizontal grid resolution averaged over 5 years 

deposition velocities across NOy uncertainties regarding the depospecies such as NO2 pose ongoin

a shortage of NOy species meastechniques to directly measure dprogress on improving parametedeposition. 

DepNHx

Annual deposition of reduced nitrogen mass from dry deposition of (NH3 and SO4) and wet 

NH4 derived from CMAQ 

12km horizontal grid resolution averaged over 5 years 

both low medium 

NHx deposition also is quantifieapplications. The well dispersebased emissions that are influenmeteorological and surface /soil 

to challenge characterization of Recent incorporation of a bi-dire

CMAQ improves consistency wunderstanding and yields improvof limited observations with moammonia and ammonium ambieto compromise our ability to chaour treatment of NHx. As with estimates, technologies for direcavailable routinely. Both NHx deposition are assigned low valua general dominating role of sulf

Wet deposition (generically – N and S species) 

Wet component of total deposition as described in the Dep terms, above 

both low low 

Wet deposition remains an attribconfidence based on the ability tchemical components in precipitHowever, given the stochastic n
55

Source Description 

Potential influence of 

uncertainty in element 

KnowledgeBase 

uncertainty Comme

Direction 

(negative 

implies 

less 

relative 

protection) Magnitude 

models have a difficult time in mThe use of 5 year averages and padjustments have reduced uncerobservations and modeled estim

Dry deposition (generically – N and S species) 

Dry component of total deposition as described in the Dep terms, above 

both medium Medium-high 

The absence of direct dry deposicombined with the significant vathat influence dry deposition velconfidence level in dry depositiodeposition. 

Deposition Transference Ratios 

CMAQ derived ratio of total oxidized deposition to concentration averaged over one year 

both low unknown 

Transference ratios enable the codeposition and the policy relevanNOy and (SO2 + SO4). They ar

construct and cannot be evaluateobservation context. The low sto emission changes and inter ancombined with low spatial variaratios are necessarily stable. 

CNOy Ambient concentrations of NOy through observations. negative low Low-medium 

Adequate spatial coverage of NOexist, but will be addressed in thmonitoring technology only oveperceived as “routine” based on NCore network. However FRM instruments currently is not avaidirection is a standard caveat to internal air stream conversion of to detection. 

CSOx Ambient concentrations of both low Low A lack of adequate spatial cover
56

Source Description 

Potential influence of 

uncertainty in element 

KnowledgeBase 

uncertainty Comme

Direction 

(negative 

implies 

less 

relative 

protection) Magnitude 

NOy through observations. for SO2 + SO4 observations. F

for SO4; although the long track precise CASTNET FP measuremachieving FRM status is a low h

Ecosystem Components 

BC0* Pre-industrial base cation concentrations negative Medium- high high 

Both the F-factor approach and pmodeling were used to generate 

agreement between both approacShenandoah streams. The more requirements of MAGIC limit itAdirondacks, although for consiapproach was applied nationwidillustrated greater divergence at areas with greater acid buffering cation levels. These conditions our population distribution analydo not affect the location within more sensitive water bodies. Sipreferred approach) tends to ove

the F factor approach, and the F-

applied nationally, the BC0* estim

conservative leading to a slight pcritical loads. Although we haestimates of BC0*, there is a lack 

of BC weathering rates. 

Neco positive low medium The term Neco, as defined, has a 

confidence level and is a direct f
57

Source Description 

Potential influence of 

uncertainty in element 

KnowledgeBase 

uncertainty Comme

Direction 

(negative 

implies 

less 

relative 

protection) Magnitude 

inherent in the deposition estimasurface measurements of NO3. 

“measurement” difference approall influencing processes (dinitriimmobilization) over the time peConsequently, there is an inhererelatively static system (Neco is model) that generally is not testesteady state vision of Neco woulforested ecosystem. The relativlargely, to the relative productivchallenge in determining any podetermine the relative “maturatiowhich requires knowledge of futareas of high land use restrictionNeco would be assumed to be ovrelative magnitude of Neco often dominance of SOx in controlling many systems. Furthermore, it any stored N will be released bais assumed to not occur in the lin

Q 

Annual runoff rate (distance/time) for a catchement. 

both low high 

Data used to calculate Q wStreamflow data were collectestations during 1951-80; 5,951 the analysis. See Gebert and otdescription of how the runoff streamflow datF. Appropriate m
58

Source Description 

Potential influence of 

uncertainty in element 

KnowledgeBase 

uncertainty Comme

Direction 

(negative 

implies 

less 

relative 

protection) Magnitude 

the geographical distribution of for the years 1951-80 and can devariations of runoff nationwide. reflect the runoff of tributary strrivers in order to represent mosmall scale variation in runoff wgeographical characteristics. t, W.F., Graczyk, D.J., and Krannual runoff in the United Geological Survey Hydrologic 710, scale 1:7,500,000. 

DOC Surface water dissolved organic carbon negative low medium 

Water bodies with high DOC lescreened out of the critical load avoid naturally acidic systems. assumption of ANC = ∑strong C

explicitly account for contributioConsequently, a small positive bload calculations (i.e., the CL esknowledge base value of M refleDOC datF. 
September, 2010 Draft – Do Not Quote or Cite 7-40

1

2

3 REFERENCES:

4 Aiyyer, A, Cohan, D., Russell, F., Stockwell, W., Tanrikulu, S., Vizuete, W., and Wilczak, J. 

5 2007. Final Report: Third Peer Review of the CMAQ Model. 

6 Altieri, K.E., B.J. Turpin and S.B. Seitzinger, 2009, Composition of dissolved organic nitrogen 

7 in continental precipitation investigated by bu ultra-high resolution FT_ICR mass 

8 spectrometry, Environmental Science and Technology, 43, 18, 6950 – 6955. 

9 Byun, D.W., and Schere, K.L. 2006. Review of the Governing Equations, Computational 

10 Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality 

11 (CMAQ) Modeling System. J. Applied Mechanics Reviews, 59 (2), 51–77. 

12 Dennis, R. and K. Foley, 2009, Adapting CMAQ deposition fields for critical loads analyses, 

13 presented at 2009 NADP Confernce; manuscript in preparation 

14 Dennis, R., R. Mathur, J.E. Pleim and J.T. Walker, 2010, Fate of Ammonia Emissions at the 

15 Local to Regional Scale as Simulated by the Community Multiscale Air Quality Model, 

16 accepted by Atmospheric Pollution Research 

17 Foley, K.M., Roselle, S.J, Appel, K.W., Phave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, 

18 G., Young, J.O., Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, F.B., Bash, J.O. (2010) 

19 Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system 

20 version 4.7, Geosci. Model Dev., 3, 205-226. 

21 Otte, T.L. and Pleim, J.E. (2010) The Meteorology-Chemistry Interface Processor (MCIP) for 

22 the CMAQ modeling system, Geosci. Model Dev., 3, 243-256. 

23 PRISM Climate Group, Oregon State University, http://www.prismclimate.org, created 4 Feb 

24 2004 

25
January 14, 2011 G - 1

Appendix G

Cumulative Uncertainty Analysis

This appendix provides analyses of the relative uncertainty in the AAI equation. The 

analysis involves a Monte Carlo style approach that incorporates the relative uncertainties of the 

input parameters. Cumulative results are illustrated in the form of the trade-off curve of the 

allowable atmospheric concentrations of the oxides of N and S.

From the general form of the standard:

AAI = {ANClim + CLr/Qr } – NHx/Qr - TNOy [NOy]/Qr - TSOx[SOx]/Qr

There are six sources of uncertainty in calculating an AAI value for measured 

atmospheric concentrations of the oxides of N and S; however these individual uncertainties are 

not applied in the same manner. Four of these, Neco, NHx, Tnoy and Tsox rely on data from 

CMAQ modeling. The uncertainty in CMAQ values can be high (see appendix F for detailed 

discussion of CMAQ modeling). These values as they are used in the AAI equation are averaged 

either across individual water bodies in the ecoregion or across CMAQ 12 km grids within the 

ecoregion. In order to translate the uncertainty to the AAI equation, these averaged values were 

bootstrapped using uncertainty in CMAQ modeling to obtain an estimated uncertainty in the 

average values. Uncertainty in the parameters BCo and Q affect the selection of a representative 

Critical Load (CL) value for the region. To analyze this, the CL value was bootstrapped to 

generate a distribution of CL values reflecting the underlying uncertainty in BCo and Q. In all 

cases the error was modeled as a normal Gaussian distribution and the bootstrapping was 

repeated 20,000 times to generate the distributions. 

Two ecoregions were chosen to assess the uncertainty, the Northern Highlands (region 

5.3.1) and the Northern Great Plains (region 9.3.3). Region 5.3.1 was chosen because it had the 

largest number of critical load data points to work with (819). Region 9.3.3 was chosen because 

it is a relatively less acid sensitive region that had a large enough number of Critical Load data 

points (164) to use for analyses. For all of these analyses an ANC level of 50 and a percentile of 

75% were selected.
January 14, 2011 G - 2

Modeled nitrogen deposition (Ndep) is used in two different components of the AAI, the Neco

value and in Tnoy. Figure G-1 shows the results of bootstrapping analyses. These analyses 

allowed for 100% uncertainty in the deposition at the grid level. Averaged over the entire 

regions the bootstrapped distribution has uncertainty that is +15% in Region 5.3.1 and +10% in 

Region 9.3.3.

Nitrogen Deposition (Ndep)

A. B.

Figure G-1. Bootstrap distribution of mean nitrogen deposition for Regions 5.3.1 (A) and 

9.3.3 (B)

The Neco term directly uses the mean deposition. The Neco is calculated as the Average Neco = 

Ndep – Nitrogen Leaching (Nleach), where Ndep is the regional average and Nleach is a lake 

specific value. Neco is calculated at each lake individually, and the mean Neco for the region is 

used in the AAI equation. Ndep was selected randomly from the distribution generated above

(Figure G-1). Nleach was allowed to have 30% uncertainty. Figure G-2 shows the results of the 

Neco bootstrapping analysis of the mean values for the two regions. The results indicate that the 

Nitrogen Uptake (Neco)
January 14, 2011 G - 3

mean Neco term varies widely with a range of <20 to >120 in Region 5.3.1 and <10 to 60 in 

Region 9.3.3. This equates to a range of uncertainty that is from approximately -75% to 135% in 

Region 5.3.1 and -65% to 218% in Region 9.3.3.

A. B.

Figure G-2. Bootstrap distribution of mean Nitrogen uptake (Neco) for Regions 5.3.1 (A) 

and 9.3.3 (B)

Transference Ratios (Tnoy, Tsox)

The Nitrogen transference ratio (Tnoy) also uses CMAQ derived values for Nitrogen deposition, 

but Tnoy, and the transference ratio for S, Tsox) are calculated at the grid level. The transference 

ratio analyses included uncertainty at the grid level in N and S deposition and in the ambient 

concentrations of NOy and Sox (Nconc and Sconc), also modeled by CMAQ. The transference 

ratios were calculated as Tnoy = Ndep/Nconc and Tsox = Sdep/Sconc. Uncertainty in the 

CMAQ values was included randomly before the ratio was calculated, with uncertainty in 

deposition (Ndep and Sdep) up to 75%, and uncertainty in concentration (Nconc and Sconc) up to

25%. The uncertainty in the deposition terms is lower than used above because this value

incorporates the concentration terms in CMAQ, so a portion of the uncertainty in these two terms 

will covary and would be directly offset in the calculation of the transference ratios. The mean 

value for the region was then calculated. The results of these analyses are shown in figures G-3 
January 14, 2011 G - 4

and G-4. In both analyses there were a small number of extreme outliers and the axes in the 

figures have been truncated to allow better visualization of the results. These outliers were 

included in the final analyses. The results were similar for Tnoy and Tsox. The range of values, 

excluding outliers, was larger for both ratios in Region 5.3.1, but in both regions the uncertainty 

ranged from -75% to > 100%, excluding the extreme outliers. 

A. B.

Figure G-3. Bootstrap distribution of mean nitrogen transference ratios (Tnoy) for Regions 

5.3.1 (A) and 9.3.3 (B)
January 14, 2011 G - 5

A. B.

Figure G-4. Bootstrap distribution of mean sulfur transference ratios (Tsox) for Regions 

5.3.1 (A) and 9.3.3 (B)

Reduced Nitrogen

Reduced nitrogen deposition (NHx) was analyzed in the same way as Ndep with the same 

uncertainty of 100%. The results are show in figure G-5. The average NHx deposition is higher 

in Region 5.3.1, but in both regions the range or uncertainty is approximately +20%.
January 14, 2011 G - 6

A. B.

Figure G-5. Bootstrap distribution of mean reduced nitrogen deposition (NHx) for Regions 

5.3.1 (A) and 9.3.3 (B)

Critical Load

The critical load (CL) that is selected to represent the ecoregion is dependent on the Target 

Percentile and level set as part of the standard. This value is represented in the AAI equation as 

the Base Cation weathering (BCo) and the hydrologic term, Q. Uncertainty in these two terms 

will affect the value of the CL. As represented in the AAI equation, BCo and Q are constants 

that, together with the selected ANC level, are equal to the representative CL. To model this 

uncertainty the CL values were bootstrapped including 70% uncertainty in the BCo term and 5% 

uncertainty in the Q term. These results are shown in Figure G-6. The CL has an uncertainty

range of approximately +30% in Region 5.3.1 and -25% to 35% in Region 9.3.3.
January 14, 2011 G - 7

A. B.

Figure G-6. Bootstrap distribution of representative critical load values for Regions 5.3.1 

(A) and 9.3.3 (B)

Combined Uncertainty in the AAI

To illustrate the combined effect of the uncertainty developed above a trade-off curve 

representing the allowable combined concentrations of S and N was developed. The trade-off 

curve was derived using the raw data for these ecoregions (Figure F-7, dashed lines). The curve 

was then derived again using values from the derived distributions of Neco, NHx, Tnoy, Tsox, 

and the CL. This was repeated 20,000 times with the resulting curves plotted in light gray in 

Figure G-7. The solid black lines represent 95% confidence intervals, the inner quartiles (25th

and 75th percentiles) and the mean. For this analysis the Neco term was treated separately from 

the CL selection to allow plotting of the tradeoff curves.

In Region 5.3.1 the mean value derived from the modeling is the same as the value 

derived from the raw data and the curves are directly on top of each other. The 95% confidence 

intervals represent a range of approximately +15% and the inner quartile approximately +5% 

relative to the maximum allowable concentration of S oxides. The uncertainty associated with 

the maximum allowable concentration of NOx is larger when looking at the 95% confidence 
January 14, 2011 G - 8

interval, approximately +45%, and at the inner quartiles, +20% uncertainty. The larger 

uncertainty on the NOx axis is due to the Neco and NHx terms.

In Region 9.3.3 the mean value derived from the modeling is the same as the curve 

derived from the raw data. The 95% confidence intervals represent a range of uncertainty that is 

approximately +25% and +10% for the inner quartiles relative to the maximum allowable 

concentration of S oxides. The uncertainty associated with the maximum allowable 

concentration of NOx is in the same range for this region.

A. B.

Figure G-7. NOy/Sox tradeoff curve with results of cumulative uncertainty analysis for 

Regions 5.3.1 (A) and 9.3.3 (B)
United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division

Research Triangle Park, NC

Publication No. EPA-452/R-11-004b

January 2011