Risk and Exposure Assessment for Review 

of the Secondary National Ambient Air Quality 

Standards for Oxides of Nitrogen and 

Oxides of Sulfur 

Final 

Appendices 

Photo courtesy of the National Park Service 

EPA-452/R-09-008b 

September 2009 

Risk and Exposure Assessment for Review of the Secondary National Ambient Air Quality 

Standards for Oxides of Nitrogen and Oxides of Sulfur 

Final 

Appendices 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 
DISCLAIMER 

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

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

Clean Air Markets Division, Office of Air Programs, the National Center for Environmental 

Assessment, Office of Research and Development, and the National Health and Environmental 

Effects Research Laboratory, Office of Research and Development, U.S. Environmental 

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

authors and do not necessarily reflect the views of EPA. This document is being circulated to 

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

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

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

Research Triangle Park, North Carolina 27711 (email: rea.anne@epa.gov). 
September 2009 

Appendix 1 

Aquatic Nutrient Enrichment Case Study 

Community Multiscale Air Quality (CMAQ) Model 

Final 

 

Prepared by 

U.S. Environmental Protection Agency 

Office of Atmospheric Programs 

Washington, DC 

Community Multiscale Air Quality (CMAQ) Model 

TABLE OF CONTENTS 

Acronyms and Abbreviations ..................................................................................................... 1-iv

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

1.1 Overview of CMAQ Model Application ................................................................................... 1-1

1.2 CMAQ Model Performance Evaluation .................................................................................... 1-3

1.2.1 CMAQv4.6 2002 Annual Average Predictions versus Observations for 

the Eastern United States .................................................................................. 1-5

1.2.2 CMAQv4.6 2002 Annual Average Predictions versus Observations for 

the Western United States ................................................................................. 1-9

1.2.3 CMAQv4.7 2002 through 2005 Annual and Monthly Average 

Predictions versus Observations ..................................................................... 1-13

1.2.4 Extended Evaluation of CMAQv4.6 2002 Predictions versus 

Observations ................................................................................................... 1-21

2.0 References ............................................................................................................................ 1-29

LIST OF FIGURES 

Figure 1.1-1. CMAQ continental United States and eastern and western modeling 

domains. ............................................................................................................... 1-2

Figure 1.2-1. 2002 CMAQv4.6 annual average SO2 predicted concentrations versus 

observations at CASTNet sites in the eastern domain. ........................................ 1-6

Figure 1.2-2. 2002 CMAQv4.6 annual average SO2-

4 predicted concentrations versus 

observations at CASTNet sites in the eastern domain. ........................................ 1-6

Figure 1.2-3. 2002 CMAQv4.6 annual average TNO3 predicted concentrations versus 

observations at CASTNet sites in the eastern domain. ........................................ 1-7

Figure 1.2-4. 2002 CMAQv4.6 annual average NH4

+

 predicted concentrations versus 

observations at CASTNet sites in the eastern domain. ........................................ 1-7

Figure 1.2-5. 2002 CMAQv4.6 annual average SO4

2- predicted wet deposition versus 

observations at NADP sites in the eastern domain. ............................................. 1-8

Figure 1.2-6. 2002 CMAQv4.6 annual average NO3

-

 predicted wet deposition versus 

observations at NADP sites in the eastern domain. ............................................. 1-8

Figure 1.2-7. 2002 CMAQv4.6 annual average NH4

+

 predicted wet deposition versus 

observations at NADP sites in the eastern domain. ............................................. 1-9

Figure 1.2-8. 2002 CMAQv4.6 annual average SO4

2- predicted concentrations versus 

observations at CASTNet sites in the western domain. ..................................... 1-10

Figure 1.2-9. 2002 CMAQv4.6 annual average SO2 predicted concentrations versus 

observations at CASTNet sites in the western domain. ..................................... 1-10

Figure 1.2-10. 2002 CMAQv4.6 annual average TNO3 predicted concentrations versus 

observations at CASTNet sites in the western domain. ..................................... 1-11

Final Risk and Exposure Assessment September 2009 

Appendix 1 - i 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-11. 2002 CMAQv4.6 annual average NH4

+

 predicted concentrations versus 

observations at CASTNet sites in the western domain. ..................................... 1-11

Figure 1.2-12. 2002 CMAQv4.6 annual average SO4

2- predicted wet deposition versus 

observations at NADP sites in the western domain. .......................................... 1-12

Figure 1.2-13. 2002 CMAQv4.6 annual average NO3

-

 predicted wet deposition versus 

observations at NADP sites in the western domain. .......................................... 1-12

Figure 1.2-14. 2002 CMAQv4.6 annual average NH4

+

 predicted wet deposition versus 

observations at NADP sites in the western domain. .......................................... 1-13

Figure 1.2-15. 2002–2005 Domain-wide average SO4

2- predicted concentrations and 

observations by month at CASTNet Sites in the eastern domain. ..................... 1-15

Figure 1.2-16. 2002–2005 Domain-wide monthly aggregate model performance statistics 

for SO4

2- concentrations based on CASTNet sites in the eastern domain. ........ 1-15

Figure 1.2-17. 2002–2005 Domain-wide average TNO3 predicted concentrations and 

observations by month at CASTNet sites in the eastern domain. ...................... 1-16

Figure 1.2-18. 2002–2005 Domain-wide monthly aggregate model performance statistics 

for TNO3 concentrations based on CASTNet Sites in the eastern domain. ....... 1-16

Figure 1.2-19. 2002–2005 Domain-wide average NH4

+

 predicted concentrations and 

observations by month at CASTNet sites in the eastern domain. ...................... 1-17

Figure 1.2-20. 2002–2005 domain-wide monthly aggregate model performance statistics 

for NH4

+

 concentrations based on CASTNet sites in the eastern domain. ........ 1-17

Figure 1.2-21. 2002–2005 domain-wide average SO4

2- predicted deposition and 

observations by month at NADP sites in the eastern domain. ........................... 1-18

Figure 1.2-22. 2002–2005 domain-wide monthly aggregate model performance statistics 

for SO4

2- deposition based on NADP sites in the eastern domain. .................... 1-18

Figure 1.2-23. 2002–2005 domain-wide average NO3

-

 predicted deposition and 

observations by month at NADP sites in the eastern domain. ........................... 1-19

Figure 1.2-24. 2002–2005 domain-wide monthly aggregate model performance statistics 

for NO3

-

 deposition based on NADP sites in the eastern domain. ..................... 1-19

Figure 1.2-25. 2002–2005 domain-wide average NH4

+

 predicted deposition and 

observations by month at NADP sites in the eastern domain. ........................... 1-20

Figure 1.2-26. 2002–2005 domain-wide monthly aggregate model performance statistics 

for NH4

+

 deposition based on NADP sites in the eastern domain. .................... 1-20

Final Risk and Exposure Assessment September 2009 

Appendix 1 - ii 
Community Multiscale Air Quality (CMAQ) Model 

LIST OF TABLES 

Table 1.1-1. CMAQ Nitrogen and Sulfur Deposition Species .................................................... 1-3

Table 1.1-2. Formulas for Calculating Nitrogen and Sulfur Deposition ..................................... 1-3

Table 1.2-1. Normalized Mean Bias Statistics for Predicted and Observed Pollutant 

Concentration ..................................................................................................... 1-13

Table 1.2-2. Normalized Mean Bias Statistics for Predicted and Observed Pollutant Wet 

Deposition .......................................................................................................... 1-14

Table 1.2-3. CMAQv4.6 Model Performance Statistics for PM2.5 and Selected 

Component Species Concentrations .................................................................. 1-22

Table 1.2-4. CMAQ v4.6 2002 Model Performance Statistics for Nitric Acid and Sulfur 

Dioxide ............................................................................................................... 1-25

Table 1.2-5. CMAQv4.6 2002 Model Performance Statistics for Annual Sulfate and 

Nitrate Wet Deposition Model Performance Statistics. ..................................... 1-26

Table 1.2-6. CMAQv4.6 Model Performance Statistics for Hourly Ozone Concentrations ..... 1-27

Table 1.2-7. CMAQv4.6 Model Performance Statistics for 8-hour Daily Maximum 

Ozone Concentrations ........................................................................................ 1-28

Final Risk and Exposure Assessment September 2009 

Appendix 1 - iii 
Community Multiscale Air Quality (CMAQ) Model 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - iv 

ACRONYMS AND ABBREVIATIONS 

ANO3 particle nitrate 

ASO4 particle sulfate 

CMAQ Community Multiscale Air Quality 

EPA U.S. Environmental Protection Agency 

ha hectare 

HONO nitrous acid 

HNO3 nitric acid 

kg kilogram 

NADP National Atmospheric Deposition Program 

N2O5 nitrogen pentoxide 

NH4

+

 ammonium 

NHx total reduced nitrogen 

NO nitric oxide 

NO2 nitrogen dioxide 

NO3

- nitrate 

NOx nitrogen oxides 

NOy oxidized nitrogen 

NMB normalized mean bias 

NTR organic nitrate 

PAN peroxyacl nitrate 

PM particulate matter 

ORD Office of Research and Development 

SO2 sulfur dioxide 

SO4

2- sulfate 

SOx sulfur oxides 
Community Multiscale Air Quality (CMAQ) Model 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 1 

1.0 INTRODUCTION 

This appendix provides an overview of the Community Multiscale Air Quality (CMAQ) 

model and the modeling system used for simulating pollutant concentrations and deposition for 

the years 2002 through 2005. Included in this appendix are the results of a model performance 

evaluation in which model predictions of sulfur dioxide (SO2), nitrogen dioxide (NO2), SO4

2-, 

total NO3

-*, and ammonium (NH4+

) concentrations and SO4

2-, NO3-

, and NH4

+

 wet deposition are 

compared to observations. 

1.1 OVERVIEW OF CMAQ MODEL APPLICATION 

The CMAQ model is a comprehensive, peer-reviewed (Aiyyer et al., 2007), threedimensional grid-based Eulerian air quality model designed to simulate the formation and fate of 

gaseous and particle (i.e., particulate matter or PM) species, including ozone, oxidant precursors, 

and primary and secondary PM concentrations and deposition over urban, regional, and larger 

spatial scales (Dennis et al., 1996; U.S. EPA, 1999; Bryun and Schere, 2006). CMAQ is run for 

user-defined input sets of meteorological conditions and emissions. For this analysis, we are 

using predictions from several existing CMAQ runs. These runs include annual simulations for 

2002 using CMAQv4.6 and annual simulations for each of the years 2002 through 2005 using 

CMAQv4.7. CMAQv4.6 was released by the U.S. Environmental Protection Agency’s (EPA’s) 

Office of Research and Development (ORD) in October 2007. CMAQv4.7 along with an 

updated version of CMAQ’s meteorological preprocessor (MCIPv3.4)† were released in October 

2008‡. The CMAQ modeling regions (i.e., modeling domains), are shown in Figure 1.1-1. The 

2002 simulation with CMAQv4.6 was performed for both the Eastern and Western domains. The 

horizontal spatial resolution of the CMAQ grid cells in these domains is approximately 12 x 12 

km. The 2002 through 2005 simulations with CMAQv4.7 were preformed for the eastern 12-km 

domain and for the continental United States domain, which has a grid resolution of 36 x 36 km. 

The CMAQv4.6 and v4.7 annual simulations feature year-specific meteorology, as well as yearspecific emissions inventories for key source sectors, such as utilities, on-road vehicles, nonroad 

 

* Total NO3

-

 includes the mass of nitric acid gas and particulate nitrate. 

† 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 

‡ 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. 
Community Multiscale Air Quality (CMAQ) Model 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 2 

vehicles, wild fires, and natural biogenic sources. Emissions for other sectors of the inventory for 

each of the years modeled rely on inventories for 2002. Details on the development of emissions, 

meteorology, and other inputs to the 2002 CMAQv4.6 runs can be found in a separate report 

(U.S. EPA, 2008). Inputs for the CMAQv4.7 runs for 2002 through 2005 were derived using 

procedures similar to those for the CMAQv4.6 2002 runs. 

CONUS Domain

Figure 1.1-1. CMAQ continental United States and eastern and western modeling 

domains. 

Each CMAQ model run produces hourly concentrations and wet and dry deposition of 

individual pollutant species in each grid cell within the domain. Concentration predictions for 

NOy* and SO2, both in units of parts per billion (ppb), are produced as part of our standard model 

output. The CMAQ deposition data for nitrogen and sulfur species are used to calculate oxidized 

and reduced wet and dry nitrogen deposition, wet and dry sulfur deposition, and total reactive 

 

* NOy is defined as the sum of CMAQ predictions for NO, NO2, HNO3, and PAN. 
Community Multiscale Air Quality (CMAQ) Model 

nitrogen and total sulfur deposition. These composite deposition variables are derived from the 

species identified in Table 1.1-1 as applied in the formulas shown in Table 1.1-2. The CMAQ 

deposition data are in units of kilograms per hectare (kg/ha). We are also including in the 

analysis gridded precipitation data that were input to the CMAQ runs to help understand the 

temporal and spatial behavior of wet deposition. 

Table 1.1-1. CMAQ Nitrogen and Sulfur Deposition Species 

CMAQ Species Chemical Name 

ANO3 Particle Nitrate 

HNO3 Nitric Acid 

N2O5 Nitrogen Pentoxide 

HONO Nitrous Acid 

NO Nitric Oxide 

NO2 Nitrogen Dioxide 

PAN Peroxyacyl Nitrate 

NTR Organic Nitrate 

ASO4 Particle Sulfate 

SO2 Sulfur Dioxide 

Table 1.1-2. Formulas for Calculating Nitrogen and Sulfur Deposition 

Deposition Type Formula 

Oxidized Nitrogen 

0.2258*ANO3 + 0.2222*HNO3 + 0.4667*NO + 0.3043*NO2 + 0.2592*N2O5 + 

0.1157*PAN + 0.2978*HONO + 0.1052*NTR 

Reduced Nitrogen 0.7777*NH4 + 0.8235*NH3

Sulfur 0.3333*ASO4 + 0.5000*SO2

1.2 CMAQ MODEL PERFORMANCE EVALUATION 

This evaluation of CMAQ focuses on model performance for concentrations and 

deposition of nitrogen and sulfur species. For the most part, these comparisons are based on 

predictions and observations using annual average concentrations and deposition, to be 

consistent with the use of annual predictions in this assessment. A more comprehensive set of 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 3 
Community Multiscale Air Quality (CMAQ) Model 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 4 

model performance information for the 2002 CMAQ simulation is provided in Section 1-2.5, 

below. 

The purpose of this evaluation is to provide information on how well model predictions 

of performance for concentrations and deposition of nitrogen and sulfur species match the 

observed data on a regional basis, and not to evaluate performance for an individual location or 

area. The CMAQ predictions of SO2, SO4

2-, total NO3-* and NH4+

 concentrations, as well as 

SO4

2-, NO3-

, and NH4

+

 wet deposition, were compared to the corresponding measured data for 

the years 2002 through 2005†. This analysis compared the annual average predictions of SO2, 

SO4

2-, total NO3-

, and NH4

+

 concentrations to measurements from CASTNet sites. It also 

compared the CMAQ annual total SO4

2-, NO3-

, and NH4

+

 wet deposition to measurements of 

these species at National Atmospheric Deposition Program (NADP) sites. In all cases, the model 

predictions and observations were paired in space and time to align with the corresponding 

observations. 

For the 2002 CMAQv4.6 runs, model performance results are provided for both the 

eastern and western modeling domains. For the 2002 through 2005 CMAQv4.7, runs have 

performance results for concentrations and deposition for the eastern modeling domain only.‡

The CMAQ v4.7 performance results for 2002 through 2005 are courtesy of EPA’s ORD. The 

equations used to calculate model performance statistics for the CMAQv4.6 and v4.7 simulations 

are described elsewhere (U.S. EPA, 2008). 

The “acceptability” of model performance is judged by comparing the CMAQ 

performance results to the range of performance found in other recent regional photochemical 

model applications. (U.S. EPA, 2006a; U.S. EPA 2006b; Dentener and Crutzen, 1993) These 

other modeling studies represent a wide range of modeling analyses, which cover various 

models, model configurations, domains, years and/or episodes, chemical mechanisms, and 

aerosol modules. The CMAQv4.6 and v4.7 performance results are within the range found by 

these other studies. A key limitation of this evaluation, as noted in Section 3.5, is the lack of 

“true” dry deposition measurements for nitrogen and sulfur that are appropriate for comparison 

to CMAQ deposition predictions. Thus, this evaluation relies upon the model performance 

 

* Total nitrate includes nitric acid gas and particulate nitrate. 

† There are insufficient non-urban measurements of NO2 to provide for a meaningful regional evaluation of this 

pollutant for the purposes of this assessment. 

‡ CMAQv4.7 was not run for the Western 12-km domain for 2002 through 2005. 
Community Multiscale Air Quality (CMAQ) Model 

results for various gas and particle species concentrations as well as wet deposition, collectively, 

to indicate the extent that the modeling system provides a scientifically acceptable approach for 

use in this assessment. 

1.2.1 CMAQv4.6 2002 Annual Average Predictions versus Observations for the 

Eastern United States 

Figures 1.2-1 through 1.2-7 display the comparison of CMAQv4.6 predictions of annual 

average concentrations and annual average wet deposition for monitoring CASTNet 

(concentrations) and NADP (wet deposition) sites the eastern domain. Each data point in the 

figures represents an annual average paired observation and CMAQ prediction at a particular 

CASTNet or NADP site. Solid lines indicate the factor of 2 around the 1:1 line shown between 

them. 

Using the normalized mean bias (NMB) statistic, we see the 2002 CMAQ run tends to 

underpredict concentrations of SO4

2- (NMB = -12.0%) and NH4+

 (NMB = -3.4%) and 

overpredict SO2 (NMB = 33.8%) and TNO3 (NMB = 11.2%) within the eastern domain for 2002. 

For wet deposition, the 2002 CMAQ run tends to underpredict NO3

-

 (NMB = -11.4%) and NH4

+

(-6.1%) and overpredict SO4

2- wet deposition (9.4%). 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 5 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-1. 2002 CMAQv4.6 annual average SO2 predicted concentrations versus 

observations at CASTNet sites in the eastern domain. 

Figure 1.2-2. 2002 CMAQv4.6 annual average SO2-

4 predicted concentrations versus 

observations at CASTNet sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 6 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-3. 2002 CMAQv4.6 annual average TNO3 predicted concentrations versus 

observations at CASTNet sites in the eastern domain. 

Figure 1.2-4. 2002 CMAQv4.6 annual average NH4

+

 predicted concentrations versus 

observations at CASTNet sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 7 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-5. 2002 CMAQv4.6 annual average SO4

2- predicted wet deposition versus 

observations at NADP sites in the eastern domain. 

Figure 1.2-6. 2002 CMAQv4.6 annual average NO3

-

 predicted wet deposition versus 

observations at NADP sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 8 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-7. 2002 CMAQv4.6 annual average NH4

+

 predicted wet deposition versus 

observations at NADP sites in the eastern domain. 

1.2.2 CMAQv4.6 2002 Annual Average Predictions versus Observations for the 

Western United States 

Figures 1.2-8 through 1.2-14 display the comparison of CMAQv4.6 predictions of 

annual average concentrations and annual average wet deposition for monitoring CASTNet 

(concentrations) and NADP (wet deposition) sites the western domain. Each data point in the 

figures represents an annual average paired observation and CMAQ prediction at a particular 

CASTNet or NADP site. Solid lines indicate the factor of 2 around the 1:1 line shown between 

them. 

Using the NMB statistic, the 2002 CMAQ run tends to underpredict concentrations of 

SO4

2- (NMB = -20.8%), NH4+

 (NMB = -16.2%), and TNO3 (NMB = -19.6%) and to overpredict 

SO2 (NMB = 31.2%) within the western domain for 2002. For wet deposition, the 2002 CMAQ 

run tends to underpredict NO3

-

 (NMB = -43.6%), NH4

+

 (NMB = -43.4%), and SO4

2- wet 

deposition (NMB = -20.5%). In general, model performance for the western domain is degraded 

somewhat compared to the results found for the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 9 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-8. 2002 CMAQv4.6 annual average SO4

2- predicted concentrations versus 

observations at CASTNet sites in the western domain. 

Figure 1.2-9. 2002 CMAQv4.6 annual average SO2 predicted concentrations versus 

observations at CASTNet sites in the western domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 10 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-10. 2002 CMAQv4.6 annual average TNO3 predicted concentrations versus 

observations at CASTNet sites in the western domain. 

Figure 1.2-11. 2002 CMAQv4.6 annual average NH4

+

 predicted concentrations versus 

observations at CASTNet sites in the western domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 11 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-12. 2002 CMAQv4.6 annual average SO4

2- predicted wet deposition versus 

observations at NADP sites in the western domain. 

Figure 1.2-13. 2002 CMAQv4.6 annual average NO3

-

 predicted wet deposition versus 

observations at NADP sites in the western domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 12 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-14. 2002 CMAQv4.6 annual average NH4

+

 predicted wet deposition versus 

observations at NADP sites in the western domain. 

1.2.3 CMAQv4.7 2002 through 2005 Annual and Monthly Average Predictions 

versus Observations 

The annual normalized mean bias statistics for the CMAQv4.7 2002 through 2005 

simulations are presented in Table 1.2-1 and Table 1.2-2 for annual average concentrations and 

annual total wet deposition, respectively. In general, model performance for each species is 

similar in each of the 4 years. The CMAQv4.7 performance for 2002 is similar to that of 

CMAQv4.6 for most species. Notable differences are seen for concentrations of TNO3 and NH4

+

. 

For TNO3, the overprediction in CMAQv4.7 is about twice that of CMAQv4.6. For NH4

+

, 

CMAQv4.7 slightly overpredicts observations (4%), while CMAQv4.7 slightly underpredicts 

observations (-3%). 

Table 1.2-1. Normalized Mean Bias Statistics for Predicted and Observed Pollutant 

Concentration 

Pollutant 

Concentrations 2002 2003 2004 2005 

SO2 45% 39% 47% 41% 

SO4

2- -13% -9% -13% -17% 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 13 
Community Multiscale Air Quality (CMAQ) Model 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 14 

Pollutant 

Concentrations 2002 2003 2004 2005 

TNO3 22% 26% 22% 24% 

NH4

+ 4% 11% 7% 2% 

Table 1.2-2. Normalized Mean Bias Statistics for Predicted and Observed Pollutant Wet 

Deposition 

Pollutant 

Deposition 2002 2003 2004 2005 

SO4

2- 11% 6% 11% 6% 

NO3

- -13% -17% -14% -17% 

NH4

+ -11% -16% -9% -13% 

Figures 1.2-15 through 1.2-26 provide a comparison of observed and predicted monthly 

concentrations and monthly total wet deposition for an aggregate of monitoring sites the eastern 

domain. This time series format is intended to reveal differences and similarities in performance 

within and across the 4-year period. Figures 1.2-15 and 1.2-21 indicate that the predictions of 

SO4

2- concentration and SO42- wet deposition closely track the temporal patterns exhibited by the 

observations. However, the correlation is higher and the error is lower for SO4

2- concentrations 

than the corresponding statistics for SO4

2- wet deposition (see Figures 1.2-16 and 1.2-22). 

Predictions of NO3

-

 concentrations, although highly correlated with the observations in most 

months, show relatively large error and positive bias in the fall with a peak in October for each 

year. Model performance for wet deposition of NO3

-

 also has a seasonal pattern, with 

underprediction of approximately 40% in the late spring and summer and overprediction from 

October through December. Observed concentrations of NH4

+

 are overpredicted by CMAQ in 

the spring and fall and underpredicted in the summer. The overprediction in the spring peaks in 

March/April, while the peak overprediction in the fall occurs in October/November of each year. 

Model predictions of NH4

+

 wet deposition more closely track the temporal patterns of 

observations than do the predictions of NH4

+

 concentrations. There does not appear to be strong 

seasonal differences in performance across the 4 years as seen in NH4

+

 concentrations; however, 

the greatest underprediction appears to occur in May in each year. The differences and 

similarities in the seasonal patterns in model performance for various species are being analyzed 

by EPA to understand and explain these relationships, with the goal of improving model 
Community Multiscale Air Quality (CMAQ) Model 

performance through improvements to emissions and meteorological inputs and scientific 

formulation. 

Figure 1.2-15. 2002–2005 Domain-wide average SO4

2- predicted concentrations and 

observations by month at CASTNet Sites in the eastern domain. 

Figure 1.2-16. 2002–2005 Domain-wide monthly aggregate model performance statistics 

for SO4

2- concentrations based on CASTNet sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 15 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-17. 2002–2005 Domain-wide average TNO3 predicted concentrations and 

observations by month at CASTNet sites in the eastern domain. 

Figure 1.2-18. 2002–2005 Domain-wide monthly aggregate model performance statistics 

for TNO3 concentrations based on CASTNet Sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 16 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-19. 2002–2005 Domain-wide average NH4

+

 predicted concentrations and 

observations by month at CASTNet sites in the eastern domain. 

Figure 1.2-20. 2002–2005 domain-wide monthly aggregate model performance statistics 

for NH4

+

 concentrations based on CASTNet sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 17 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-21. 2002–2005 domain-wide average SO4

2- predicted deposition and 

observations by month at NADP sites in the eastern domain. 

Figure 1.2-22. 2002–2005 domain-wide monthly aggregate model performance statistics 

for SO4

2- deposition based on NADP sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 18 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-23. 2002–2005 domain-wide average NO3

-

 predicted deposition and 

observations by month at NADP sites in the eastern domain. 

Figure 1.2-24. 2002–2005 domain-wide monthly aggregate model performance statistics 

for NO3

-

 deposition based on NADP sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 19 
Community Multiscale Air Quality (CMAQ) Model 

Figure 1.2-25. 2002–2005 domain-wide average NH4

+

 predicted deposition and 

observations by month at NADP sites in the eastern domain. 

Figure 1.2-26. 2002–2005 domain-wide monthly aggregate model performance statistics 

for NH4

+

 deposition based on NADP sites in the eastern domain. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 20 
Community Multiscale Air Quality (CMAQ) Model 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 21 

1.2.4 Extended Evaluation of CMAQv4.6 2002 Predictions versus Observations 

In addition to the preceding evaluation of annual and monthly average concentrations and 

deposition for nitrogen and sulfur species, model performance statistics are also calculated and 

predicted for CMAQv4.6 for a more comprehensive set of species, with comparisons of 

observations and predictions paired for the native averaging times of the observations (i.e., 

hourly or weekly)*. These statistics are presented separately for each of four subregions†: 

Northeast, Midwest, Southeast, Central, and West for the following species‡: PM2.5 total mass, 

selected PM2.5 component species (i.e., SO4

2-, NO3-

, NH4

+

, elemental carbon, and organic 

carbon), HNO3, total nitrate, SO2, NO2*, ozone†, and wet deposition of SO4

2-, NO3-

, and NH4

+

.

The performance statistics are provided in Tables 1.2-3 through 1.2-7. As one way of 

comparing performance for sulfur and nitrogen containing species across the five subregions, the 

species/subregion combinations are identified, which had NMB estimates within + 15%, between 

+ 15% and + 30%, and beyond + 30%. The results of categorizing the statistics in this manner 

lead to the following findings: 

1. Sulfate Concentration 

• NMB is within + 15%, except for the Central subregion and the West subregion, 

where NMB is between -15% and - 30%. 

2. Sulfur Dioxide Concentration 

• NMB is between +15% and + 30% in the Northeast and West subregions and 

greater than + 30% in the Midwest, Southeast, and Central subregions. 

3. Sulfate Wet Deposition 

• NMB is within + 15%, except for the Midwest subregion, where NMD is between 

+ 15% and + 30%. 

 

* Measurement sampling periods used in this analysis are as follows: Speciation Trends Network (STN) and 

IMPROVE - 24-hour average concentrations; CASTNet and NADP - weekly average concentrations and weekly 

total deposition, respectively; and Air Quality System (AQS) – hourly average concentrations. 

† The subregions are defined as groups of states: Midwest includes IL, IN, MI, OH, and WI; Northeast CT, DE, MA, 

MD, ME, NH, NJ, NY, PA, RI, and VT; Southeast includes AL, FL, GA, KY, MS, NC, SC, TN, VA, and WV; 

Central includes AR, IA, KS, LA, MN, MO, NE, OK, and TX; and West includes AZ, CA, CO, ID, MT, ND, 

NM, NV, OR, SD, UT, WY, and WY. 

‡ Measurements of NO2 and NOy from sites in the Southeastern Aerosol Research and Characterization (SEARCH) 

network were not available for use in this assessment. 

* The model performance statistics for NO2 were not calculated in the West subregion because the results would be 

disproportionately skewed to California where most of the NO2 AQS sites in this subregion are located. 

† Note that performance statistics for ozone excluded any observed or predicted hourly ozone concentrations less 

than a threshold of 40ppb. 
Community Multiscale Air Quality (CMAQ) Model 

4. Nitrate Concentration 

• NMB is within + 15%, except for the Northeast subregion, where NMB exceeds + 

30% and the West subregion where NMB is less than - 30%. 

5. Nitric Acid Concentration 

• NMB is within + 15%, except for the Southeast subregion, where NMB is 

between +15% and + 30%. 

6. Nitrogen Dioxide Concentration 

• NMB is within + 15%, except for the Central subregion, where NMB exceeds + 

30%. 

7. Nitrate Wet Deposition 

• NMB is within + 15%, except for the Central subregion, where NMB is between -

15% and - 30%, and the West subregion, where NMB is less than - 30%. 

8. Ammonium Concentration 

• NMB is within + 15%, except for the West subregion, where NMB exceeds + 

30%. 

9. Ammonium Wet Deposition 

• NMB is within + 15%, except for the Central subregion, where NMB is between -

15% and - 30%, and the West subregion, where NMB is less than - 30%. 

Table 1.2-3. CMAQv4.6 Model Performance Statistics for PM2.5 and Selected Component 

Species Concentrations 

CMAQ 2002 No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

PM2.5 

Total Mass STN 

12-km EUS 10307 2.4 39.5 -3.2 42.1 

12-km WUS 3000 -5.8 46.9 -3.1 45.0 

Northeast 2780 10.7 41.6 7.5 39.3 

Midwest 1516 8.4 32.8 6.2 33.9 

Southeast 2554 -9.4 35.6 -17.2 41.4 

Central 2738 4.4 46.5 -3.4 50.2 

West 2487 -7.4 46.8 -4.5 44.8 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 22 
Community Multiscale Air Quality (CMAQ) Model 

CMAQ 2002 No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

PM2.5 

Total Mass 

(continued) 

IMPROVE 

12-km EUS 8436 -12.0 44.8 -15.1 50.5 

12-km WUS 10123 -26.4 53.5 -26.3 57.5 

Northeast 2060 4.6 47.2 3.5 45.6 

Midwest 592 -0.8 36.1 -6.0 39.2 

Southeast 1803 -19.7 40.7 -28.8 51.8 

Central 1624 -20.1 48.9 -25.3 58.6 

West 9543 -27.8 53.1 -27.1 57.2 

Sulfate 

STN 

12-km EUS 10157 -5.3 34.1 -11.3 39.3 

12-km WUS 2926 -20.6 41.9 -12.2 43.5 

Northeast 2730 -4.1 29.6 -8.4 33.5 

Midwest 1487 7.1 36.3 0.9 36.9 

Southeast 2541 -9.8 33.7 -18.7 39.8 

Central 2686 -9.2 39.4 -13.0 46.4 

West 2446 -26.1 44.9 -15.8 44.8 

IMPROVE 

12-km EUS 8532 -11.7 33.8 -8.8 41.8 

12-km WUS 10232 -7.5 42.4 7.6 45.7 

Northeast 2070 -12.7 30.8 -11.4 37.7 

Midwest 597 -3.1 30.6 -6.9 35.8 

Southeast 1805 -10.8 33.2 -18.8 41.3 

Central 1671 -18.5 37.0 -19.3 44.6 

West 9645 -5.5 43.5 8.6 45.9 

Sulfate 

(continued) CASTNet 

12-km EUS 3173 -12.3 20.3 -17.2 25.7 

12-km WUS 1158 -21.3 34.6 -11.2 35.9 

Northeast 839 -13.3 18.7 -16.9 22.2 

Midwest 663 -8.2 17.9 -14.4 21.6 

Southeast 1085 -12.9 21.4 -19.9 27.4 

Central 229 -22.9 28.6 -29.9 35.8 

West 1118 -20.4 35.3 -10.7 36.1 

Nitrate STN 

12-km EUS 8770 0.1 61.1 -39.8 85.8 

12-km WUS 2726 -45.0 63.1 -70.6 95.0 

Northeast 2731 9.8 60.6 -26.2 77.5 

Midwest 1488 2.7 55.9 -14.5 70.4 

Southeast 2540 -5.8 78.2 -70.0 106.9 

Central 1298 -3.8 53.7 -21.5 73.8 

West 2446 -47.5 62.8 -73.8 95.4 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 23 
Community Multiscale Air Quality (CMAQ) Model 

CMAQ 2002 No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

Nitrate 

(continued) IMPROVE 

12-km EUS 8514 13.3 85.1 -66.5 115.4 

12-km WUS 10110 -34.8 80.6 -101.0 130.0 

Northeast 2069 56.3 99.1 -19.6 97.6 

Midwest 597 11.5 72.04 -50.4 101.5 

Southeast 1803 3.8 94.8 -91.4 132.6 

Central 1672 0.8 77.3 -64.7 115.8 

West 9522 -39.6 81.1 -104.0 131.1 

Total Nitrate 

(NO3+HNO3) CASTNet 

12-km EUS 3171 11.1 30.5 7.2 31.3 

12-km WUS 1157 -19.5 44.2 -12.0 46.0 

Northeast 839 18.1 30.4 15.4 29.7 

Midwest 662 10.2 27.9 7.7 25.6 

Southeast 1085 12.4 33.5 8.3 33.9 

Central 229 -2.9 33.2 -6.5 35.3 

West 1117 -20.4 45.8 -12.1 46.6 

Ammonium 

STN 

12-km EUS 10157 5.0 39.5 7.0 45.7 

12-km WUS 2926 -23.6 55.7 7.2 58.1 

Northeast 2731 5.4 35.3 12.1 40.1 

Midwest 1488 11.8 39.1 17.3 43.4 

Southeast 2540 1.9 38.6 -0.7 43.4 

Central 2685 5.1 46.9 4.7 54.8 

West 2446 -30.6 56.7 2.9 59.7 

CASTNet 

12-km EUS 3166 28.2 -3.1 30.8 0.8 

12-km WUS 1156 -16.8 42.5 -13.0 41.1 

Northeast 837 -3.2 27.8 -0.8 28.4 

Midwest 661 7.1 26.5 5.6 26.1 

Southeast 1085 -13.7 29.9 -17.6 35.1 

Central 229 0.5 31.5 -4.2 35.9 

West 1116 -21.1 43.5 -14.4 41.4 

Elemental 

Carbon STN 

12-km EUS 10031 33.0 72.4 12.6 56.1 

12-km WUS 2975 43.1 82.6 18.2 61.3 

Northeast 2744 39.1 68.7 16.1 53.0 

Midwest 1498 25.9 53.1 14.0 47.5 

Southeast 2506 10.1 63.8 -0.1 52.2 

Central 2570 76.4 107.5 31.5 66.1 

West 2475 49.0 86.2 17.1 62.7 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 24 
Community Multiscale Air Quality (CMAQ) Model 

CMAQ 2002 No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

Elemental 

Carbon 

(continued) 

IMPROVE 

12-km EUS 8282 48.8 -23.5 -34.1 56.1 

12-km WUS 10069 -14.1 67.2 -29.5 62.1 

Northeast 2056 -4.1 51.8 -15.0 50.6 

Midwest 599 -31.2 39.4 -39.3 51.3 

Southeast 1795 -39.9 47.0 -53.6 62.2 

Central 1532 -31.1 50.4 -38.8 61.2 

West 9493 -15.5 67.8 -31.3 62.7 

Organic Carbon 

STN 

12-km EUS 9726 -45.8 57.5 -50.4 72.8 

12-km WUS 2903 -37.6 60.3 -40.4 69.3 

Northeast 2641 -35.2 58.2 -30.3 66.8 

Midwest 1447 -50.5 60.6 -51.1 75.1 

Southeast 2474 -50.6 56.0 -60.7 73.4 

Central 2504 -49.1 56.2 -61.2 75.6 

West 2408 -36.3 61.4 -37.9 70.2 

IMPROVE 

12-km EUS 8287 -41.3 59.0 -49.0 70.6 

12-km WUS 10082 -34.8 60.0 -31.2 63.0 

Northeast 2057 -21.8 58.8 -16.9 57.4 

Midwest 598 -49.6 54.9 -52.5 66.4 

Southeast 1800 -51.9 59.8 -80.9 87.8 

Central 1531 -53.6 63.3 -71.0 85.1 

West 9508 -34.5 59.6 -29.7 61.9 

Table 1.2-4. CMAQ v4.6 2002 Model Performance Statistics for Nitric Acid and Sulfur Dioxide 

CMAQ 2002 No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

Nitric Acid CASTNet 

12-km EUS 3172 10.0 39.1 9.0 40.3 

12-km WUS 1157 6.1 47.7 17.0 49.0 

Northeast 839 3.8 34.2 4.4 35.4 

Midwest 662 9.2 38.8 3.0 40.4 

Southeast 1086 17.7 44.0 20.5 44.0 

Central 229 2.8 31.3 2.4 31.3 

West 1117 5.5 48.5 17.2 49.5 

Sulfur Dioxide CASTNet 

12-km EUS 3174 33.08 46.61 31.6 46.4 

12-km WUS 1157 26.5 71.4 14.9 57.7 

Northeast 839 24.7 39.4 29.7 40.8 

Midwest 663 35.3 47.9 38.7 45.3 

Southeast 1086 39.0 51.6 29.8 51.4 

Central 229 91.6 99.9 48.5 61.0 

West 1117 19.4 68.1 12.9 57.2 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 25 
Community Multiscale Air Quality (CMAQ) Model 

CMAQ 2002 No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

Sulfur Dioxide 

(continued) SEARCH 12-km EUS 61760 28.1 98.4 24.4 87.1 

Nitrogen 

Dioxide AQS 

12-km EUS 921032 13.6 54.7 4.9 56.5 

Northeast 620014 -2.9 45.2 -13.0 52.5 

Midwest 14913 12.3 44.0 5.8 43.8 

Southeast 342422 7.3 57.2 2.0 60.8 

Central 625924 34.3 70.8 10.9 61.1 

Table 1.2-5. CMAQv4.6 2002 Model Performance Statistics for Annual Sulfate and Nitrate Wet 

Deposition Model Performance Statistics. 

CMAQ 2002 Annual No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

Sulfate NADP 

12-km EUS 6896 5.53 63.7 1.0 70.9 

12-km WUS 2354 -18.0 73.5 -23.5 94.7 

Northeast 1405 8.2 57.6 3.0 59.7 

Midwest 1413 17.0 62.9 16.4 64.0 

Southeast 1856 4.8 68.6 1.4 73.2 

Central 1246 -10.2 62.3 -2.5 71.6 

West 2058 -10.5 76.6 -20.5 94.7 

Nitrate NADP 

12-km EUS 6896 -16.2 59.2 -22.2 73.3 

12-km WUS 2354 -42.4 72.3 -59.4 99.2 

Northeast 1413 -7.1 56.8 -6.0 66.0 

Midwest 1405 -9.5 52.7 -13.6 60.7 

Southeast 1856 -12.5 64.3 -14.8 74.2 

Central 1246 -25.8 58.9 -24.1 72.8 

West 2058 -45.4 72.0 -59.8 99.3 

Ammonium NADP 

12-km EUS 6896 -11.5 65.2 -10.2 76.0 

12-km WUS 2354 -40.4 73.1 -55.5 100.6 

Northeast 1413 -8.0 61.9 0.4 68.5 

Midwest 1405 -2.0 63.1 1.3 67.3 

Southeast 1856 1.1 75.2 -0.2 77.9 

Central 1246 -24.4 60.9 -16.5 75.0 

West 2058 -41.1 74.2 -54.8 100.6 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 26 
Community Multiscale Air Quality (CMAQ) Model 

Table 1.2-6. CMAQv4.6 Model Performance Statistics for Hourly Ozone Concentrations 

CMAQ 2002 Hourly Ozone: 

Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

May 

12-km EUS 241185 -0.7 15.9 -2.0 17.1 

12-km WUS 124931 -3.7 15.9 -5.0 17.3 

Northeast 51055 1.2 17.1 -0.3 18.2 

Midwest 55859 3.3 16.2 2.4 16.9 

Southeast 69073 -2.5 14.1 -3.1 14.8 

Central 41728 -6.4 17.3 -9.2 20.3 

West 111385 -3.9 16.1 -5.2 17.6 

June 

12-km EUS 256263 -7.5 16.8 -9.0 18.6 

12-km WUS 125662 -8.37 17.7 -9.3 19.1 

Northeast 61354 -8.46 17.3 -9.9 19.1 

Midwest 54515 -7.19 17.9 -8.3 19.6 

Southeast 67867 -7.2 15.3 -7.6 16.3 

Central 46026 -10.0 17.5 -13.5 21.2 

West 109157 -8.8 18.2 -9.9 19.7 

July 

12-km EUS 257076 -5.3 17.7 -6.6 19.2 

12-km WUS 116785 -12.0 21.5 -14.9 24.3 

Northeast 66774 -3.9 17.0 -4.8 18.0 

Midwest 59360 -10.5 19.4 -12.3 21.7 

Southeast 68619 -3.6 16.5 -3.9 17.2 

Central 36021 -3.6 18.7 -6.3 21.1 

West 104321 -13.6 21.8 -16.8 24.9 

August 

12-km EUS 235090 -8.7 17.8 -10.2 19.7 

12-km WUS 125575 -7.91 20.1 -10.2 22.1 

Northeast 53837 -6.4 16.7 -7.4 18.0 

Midwest 54179 -10.8 19.1 -12.4 21.4 

Southeast 62506 -9.4 17.3 -9.9 18.5 

Central 41456 -9.3 18.7 -12.8 22.4 

West 110225 -8.5 20.6 -11.1 22.8 

September 

12-km EUS 179156 -9.9 17.2 -11.8 19.5 

12-km WUS 99710 -10.7 19.0 -12.7 21.1 

Northeast 44678 -8.7 16.3 -10.6 18.4 

Midwest 34285 -11.4 18.5 -12.9 20.4 

Southeast 41627 -8.2 16.5 -9.0 17.8 

Central 41549 -12.8 18.8 -16.6 22.8 

West 83921 -11.7 20.0 -13.8 22.1 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 27 
Community Multiscale Air Quality (CMAQ) Model 

CMAQ 2002 Hourly Ozone: 

Threshold of 40 ppb 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

Seasonal Aggregate 

12-km EUS 1168770 -6.4 17.1 -7.7 18.8 

12-km WUS 592663 -8.4 18.8 -10.3 20.7 

Northeast 277698 -5.4 16.9 -6.5 18.4 

Midwest 258198 -7.3 18.3 -8.4 20.0 

Southeast 309692 -6.0 15.9 -6.4 16.8 

Central 206780 -8.6 18.2 -11.9 21.6 

West 519009 -9.2 19.3 -11.2 21.3 

Table 1.2-7. CMAQv4.6 Model Performance Statistics for 8-hour Daily Maximum Ozone 

Concentrations 

CMAQ 2002 8-Hour 

Maximum Ozone: 

Threshold of 40 ppb 

No. of 

Obs. NMB (%) NME (%) FB (%) FE (%) 

May 

12-km EUS 18115 4.0 12.7 4.4 12.7 

12-km WUS 9188 0.1 12.6 0.5 12.8 

Northeast 4123 7.1 13.1 7.4 12.9 

Midwest 5596 13.9 19.7 14.6 19.7 

Southeast 5257 0.5 11.1 0.8 11.2 

Central 2921 0.4 12.3 0.8 12.5 

West 1199 -1.7 11.7 -1.6 11.8 

June 

12-km EUS 18341 -4.3 12.3 -3.8 12.5 

12-km WUS 8788 -4.9 14.0 -4.3 14.2 

Northeast 4038 -3.0 12.7 -2.0 12.8 

Midwest 5388 -0.8 14.7 2.6 16.4 

Southeast 4901 -5.5 11.9 -5.0 12.1 

Central 3137 -6.1 12.4 -6.2 13.0 

West 7789 -5.4 14.5 -4.8 14.7 

July 

12-km EUS 19499 -0.9 13.5 -0.5 13.6 

12-km WUS 8809 -7.5 17.0 -8.2 18.0 

Northeast 4300 -5.5 14.0 -4.9 14.2 

Midwest 5612 2.0 13.6 3.1 13.7 

Southeast 5397 -0.6 13.2 -0.1 13.2 

Central 2799 2.1 14.3 1.9 14.6 

West 1242 -14.1 20.3 -16.1 22.2 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 28 
Community Multiscale Air Quality (CMAQ) Model 

CMAQ 2002 8-Hour 

Maximum Ozone: 

Threshold of 40 ppb 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) 

FB 

(%) 

FE 

(%) 

August 

12-km EUS 18204 -4.4 13.0 -3.8 13.1 

12-km WUS 9516 -2.9 15.8 -3.2 16.1 

Northeast 3956 -7.3 13.5 -6.7 13.7 

Midwest 5620 2.6 14.3 4.7 14.8 

Southeast 4882 -5.8 13.5 -4.9 13.5 

Central 3104 -3.6 13.0 -3.7 13.6 

West 8283 -3.2 16.1 -3.7 16.4 

September 

12-km EUS 14921 -5.7 12.6 -5.5 13.0 

12-km WUS 8138 -6.7 15.0 -6.9 15.5 

Northeast 3075 -7.0 13.4 -6.4 13.4 

Midwest 5439 -0.1 14.2 2.6 15.6 

Southeast 3540 -3.7 12.6 -3.1 12.9 

Central 3026 -8.4 13.7 -8.8 14.5 

West 6864 -7.4 15.9 -7.7 16.4 

Seasonal Aggregate 

12-km EUS 89080 -2.3 12.8 -1.7 13.0 

12-km WUS 44626 -4.3 14.9 -4.3 15.3 

Northeast 19492 -3.3 13.3 -2.3 13.4 

Midwest 27655 3.2 15.1 5.6 16.1 

Southeast 23977 -3.0 12.5 -2.3 12.5 

Central 14987 -3.3 13.2 -3.3 13.6 

West 38823 -5.0 15.3 -5.0 15.7 

2.0 REFERENCES 

Aiyyer, A, Cohan, D., Russell, A., Stockwell, W., Tanrikulu, S., Vizuete, W., and Wilczak, J. 

2007. Final Report: Third Peer Review of the CMAQ Model. 

Byun, D.W., and Schere, K.L. 2006. Review of the Governing Equations, Computational 

Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality 

(CMAQ) Modeling System. J. Applied Mechanics Reviews, 59 (2), 51–77. 

Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M.A. 1996. The 

next generation of integrated air quality modeling: EPA’s Models-3. Atmospheric 

Environment, 30, 1925–1938. 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 29 
Community Multiscale Air Quality (CMAQ) Model 

Final Risk and Exposure Assessment September 2009 

Appendix 1 - 30 

Dentener F. J., and Crutzen P.J. 1993. Reaction of N2O5 on tropospheric aerosols: impact on the 

global distributions of NOx, O3, and OH. J Geophys Res, 98, 7149–7163. 

U.S. EPA (Environmental Protection Agency). 1999. Science Algorithms of EPA Models-3 

Community Multiscale Air Quality (CMAQ) Modeling System. Byun, D.W., and Ching, 

J.K.S., Eds. EPA/600/R-99/030. Office of Research and Development. 

U.S. EPA (Environmental Protection Agency). 2008. Technical Support Document for the Final 

Locomotive/Marine Rule: Air Quality Modeling Analyses. EPA 454/R-08-002. Office of 

Air Quality Planning and Standards, Research Triangle Park, NC. January 2008. 

U.S. EPA (Environmental Protection Agency). 2006a. Technical Support Document for the Final 

Clean Air Interstate Rule: Air Quality Modeling. Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. March 2005 (CAIR Docket OAR-2005-0053-

2149). 

U.S. EPA (Environmental Protection Agency). 2006b. Technical Support Document for the 

Final PM NAAQS Rule. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. 
September 2009 

Appendix 2 

Supplemental Deposition Information 

Wet Deposition Trends in or Near Case Study Areas and 

Supplemental Nationwide Maps Depicting the Ratio of 

Deposition to Concentration and Deposition to Emissions 

Final 

Prepared by 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27709 

Trends in Wet Deposition at NADP Sites 

TABLE OF CONTENTS 

1.0 Trends in Wet Deposition of Inorganic Nitrogen and Sulfate at National 

Atmospheric Deposition Program Sites in or Near Case Study Areas ............................... 1 

2.0 Supplemental Nationwide Maps Depicting the Ratio of Deposition to 

Concentration and Deposition to Emissions ..................................................................... 17 

LIST OF FIGURES 

Figure 1-1. Adirondack Case Study Area; Site: Huntington Wildlife Forest, NY 

NTN = National Trends Network ........................................................................... 1

Figure 1-2. Adirondack Case Study Area; Site: Whiteface Mountain, NY .................................... 2

Figure 1-3. Hubbard Brook Experimental Forest Case Study Area; Site: Hubbard 

Brook, NH. .............................................................................................................. 2

Figure 1-4. Kane Experimental Forest Case Study Area; Site: Kane Experimental 

Forest, PA. .............................................................................................................. 3

Figure 1-5. Potomac River/Potomac Estuary Case Study Area; Site: Arendtsville, PA ................ 3

Figure 1-6. Potomac River/Potomac Estuary Case Study Area; Site: Parsons, WV ...................... 4

Figure 1-7. Potomac River/Potomac Estuary Case Study Area; Site: Wye, MD ........................... 4

Figure 1-8. Shenandoah Case Study Area; Site: Shenandoah National Park, VA ......................... 5

Figure 1-9. Shenandoah Case Study Area; Site: Horton’s Station, VA ......................................... 5

Figure 1-10. Neuse River/Neuse River Estuary Case Study Area; Site: Finley Farm, NC ............ 6

Figure 1-11. Neuse River/Neuse River Estuary Case Study Area; Site: Beaufort, NC .................. 6

Figure 1-12. Rocky Mountain National Park Supplemental Area; Site: Beaver 

Meadows, CO ......................................................................................................... 7

Figure 1-13. Mixed Conifer Forest (Sierra Nevada Range) Case Study Area; Site: 

Yosemite National Park, CA................................................................................... 7

Figure 1-14. Mixed Conifer Forest (Transverse Range) Case Study Area; Site: Joshua 

Tree National Park, CA........................................................................................... 8

Figure 1-15. Mixed Conifer Forest (Transverse Range) Case Study Area; Site: Tanbark 

Flat, CA ................................................................................................................... 8

Figure 1-16. Adirondack Case Study Area; Site: Huntington Wildlife Forest, NY ....................... 9

Figure 1-17. Adirondack Case Study Area; Site: Whiteface Mountain, NY .................................. 9

Figure 1-18. Hubbard Brook Experimental Forest Case Study Area; Site: Hubbard 

Brook, NH. ............................................................................................................ 10

Figure 1-19. Kane Experimental Forest Case Study Area; Site: Kane Experimental 

Forest, PA. ............................................................................................................ 10

Figure 1-20. Potomac River/Potomac Estuary Case Study Area; Site: Arendtsville, PA ............ 11

Figure 1-21. Potomac River/Potomac Estuary Case Study Area; Site: Parsons, WV .................. 11

Figure 1-22. Potomac River/Potomac Estuary Case Study Area; Site: Wye, MD ....................... 12

Figure 1-23. Shenandoah Case Study Area; Site: Shenandoah National Park, VA ..................... 12

Figure 1-24. Shenandoah Case Study Area; Site: Horton’s Station, VA ..................................... 13

Figure 1-25. Neuse River/Neuse River Estuary Case Study Area; Site: Finley Farm, NC .......... 13

Figure 1-26. Neuse River/Neuse River Estuary Case Study Area; Site: Beaufort, NC ................ 14

Final Risk and Exposure Assessment September 2009 

Appendix 2 - i 
Trends in Wet Deposition at NADP Sites 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - ii 

Figure 1-27. Rocky Mountain National Park (Supplemental Area); Site: Beaver 

Meadows, CO ....................................................................................................... 14

Figure 1-28. Mixed Conifer Forest (Sierra Nevada Range) Case Study Area; Site: 

Yosemite National Park, CA................................................................................. 15

Figure 1-29. Mixed Conifer Forest (Transverse Range) Case Study Area; Site: Joshua 

Tree National Park, CA......................................................................................... 15

Figure 1-30. Mixed Conifer Forest (Transverse Range) Case Study Area; Site: Tanbark 

Flat, CA ................................................................................................................. 16

Figure 2-1. Ratio of annual total dry sulfur deposition (kg S/ha/yr) to annual average 

sulfur dioxide concentrations (μg/m3

). ................................................................. 18

Figure 2-2. Ratio of annual total wet sulfur deposition (kg S/ha/yr) to annual average 

sulfur dioxide concentrations (μg/m3

). ................................................................. 19

Figure 2-3. Ratio of annual total wet+dry sulfur deposition (kg S/ha/yr) to annual average 

sulfur dioxide concentrations (μg/m3

). ................................................................. 20

Figure 2-4. Ratio of annual total dry sulfur deposition (kg S/ha/yr) to annual total sulfur 

dioxide emissions (tons/yr). .................................................................................. 21

Figure 2-5. Ratio of annual total wet sulfur deposition (kg S/ha/yr) to annual total sulfur 

dioxide emissions (tons/yr). .................................................................................. 22

Figure 2-6. Ratio of annual total wet+dry sulfur deposition (kg S/ha/yr) to annual total 

sulfur dioxide emissions (tons/yr). ........................................................................ 23

Figure 2-7. Ratio of annual total wet oxidized nitrogen deposition (kg NOx/ha/yr) to 

annual average nitrogen dioxide concentrations (ppb). ........................................ 24

Figure 2-8. Ratio of annual total dry oxidized nitrogen deposition (kg NOx/ha/yr) to 

annual average nitrogen dioxide concentrations (ppb). ........................................ 25

Figure 2-9. Ratio of annual total wet+dry oxidized nitrogen deposition (kg NOx/ha/yr) to 

annual average nitrogen dioxide concentrations (ppb). ........................................ 26

Figure 2-10. Ratio of annual total dry oxidized nitrogen deposition (kg NOx/ha/yr) to 

annual total nitrogen dioxide emissions (tons/yr). ................................................ 27

Figure 2-11. Ratio of annual total wet oxidized nitrogen deposition (kg NOx/ha/yr) to 

annual total nitrogen dioxide emissions (tons/yr). ................................................ 28

Figure 2-12. Ratio of annual total wet+dry oxidized nitrogen deposition (kg NOx/ha/yr) to 

annual total nitrogen dioxide emissions (tons/yr). ................................................ 29

Figure 2-13. Ratio of annual total dry nitrogen deposition (kg N/ha/yr) to annual average 

nitrogen dioxide concentrations (ppb). ................................................................. 30

Figure 2-14. Ratio of annual total wet nitrogen deposition (kg N/ha/yr) to annual average 

nitrogen dioxide concentrations (ppb). ................................................................. 31

Figure 2-15. Ratio of annual total wet+dry nitrogen deposition (kg N/ha/yr) to annual 

average nitrogen dioxide concentrations (ppb). .................................................... 32

Figure 2-16. Ratio of annual total dry nitrogen deposition (kg N/ha/yr) to annual total 

nitrogen dioxide emissions (tons/yr). .................................................................... 33

Figure 2-17. Ratio of annual total wet nitrogen deposition (kg N/ha/yr) to annual total 

nitrogen dioxide emissions (tons/yr). .................................................................... 34

Figure 2-18. Ratio of annual total wet+dry nitrogen deposition (kg N/ha/yr) to annual 

total nitrogen dioxide emissions (tons/yr)............................................................. 35
Trends in Wet Deposition at NADP Sites 

1.0 TRENDS IN WET DEPOSITION OF INORGANIC 

NITROGEN AND SULFATE AT NATIONAL ATMOSPHERIC 

DEPOSITION PROGRAM SITES IN OR NEAR CASE STUDY 

AREAS 

Trends in Wet Deposition of Inorganic Nitrogen 

(Arrow on plots indicate the year 2002) 

NADP/NTN Site NY20

Annual inorganic N wet depositions, 1978–2007

Trend line

Figure 1-1. Adirondack Case Study Area; Site: Huntington Wildlife Forest, NY 

NTN = National Trends Network 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 1 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site NY98

Annual inorganic N wet depositions, 1984–2007

Trend line

Figure 1-2. Adirondack Case Study Area; Site: Whiteface Mountain, NY 

NADP/NTN Site NH02

Annual inorganic N wet depositions, 1978–2007

Trend line

Figure 1-3. Hubbard Brook Experimental Forest Case Study Area; Site: Hubbard Brook, NH. 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 2 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site PA29

Annual inorganic N wet depositions, 1978–2007

Trend line

Figure 1-4. Kane Experimental Forest Case Study Area; Site: Kane Experimental Forest, PA. 

NADP/NTN Site PA00

Annual inorganic N wet depositions, 1999–2007

Trend line

Figure 1-5. Potomac River/Potomac Estuary Case Study Area; Site: Arendtsville, PA 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 3 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site WV18

Annual inorganic N wet depositions, 1978–2007

Trend line

Figure 1-6. Potomac River/Potomac Estuary Case Study Area; Site: Parsons, WV 

NADP/NTN Site MD13

Annual inorganic N wet depositions, 1983–2007

Trend line

Figure 1-7. Potomac River/Potomac Estuary Case Study Area; Site: Wye, MD 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 4 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site VA28

Annual inorganic N wet depositions, 1981–2007

Trend line

Figure 1-8. Shenandoah Case Study Area; Site: Shenandoah National Park, VA 

NADP/NTN Site VA13

Annual inorganic N wet depositions, 1978–2007

Trend line

Figure 1-9. Shenandoah Case Study Area; Site: Horton’s Station, VA

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 5 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site NC41

Annual inorganic N wet depositions, 1978–2007

Trend line

Figure 1-10. Neuse River/Neuse River Estuary Case Study Area; Site: Finley Farm, NC

NADP/NTN Site NC06

Annual inorganic N wet depositions, 1999–2007

Trend line

Figure 1-11. Neuse River/Neuse River Estuary Case Study Area; Site: Beaufort, NC 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 6 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site CO19

Annual inorganic N wet depositions, 1980–2007

Trend line

Figure 1-12. Rocky Mountain National Park Supplemental Area; Site: Beaver Meadows, CO 

NADP/NTN Site CA99

Annual inorganic N wet depositions, 1981–2007

Trend line

Figure 1-13. Mixed Conifer Forest (Sierra Nevada Range) Case Study Area; 

Site: Yosemite National Park, CA 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 7 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site CA67

Annual inorganic N wet depositions, 2000–2007

Trend line

Figure 1-14. Mixed Conifer Forest (Transverse Range) Case Study Area; 

Site: Joshua Tree National Park, CA 

NADP/NTN Site CA42

Annual inorganic N wet depositions, 1982–2007

Trend line

Figure 1-15. Mixed Conifer Forest (Transverse Range) Case Study Area; 

Site: Tanbark Flat, CA 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 8 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site NY20

Annual SO4 wet depositions, 1978–2007

Trend line

Figure 1-16. Adirondack Case Study Area; Site: Huntington Wildlife Forest, NY 

NADP/NTN Site NY98

Annual SO4 wet depositions, 1984–2007

Trend line

Figure 1-17. Adirondack Case Study Area; Site: Whiteface Mountain, NY

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 9 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site NH02

Annual SO4 wet depositions, 1978–2007

Trend line

Figure 1-18. Hubbard Brook Experimental Forest Case Study Area; 

Site: Hubbard Brook, NH.

NADP/NTN Site PA29

Annual SO4 wet depositions, 1978–2007

Trend line

Figure 1-19. Kane Experimental Forest Case Study Area; Site: Kane Experimental Forest, PA. 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 10 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site PA00

Annual SO4 wet depositions, 1999–2007

Trend line

Figure 1-20. Potomac River/Potomac Estuary Case Study Area; Site: Arendtsville, PA 

NADP/NTN WV18

Annual SO4 wet depositions, 1978–2007

Trend line

NADP/NTN Site WV18

Annual SO4 wet depositions, 1978–2007

Figure 1-21. Potomac River/Potomac Estuary Case Study Area; Site: Parsons, WV 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 11 
Trends in Wet Deposition at NADP Sites 

NADP/NTN MD13

Annual SO4 wet depositions, 1983–2007

Trend line

NADP/NTN Site MD13

Annual SO4 wet depositions, 1983–2007

Figure 1-22. Potomac River/Potomac Estuary Case Study Area; Site: Wye, MD

NADP/NTN Site VA28

Annual SO4 wet depositions, 1981–2007

Trend line

Figure 1-23. Shenandoah Case Study Area; Site: Shenandoah National Park, VA 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 12 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site VA13

Annual SO4 wet depositions, 1978–2007

Trend line

Figure 1-24. Shenandoah Case Study Area; Site: Horton’s Station, VA 

NADP/NTN Site NC41

Annual SO4 wet depositions, 1978–2007

Trend line

Figure 1-25. Neuse River/Neuse River Estuary Case Study Area; Site: Finley Farm, NC 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 13 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site NC06

Annual SO4 wet depositions, 1999–2007

Trend line

Figure 1-26. Neuse River/Neuse River Estuary Case Study Area; Site: Beaufort, NC

NADP/NTN Site CO19

Annual SO4 wet depositions, 1980–2007

Trend line

Figure 1-27. Rocky Mountain National Park (Supplemental Area); Site: Beaver Meadows, CO 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 14 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site CA99

Annual SO4 wet depositions, 1981–2007

Trend line

Figure 1-28. Mixed Conifer Forest (Sierra Nevada Range) Case Study Area; 

Site: Yosemite National Park, CA 

NADP/NTN Site CA67

Annual SO4 wet depositions, 2000–2007

Trend line

Figure 1-29. Mixed Conifer Forest (Transverse Range) Case Study Area; 

Site: Joshua Tree National Park, CA 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 15 
Trends in Wet Deposition at NADP Sites 

NADP/NTN Site CA42

Annual SO4 wet depositions, 1982–2007

Trend line

Figure 1-30. Mixed Conifer Forest (Transverse Range) Case Study Area; 

Site: Tanbark Flat, CA 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 16 
Trends in Wet Deposition at NADP Sites 

2.0 SUPPLEMENTAL NATIONWIDE MAPS DEPICTING THE 

RATIO OF DEPOSITION TO CONCENTRATION AND 

DEPOSITION TO EMISSIONS 

This section of Appendix 2 contains the following maps (shown by figure number): 

2-1. Ratio of annual total dry sulfur deposition (kg S/ha/yr) to annual average sulfur 

dioxide concentrations (μg/m3) 

2-2. Ratio of annual total wet sulfur deposition (kg S/ha/yr) to annual average sulfur 

dioxide concentrations (μg/m3

) 

2-3. Ratio of annual total wet+dry sulfur deposition (kg S/ha/yr) to annual average sulfur 

dioxide concentrations (μg/m3

) 

2-4. Ratio of annual total dry sulfur deposition (kg S/ha/yr) to annual total sulfur dioxide 

emissions (tons/yr). 

2-5. Ratio of annual total wet sulfur deposition (kg S/ha/yr) to annual total sulfur dioxide 

emissions (tons/yr) 

2-6. Ratio of annual total wet+dry sulfur deposition (kg S/ha/yr) to annual total sulfur 

dioxide emissions (tons/yr) 

2-7. Ratio of annual total wet oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

average nitrogen dioxide concentrations (ppb) 

2-8. Ratio of annual total dry oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

average nitrogen dioxide concentrations (ppb) 

2-9. Ratio of annual total wet+dry oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

average nitrogen dioxide concentrations (ppb) 

2-10. Ratio of annual total dry oxidized nitrogen deposition (kg NOx/ha/yr) to annual total 

nitrogen dioxide emissions (tons/yr) 

2-11. Ratio of annual total wet oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

total nitrogen dioxide emissions (tons/yr) 

2-12. Ratio of annual total wet+dry oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

total nitrogen dioxide emissions (tons/yr) 

2-13. Ratio of annual total dry nitrogen deposition (kg N/ha/yr) to annual average 

nitrogen dioxide concentrations (ppb) 

2-14. Ratio of annual total wet nitrogen deposition (kg N/ha/yr) to annual average 

nitrogen dioxide concentrations (ppb) 

2-15. Ratio of annual total wet+dry nitrogen deposition (kg N/ha/yr) to annual average 

nitrogen dioxide concentrations (ppb)

2-16. Ratio of annual total dry nitrogen deposition (kg N/ha/yr) to annual total nitrogen 

dioxide emissions (tons/yr) 

2-17. Ratio of annual total wet nitrogen deposition (kg N/ha/yr) to annual total nitrogen 

dioxide emissions (tons/yr) 

2-18. Ratio of annual total wet+dry nitrogen deposition (kg N/ha/yr) to annual total 

nitrogen dioxide emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 17 
Trends in Wet Deposition at NADP Sites 

These maps were created using CMAQ-predicted 12 x 12 km concentrations and 

deposition, and CMAQ gridded emissions inputs data. 

Figure 2-1. Ratio of annual total dry sulfur deposition (kg S/ha/yr) to annual average sulfur 

dioxide concentrations (μg/m3). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 18 
Trends in Wet Deposition at NADP Sites 

Figure 2-2. Ratio of annual total wet sulfur deposition (kg S/ha/yr) to annual average sulfur 

dioxide concentrations (μg/m3). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 19 
Trends in Wet Deposition at NADP Sites 

Figure 2-3. Ratio of annual total wet+dry sulfur deposition (kg S/ha/yr) to annual average sulfur 

dioxide concentrations (μg/m3). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 20 
Trends in Wet Deposition at NADP Sites 

Figure 2-4. Ratio of annual total dry sulfur deposition (kg S/ha/yr) to annual total sulfur dioxide 

emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 21 
Trends in Wet Deposition at NADP Sites 

Figure 2-5. Ratio of annual total wet sulfur deposition (kg S/ha/yr) to annual total sulfur dioxide 

emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 22 
Trends in Wet Deposition at NADP Sites 

Figure 2-6. Ratio of annual total wet+dry sulfur deposition (kg S/ha/yr) to annual total sulfur 

dioxide emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 23 
Trends in Wet Deposition at NADP Sites 

Figure 2-7. Ratio of annual total wet oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

average nitrogen dioxide concentrations (ppb). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 24 
Trends in Wet Deposition at NADP Sites 

Figure 2-8. Ratio of annual total dry oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

average nitrogen dioxide concentrations (ppb). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 25 
Trends in Wet Deposition at NADP Sites 

Figure 2-9. Ratio of annual total wet+dry oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

average nitrogen dioxide concentrations (ppb). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 26 
Trends in Wet Deposition at NADP Sites 

Figure 2-10. Ratio of annual total dry oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

total nitrogen dioxide emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 27 
Trends in Wet Deposition at NADP Sites 

Figure 2-11. Ratio of annual total wet oxidized nitrogen deposition (kg NOx/ha/yr) to annual 

total nitrogen dioxide emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 28 
Trends in Wet Deposition at NADP Sites 

Figure 2-12. Ratio of annual total wet+dry oxidized nitrogen deposition (kg NOx/ha/yr) to 

annual total nitrogen dioxide emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 29 
Trends in Wet Deposition at NADP Sites 

Figure 2-13. Ratio of annual total dry nitrogen deposition (kg N/ha/yr) to annual average 

nitrogen dioxide concentrations (ppb). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 30 
Trends in Wet Deposition at NADP Sites 

Figure 2-14. Ratio of annual total wet nitrogen deposition (kg N/ha/yr) to annual average 

nitrogen dioxide concentrations (ppb). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 31 
Trends in Wet Deposition at NADP Sites 

Figure 2-15. Ratio of annual total wet+dry nitrogen deposition (kg N/ha/yr) to annual average 

nitrogen dioxide concentrations (ppb). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 32 
Trends in Wet Deposition at NADP Sites 

Figure 2-16. Ratio of annual total dry nitrogen deposition (kg N/ha/yr) to annual total nitrogen 

dioxide emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 33 
Trends in Wet Deposition at NADP Sites 

Figure 2-17. Ratio of annual total wet nitrogen deposition (kg N/ha/yr) to annual total nitrogen 

dioxide emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 34 
Trends in Wet Deposition at NADP Sites 

Figure 2-18. Ratio of annual total wet+dry nitrogen deposition (kg N/ha/yr) to annual total 

nitrogen dioxide emissions (tons/yr). 

Final Risk and Exposure Assessment September 2009 

Appendix 2 - 35 

September, 2009 

Appendix 3 

Components of Reactive Nitrogen Deposition Based 

on Average Deposition Over the Period 2002–2005 

Dry Deposition from CMAQ/Wet Deposition from NADP 

Final 

Prepared by 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27709 

Components of Reactive Nitrogen Deposition: 2002–2005 

LIST OF FIGURES 

Figure 1. Adirondack Case Study Area: 2002–2005. ..................................................................... 1

Figure 2. Hubbard Brook Experimental Forest Case Study Area: 2002–2005. ............................. 1

Figure 3. Kane Experimental Forest Case Study Area: 2002–2005. .............................................. 2

Figure 4. Neuse River/Neuse River Estuary Case Study Area: 2002–2005. .................................. 2

Figure 5. Potomac River/Potomac Estuary Case Study Area: 2002–2005. .................................... 3

Figure 6. Shenandoah Case Study Area: 2002–2005. .................................................................... 3

Figure 7. Rocky Mountain National Park (Supplemental Area): 2002–2005. ............................... 4

Figure 8. Mixed Conifer Forest (Sierra Nevada Range) Case Study Area: 2002–2005. ................ 4

Figure 9. Mixed Conifer Forest (Transverse Range) Case Study Area: 2002–2005. ..................... 5

Final Risk and Exposure Assessment September 2009 

Appendix 3 - i 
Components of Reactive Nitrogen Deposition: 2002–2005 

Final Risk and Exposure Assessment September 2009 

Appendix 3 - ii 

[This page intentionally left blank.] 
Components of Reactive Nitrogen Deposition: 2002–2005 

Ox N - Dry

35%

Ox N - Wet

35%

Re N - Wet

23%

Re N - Dry

7%

Figure 1. Adirondack Case Study Area: 2002–2005. 

Ox N - Dry

40%

Ox N - Wet

35%

Re N - Wet

19%

Re N - Dry

6%

Figure 2. Hubbard Brook Experimental Forest Case Study Area: 2002–2005. 

Final Risk and Exposure Assessment September 2009 

Appendix 3 - 1 
Components of Reactive Nitrogen Deposition: 2002–2005 

Ox N - Dry

41%

Ox N - Wet

33%

Re N - Wet

22%

Re N - Dry

4%

Figure 3. Kane Experimental Forest Case Study Area: 2002–2005. 

Ox N - Dry

30%

Ox N - Wet

15%

Re N - Wet

18%

Re N - Dry

37%

Figure 4. Neuse River/Neuse River Estuary Case Study Area: 2002–2005. 

Final Risk and Exposure Assessment September 2009 

Appendix 3 - 2 
Components of Reactive Nitrogen Deposition: 2002–2005 

Ox N - Dry

42%

Ox N - Wet

23%

Re N - Wet

20%

Re N - Dry

15%

Figure 5. Potomac River/Potomac Estuary Case Study Area: 2002–2005. 

Ox N - Dry

42%

Ox N - Wet

24%

Re N - Wet

18%

Re N - Dry

16%

Figure 6. Shenandoah Case Study Area: 2002–2005. 

Final Risk and Exposure Assessment September 2009 

Appendix 3 - 3 
Components of Reactive Nitrogen Deposition: 2002–2005 

Ox N - Dry

32%

Ox N - Wet

25%

Re N - Wet

36%

Re N - Dry

7%

Figure 7. Rocky Mountain National Park (Supplemental Area): 2002–2005. 

Ox N - Dry

46%

Ox N - Wet

13%

Re N - Wet

18%

Re N - Dry

23%

Figure 8. Mixed Conifer Forest (Sierra Nevada Range) Case Study Area: 2002–2005. 

Final Risk and Exposure Assessment September 2009 

Appendix 3 - 4 
Components of Reactive Nitrogen Deposition: 2002–2005 

Ox N - Dry

74%

Ox N - Wet

6%

Re N - Wet

6%

Re N - Dry

14%

Figure 9. Mixed Conifer Forest (Transverse Range) Case Study Area: 2002–2005. 

Final Risk and Exposure Assessment September 2009 

Appendix 3 - 5 

September 2009 

Appendix 4 

Aquatic Acidification Case Study 

Final 

Prepared by 

U.S. Environmental Protection Agency 

Office of Atmospheric Programs 

Washington, DC 

Aquatic Acidification Case Study 

TABLE OF CONTENTS 

Acronyms and Abbreviations ....................................................................................................... vii

1.0 Purpose ...................................................................................................................................... 1

2.0 Background ............................................................................................................................... 1

2.1 Acidification .................................................................................................................... 1

2.2 Indicators of Acidification ............................................................................................... 2

2.3 Biological Response to Acidification and Acid Neutralizing Capacity........................... 4

3.0 Case Studies .............................................................................................................................. 6

3.1 Surface Waters Acidification in the Eastern United States ............................................. 6

3.2 Objectives ........................................................................................................................ 8

3.3 Adirondack Case Study Area ........................................................................................... 9

3.3.1 General Description ............................................................................................. 9

3.3.2 Levels of Air Pollution and Acidifying Deposition ........................................... 10

3.3.3 Levels of Sulfate, Nitrate, and ANC Concentrations in Surface Water ............. 10

3.4 Shenandoah Case Study Area ........................................................................................ 14

3.4.1 General Description ........................................................................................... 14

3.4.2 Levels of Air Pollution and Acidifying Deposition ........................................... 15

3.4.3 Levels of Sulfate, Nitrate, and ANC Concentrations in Surface Water ............. 15

4.0 Methods................................................................................................................................... 19

4.1 Biological Response to Acidification ............................................................................ 19

4.2 Past, Present, and Future Surface Water Chemistry—the MAGIC Modeling 

Approach ........................................................................................................................ 23

4.3 Connecting Current Nitrogen and Sulfur Deposition to Acid-Base Conditions of 

Lakes and Streams: The Critical Load Approach .......................................................... 24

4.3.1 Regional Assessment of Adirondack Case Study Area Lakes and 

Shenandoah Case Study Area Trout Streams .................................................... 27

4.3.1.1 Adirondack Case Study Area .............................................................. 27

4.3.1.2 Shenandoah Case Study Area ............................................................. 27

5.0 Results ..................................................................................................................................... 29

5.1 Adirondack Case Study Area ......................................................................................... 29

5.1.1 Current and Preacidification Conditions of Surface Waters .............................. 29

5.1.2 ANC Inferred Condition—Aquatic Status Categories. ...................................... 31

5.1.3 The Biological Risk from Current Nitrogen and Sulfur Deposition: 

Critical Load Assessment .................................................................................. 33

5.1.4 Representative Sample of Lakes in the Adirondack Case Study Area .............. 37

5.1.5 Recovery from Acidification Given Current Emission Reductions .................. 38

5.2 Shenandoah Case Study Area ........................................................................................ 39

5.2.1 Current and Preacidification Conditions of Surface Waters .............................. 39

5.2.2 ANC Inferred Condition—Aquatic Status Categories ....................................... 41

5.2.3 The Biological Risk from Current Nitrogen and Sulfur Deposition: 

Critical Load Assessment .................................................................................. 43

5.2.4 Regional Assessment of Trout Streams in the Shenandoah Case Study 

Area .................................................................................................................... 45

Final Risk and Exposure Assessment September 2009 

Appendix 4 - i 
Aquatic Acidification Case Study 

5.2.5 Recovery from Acidification Given Current Emission Reductions .................. 45

6. References ............................................................................................................................... 47

ATTACHMENT A ......................................................................................................................... 1

1. Modeling Descriptions ..................................................................................................... 1

1.1 MAGIC ................................................................................................................ 1

1.1.1 Input Data and Calibration .................................................................... 2

1.1.2 Lake, Stream, and Soil Data for Calibration ......................................... 2

1.1.3 Wet Deposition and Meteorology Data for Calibration ........................ 3

1.1.4 Wet Deposition Data (Reference Year and Calibration Values) .......... 5

1.1.5 Dry, Cloud, and Fog Deposition Data and Historical 

Deposition Sequences ........................................................................... 6

1.1.6 Protocol for MAGIC Calibration and Simulation at Individual 

Sites ....................................................................................................... 7

1.1.7 Combined Model Calibration and Simulation Uncertainty .................. 9

1.1.8 Results of the Uncertainty Analysis .................................................... 10

1.2 Critical Loads: Steady-State Water Chemistry Models ..................................... 15

1.2.1 Preindustrial Base Cation Concentration ............................................ 18

1.2.2 F-factor ................................................................................................ 19

1.2.3 ANC Limits ......................................................................................... 20

1.2.4 Sea Salt Corrections ............................................................................ 20

1.2.5 Critical load exceedance ..................................................................... 21

1.2.6 Lake-to-Catchment-Area Ratios ......................................................... 23

1.2.7 Denitrification and N Immobilization in Soils .................................... 23

1.2.8 Uncertainty and Variability ................................................................. 23

1.2.8.1 Results of the uncertainty analysis ...................................................... 26

ATTACHMENT B ......................................................................................................................... 1

1. EMAP/TIME/LTM Programs .......................................................................................... 1

2. Temporally Integrated Monitoring of Ecosystems and Long-Term Monitoring 

Programs .......................................................................................................................... 2

2.1 TIME Program ..................................................................................................... 2

2.2 LTM Program ...................................................................................................... 3

LIST OF FIGURES

Figure 2.3-1. (a) Number of fish species per lake or stream versus acidity, expressed as 

ANC for Adirondack Case Study Area lakes (Sullivan et al., 2006). (b) 

Number of fish species among 13 streams as a function of ANC in the 

Shenandoah Case Study Area. Values of ANC are means based on 

quarterly measurements from 1987 to 1994. The regression analysis shows 

a highly significant relationship (p <.0001) between mean stream ANC 

and the number of fish species. ............................................................................... 6

Figure 3.1-1. Regions containing ecosystems sensitive to acidifying deposition in the 

eastern United States (U.S. EPA, based on NAPAP, 2005). .................................. 7

Final Risk and Exposure Assessment September 2009 

Appendix 4 - ii 
Aquatic Acidification Case Study 

Figure 3.3-1. Annual average total wet deposition (kg/ha/yr) for the period 1990 to 2006 

in SO4

2- (blue) and NO3-

 (green) from eight NADP/NTN sites in the 

Adirondack Case Study Area (shown with linear trend regression lines). ........... 10

Figure 3.3-2. Trends over time for annual average SO4

2

- (blue) and NO3

-

 (green) 

concentrations and ANC (red) in 50 Adirondacks Long-Term Monitoring 

monitored lakes in the Adirondack Case Study Area (shown with linear 

trend regression lines). Both SO4

2- and NO3-

 concentrations have 

decreased in surface waters by approximately 26% and 13%, respectively. ........ 11

Figure 3.3-3. Current yearly average for 2005 to 2006 (a) SO4

2-concentrations (μeq/L), 

(b) NO3

-

 concentrations (μeq/L), and (c) ANC (μeq/L) in surface waters 

from 88 monitored lakes in the Temporally Integrated Monitoring of 

Ecosystems (38 Lakes) and Adirondacks Long-Term Monitoring (50 

Lakes) networks in the Adirondack Case Study Area. ......................................... 13

Figure 3.4-1. Air pollution concentrations and wet deposition for the period 1990 to 2006 

using a CastNET (SHN418) and seven NADP/NTN sites in the 

Shenandoah Case Study Area. (a) Annual average atmospheric 

concentrations (ug/m3

) of SO2 (blue), oxidized nitrogen (red), SO4

2-

(green), and reduced nitrogen (black). (b) Annual average total wet 

deposition (kg/ha/yr) of SO4

2- (green) and NO3-

 (blue). ....................................... 15

Figure 3.4-2. Trends over time for annual average SO4

2- (blue) and NO3- (green) 

concentrations, and ANC (red) in 67 streams in the Surface Water 

Acidification Study, Virginia Trout Stream Sensitivity Survey, and LongTerm Monitoring programs in the Shenandoah Case Study Area (shown 

with linear trend regression lines). ........................................................................ 16

Figure 3.4-3. Current yearly average for 2005 to 2006 (a) SO4

2- concentration, (b) NO3-

concentration, and (c) ANC (μeq/L) in surface waters from 67 monitored 

streams in the Surface Water Acidification Study, Virginia Trout Stream 

Sensitivity, and Long-Term Monitoring network in the Shenandoah Case 

Study Area. ........................................................................................................... 18

Figure 4.1-1. Relationship between summer and spring ANC values at LTM sites in New 

England, the Adirondack Mountains, and the Northern Appalachian 

Plateau. Values are mean summer values for each site for the period 1990 

to 2000 (horizontal axis) and mean spring minima for each site for the 

same time period. On average, spring ANC values are at least 30 μeq/L 

lower than summer values. ................................................................................... 22

Figure 4.1-2. Number of fish species per lake or stream versus ANC level and aquatic 

status category (represented by color) for lakes in the Adirondack Case 

Study Area (Sullivan et al., 2006). The five aquatic status categories are 

described in Table 4.1-1. ....................................................................................... 22

Figure 4.3-1. (a) The location of lakes in the Adirondack Case Study Area used for 

MAGIC (red circles) and critical load (green circles) modeling. (b) The 

location of streams in the Shenandoah Case Study Area used for both 

MAGIC and critical load modeling. ..................................................................... 25

Figure 5.1-1. Average NO3

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

(blue) for the 44 lakes in the Adirondack Case Study Area modeled using 

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

Final Risk and Exposure Assessment September 2009 

Appendix 4 - iii 
Aquatic Acidification Case Study 

Figure 5.1-2. (a) NO3

-

 and (b) SO4

2- concentrations (μeq/L) of preacidification (1860) and 

current (2006) conditions based on hindcasts of 44 lakes in the 

Adirondack Case Study Area modeled using MAGIC. ........................................ 31

Figure 5.1-3. Percentage of lakes in the five classes (Acute, Severe, Elevated, Moderate, 

Low) for years 1860 (preacidification) and 2006 (current) conditions for 

44 lakes modeled using MAGIC. Error bars indicate the 95% confidence 

interval. ................................................................................................................. 32

Figure 5.1-4. ANC concentrations of preacidification (1860) and current (2006) 

conditions based on hindcasts of 44 lakes in the Adirondack Case Study 

Area modeled using MAGIC. ............................................................................... 33

Figure 5.1-5. Critical loads of acidifying deposition that each surface water location can 

receive in the Adirondack Case Study Area while maintaining or 

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

Watersheds with critical load values <100 meq/m2

/yr (red and orange 

circles) are most sensitive to surface water acidification, whereas 

watersheds with values >100 meq/m2

/yr (yellow and green circles) are the 

least sensitive sites. ............................................................................................... 34

Figure 5.1-6. Critical load exceedances (red circles) in the Adirondack Case Study Area 

based on the year 2002 deposition magnitudes for waterbodies where the 

critical limit ANC concentration is 0, 20, 50, and 100 μeq/L, respectively. 

Green circles represent lakes where deposition is below the critical load. 

See Table 5.1-3. .................................................................................................... 36

Figure 5.1-7. Percentage of lakes in each of the five classes of acidification (Acute, 

Severe, Elevated, Moderate, Low) for the years 2006, 2020, and 2050 for 

44 lakes modeled using MAGIC, where current emissions are held 

constant. Error bars indicate the 95% confidence interval. .................................. 38

Figure 5.2-1. Average NO3

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

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

modeled using MAGIC for the period 1850 to 2050. ........................................... 39

Figure 5.2-2. (a) NO3

-

 and (b) SO4

2- concentrations (μeq/L) of 1860 (preacidification) and 

2006 (current) conditions based on hindcasts of 60 streams in the 

Shenandoah Case Study Area modeled using MAGIC. ....................................... 40

Figure 5.2-3. Percentage of streams in the five classes of acidification (Acute, Severe, 

Elevated, Moderate, Low) for years 1860 (preacidification) and 2006 

(current) conditions for 60 streams modeled using MAGIC. The number 

of streams in each category is above the bar. Error bars indicate the 95% 

confidence interval. ............................................................................................... 42

Figure 5.2-4. ANC levels of 1860 (preacidification) and 2006 (current) conditions based 

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

using MAGIC........................................................................................................ 42

Figure 5.2-5. Critical loads of surface water acidity for an ANC of 50 μeq/L for 

Shenandoah Case Study Area streams. Each dot represents an estimated 

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

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

Watersheds with critical load values <100 meq/m2

/yr (red and orange 

circles) are most sensitive to surface water acidification, whereas 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - iv 
Aquatic Acidification Case Study 

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

least sensitive sites. ............................................................................................... 43

Figure 5.2-6. Critical load exceedances for ANC levels of 0, 20, 50, and 100 μeq/L for 

Shenandoah Case Study Area streams. Green circles represent streams 

where current nitrogen and sulfur deposition is below the critical load and 

that maintain an ANC level of 0, 20, 50, and 100 μeq/L, respectively. Red 

circles represent streams where current nitrogen and sulfur deposition 

exceeds the critical load, indicating they are currently impacted by 

acidifying deposition. See Table 5.2-3. ................................................................ 44

Figure 5.2-7. Percentage of streams in the five categories of acidification (Acute, Severe, 

Elevated, Moderate, Low) for the years 2006, 2020, and 2050 for 60 

streams in the Shenandoah Case Study Area modeled using MAGIC. The 

number of streams in each category is above the bar. Error bars indicate 

the 95% confidence interval. ................................................................................. 46

Figure 1.1-1. Simulated versus observed annual average surface water SO4

2-, NO3-

. ANC, 

and pH during the model calibration period for each of the 44 lakes in the 

Adirondacks Case Study Area. The black line is the 1:1 line. .............................. 12

Figure 1.1-2. Simulated versus observed annual average surface water SO4

2-, NO3-

. ANC, 

and pH during the model calibration period for each of the 60 streams in 

the Shenandoah Case Study Area. The black line is the 1:1 line. ......................... 13

Figure 1.1-3. MAGIC simulated and observed values of ANC for two lakes in the 

Shenandoah Case Study Area. Red points are observed data and the 

simulated values are the line. The Root Mean Squared Error (RMSE) for 

ANC was 11.8 μeq/L for Helton Creek and 4.0 μeq/L for Nobusiness 

Creek. .................................................................................................................... 14

Figure 1.1-4. MAGIC simulated and observed values of ANC for two lakes in the 

Shenandoah Case Study Area. Red points are observed data and the 

simulated values are the line. The Root Mean Squared Error (RMSE) for 

ANC was 11.8 μeq/L for Helton Creek and 4.0 μeq/L for Nobusiness 

Creek. .................................................................................................................... 15

Figure 1.2-1. The depositional load function defined by the model. ............................................ 22

Figure 1.2-2. The inverse cumulative frequency distribution for Little Hope Pond. The xaxis shows critical load exceedance in meq/ha/yr and y-axis is the 

probability. The dashed lines represent zero exceedance. In the case of 

Little Hope Pond, the dash line divides mostly the probability distribution 

on the left hand side, indicating Little Hope Pond has a relative low 

probability of being exceeded (0.3). Critical load and exceedances values 

were based on a critical level of protection of ANC = 50 μeq/L. ......................... 25

Figure 1.2-3. Coefficients of variation of surface water critical load for acidity CL(A) and 

exceedances (EX(A)). Critical load and exceedances values were based on 

a critical level of protection of ANC = 50 μeq/L. ................................................. 27

Figure 1.2-4. Probability of exceedance of critical load for acidity for 2002. .............................. 28

LIST OF TABLES 

Table 4.1-1. Aquatic Status Categories ......................................................................................... 20

Final Risk and Exposure Assessment September 2009 

Appendix 4 - v 
Aquatic Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - vi 

Table 5.1-1. Estimated Average Concentrations of Surface Water Chemistry for 44 Lakes 

in the Adirondack Case Study Area Modeled Using MAGIC for 

Preacidification (1860) and Current (2006) Conditions ....................................... 30

Table 5.1-2. Percentage of Lakes in the Five Aquatic Status Categories Based on Their 

Surface Water ANC Levels for 44 Lakes Modeled Using MAGIC and 88 

Lakes in the TIME/LTM Monitoring Network. Results Are for the 

Adirondack Case Study Area for the Years 2005 to 2006. ................................... 32

Table 5.1-3. Adirondack Case Study Area Critical Load Exceedances, Where Nitrogen 

and Sulfur Deposition Is Larger Than the Critical Load for Four Different 

ANC Critical Limit Thresholds, for 169 Modeled Lakes within the 

TIME/LTM and EMAP Monitoring and Survey Programs. “No. 

Exceedances” Indicates the Number of Lakes at or below the Given ANC 

Critical Limit, and “% Lakes” Indicates the Total Percentage of Lakes at 

or below the Given ANC Critical Limit. .............................................................. 35

Table 5.1-4. Critical Load Exceedances for the Regional Population of 1,842 Lakes in the 

Adirondack Case Study Area That Are from 0.5 to 2,000 ha in Size and at 

Least 1 m in Depth for the Four Critical Limit ANC Levels (0, 20, 50, and 

100 μeq/L). Estimates Use the Exceedances for the Subset of 169 Lakes 

Using 2002 Deposition Magnitudes (Table 5.1-3) and Are Extrapolated to 

the Full Population Based on the EMAP Lake Probability Survey of 1991 

to 1994. “No. Exceedances” Indicates the Number of Lakes at or Below 

the Given ANC Critical Limit; “% Lakes” Indicates the Total Percentage 

of Lakes at or Below the Given ANC Critical Limit. ........................................... 37

Table 5.2-1. Estimated Average Concentrations of Surface Water Chemistry for 60 

Streams in the Shenandoah Case Study Area Modeled Using MAGIC for 

Preacidification (1860) and Current (2006) Conditions ....................................... 41

Table 5.2-2. Percentage of Streams in the Five Aquatic Status Categories Based on Their 

Surface Water ANC Concentrations for 60 Streams Modeled Using 

MAGIC and 67 Streams in the SWAS-VTSSS LTM Network. Results are 

for the Shenandoah Case Study Area for the Year 2006. ..................................... 41

Table 5.2-3. Critical Load Exceedances (Nitrogen + Sulfur Deposition > Critical Load) 

for 60 Modeled Streams within the VTSSS LTM Program in the 

Shenandoah Case Study Area. “No. Exceedances” Indicates the Number 

of Streams at the Given ANC Limit; “% Streams” Indicates the Total 

Percentage of Streams at the Given ANC Limit. .................................................. 45

Table 1.2-1. Parameters Used and their Uncertainty Range. The Range of Surface Water 

Parameters (e.g., CA, MG, CL, NA, NO3, SO4) were Determined from 

Surface Water Chemistry Data for the Period from 1992 to 2006 from the 

LTM-TIME Monitoring Network. Runoff(Q) and Acidic Deposition were 

Set at 50% and 25% .............................................................................................. 24

Table 1.2-2. Means and Coefficients of Variation of Critical Loads and Exceedances for 

Surface Water. ....................................................................................................... 26
Aquatic Acidification Case Study 

ACRONYMS AND ABBREVIATIONS 

Al3+ aluminum 

Al(OH)3 aluminum hydroxide 

ALTM Adirondack Long-Term Monitoring 

ANC acid neutralizing capacity 

ASTRAP Advanced Statistical Trajectory Regional Air Pollution 

Ca2+ calcium 

Cl-

 chloride 

CL(A) critical loads of acidity 

CO2 carbon dioxide 

DDF dry and occult deposition factor 

EMAP Environmental Monitoring and Assessment Program 

eq/ha/yr equivalents per hectare per year 

F-

 fluoride 

FAB First-Order Acidity Balance 

H+ hydrogen ion 

H4SiO4 silicic acid 

ha hectare 

ISA Integrated Science Assessment 

K+ potassium 

kg/ha/yr kilograms/hectare/year 

km kilometer 

LTM Long-Term Monitoring 

m meter 

m/yr meters/year 

MAGIC Model of Acidification of Groundwater in Catchments 

MAHA Mid-Atlantic Highlands Assessment 

meq/m2.yr) milliequivalents per square meter per year 

Mg2+ magnesium 

Na+ sodium 

NADP National Atmospheric Deposition Program 

NH4

+ ammonium

NO3

- nitrate 

NOx nitrogen oxides 

NSWS National Lake/Stream Surveys 

NTN National Trends Network 

O3 ozone 

PnET-BGC biogeochemical model 

Si silicon 

SO2 sulfur dioxide 

SO4

2- sulfate 

SOx sulfur oxides 

SWAS Shenandoah Nation Park Surface Water Acidification Study 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - vii 
Aquatic Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - viii 

SSWC Steady-State Water Chemistry 

TIME Temporally Integrated Monitoring of Ecosystems 

μeq/L microequivalents per liter 

μM micrometer 

VTSSS Virginia Trout Stream Sensitivity Survey 
Aquatic Acidification Case Study 

1.0 PURPOSE 

This case study is intended to estimate the ecological exposure and risk to aquatic 

ecosystems from acidification associated with the deposition of nitrogen and sulfur for two 

sensitive regions of eastern United States: the Adirondack Mountains in New York (hereafter 

referred to as the Adirondack Case Study Area) and Shenandoah National Park and the 

surrounding areas of Virginia (hereafter referred to as the Shenandoah Case Study Area). 

2.0 BACKGROUND 

2.1 ACIDIFICATION 

Sulfur oxides (SOx) and nitrogen oxides (NOx) compounds in the atmosphere undergo a 

complex mix of reactions and thermodynamic processes in gaseous, liquid, and solid phases to 

form various acidic compounds. These acidic compounds are removed from the atmosphere 

through deposition: either wet (e.g., rain, snow), fog or cloud, or dry (e.g., gases, particles). 

Deposition of these acidic compounds leads to ecosystem exposure and effects on ecosystem 

structure and function. Following deposition, these compounds can, in some instances, leach out 

of the soils in the form of sulfate (SO4

2-) and nitrate (NO3-

), leading to the acidification of surface 

waters. The effects on ecosystems depend on the magnitude of deposition, as well as a host of 

biogeochemical processes occurring in the soils and waterbodies. 

When sulfur or nitrogen leaches from soils to surface waters in the form of SO4

2- or NO3-

, 

an equivalent amount of positive cations, or countercharge, is also transported. This maintains 

electroneutrality. If the countercharge is provided by base cations, such as calcium (Ca2+), 

magnesium (Mg2+), sodium (Na+), or potassium (K+), rather than hydrogen (H+) and dissolved 

inorganic aluminum, the acidity of the soil water is neutralized, but the base saturation of the soil 

is reduced. Continued SO4

2- or NO3-

 leaching can deplete the base cation supply of the soil. As 

the base cations are removed, continued deposition and leaching of SO4

2- and/or NO3-

 (with H+

and Al3+) leads to acidification of soil water, and by connection, surface water. Loss of soil base 

saturation is a cumulative effect that increases the sensitivity of the watershed to further 

acidifying deposition. Base cations are replenished through the natural weathering of the rocks 

and soils, but weathering is a slow process, which results in the depletion of cations in the soil in 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 1 
Aquatic Acidification Case Study 

the presence of SO4

2- and/or NO3-

 pollution. A watershed’s ability to neutralize acidic deposition 

is determined by a host of biogeophysical factors, including base cation concentrations, 

weathering rates, uptake by vegetation, rate of surface water flow, soil depth, and bedrock. 

Following deposition, SO4

2- can absorb to, or bind with, soil particles, a process that 

removes it from the aqueous soil solution, and therefore, prevents the leaching of base cations (at 

least temporarily) and further acidifying of the soil water. This process results in an 

accumulation of sulfur in the soil. This process is potentially reversible and can contribute to soil 

acidification if, and when, the SO4

2- is desorbed and released back into the soil water solution. 

The degree to which SO4

2- adsorbs on soil is dependent on soil characteristics. The locations of 

soils in the United States that most effectively adsorb SO4

2- are found south of the areas that 

experienced glaciation during the most recent ice age (Rochelle and Church, 1987; Rochelle et 

al., 1987). SO4

2- adsorption is strongly pH-dependent, and a decrease in soil pH resulting from 

acidifying deposition can enhance the ability of soil to adsorb SO4

2-. Consequently, as deposition 

increases, the soil potentially stores a disproportionate amount of SO4

2-. When deposition 

decreases, this stored SO4

2- is slowly, but continually, released, keeping soil water acidified 

and/or depleting the base cation supply. 

2.2 INDICATORS OF ACIDIFICATION 

The chemistry of the surface water is directly related to the biotic integrity of freshwater 

ecosystems. There are numerous chemical constituents in surface water that can be used to 

indicate the acidification condition of lakes and streams and to assess the effects of acidifying 

deposition on ecosystem components. These include surface water pH (log[H+]) and 

concentrations of SO4

2-, NO3-

, Al3+, and Ca2+; the sum of base cations; the recently developed 

base cation surplus; and the acid neutralizing capacity (ANC). Each of these chemical indicators 

provides direct links to the health of individual biota and the overall health and integrity of 

aquatic ecosystems as a result of surface water acidification. 

Although ANC does not directly affect the health of biotic communities, it is calculated 

(or measured) based on the concentrations of chemical constituents that directly contribute to or 

ameliorate acidity-related stress, in particular, pH, Ca2+, and dissolved inorganic aluminum. 

Furthermore, numerical models of surface water acidification can more accurately estimate ANC 

than all of the individual constituents that comprise it. Consequently, for the purpose of this case 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 2 
Aquatic Acidification Case Study 

study, annual average ANC of surface waters was used as the primary metric to quantify the 

current acidic conditions and biological impacts for a subset of waterbodies in the study areas. 

The remainder of this section focuses on a description of ANC. 

ANC reflects the relative balance between base cations and strong acid anions. It 

accounts for the cumulative effects of all of the ionic interactions that occur as the acidic 

compounds are removed from the atmosphere to the catchment and drainage water to emerge in 

a stream or lake. ANC of surface waters is defined (in this study) as the total amount of strong 

base ions minus the total amount of strong acid anions: 

ANC = (Ca2+ + Mg2++ K+ + Na+ + NH4

+

) – (SO4

2- + NO3- + Cl-

) (1) 

The unit of ANC is microequivalents per liter (μeq/L). If the sum of the equivalent 

concentrations of the base cations exceeds those of the strong acid anions, then the ANC of a 

waterbody will be positive. To the extent that the base cation sum exceeds the strong acid anion 

sum, the ANC will be higher. Higher ANC is generally associated with high pH and Ca2+

concentrations, and lower ANC is generally associated with low pH and high dissolved inorganic 

aluminum concentrations and a greater likelihood of toxicity to biota. 

ANC samples from waterbodies are typically measured using the Gran titration approach. 

Process-based numerical models, such as Model of Acidification of Groundwater in Catchments 

(MAGIC) utilize the ANC calculated from the charge balance. For assessment purposes, 

including resource characterization and Long-Term Monitoring (LTM) programs, it is always 

best to use both directly measured and numerically estimated ANC values. The difference 

between the two can be used to quantify uncertainty and reveal the influences of natural organic 

acidity and/or dissolved inorganic aluminum on the overall acid-base chemistry of the water. 

Relative to some individual chemical parameters, such as pH, ANC reflects sensitivity to 

acidifying deposition input and effects on surface water chemistry in a linear fashion across the 

full range of ANC values. Consequently, ANC is a preferred indicator variable for surface water 

acidification. Other parameters, such as surface water pH, can complement the assessment of 

surface water acidification; however, the response of this parameter to inputs is not necessarily 

linear throughout its range. For example, at pH values >6.0, pH is not a good indicator of either 

sensitivity to acidification or level of biological effect. In addition, pH measurements (especially 

at these higher values) are sensitive to and can be confounded by the level of dissolved carbon 

dioxide (CO2) in the water. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 3 
Aquatic Acidification Case Study 

2.3 BIOLOGICAL RESPONSE TO ACIDIFICATION AND ACID 

NEUTRALIZING CAPACITY 

Ecological effects occur at four levels of biological organization: (1) the individual; (2) 

the population, which is composed of a single species of individuals; (3) the biological 

community, which is composed of many species; and (4) the ecosystem. Low ANC 

concentrations are linked with negative effects on aquatic systems at all four of these biological 

levels. For the individual level, impacts are assessed in terms of fitness (i.e., growth, 

development, and reproduction) or sublethal effects on condition. Surface water with low ANC 

concentrations can directly influence aquatic organism fitness or mortality by disrupting ion 

regulation and can mobilize dissolved inorganic aluminum, which is highly toxic to fish under 

acidic conditions (i.e., pH <6 and ANC <50 μeq/L). For example, research showed that as the pH 

of surface waters decreased to <6, many aquatic species, including fish, invertebrates, 

zooplankton, and diatoms, tended to decline sharply causing species richness to decline 

(Schindler, 1988). Van Sickle et al. (1996) also found that blacknose dace (Rhinichthy spp.) were 

highly sensitive to low pH and could not tolerate inorganic Al concentrations greater than about 

3.7 micromolar (μM) for extended periods of time. For example, they found that after 6 days of 

exposure to high inorganic Al, blacknose dace mortality increased rapidly to nearly 100%. 

At the community level, species richness and community structure can be used to 

evaluate the effects of acidification. Species composition refers to the mix of species that are 

represented in a particular ecosystem, whereas species richness refers to the total number of 

species in a stream or lake. Acidification alters species composition and richness in aquatic 

ecosystems. There are a number of species common to many oligotrophic waterbodies that are 

sensitive to acidification and cannot survive, compete, or reproduce in acidic waters. In response 

to small to moderate changes in acidity, acid-sensitive species are often replaced by other more 

acid-tolerant species, resulting in changes in community composition and richness, but with little 

or no change in total community biomass. The effects of acidification are continuous, with more 

species being affected at higher degrees of acidification. At a point, typically a pH <4.5 and an 

ANC <0 μeq/L, complete to near-complete loss of many classes of organisms occur, including 

fish and aquatic insect populations, whereas others are reduced to only a few acidophilic forms. 

These changes in species integrity are because energy cost in maintaining physiological 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 4 
Aquatic Acidification Case Study 

homeostasis, growth, and reproduction is high at low ANC levels (Schreck, 1981, 1982; 

Wedemeger et al., 1990). 

Decreases in species richness related to acidification have been observed in the 

Adirondack Mountains and Catskill Mountains of New York (Baker et al., 1993), the Upper 

Midwest of the United States (Schindler et al., 1989), New England and Pennsylvania (Haines 

and Baker, 1986), and Virginia (Bulger et al., 2000). Studies on fish species richness in the 

Adirondack Case Study Area demonstrated the effect of acidification; of the 53 fish species 

recorded in Adirondack Case Study Area lakes, only 27 species were found in lakes with a pH 

<6.0. The 26 species missing from lakes with a pH <6.0 include important recreational species, 

such as Atlantic salmon, tiger trout (Salmo trutta X Salvelinus fontinalis), redbreast sunfish 

(Lepomis auritus), bluegill (Lepomis macrochirus), tiger musky (Esox masquinongy X lucius), 

walleye (Sander vitreus), alewife (Alosa pseudoharengus), and kokanee (Oncorhynchus nerka) 

(Kretser et al., 1989), as well as ecologically important minnows that are commonly eaten by 

sport fish. A survey of 1,469 lakes in the late 1980s found 346 lakes to be devoid of fish. Among 

lakes with fish, there was a relationship between the number of fish species and lake pH, ranging 

from about one species per lake for lakes having a pH <4.5 to about six species per lake for lakes 

having a pH >6.5 (Driscoll et al., 2001; Kretser et al., 1989). 

These decreases in species richness due to acidifying deposition are positively correlated 

with ANC concentrations (Kretser et al., 1989; Rago and Wiener, 1986). Most notably, Sullivan 

et al. (2006) found a logistic relationship between fish species richness and ANC category for 

Adirondack Case Study Area lakes (Figure 2.3-1, a), which indicates the probability of 

occurrence of an organism for a given value of ANC. In addition, a similar relationship has been 

found for the Shenandoah Case Study Area, where a statistically robust relationship between 

ANC and fish species richness was documented (Figure 2.3-1, b). In fact, ANC has been found 

in studies to be the best single indicator of the biological response and health of aquatic 

communities in acid-sensitive systems (Lien et al., 1992; Sullivan et al., 2006). 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 5 
Aquatic Acidification Case Study 

(a) (b) 

Figure 2.3-1. (a) Number of fish species per lake or stream versus acidity, expressed 

as ANC for Adirondack Case Study Area lakes (Sullivan et al., 2006). (b) Number 

of fish species among 13 streams as a function of ANC in the Shenandoah Case 

Study Area. Values of ANC are means based on quarterly measurements from 1987 

to 1994. The regression analysis shows a highly significant relationship (p <.0001) 

between mean stream ANC and the number of fish species. 

3.0 CASE STUDIES 

3.1 SURFACE WATERS ACIDIFICATION IN THE EASTERN UNITED 

STATES 

The regions of the United States with low average annual surface water ANC values are 

the locations that are sensitive to acidifying deposition. The majority of lakes and streams in the 

United States have ANC levels >200 μeq/L and are not sensitive to the acidifying deposition of 

NOx and SOx air pollution at their existing ambient concentration levels. Figure 3.1-1 shows the 

acid-sensitive regions of the eastern United States with the potential for low surface water ANC, 

as determined by geology and surface water chemistry. 

Freshwater surveys and monitoring in the eastern United States have been conducted by 

many programs since the mid-1980s, including the National Lake/Stream Surveys (NSWS), 

EPA’s Environmental Monitoring and Assessment Program (EMAP), the Temporally Integrated 

Monitoring of Ecosystems (TIME) monitoring program (Stoddard, 1990), and LTM project 

(Ford et al., 1993; Stoddard et al., 1998) (Appendix Attachment B). The purpose of these 

programs is to determine the current state and document the trends over time in surface water 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 6 
Aquatic Acidification Case Study 

chemistry for regional populations of lakes or streams impacted by acidifying deposition. Based 

on extensive surveys and surface water data from these programs, it was determined that the 

most sensitive lakes and streams (i.e., ANC less than about 50 μeq/L) in the eastern United States 

are found in New England, the Adirondack Mountains, the Appalachian Mountains (northern 

Appalachian Plateau and Ridge/Blue Ridge region), northern Florida, and the Upper Midwest. 

These areas are estimated to contain 95% of the lakes and 84% of the streams in the United 

States that have been anthropogenically acidified through deposition (see Annex 4.3.3.2 of the 

Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur–Ecological Criteria 

(Final Report) (ISA) (U.S. EPA, 2008). 

Northern Florida 

Highlands 

Appa

Plateau 

Ridge/Blue Ridge 

Upper Mid-west 

New England 

Adirondack

Mountains 

Northern Florida 

Highlands 

Appalachian

Plateau 

Ridge/Blue Ridge 

Upper Midwest 

New England 

Mountains 

Figure 3.1-1. Regions containing ecosystems sensitive to acidifying deposition in 

the eastern United States (U.S. EPA, based on NAPAP, 2005). 

The number and proportion of acidic waterbodies in these regions, defined as having a 

pH <5.0 and ANC <0 μeq/L, are substantial. The Adirondack Case Study Area had a large 

proportion of acidic surface waters (14%) in the NSWS; from 1984 to 1987, the Adirondack 

Lakes Survey Corporation sampled 1,469 Adirondack Case Study Area lakes >0.5 hectares (ha) 

in size and estimated that many more (26%) were acidic (Driscoll et al., 1991). The proportion of 

lakes estimated by NSWS to be acidic was smaller in New England and the Upper Midwest (5% 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 7 
Aquatic Acidification Case Study 

and 3%, respectively), but because of the large number of lakes in these regions, there were 

several hundred acidic waters in each of these two regions. The Shenandoah Case Study Area 

had 5.5% and 6% acidic sites, respectively, based on data from the early 1990s. Portions of 

northern Florida also contain many acidic and low-ANC lakes and streams, although the role of 

acidifying deposition in these areas is less clear. In 2002, Stoddard et al. (2003) suggested that 

~8% of lakes in the Adirondack Mountains and from 6% to 8% of streams in the northern 

Appalachian Plateau and Ridge/Blue Ridge region were acidic at base-flow conditions. 

The Adirondack Case Study Area and the Shenandoah Case Study Area provide ideal 

areas to assess the risk to aquatic ecosystems from NOx and SOx acidifying deposition. Four 

main reasons support the selection of these two areas. First, both regions fall within the areas of 

the United States known to be sensitive to acidifying deposition because of a host of 

environmental factors that make these regions predisposed to acidification. Second, these areas 

are representative of other areas sensitive to acidification, which will allow the results of this 

case study to be generalized. Third, these regions have in the past and continue to experience 

substantial exposure to NOx and SOx air pollution. Fourth, these areas have been extensively 

studied (e.g., from atmospheric concentrations, soil characteristics, surface water chemistry, to 

the changes in biological communities in response to aquatic acidification) over the last 3 

decades (see Section 4 of the ISA Report) (U.S. EPA, 2008). For example, extensive water 

quality data exists from monitoring networks in operation since the 1980s, along with numerous 

research studies that directly link the biological harm of individuals, populations, communities, 

and ecosystems to aquatic acidification. The sections below describe each of the case studies 

areas, in turn, indicating past impacts of acidifying deposition, and identifying research linking 

biological and acidic conditions for each region. 

3.2 OBJECTIVES 

For the two case study areas, the Adirondack and the Shenandoah, conditions of the 

aquatic ecosystems and responses to nitrogen and sulfur deposition were evaluated by using 

multiple approaches that rely on monitoring data and modeled output. Current conditions were 

evaluated by a three-step process: 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 8 
Aquatic Acidification Case Study 

 By evaluating the status and trends of surface water chemistry data to establish linkages 

between current ambient air pollution levels of nitrogen and sulfur and the total amount 

of deposition 

 By evaluating the biological risk to individuals, populations, and communities from 

acidification 

 By evaluating the response of the aquatic ecosystem to current and future deposition 

compared with the likelihood for recovery of currently impacted aquatic waterbodies. 

In evaluating these conditions, this case study addresses the welfare effects of 

acidification by building linkages between ambient pollutant levels, deposition, surface water 

chemistry, and the resulting response in the biological communities. 

3.3 ADIRONDACK CASE STUDY AREA 

3.3.1 General Description 

The Adirondack Case Study Area is situated in northeastern New York and is 

characterized by dense forest cover of evergreen and deciduous trees and abundant surface 

waters, with 46 peaks that extend up to 1,600 meters. The Adirondack Case Study Area has long 

been a nationally important recreation area for fishing, hiking, boating, and other outdoor 

activities. The area includes the headlands of five major drainage basins: Lake Champlain and 

the Hudson, Black, St. Lawrence, and Mohawk rivers, which all draw water from the preserve. 

There are more than 2,800 lakes and ponds, and more than 1,500 miles of rivers that are fed by 

an estimated 30,000 miles of brooks and streams. The Adirondack Case Study Area, particularly 

its southwestern section, is sensitive to acidifying deposition because it receives high 

precipitation amounts with high concentrations of pollutants, has shallow base-poor soils, and is 

underlain by igneous bedrock with low weathering rates and buffering ability (Driscoll et al., 

1991; Sullivan et al., 2006). This case study area is among the most severely acid-impacted 

regions in North America (Driscoll et al., 2003; Landers et al., 1988; Stoddard et al., 2003). It 

has long been used as an indicator of the response of forest and aquatic ecosystems to changes in 

emissions of sulfur dioxide (SO2) and NOx resulting, in part, from the Clean Air Act 

Amendments of 1990 (NAPAP, 1998; U.S. EPA, 1995).

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 9 
Aquatic Acidification Case Study 

3.3.2 Levels of Air Pollution and Acidifying Deposition 

Wet deposition in the Adirondack Case Study Area has been monitored by the National 

Atmospheric Deposition Program/National Trends Network (NADP/NTN) since 1978 at two 

sites (i.e., Huntington Forest and Whiteface Mountain) and at seven other sites since the 1980s. 

Since 1990, wet SO4

2- and NO3-

 deposition at these NADP/NTN sites in the Adirondack Case 

Study Area has declined by about 45% and 40%, respectively (Figure 3.3-1). However, annual 

total wet deposition is still >15 and 10 kilograms/hectare/year (kg/ha/yr) of SO4

2- and NO3-

, 

respectively. 

0

5

10

15

20

25

30

35

1990 1992 1994 1996 1998 2000 2002 2004 2006

Kg/ha

SO4

NO3

Source: NADP

NADP Sites: NY08,NY20,NY52,NY68,NY98,NY99,VT01VT99

Figure 3.3-1. Annual average total wet deposition (kg/ha/yr) for the period 1990 to 2006 in 

SO4

2- (blue) and NO3-

 (green) from eight NADP/NTN sites in the Adirondack Case Study Area 

(shown with linear trend regression lines). 

3.3.3 Levels of Sulfate, Nitrate, and ANC Concentrations in Surface Water 

 Environmental monitoring data reported above demonstrate decreasing trends in 

depositional loading, reflecting decreases in air pollution. Figure 3.3-2 shows annual average 

trends since 1990 in SO4

2-, NO3-

, and ANC in surface water from 50 lakes in the Adirondack 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 10 
Aquatic Acidification Case Study 

Case Study Area monitored through the Adirondack Long-Term Monitoring (ALTM) program. 

As a result of decreases in air pollution and depositional loading, regional annual average SO4

2-

concentrations in these lakes has dropped by approximately 26% since the mid-1990s. While 

inter-annual variability in annual average NO3

-

 concentrations is evident in the Adirondack Case 

Study Area monitored lakes, the overall trend is modestly downward (13% over the entire 

period). An increase in long-term annual average ANC of +0.8 μeq/L/yr has corresponded to the 

declines in NO3

-

 and SO4

2-

. However, this increase in ANC also correlates with reductions in 

annual average base cations of calcium (Ca2+) and magnesium (Mg2+) during the same period of 

time (data not shown). In the Adirondack Case Study Area, toxic levels of dissolved inorganic 

aluminum also declined slightly (data not shown). 

0.0

20.0

40.0

60.0

80.0

100.0

120.0

1990 1992 1994 1996 1998 2000 2002 2004 2006

ueq/L

SO4

NO3

ANC

Annual Average Suface Water Trends 1990-2006

(Adirondack LTM Lakes)

Source: ALTM - 50 Lakes

Figure 3.3-2. Trends over time for annual average SO4

2

- (blue) and NO3

-

 (green) concentrations 

and ANC (red) in 50 Adirondacks Long-Term Monitoring monitored lakes in the Adirondack 

Case Study Area (shown with linear trend regression lines). Both SO4

2-

 and NO3

-

 concentrations 

have decreased in surface waters by approximately 26% and 13%, respectively. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 11 
Aquatic Acidification Case Study 

Despite decreases in deposition and surface water concentrations of SO4

2- and NO3-

, 

levels remain elevated in monitored lakes. Figure 3.3-3 shows current yearly average (2005 to 

2006) SO4

2-, NO3-

, and ANC for 88 Adirondack Case Study Area lakes monitored through the 

ALTM (50 Lakes) and TIME (38 lakes) programs. The yearly averages for the period from 2005 

to 2006 of SO4

2-, NO3-

, and ANC are 70.88 ± 19.87, 9.18 ± 10.51, and 33.84 ± 44.40 μeq/L, 

respectively. 

There is still a substantial number of lakes in the Adirondack Case Study Area that have 

low ANC values (<50 μeq/L) based on the observed yearly average of ANC from the years 2005 

and 2006 for the waterbodies in the TIME/ALTM monitoring networks. Of the 88 monitored 

lakes, 24% have ANC values >50 μeq/L, whereas 76% of the monitored lakes have ANC values 

<50 μeq/L. Of the 73 monitored lakes with >50 μeq/L, 17 are chronically acidic (ANC <0 

μeq/L). Twenty-seven of the lakes have <20 μeq/L, making their biological communities 

susceptible to episodic acidification. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 12 
Aquatic Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 13 

Figure 3.3-3. Current yearly average for 2005 to 2006 (a) SO4

2-

concentrations 

(μeq/L), (b) NO3

-

 concentrations (μeq/L), and (c) ANC (μeq/L) in surface waters 

from 88 monitored lakes in the Temporally Integrated Monitoring of Ecosystems 

(38 Lakes) and Adirondacks Long-Term Monitoring (50 Lakes) networks in the 

Adirondack Case Study Area. 

(a) (b) 

(c) 
Aquatic Acidification Case Study 

3.4 SHENANDOAH CASE STUDY AREA 

3.4.1 General Description 

The Shenandoah Case Study Area straddles the crest of the Blue Ridge Mountains in 

western Virginia, on the eastern edge of the central Appalachian Mountain region. Several areas 

in Shenandoah National Park have been designated Class 1 Wilderness areas. Shenandoah 

National Park is known for its scenic beauty, outstanding natural features, and biota. The Skyline 

Drive, a scenic 165-kilometer (km) parkway, provides the opportunity for views of the Blue 

Ridge Mountains and surrounding areas. The Appalachian National Scenic Trail is the backbone 

of the park’s trail system. The natural features and biota of the park include the well-exposed 

rock strata of the Appalachians, which is one of the oldest mountain ranges in the world. The 

park comprises one of the nation’s most diverse botanical reserves and wildlife habitats. A 

congressionally designated wilderness area within the park is the largest in the mid-Atlantic 

states and provides a comparatively accessible opportunity for solitude, study, and experience in 

a natural area. 

Air pollution within the Shenandoah Case Study Area, including concentrations of sulfur, 

nitrogen, and ozone (O3), is higher than in most other national parks in the United States. This 

area is sensitive to acidifying deposition because of the noncarbonate composition and 

weathering-resistant characteristics of much of the underlying bedrock, which result in base-poor 

soils with low weathering rates and poor buffering capacity. At base flow conditions, Lynch and 

Dise (1985) determined that stream water ANC, pH, and base cation concentrations in this region 

are strongly correlated with bedrock geology. This landscape includes three major bedrock types: 

siliceous (e.g., quartzite and sandstone), felsic (e.g., granitic), and mafic (e.g., basaltic). Each of 

these bedrock types influence about one-third of the stream miles in this region. ANC 

concentrations for streams associated with siliceous bedrock are extremely low. Almost half of 

the sampled streams had ANC in the chronically acidic range (<0 μeq/L). The balance of the 

streams associated with siliceous bedrock had ANC in the episodically acidic range (0 to 20 

μeq/L). Consequently, this region is among the most severely acid-impacted areas in North 

America (Stoddard et al., 2003; Webb et al., 2004). 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 14 
Aquatic Acidification Case Study 

3.4.2 Levels of Air Pollution and Acidifying Deposition 

Annual average atmospheric concentrations of sulfur dioxide (SO2), NOx, SO4

2-, and 

reduced nitrogen at the Big Meadow air monitoring location within the Shenandoah National 

Park have all decreased since the 1990s, with the exception of reduced nitrogen (Figure 3.4-1, 

a). As a result, wet deposition in the Shenandoah National Park monitored at 7 sites by the 

NADP/NTN since the 1980s shows wet SO4

2- and NO3-

 deposition declining by about 28% and 

20%, respectively (Figure 3.4-1, b). However, annual total deposition is still 15 and 10 kg/ha/yr 

of SO4

2- and NO3-

, respectively. 

0

2

4

6

8

10

1990 1992 1994 1996 1998 2000 2002 2004 2006

ug/m3

SO2

SO4

Oxidized N

NH4

Source: CASTNET

CASTNET Sites: SHN418

0

5

10

15

20

25

30

35

1990 1992 1994 1996 1998 2000 2002 2004 2006

Kg/ha

SO4

NO3

Source: NADP

NADP Sites: NY08,NY20,NY52,NY68,NY98,NY99,VT01VT99

Figure 3.4-1. Air pollution concentrations and wet deposition for the period 1990 

to 2006 using a CastNET (SHN418) and seven NADP/NTN sites in the 

Shenandoah Case Study Area. (a) Annual average atmospheric concentrations 

(ug/m3

) of SO2 (blue), oxidized nitrogen (red), SO4

2- (green), and reduced 

nitrogen (black). (b) Annual average total wet deposition (kg/ha/yr) of SO4

2-

(green) and NO3

-

 (blue).

3.4.3 Levels of Sulfate, Nitrate, and ANC Concentrations in Surface Water 

Figure 3.4-2 shows trends in annual average surface water concentrations of SO4

2- and 

NO3

-

 and ANC values for streams in the Shenandoah Case Study Area monitored through the 

Shenandoah Nation Park Surface Water Acidification Study (SWAS), Virginia Trout Stream 

Sensitivity Survey (VTSSS), and LTM programs. The annual average for 67 streams for the 

period 2005 to 2006 of SO4

2-, NO3-

, and ANC are 59.29 ± 28.2, 3.74 ± 7.0, and 60.60 ± 72.7 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 15 
Aquatic Acidification Case Study 

μeq/L, respectively (Figure 3.4-3). Despite the decreases in pollution and regional acidifying 

deposition, SO4

2- and NO3-

 concentrations in these streams have seen slight improvements since 

the mid-1990s. There is a slight decline in SO4

2- concentrations (-0.04 μeq/L/yr) in surface 

waters, whereas NO3

- declined by only -0.3 μeq/L/yr. On the other hand, average ANC values of 

the 67 streams increased to 79 μeq/L until the year 2002, from about 50 μeq/L in the early 1990s. 

However, since 2002, ANC levels have fluctuated by first declining back to early 1990s levels 

and then increasing to 67 μeq/L in 2006. Despite improvement in deposition, surface water 

concentrations of SO4

2- and NO3-

 levels remain elevated in monitored streams in the Shenandoah 

Case Study Area. 

0.0

20.0

40.0

60.0

80.0

100.0

120.0

1990 1992 1994 1996 1998 2000 2002 2004 2006

ueq/L

SO4

NO3

ANC

Annual Average Suface Water Trends 1990-2006

(SWAS-VTSSS-LTM)

Source: SWAS-VTSSS-LTM - 67 Streams 

Figure 3.4-2. Trends over time for annual average SO4

2- (blue) and NO3- (green) 

concentrations, and ANC (red) in 67 streams in the Surface Water Acidification Study, 

Virginia Trout Stream Sensitivity Survey, and Long-Term Monitoring programs in the 

Shenandoah Case Study Area (shown with linear trend regression lines). 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 16 
Aquatic Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 17 

There are a significant number of the 67 streams in SWAS-VTSSS and LTM programs 

that currently have ANC <50 μeq/L based on the observed annual average ANC concentrations 

(Figure 3.4-3). Forty-five percent of all monitored streams have ANC values >50 μeq/L, 

whereas 55% have <50 μeq/L. Of the 55% <50 μeq/L, 18% experience episodic acidification 

(<20 μeq/L) and 12% are chronically acidic (<0 μeq/L) at the current level of acidifying 

deposition and ambient concentrations of NOx and SO2. 
Aquatic Ac

Figure 3.4-3. Current yearly average for 2005 to 2006 (a) SO42- concentration, (b) NO3- concentration, ansurface waters from 67 monitored streams in the Surface Water Acidification Study, Virginia Trout Strea

Long-Term Monitoring network in the Shenandoah Case Study Area. 

Final Risk and Exposure Assessment 

Appendix 4 - 18 
Aquatic Acidification Case Study 

4.0 METHODS 

4.1 BIOLOGICAL RESPONSE TO ACIDIFICATION 

Because there is a continuum in the relationship between ANC concentrations and 

resulting biological effects, a range of ANC values related to specific biological effects is needed 

for the following reasons: 

(1) ANC values within a waterbody are not constant; there is variation with time and 

season. For example, during spring, following snowmelt and the resulting influx of 

acidifying compounds, surface water ANC levels can substantially drop. There is also 

spatial uncertainty. Consequently, the length of exposure (i.e., chronic vs. episodic) can 

affect biological responses. 

(2) The biological effects of particular ANC values vary between individual organisms 

because of differences in developmental stage and size, innate differences between 

different species of the same general types of organisms, and differences between 

different kingdoms, phyla, and classes of organisms. 

Therefore, five categories of ANC values were used that link specific biological health 

conditions to the effects of aquatic communities, ranging from no impacts to complete loss of 

populations. These five classes are based on the relationships among ANC and ecological 

attributes, including richness, diversity, community structure, and individual fitness of 

organisms. The following paragraphs describe the biological impacts, given a range of ANC 

values and the scientific research that supports the grouping. Section AX4 of the Annexes to the 

ISA (U.S. EPA, 2008) presents a more in-depth description of the biological relationship used in 

this case study. 

For freshwater systems, ANC values are grouped into five major categories: Acute 

Concern (<0 μeq/L; acidic), Severe Concern (0 to 20 μeq/L), Elevated Concern (20 to 50 μeq/L), 

Moderate Concern (50 to 100 μeq/L), and Low Concern (>100 μeq/L), with each range 

representing a probability of ecological damage to the community (Table 4.1-1). 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 19 
Aquatic Acidification Case Study 

Table 4.1-1. Aquatic Status Categories 

Category Label ANC Levels* Expected Ecological Effects 

Acute 

Concern 

<0 μeq/L 

 (Acidic) 

Near complete loss of fish populations is expected. Planktonic 

communities have extremely low diversity and are dominated by 

acidophilic forms. The number of individuals in plankton species that 

are present is greatly reduced. 

Severe 

Concern 

 0–20 μeq/L Highly sensitive to episodic acidification. During episodes of high 

acidifying deposition, brook trout populations may experience lethal 

effects. Diversity and distribution of zooplankton communities decline 

sharply. 

Elevated 

Concern 

20–50 μeq/L Fish species richness is greatly reduced (i.e., more than half of expected 

species can be missing). On average, brook trout populations 

experience sublethal effects, including loss of health, reproduction 

capacity, and fitness. Diversity and distribution of zooplankton 

communities decline. 

Moderate 

Concern 

50–100 

μeq/L 

Fish species richness begins to decline (i.e., sensitive species are lost 

from lakes). Brook trout populations are sensitive and variable, with 

possible sublethal effects. Diversity and distribution of zooplankton 

communities also begin to decline as species that are sensitive to 

acidifying deposition are affected. 

Low 

Concern 

>100 μeq/L Fish species richness may be unaffected. Reproducing brook trout 

populations are expected where habitat is suitable. Zooplankton 

communities are unaffected and exhibit expected diversity and 

distribution. 

Low Concern – Biota is generally not harmed when ANC values are >100 μeq/L. For 

example, the number of fish species tend to peak at ANC values >100 μeq/L (Bulger et al., 1999; 

Driscoll et al., 2001; Kretser et al., 1989; Sullivan et al., 2006). Typically, with ANC 

concentrations >100 μeq/L, the diversity of the aquatic community is more influenced by other 

environmental factors, such as habitat availability, than the acid-base balance of the surface 

water. 

Moderate Concern – At ANC levels 50 to 100 μeq/L, declines in the fitness and 

recruitment of species sensitive to acidity (e.g., some fish and invertebrate organisms) have been 

demonstrated and may result in decreases in community-level diversity as the few highly acidsensitive species are lost (see Figure 2.3-1). However, minimal (no measurable) change in total 

community abundance or production generally occurs, resulting in good overall health of the 

community.

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 20 
Aquatic Acidification Case Study 

Elevated Concern – When ANC values drop between 20 and 50 μeq/L, they are generally 

associated with negative effects on the fitness and recruitment of aquatic biota. Kretser et al. 

(1989) showed that a 50% reduction in the number of fish species occurred when ANC values 

dropped to <50 μeq/L in lakes that were surveyed. Furthermore, Dennis and Bulger (1995) 

showed that when ANC values drop to between 20 and 50 μeq/L, the overall fitness of most fish 

species are reduced, such as sensitive species of minnows and daces (e.g., fathead minnow and 

blacknose dace), and recreation fish species (e.g., lake trout and walleye). In addition to the 

changes in the fish community, a drop in ANC values can cause some loss of common 

invertebrate species from zooplankton and benthic communities, which include many species of 

snails, clams, mayflies, and amphipods. These losses of sensitive species often result in distinct 

decreases in species richness and changes in species composition of the biota. However, the total 

community abundance or production remains high, with little if any change.

Severe Concern – When ANC levels drop <20 μeq/L, almost all biota exhibit some level 

of negative effects. Fish and plankton diversity and the structure of the communities continue to 

decline sharply to levels where acid-tolerant species begin to outnumber all other species 

(Driscoll et al., 2001; Matuszek and Beggs, 1988). Loss of several important sport fish species is 

possible, including lake trout, walleye, and rainbow trout, and losses of additional nongame 

species, such as creek chub, occur. In addition, several other invertebrate species, including all 

snails, most slams, and many species of mayflies, stoneflies, and other benthic invertebrates, are 

lost or greatly reduced in population size, which further depresses species composition and 

community richness. Also, at <20 μeq/L, surface waters are susceptible to episodic acidification, 

and a total loss of biota can occur when ANC concentration goes to <0 μeq/L for a short period 

of time. Stoddard et al. (2003) showed that to protect biota from episodic acidification in the 

spring, base flow (i.e., summer nonstorm event) ANC levels need to have an ANC of at least 30 

to 40 μeq/L (Figure 4.1-1). 

Acute Concern – Near complete loss of fish populations and extremely low diversity of 

planktonic communities occur with ANC levels <0 μeq/L. Only acidophilic species are present, 

but their population numbers are sharply reduced. For example, lakes in the Adirondack Case 

Study Area have been shown to be fishless when the average ANC is <0 μeq/L (Sullivan et al., 

2006). A summary of the five categories of ANC and expected ecological effects can be found in 

Figure 4.1-2 and Table 4.1-1.

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 21 
Aquatic Acidification Case Study 

Figure 4.1-1. Relationship between summer and spring ANC values at LTM sites 

in New England, the Adirondack Mountains, and the Northern Appalachian 

Plateau. Values are mean summer values for each site for the period 1990 to 2000 

(horizontal axis) and mean spring minima for each site for the same time period. 

On average, spring ANC values are at least 30 μeq/L lower than summer values. 

Figure 4.1-2. Number of fish species per lake or stream versus ANC level and aquatic 

status category (represented by color) for lakes in the Adirondack Case Study Area 

(Sullivan et al., 2006). The five aquatic status categories are described in Table 4.1-1.

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 22 
Aquatic Acidification Case Study 

4.2 PAST, PRESENT, AND FUTURE SURFACE WATER 

CHEMISTRY—THE MAGIC MODELING APPROACH 

The preacidification condition of a waterbody is rarely known because historical 

measurements are not available. Likewise, it is also difficult to determine if a waterbody has or 

will recover from acidification as acidifying deposition inputs decline, because recovery may 

take many years to decades to occur. For these reasons, biogeochemical models, such as 

MAGIC, enable estimates of past, present, and future water chemistry levels that can be used to 

evaluate the associated risk and uncertainty of the current levels of acidification compared with 

estimated preacidification conditions and to evaluate whether a system will recover as a result of 

reduction in acidifying deposition. 

Dynamic hydrological models use surface water measurements of multiple parameters 

from the long-term record, information about the current exposure (i.e., ambient pollutant 

concentrations, deposition estimates), and known/measurable biogeochemical factors to 

characterize a watershed and estimate its preindustrial (i.e., preacidification) state and to estimate 

its response to changes in deposition in the future. 

In both case study areas, MAGIC was used to estimate the past (i.e., preacidification, 

1860), present (i.e., the years 2002 and 2006), and future (i.e., the years 2020 and 2050) acidic 

conditions of 44 lakes in the Adirondack Case Study Area and 60 streams in the Shenandoah 

Case Study Area (Figure 4.3-1). Fewer lakes and streams were modeled with MAGIC because 

calibration data are not available for all sites in the ALTM and SWAS-VTSSS/LTM monitoring 

networks. Furthermore, MAGIC was used to quantify the associated uncertainty in these 

estimates, as well as in input parameters used in MAGIC. The MAGIC model output for each 

waterbody was summarized into five ANC levels that correspond to the aquatic status categories 

in Table 4.1-1. This grouping permits an assessment of the risk to the biological communities for 

each of the conditions. The hydrological model, MAGIC, along with all the necessary inputs and 

calibration procedure, is described in detail in Appendix Attachment A. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 23 
Aquatic Acidification Case Study 

4.3 CONNECTING CURRENT NITROGEN AND SULFUR 

DEPOSITION TO ACID-BASE CONDITIONS OF LAKES AND 

STREAMS: THE CRITICAL LOAD APPROACH 

Critical loads were calculated for lakes and streams in both case study areas. The critical 

load for a lake or stream provides a means to gauge the extent to which a waterbody has 

recovered from past acidifying deposition or is potentially at risk because of current deposition 

levels. The critical load approach provides a quantitative estimate of the level of exposure to one 

or more pollutants, below which significant harmful effects on specific sensitive elements of the 

environment do not occur, according to present knowledge. 

The critical load approach relates specific amounts of deposition to particular ANC levels 

for individual waterbodies, using the relationships established between the biogeochemical state 

of the environment, current pollutant deposition, the surface water chemistry, and the response of 

the biological communities to deposition. Conversely, it is possible to specify a “critical limit” 

ANC level and to estimate the “critical load” of deposition required to cause the stream to have 

that specified ANC level. The past, current, or estimated future levels of deposition can be 

compared with the critical load estimate. For example, a critical limit ANC value of 50 μeq/L 

could be specified for a particular stream or lake. The amount of deposition that the stream or 

lake could take and maintain an ANC of 50 μeq/L would be its critical load. Clearly, if the 

critical limit ANC value is lower (20 μeq/L), the critical load would increase—it would take 

more deposition to lower the stream’s ANC to that new value. 

A critical load estimate is analogous to a “susceptibility” estimate, relating the sensitivity 

of the waterbody to become acidified from the deposition of nitrogen and sulfur to the critical 

limit ANC concentration. Low critical load values (e.g., less than 50 milliequivalents per square 

meter per year (meq/m2

/yr)) mean that the watershed has a limited ability to neutralize the 

addition of acidic anions, and hence, it is at risk or susceptible to acidification and the resulting 

deleterious effects. The greater the critical load value, the greater the ability of the watershed to 

neutralize the additional acidic anions and resist acidification, thereby protecting the aquatic 

ecosystem. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 24 
Aquatic Acidification Case Study 

Figure 4.3-1. (a) The location of lakes in the Adirondack Case Study Area used for 

MAGIC (red circles) and critical load (green circles) modeling. (b) The location of 

streams in the Shenandoah Case Study Area used for both MAGIC and critical load 

modeling.

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 25 
Aquatic Acidification Case Study 

Applied at locations over a region, the critical load approach provides a method to 

quantify the number of lakes or streams in a given area that receive harmful levels of deposition. 

The magnitude of the biological harm is defined by the critical limit ANC level (e.g., ANC of 

50 μeq/L) (see Table 4.1-1). Critical load exceedance (i.e., the amount of actual deposition 

above the critical load, if any) can be calculated for each waterbody in the region. Lakes and 

streams with positive exceedance values, where actual deposition was above its critical load, are 

not protected at that critical limit, whereas negative exceedance values, where deposition of 

nitrogen and sulfur was below its critical load, are protected. 

Critical loads and their exceedances were calculated for four critical limit thresholds (i.e., 

ANC of 0, 20, 50, and 100 μeq/L), separating the five ANC categories of biological protections 

(Table 4.1-1) for 169 lakes in the Adirondack Case Study Area and 60 streams in the 

Shenandoah Case Study Area. There are numerous methods and models that can be used to 

calculate critical loads for acidity. Drawing on the peer-reviewed scientific literature (Dupont et 

al., 2005), this case study used a steady-state critical load model that uses surface water 

chemistry as the base for calculating the critical load. A combination of the Steady-State Surface 

Water Chemistry (SSWC) and First-Order Acidity Balance (FAB) models were used to calculate 

the critical load.This analysis uses water chemistry data from the TIME and LTM programs that 

are part of the Environmental Monitoring and Assessment Program (see discussion of surface 

water trends in Section 5.1.1). This case study focuses on the combined load of sulfur and 

nitrogen deposition, below which the ANC level would still support healthy aquatic ecosystems. 

For each waterbody, the actual current total deposition in the year 2002 was compared with the 

estimated critical loads for the four critical limit thresholds to determine which sites exceed their 

critical limit of deposition and biological protection level. Estimates of actual current deposition 

were based on the sum of measured wet deposition values from the year 2002 NADP network 

and modeled dry deposition values based on the year 2002 emissions and meteorology using the 

Community Multiscale Air Quality (CMAQ) model, respectively. 

The actual deposition was compared with the critical load for each of the waterbodies 

within the case study areas for each of the critical limit levels, and exceedances were determined. 

Results for an individual lake were grouped by whether or not the lake exceeded its critical load. 

For each of two case study areas, the number and percentage of lakes that receive acidifying 

deposition above their critical load for each of the ANC critical limits of 0, 20, 50, and 100 μeq/L 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 26 
Aquatic Acidification Case Study 

were determined. The critical load models and their inputs are described in detail in Appendix 

Attachment A. 

4.3.1 Regional Assessment of Adirondack Case Study Area Lakes and Shenandoah 

Case Study Area Trout Streams 

4.3.1.1 Adirondack Case Study Area 

In the Adirondack Case Study Area, critical load exceedances were extrapolated to lakes 

defined by the New England EMAP probability survey. The EMAP probability survey was 

designed to estimate, with known confidence, the status, extent, change, and trends in condition 

of the nation’s ecological resources, such as surface water quality. In probability sampling, the 

inclusion probability for each sampled lake represents a proportion of the target population. 

Lakes selected with relatively high probability represent relatively few lakes in the population; 

therefore, they carry relatively low weight and influence the final inferences less than lakes 

selected with low probability. These inclusion probabilities (i.e., weighting or expansion factors) 

are used to infer or estimate population frequency distributions and to evaluate sampling 

uncertainty. 

For the Adirondack Case Study Area, the regional EMAP probability survey of 117 lakes 

(i.e., weighting factors) were used to infer the number of lakes and percentage of lakes that 

receive acidifying deposition above their critical load of a target population of 1,842 lakes. The 

target population of 1,842 lakes represents all lakes from 0.5 to 2,000 ha with a depth of 

>1 meter (m) and >1,000 m2

 of open water in the Adirondack Case Study Area. ANC limits of 

20, 50, and 100 μeq/L were examined. 

The 117 lakes in the regional Adirondack probability survey represent a subset of 344 

sampled lakes throughout New England (e.g., lakes in Maine, New Hampshire, Vermont, Rhode 

Island, Massachusetts, Connecticut, New York, New Jersey) from 1991 through 1994. For New 

England, 11,076 lakes are represented in the target population (Larsen et al., 1994). 

4.3.1.2 Shenandoah Case Study Area 

In the Shenandoah Case Study Area, critical load exceedances were extrapolated using 

the SWAS-VTSSS LTM quarterly monitored sites to the population of brook trout streams that 

do not lie on limestone bedrock and/or are not significantly affected by human activity within the 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 27 
Aquatic Acidification Case Study 

watershed. The total number of brook trout streams represented by the SWAS-VTSSS LTM 

quarterly monitored sites is approximately 310 streams out of 440 mountain headwater streams 

known to support reproducing brook trout in the Shenandoah Case Study Area. 

The SWAS-VTSSS LTM programs were designed to track the effects of acidifying 

deposition and other factors that determine water quality and related ecological conditions in the 

Shenandoah Case Study Area’s native trout streams. The SWAS-VTSSS LTM began in spring 

1987, when water samples were collected from 440 streams known to have brook trout. 

Following the 1987 survey, a representative subset of 69 streams was selected for long-term 

quarterly monitoring of water quality, mostly located on National Forest lands or within the 

Shenandoah National Park Case Study Area (14 SWAS and 55 VTSSS streams). These streams 

were selected to achieve geographic distribution and representation of major bedrock types 

(Webb et al., 1994), allowing the streams to be stratified into bedrock type. This enabled results 

from the monitored streams (n=69) to be extrapolated to the entire regional population of trout 

streams (440). Webb et al. (1994) identified six bedrock classes that account for much of the 

spatial variation in ANC among the SWAS-VTSSS LTM quarterly sampled streams. The 

landscape classes adopted for this study and the number of selected stream sites within each of 

the classes include Blue Ridge siliciclastic (16 streams), Blue Ridge granitic (18 streams), Blue 

Ridge basaltic (four streams), and Valley and Ridge siliciclastic (22 streams). Streams in the 

carbonate classes (13) were not included because they are not considered susceptible to 

acidification. A weighting scheme based on the number of monitoring streams in each of the 

bedrock classes was used to extrapolate to the regional population of trout streams. For example, 

103 VTSSS streams lie on granitic bedrock, of which 18 were monitored quarterly, resulting in a 

weighting factor of 5.7 (=103/18). The weights for streams in the other bedrock classes are 6.25 

(= 25/4) for basaltic, 4.3 (= 69/16) for Blue Ridge siliciclastic, and 4.86 (=107/22) for Valley and 

Ridge siliciclastic. Thus, the total number of brook trout streams represented in the Shenandoah 

Case Study Area is approximately 310; these are all brook trout streams that do not lie on 

limestone (10% of 440 streams) and/or have not been significantly affected by human activity 

within the watersheds (20% of the streams). 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 28 
Aquatic Acidification Case Study 

5.0 RESULTS 

5.1 ADIRONDACK CASE STUDY AREA 

5.1.1 Current and Preacidification Conditions of Surface Waters 

Since the mid-1990s, lakes in the Adirondack Case Study Area have shown signs of 

improvement in ANC, NO3

-

, and SO4

2- levels in surface waters, as shown in Figure 3.3-2. 

However, current average surface water concentrations of NO3

-

 and SO4

2- are still well above 

preacidification conditions based on MAGIC model simulations of 44 lakes (Figure 5.1-1), 

resulting in lower than average ANC surface water chemistry. 

0

20

40

60

80

100

120

140

1850 1900 1950 2000 2050

ANC Concentration

(μeq/L)

Years

Figure 5.1-1. Average NO3

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

for the 44 lakes in the Adirondack Case Study Area modeled using MAGIC for the period 1850 

to 2050. 

On average, simulated SO4

2- and NO3-

 surface water concentrations are 5- and 17-fold 

higher today, respectively, compared with 1860 acidification levels, whereas ANC has dropped 

by a factor of 2 (Table 5.1-1, Figure 5.1-2). Although NO3

-

 deposition can be an important 

factor in acid precipitation and waterbody acidification, the strong inverse relationship between 

SO4

2- and ANC, in combination with the low levels of NO3- in surface waters, suggests that 

acidification in the Adirondack Case Study Area has and continues to be driven by SO4

2-

deposition. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 29 
Aquatic Acidification Case Study 

Table 5.1-1. Estimated Average Concentrations of Surface Water Chemistry for 44 Lakes in the 

Adirondack Case Study Area Modeled Using MAGIC for Preacidification (1860) and Current 

(2006) Conditions

Preacidification Current 

μeq/L Ave. (±) Ave. (±) 

ANC 120.3 13.6 62.1 15.7 

SO4

2- 12.4 2.1 66.1 1.24 

NO3

- 0.2 1.7 3.4 14.8 

NH4

+ 0.0 0.0 0.1 0.1 

An estimate of how much of this current condition is attributed to the effects of 

industrially generated acidifying deposition can be made by examining the hindcast conditions of 

the streams. Based on the MAGIC model simulations, the preacidification average ANC 

concentration of 44 modeled lakes is 120.3 ueq/L (95% CI 106.8 to 134.0) compared with 62.1 

μeq/L (95% CI 50.5 to 81.8) for today (Table 5.1-1). 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 30 
Aquatic Acidification Case Study 

Figure 5.1-2. (a) NO3

-

 and (b) SO4

2- concentrations (μeq/L) of 

preacidification (1860) and current (2006) conditions based on hindcasts 

of 44 lakes in the Adirondack Case Study Area modeled using MAGIC. 

5.1.2 ANC Inferred Condition—Aquatic Status Categories. 

The deposition of sulfur and nitrogen and resulting changes in water quality has effects 

on ANC values, and by consequence, the biological integrity of the water ecosystem. By 

comparing their current surface water condition with their preindustrial (i.e., preacidification or 

1860) condition through the MAGIC model simulations of 44 lakes, it is possible to estimate 

how the distribution of affected lakes within the Adirondack Case Study Area has changed over 

time. Furthermore, grouping each lake into aquatic status categories provides the range of 

biological condition. The percentage of lakes in each of the five aquatic status categories is 

shown in Table 5.1-2. Eighty-nine percent of the modeled lakes were likely Low Concern or 

Moderate Concern (i.e., ANC levels >50 μeq/L) prior to the onset of acidifying deposition 

(Figure 5.1-3), whereas the remaining 11% of lakes have ANC >20 μeq/L. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 31 
Aquatic Acidification Case Study 

Table 5.1-2. Percentage of Lakes in the Five Aquatic Status Categories Based on Their Surface 

Water ANC Levels for 44 Lakes Modeled Using MAGIC and 88 Lakes in the TIME/LTM 

Monitoring Network. Results Are for the Adirondack Case Study Area for the Years 2005 to 2006. 

Concern ANC 

(μeq/L) 

Modeled PreAcidification 

(% of Lakes) 

Modeled Current 

Condition 

(% of Lakes) 

Measured Current 

Condition 

(% of Lakes) 

Low >100 50 20 6 

Moderate 50–100 39 36 16 

Elevated 20–50 11 23 32 

Severe 0–20 0 9 29 

Acute <0 0 11 17 

In contrast, the current simulated condition of the lakes has shifted toward the chronically 

acidified categories. Only 20% and 36% of the lakes currently experience the Low Concern or 

Moderate Concern condition, having ANC concentrations >50 μeq/L, whereas 43% of the lakes 

are in either the Acute Concern, Severe Concern, or Elevated Concern condition. The result that 

the hindcast simulations produced no lakes with Acute Concern or Severe Concern 

preacidification suggests that current and recent historical ambient concentrations of NOx and 

SOx and their associated levels of NO3

-

 and SO4

2- deposition have substantially contributed to 

acidification (<50 μeq/L) to approximately 32% of modeled lakes. Figure 5.1-4 shows the 

spatial extent of preacidification and the current annual average ANC levels in the Adirondack 

Case Study Area. 

0

10

20

30

40

50

60

70

1860 2006

Acute (Below 0 :eq/L)

Severe (0–20 :eq/L)

Elevated (20–50 :eq/L)

Moderate (50–100 :eq/L)

Low (Above 100 :eq/L)

Figure 5.1-3. Percentage of lakes in the five classes (Acute, Severe, Elevated, 

Moderate, Low) for years 1860 (preacidification) and 2006 (current) conditions 

for 44 lakes modeled using MAGIC. Error bars indicate the 95% confidence 

interval. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 32 
Aquatic Acidification Case Study 

Figure 5.1-4. ANC concentrations of preacidification (1860) and current (2006) 

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

modeled using MAGIC.

5.1.3 The Biological Risk from Current Nitrogen and Sulfur Deposition: Critical 

Load Assessment 

The amount of acidifying deposition of sulfur and nitrogen that a watershed can receive 

and effectively neutralize varies with location, depending on the biogeochemical properties of 

the watershed. A critical load of deposition approach, where the amount of deposition that a 

watershed can receive and maintain an ANC critical limit level, can provide insight into the 

sensitivity of the waterbody to deposition and can allow an assessment of what the current 

condition of the lake might be under current deposition loads. 

A critical load of deposition analysis for a critical limit ANC threshold level of 50 μeq/L 

was done for 169 waterbodies in the Adirondack Case Study Area. Sites that are unable to 

maintain the critical limit ANC level of 50 μeq/L while experiencing 100 meq/m2

/yr or less of 

deposition are classified as “highly” or “moderately sensitive,” indicating that they have a 

limited ANC ability and could shift toward acidic aquatic status levels with modest acidifying 

deposition inputs. Figure 5.1-5 shows the locations and relative sensitivity of the 169 

waterbodies for the critical load analysis (with the 50 μeq/L ANC critical limit). Sites labeled by 

red or orange circles have less neutralizing ability than lakes labeled with yellow and green 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 33 
Aquatic Acidification Case Study 

circles, and therefore, are those lakes most sensitive to acidifying deposition. Approximately 

50% of the 169 lakes modeled in the Adirondack Case Study Area are sensitive, or at risk, to 

acidifying deposition. 

In Figure 5.1-6, a critical load exceedance “value” indicates combined sulfur and 

nitrogen deposition for the year 2002 is greater than the amount of deposition the lake could 

neutralize and still maintain an ANC level at or above the critical limit threshold. For the 

deposition load for the year 2002, 18%, 28%, 44%, and 58% of the 169 lakes modeled received 

levels of combined sulfur and nitrogen deposition that exceeded their critical load for the critical 

limit ANC values of 20, 50, and 100 μeq/L, respectively (Table 5.1-3). 

Figure 5.1-5. Critical loads of acidifying deposition that each surface water 

location can receive in the Adirondack Case Study Area while maintaining or 

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

with critical load values <100 meq/m2

/yr (red and orange circles) are most 

sensitive to surface water acidification, whereas watersheds with values >100 

meq/m2

/yr (yellow and green circles) are the least sensitive sites. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 34 
Aquatic Acidification Case Study 

Table 5.1-3. Adirondack Case Study Area Critical Load Exceedances, Where Nitrogen and 

Sulfur Deposition Is Larger Than the Critical Load for Four Different ANC Critical Limit 

Thresholds, for 169 Modeled Lakes within the TIME/LTM and EMAP Monitoring and Survey 

Programs. “No. Exceedances” Indicates the Number of Lakes at or below the Given ANC 

Critical Limit, and “% Lakes” Indicates the Total Percentage of Lakes at or below the Given 

ANC Critical Limit. 

ANC 

Critical Limit 

No. Exceedances 

(out of 169 Lakes) 

% 

Lakes 

100 μeq/L 98 58 

50 μeq/L 74 44 

20 μeq/L 47 28 

0 μeq/L 30 18 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 35 
Aquatic Acidification Case Study 

Figure 5.1-6. Critical load exceedances (red circles) in the Adirondack Case 

Study Area based on the year 2002 deposition magnitudes for waterbodies where 

the critical limit ANC concentration is 0, 20, 50, and 100 μeq/L, respectively. 

Green circles represent lakes where deposition is below the critical load. See 

Table 5.1-3.

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 36 
Aquatic Acidification Case Study 

5.1.4 Representative Sample of Lakes in the Adirondack Case Study Area 

Estimating the acidification risk to the entire population of lakes in the Adirondack Case 

Study Area from the current the levels of NOx and SOx ambient concentrations and deposition 

requires extrapolating from the 169 modeled lakes to the broader population of lakes in the 

Adirondack Case Study Area. One hundred seventeen lakes of the 169 lakes modeled for critical 

loads are part of a subset of 1,842 lakes in the Adirondack Case Study Area, which include all 

lakes from 0.5 to 2000 ha in size and at least 1 m in depth. Using weighting factors for frequency 

of occurrence derived from the EMAP probability survey and critical load calculations from the 

117 lakes, exceedances estimates were derived for the 1,842 lakes in the Adirondack Case Study 

Area. Based on this approach, 945, 666, 242, and 135 lakes exceed their critical load for the year 

2002 deposition with critical limits of 100, 50, 20, and 0 μeq/L, respectively (Table 5.1-4). The 

current effect level for a moderate protective (i.e., ANC limit of 20 and 50 μeq/L) is 13% of the 

total population and 36% of the total population, whereas it is 51% of the total population for the 

most protective level (i.e., ANC limit of 100 μeq/L). 

Table 5.1-4. Critical Load Exceedances for the Regional Population of 1,842 Lakes in the 

Adirondack Case Study Area That Are from 0.5 to 2,000 ha in Size and at Least 1 m in Depth for 

the Four Critical Limit ANC Levels (0, 20, 50, and 100 μeq/L). Estimates Use the Exceedances 

for the Subset of 169 Lakes Using 2002 Deposition Magnitudes (Table 5.1-3) and Are 

Extrapolated to the Full Population Based on the EMAP Lake Probability Survey of 1991 to 

1994. “No. Exceedances” Indicates the Number of Lakes at or Below the Given ANC Critical 

Limit; “% Lakes” Indicates the Total Percentage of Lakes at or Below the Given ANC Critical 

Limit. 

ANC Critical Limit No. Exceedances 

(out of 1842) 

% Lakes 

100 μeq/L 945 51 

50 μeq/L 666 36 

20 μeq/L 242 13 

0 μeq/L 13 7 

Because some lakes in the Adirondack Case Study Area have natural sources of acidity, 

they would never have ANC levels >50 or 100 μeq/L, even in the absence of all anthropogenicderived acidifying deposition. Based on the hindcast simulations of 44 lakes using the MAGIC 

model, no modeled lakes have ANC levels <20 μeq/L. However, 5 modeled lakes, or 11% of 

modeled lakes, have ANC concentrations between 22 and 47 μeq/L. This equates to 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 37 
Aquatic Acidification Case Study 

approximately 300 lakes, or 16%, of the representative population of lakes in the Adirondack 

Case Study Area that likely had preacidification ANC levels <50 μeq/L. On the other hand, 

potentially >52% of lakes likely had preacidification ANC levels <100 μeq/L. The higher 

percentage of lakes in the regional population compared with the modeled population is because 

the lake classes or sizes that are likely to have preacidification ANC levels <50 or 100 μeq/L are 

more abundant in the Adirondack Case Study Area than lakes with preacidification ANC levels 

>50 or 100 μeq/L. 

5.1.5 Recovery from Acidification Given Current Emission Reductions 

The question is whether lakes can recover to healthy systems (i.e., ANC >50, or 100 

μeq/L) given the current surface water and ecosystem conditions of the lakes and current 

emission and deposition levels in the Adirondack Case Study Area. The forecast model runs of 

44 lakes using MAGIC were used to determine if current deposition could lead to recovery of the 

acidified lakes. Based on a deposition scenario that maintains current emission levels up to years 

2020 and 2050, the simulation forecast indicates no improvement in water quality over either of 

these periods. The percentage of lakes within the Elevated Concern to Acute Concern categories 

remains the same in the years 2020 and 2050 (Figure 5.1-7). Moreover, the percentage of 

modeled lakes classified as not acidic remains the same, suggesting that current emission levels 

will not likely improve their recovery from acidification in the Adirondack Case Study Area. 

0

5

10

15

20

25

30

35

40

45

2006 2020 2050

Acute (Below 0 μeq/L) Severe (0-20 μeq/L)

Elevated (20-50 μeq/L) Moderate (50-100 μeq/L)

Low (> Above 100 μeq/L)

Acute (Below 0 μeq/L)

Elevated (20–50 μeq/L)

Low (Above 100 μeq/L)

Severe (0–20 μeq/L)

Moderate (50–100 μeq/L)

0

5

10

15

20

25

30

35

40

45

2006 2020 2050

Acute (Below 0 μeq/L) Severe (0-20 μeq/L)

Elevated (20-50 μeq/L) Moderate (50-100 μeq/L)

Low (> Above 100 μeq/L)

Acute (Below 0 μeq/L)

Elevated (20–50 μeq/L)

Low (Above 100 μeq/L)

Severe (0–20 μeq/L)

Moderate (50–100 μeq/L)

Figure 5.1-7. Percentage of lakes in each of the five classes of acidification (Acute, 

Severe, Elevated, Moderate, Low) for the years 2006, 2020, and 2050 for 44 lakes 

modeled using MAGIC, where current emissions are held constant. Error bars 

indicate the 95% confidence interval. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 38 
Aquatic Acidification Case Study 

5.2 SHENANDOAH CASE STUDY AREA 

5.2.1 Current and Preacidification Conditions of Surface Waters 

Since the mid-1990s, streams in the Shenandoah Case Study Area have shown slight 

signs of improvement in NO3

-

 and SO4

2- concentrations in surface waters (see Figure 3.4-2). 

However, current concentrations of NO3

-

 and SO4

2- are still well above preacidification 

conditions based on MAGIC model simulations, resulting in lower ANC levels of surface water 

(Figure 5.2-1). 

0

20

40

60

80

100

120

1850 1900 1950 2000 2050

Years

ANC Concentration 

(μeq/L)

2050

Figure 5.2-1. Average NO3

- concentrations orange), SO42-concentrations (red), 

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

modeled using MAGIC for the period 1850 to 2050. 

Figure 5.2-2 shows the condition of the streams in the year 1860 (i.e., preacidification) 

and in the year 2006 (i.e., current) conditions. On average, NO3

-

 and SO4

2- concentrations are 32- 

and 10-fold higher today (Table 5.2-1). These results also demonstrate that acidification in most 

streams in the Shenandoah Case Study Area are currently being driven by SO4

2- deposition 

because the current average SO4

2- concentration is 11-folder greater than NO3- concentrations in 

surface waters. 

An estimate of how much of this current condition is attributed to the effects of 

industrially generated acidifying deposition can be made by examining the hindcast conditions of 

the streams. Based on the MAGIC model simulations, preacidification average ANC level of the 

60 modeled streams is 101.4 (95% CI 91.9. to 110.9) μeq/L compared with 57.9 (95% CI. 53.4 to 

62.4) μeq/L for today (Table 5.2-1). 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 39 
Aquatic Acidification Case Study 

Figure 5.2-2. (a) NO3

-

 and (b) SO4

2- concentrations (μeq/L) of 1860 

(preacidification) and 2006 (current) conditions based on hindcasts of 60 streams 

in the Shenandoah Case Study Area modeled using MAGIC. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 40 
Aquatic Acidification Case Study 

Table 5.2-1. Estimated Average Concentrations of Surface Water 

Chemistry for 60 Streams in the Shenandoah Case Study Area Modeled 

Using MAGIC for Preacidification (1860) and Current (2006) Conditions 

Preacidification Current 

μeq/L Avg. (±) Avg. (±) 

ANC 101.4 9.5 57.9 4.5 

SO4

2- 2.1 0.1 68.0 8.4 

NO3

- 0.6 0.01 6.2 0.1 

NH4

+ n/a n/a n/a n/a 

5.2.2 ANC Inferred Condition—Aquatic Status Categories 

The percentage of streams in each of the five aquatic status categories is shown in Table 

5.2-2 and in the graph in Figure 5.2-3. Ninety-two percent of the modeled streams likely were at 

the Low Concern or Moderate Concern conditions prior to the onset of acidifying deposition. 

The other 8% of streams have ANC of >27 μeq/L. The hindcast simulations produced no streams 

with Acute Concern or Severe Concern conditions.

Table 5.2-2. Percentage of Streams in the Five Aquatic Status Categories Based on Their 

Surface Water ANC Concentrations for 60 Streams Modeled Using MAGIC and 67 Streams in 

the SWAS-VTSSS LTM Network. Results are for the Shenandoah Case Study Area for the Year 

2006. 

Concern ANC 

(μeq/L) 

Modeled 

Preacidification 

Condition 

(% of Streams) 

Modeled Current 

Condition 

(% of Streams) 

Measured Current 

Condition 

(% of Streams) 

Low >100 42 27 15 

Moderate 50–100 50 20 30 

Elevated 20–50 8 28 25 

Severe 0–20 0 12 18 

Acute <0 0 13 12 

In contrast, the current simulated condition of the streams has shifted toward the 

chronically acidified categories. Only 47% of the streams currently experience the Low Concern 

or Moderate Concern conditions, whereas 53% of the streams experience the Acute Concern, 

Severe Concern, or Elevated Concern conditions. These results based on model reconstructions 

suggest that anthropogenic acidifying deposition is responsible for acidifying (ANC <50 μeq/L) 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 41 
Aquatic Acidification Case Study 

approximately 45% of streams modeled in the Shenandoah Case Study Area. Figure 5.2-4 shows 

the spatial extent of preacidification and current annual average ANC levels in the Shenandoah 

Case Study Area.

0

10

20

30

40

50

60

1860 2006

Acute (Below 0 :eq/L)

Severe (0–20 :eq/L)

Elevated (20–50 :eq/L)

Moderate (50–100 :eq/L)

Low (Above 100 :eq/L)

Figure 5.2-3. Percentage of streams in the five classes of acidification (Acute, 

Severe, Elevated, Moderate, Low) for years 1860 (preacidification) and 2006 

(current) conditions for 60 streams modeled using MAGIC. The number of 

streams in each category is above the bar. Error bars indicate the 95% confidence 

interval. 

Figure 5.2-4. ANC levels of 1860 (preacidification) and 2006 (current) conditions 

based on hindcasts of 60 streams in the Shenandoah Case Study Area modeled 

using MAGIC. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 42 
Aquatic Acidification Case Study 

5.2.3 The Biological Risk from Current Nitrogen and Sulfur Deposition: Critical 

Load Assessment 

In Figure 5.2-5, sites labeled by red or orange circles have less ability to neutralize acid 

inputs than streams labeled with yellow and green circles, and therefore, are streams most 

sensitive to acidifying deposition. Approximately 75% of the 60 streams modeled in the 

Shenandoah Case Study Area are sensitive or at risk to acidifying deposition. 

Figure 5.2-5. Critical loads of surface water acidity for an ANC of 50 μeq/L for 

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

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

receive and still maintain a surface water ANC >50 μeq/L. Watersheds with 

critical load values <100 meq/m2

/yr (red and orange circles) are most sensitive to 

surface water acidification, whereas watersheds with values >100 meq/m2

/yr 

(yellow and green circles) are the least sensitive sites.

In Figure 5.2-6, a critical load exceedance “value” indicates combined sulfur and 

nitrogen deposition in the year 2002 that is greater than the amount of deposition the stream 

could buffer and still maintain the ANC level of above each of the four different ANC limits of 

0, 20, 50, and 100 μeq/L. For the year 2002, 52%, 72%, 85%, and 92% of the 60 streams 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 43 
Aquatic Acidification Case Study 

modeled receive levels of combined sulfur and nitrogen deposition that exceeded their critical 

load with critical limits of 0, 20, 50, and 100 μeq/L, respectively (Table 5.2-3). 

Figure 5.2-6. Critical load exceedances for ANC levels of 0, 20, 50, and 100 μeq/L 

for Shenandoah Case Study Area streams. Green circles represent streams where 

current nitrogen and sulfur deposition is below the critical load and that maintain an 

ANC level of 0, 20, 50, and 100 μeq/L, respectively. Red circles represent streams 

where current nitrogen and sulfur deposition exceeds the critical load, indicating they 

are currently impacted by acidifying deposition. See Table 5.2-3.

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 44 
Aquatic Acidification Case Study 

Table 5.2-3. Critical Load Exceedances (Nitrogen + Sulfur Deposition > Critical Load) for 60 

Modeled Streams within the VTSSS LTM Program in the Shenandoah Case Study Area. “No. 

Exceedances” Indicates the Number of Streams at the Given ANC Limit; “% Streams” Indicates 

the Total Percentage of Streams at the Given ANC Limit. 

ANC Critical Limit 100 

μeq/L 

50 

μeq/L 20 μeq/L 0 μeq/L 

No. Exceedances (out 

of 60) 55 51 43 31 

% Streams 92 85 72 52 

5.2.4 Regional Assessment of Trout Streams in the Shenandoah Case Study Area 

The 60 trout streams modeled are characteristic of first- and second-order streams on 

nonlimestone bedrock in the Blue Ridge Mountains of Virginia. Because of the strong 

relationship between bedrock geology and ANC in this region, it is possible to consider the 

results in the context of similar trout streams in the Southern Appalachians that have the same 

bedrock geology and size. In addition, the 60 streams are a subset of 344 streams sampled by the 

Virginia Trout Stream Sensitivity Study, which can be applied to 304 of the original 344 streams. 

Using the 304 streams to which the analysis applies directly as the total, 279, 258, 218, and 157 

streams exceed their critical load for the year 2002 deposition with critical limits of 100, 50, 20, 

and 0 μeq/L, respectively. However, it is likely that many more of the ~12,000 trout streams in 

Virginia would exceed their critical load, given the extent of similar bedrock geology outside of 

the case study area in the southern Appalachian Mountains. 

5.2.5 Recovery from Acidification Given Current Emission Reductions 

Based on a deposition scenario that maintains current emission levels to the years 2020 

and 2050, there will still be a large number of streams in Virginia that have Elevated Concern to 

Acute Concern problems with acidity (Figure 5.2-7). In the short term (i.e., by the year 2020) 

and in the long term (i.e., by the year 2050), the response of the 60 modeled streams shows no 

improvement in the number of streams that have Moderate Concern conditions. In fact, under 

current emission levels, the modeling suggests that conditions may degrade by the year 2050. In 

Figure 5.2-7 the percentage of streams in Acute Concern condition increases by 5%, whereas 

streams in Moderate Concern condition decreases by 5%. 

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 45 
Aquatic Acidification Case Study 

0

10

20

30

40

50

60

2006 2020 2050

Acute (Below 0 μeq/L) Severe (0-20 μeq/L)

Elevated (20-50 μeq/L) Moderate (50-100 μeq/L)

Low (> Above 100 μeq/L)

Acute (Below 0 μeq/L)

Elevated (20–50 μeq/L)

Low (Above 100 μeq/L)

Severe (0–20 μeq/L)

Moderate (50–100 μeq/L)

Figure 5.2-7. Percentage of streams in the five categories of acidification (Acute, 

Severe, Elevated, Moderate, Low) for the years 2006, 2020, and 2050 for 60 streams in 

the Shenandoah Case Study Area modeled using MAGIC. The number of streams in 

each category is above the bar. Error bars indicate the 95% confidence interval.

Final Risk and Exposure Assessment September 2009 

Appendix 4 - 46 
Aquatic Acidification Case Study 

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Final Risk and Exposure Assessment September 2009 

Appendix 4 - 56 
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ATTACHMENT A 

1. MODELING DESCRIPTIONS 

1.1 MAGIC 

Model of Acidification of Groundwater in Catchments (MAGIC) is a lumped-parameter 

model of intermediate complexity, developed to predict the long-term effects of acidic deposition 

on surface water chemistry (Cosby et al., 1985a, b). The model simulates soil solution chemistry 

and surface water chemistry to predict the monthly and annual average concentrations of the 

major ions in these waters. MAGIC consists of (1) a 2–10 submodel in which the concentrations 

of major ions are assumed to be governed by simultaneous reactions involving sulfate (SO4

2-) 

adsorption, cation exchange, dissolution-precipitation- speciation of aluminum (Al), and 

dissolution-speciation of inorganic carbon; and (2) a mass balance submodel 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 in response to changes in atmospheric deposition, the chemical equilibria 

between soil and soil solution shift, resulting in changes in surface water chemistry. Thus, the 

degree and rate of change of surface water acidity depend both on flux factors and the inherent 

biogeochemical characteristics of the affected soils. 

Cation exchange is modeled using equilibrium (Gaines-Thomas) equations with 

selectivity coefficients for each base cation and Al. SO4

2- adsorption is represented by a 

Langmuir isotherm. Al dissolution and precipitation are assumed to be controlled by equilibrium 

with a solid phase of aluminum hydroxide (Al(OH)3). Al speciation is calculated by considering 

hydrolysis reactions, as well as complexation with SO4

2- and fluoride (F-

). The effects of carbon 

dioxide (CO2) on pH and on the speciation of inorganic carbon are computed from equilibrium 

equations. Organic acids are represented in the model as tri-protic analogues. Weathering and the 

uptake rate of nitrogen are assumed to be constant. A set of mass balance equations for base 

cations and strong acid anions are included (Cosby et al., 1985). 

Given a description of the historical deposition at a site, the model equations are solved 

numerically to give long-term reconstructions of surface water chemistry (for complete details of 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 1 
Aquatic Acidification Case Study 

the model, see Cosby et al., 1985 a, b; Cosby et al., 1989). MAGIC was successfully used to 

reconstruct the history of acidification and to simulate the future trends on a regional basis and in 

a large number of individual catchments in both North America and Europe (e.g., Cosby et al., 

1989, 1990, 1996; Hornberger et al., 1989; Jenkins et al., 1990 a, b, c; Lepisto et al., 1988; 

Norton et al., 1992; Sullivan and Cosby, 1998; Sullivan and Cosby, 2004; Whitehead et al., 

1988; Wright et al., 1990, 1994). 

The input data required in this project for aquatic and soils resource modeling with the 

MAGIC model (i.e., stream water, catchment, soils, deposition data) were assembled and 

maintained in databases for each site modeled (electronic spreadsheets, text-based MAGIC 

parameter files). Model outputs for each site were archived as text-based time-series files of 

simulated variable values. The outputs were also concatenated across all sites and maintained in 

electronic spreadsheets. 

1.1.1 Input Data and Calibration 

The calibration procedure requires that streamwater chemistry, soil chemical and physical 

characteristics, and atmospheric deposition data be available for each watershed. The surface 

water chemistry data needed for calibration are the concentrations of the individual base cations 

(calcium [Ca2+], magnesium [Mg2+], sodium [Na+

], and potassium [K+]) and acid anions 

(chlorine [Cl-], SO4

2-, nitrate [NO3-

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

comprise physical properties, including soil depth and bulk density, and chemical properties, 

such as soil pH, soil cation-exchange capacity, and exchangeable bases in the soil (Ca2+, Mg2+, 

Na+

, and K+). The deposition inputs required for calibration include the concentrations and 

magnitudes of all major ions from wet, dry, and cloud deposition. 

The acid-base chemistry modeling for this project was conducted using the year 2002 as 

the Base Year. The effects models were calibrated to the available atmospheric deposition and 

water chemistry data and then interpolated or extrapolated to yield Base Year estimates of lake 

water chemistry in the year 2002, which served as the starting point for modeling of current 

water chemistry (e.g., the years 2002 to 2100) 

1.1.2 Lake, Stream, and Soil Data for Calibration 

Several water chemistry databases were acquired for use in model calibration. Data were 

derived primarily from the Environmental Monitoring and Assessment Program (EMAP) and 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 2 
Aquatic Acidification Case Study 

Temporally Integrated Monitoring of Ecosystems (TIME) survey and monitoring efforts. The 

required lake water and soil composition data for the modeling efforts included the following 

measurements: 

 Stream water composition— pH, acid neutralizing capacity (ANC), Ca2+, Mg2+, K+, Na+, 

SO4

2-, NO3-

, and Cl-

 Soil properties— thickness and total cation exchange capacity, exchangeable bases (Ca2+, 

Mg2+, Na+, and K+) bulk density, porosity, and pH where available; the stream water 

chemistry database also included dissolved organic and inorganic carbon, silicic acid 

(H4SiO4), and inorganic monomeric Al (i.e., Ali). 

1.1.3 Wet Deposition and Meteorology Data for Calibration 

MAGIC requires, as atmospheric inputs for each site, estimates of the total annual 

deposition (eq/ha/yr) of eight ions, and the annual precipitation volume (meters/year [m/yr]). The 

eight ions are calcium (Ca2+), magnesium (Mg2+), Na+, K+, ammonium (NH4

+

), wet sulfate (SO4), 

chlorine (Cl-), and nitrate (NO3). Total deposition of an ion at a particular site for any year can be 

represented as combined wet, dry, and occult (i.e., cloud and fog) deposition: 

TotDep = WetDep + DryDep + OccDep. (1) 

Inputs to the MAGIC model are specified as wet deposition (the annual flux in 

meq/m2/yr) and a dry, cloud and fog deposition factor (DDF, unitless), which is multiplied by the 

wet deposition in order to get total deposition: 

TotDep = WetDep × DDF, (2) 

where DDF is the ratio of total deposition to wet deposition. It usually prescribed as equal to a 

constant fraction of the wet deposition. 

Given an annual wet deposition flux (WetDep), the ratio of dry deposition to wet 

deposition (DryDep/WetDep), and the ratio of cloud and fog deposition to wet deposition 

(OccDep/WetDep) for a given year at a site, the total deposition for that site and year is uniquely 

determined. 

In order to calibrate MAGIC, a time-series of total deposition is needed, beginning with a 

reference calibration year and including the 140 years preceding the calibration year. The 

procedure for providing a time-series of total deposition inputs to MAGIC follows. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 3 
Aquatic Acidification Case Study 

The absolute values of wet deposition and DDF for each ion are provided for a Reference 

Year at each site. For all the case study sites, a Reference Year of 2002 was used. Given the 

Reference Year deposition values, deposition data for the historical and calibration periods, and 

potentially any future deposition scenarios, can be estimated as a fraction of the Reference Year 

value. For instance, to calculate the total deposition of a particular ion in some historical or 

future year, j: 

 TotDep(j) = [WetDep(0) × WetDepScale(j) ] × [ DDF(0) × DDF Scale(j)], (3) 

where: 

 WetDep(0) = the Reference Year wet deposition (meq/m2/yr) of the ion 

 WetDepScale(j) = the scaled value of wet deposition in year j (expressed as a fraction of the 

wet deposition in the Reference Year) 

 DDF(0) = the dry and occult deposition factor for the ion for the Reference Year 

 DDFScale(j) = the scaled value of the dry and occult deposition factor in year j (expressed 

as a fraction of the DDF in the Reference Year). 

The absolute value of wet deposition used for the Reference Year is time and space 

specific—varying geographically within the region, varying locally with elevation, and varying 

from year to year. It is desirable to have the estimates of wet deposition take into account the 

geographic location and elevation of the site, as well as the year for which calibration data are 

available. Therefore, estimates of wet deposition used for the Reference Year should be derived 

from either direct measurements or a procedure (i.e., model) that has a high spatial resolution and 

considers elevation effects. As described in Section 4.2.1.4, the absolute wet deposition values 

used for the Reference Year in this project were derived from observed data from NADP 

hybridized with high-spatially resolved estimates of rainfall. 

The value of the DDF used for the Reference Year specifies the ratio between the 

absolute amounts of wet and total deposition. While the wet deposition component varies 

spatially and temporally, this ratio is not nearly so responsive. In large part, this is because the 

varying wet deposition parameter is usually a large component of the total deposition and is 

included in both the numerator and denominator of the ratio. For example, if in a given year at a 

particular site, the wet deposition goes up, then the total deposition usually goes up; or, if the 

elevation or aspect of a given site results in lower wet deposition, the total deposition also will 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 4 
Aquatic Acidification Case Study 

often be lower. Therefore, estimates of the absolute values of DDF may be derived from a model 

that has a relatively low spatial resolution and/or temporally smoothes the data. Estimates of the 

absolute values of the DDF for the Reference Year at each site in this project were derived from 

the Advanced Statistical Trajectory Regional Air Pollution (ASTRAP) model (Shannon, 1998), 

as described below. 

The long-term scaled sequences used to specify time-series of deposition inputs for 

MAGIC simulations usually do not require detailed spatial or temporal resolution. Scaled 

sequences of wet deposition or DDF (normalized to the same reference year) at neighboring sites 

will be similar, even if the absolute wet deposition or DDF at the sites are different because of 

factors such as local aspect or elevation. Therefore, if the scaled long-term patterns of any of 

these do not vary much from place to place, estimates of the scaled sequences (as for estimates of 

absolute DDF values) may be derived from a model that has a relatively low spatial resolution. 

As described in the following sections, output from the ASTRAP model was used to construct 

scaled sequences of both wet deposition and DDF for these case study areas. 

1.1.4 Wet Deposition Data (Reference Year and Calibration Values) 

The absolute values of wet deposition used for defining the Reference Year and for the 

MAGIC calibrations must be highly site-specific. Estimated wet deposition data was used for 

each site derived from the spatial interpolation model of Grimm and Lynch (2004), referred to 

here as the Grimm model. The Grimm model is based on observed wet deposition concentrations 

at NADP monitoring stations and radar-based precipitation estimates adjusted by elevation 

effects, and provides a spatially-resolved estimate of wet deposition for each of the eight ions 

required by MAGIC. The Grimm model makes a correction for changes in precipitation volume 

(and thus wet deposition) based on the elevation at a given site. This correction arises from a 

model of orographic effects on precipitation magnitudes derived from regional climatological 

data. 

The latitude, longitude, and elevation of the case study sites were provided as inputs to 

the Grimm model. Estimates of quarterly and annual wet deposition and precipitation estimates 

for each modeling site were found for the time period from 1983 through 2002. These annual 

data were used to define the Reference Year and were used in conjunction with the ASTRAP 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 5 
Aquatic Acidification Case Study 

historical deposition sequences for MAGIC calibration and simulation. The ASTRAP historical 

sequences were scaled to match the Grimm estimates at each site. 

1.1.5 Dry, Cloud, and Fog Deposition Data and Historical Deposition Sequences 

Historical sequences of wet deposition and DDF were estimated using the ASTRAP 

model. The ASTRAP model provided estimates of historical wet, dry, and occult deposition of 

sulfur and oxidized nitrogen at modeled sites for the two case study areas. The ASTRAP sites 

included 10 NADP deposition sites. For each of the modeled sites, ASTRAP produced wet, dry, 

and occult deposition estimates of sulfur and oxidized nitrogen every 10 years, starting in 1900 

and ending in 1990. The model outputs are smoothed estimates of deposition roughly equivalent 

to a 10-year moving average centered on each of the output years. The wet, dry, and occult 

deposition outputs of ASTRAP were used to estimate the absolute DDF for each site (using the 

DryDep/WetDep and OccDep/WetDep ratios from the ASTRAP 19 output) and to set up the 

scaled sequences of historical wet deposition and historical DDF for the calibration of each site 

modeled in this project. Using the values and rates of change from the year 1900 ASTRAP 

estimates, values for each time period going back to 1850 were estimated through linear 

interpolation. 

Because the ASTRAP sites are in the same region, but are not in the identical locations as 

the MAGIC sites, and since deposition magnitudes are spatially- and elevation-sensitive, the 

historical sequences of deposition at the ASTRAP sites were scaled to align with the deposition 

estimates from the Grimm model for the MAGIC case study areas. First, the time series of wet 

deposition estimates for each ASTRAP site were used to construct historical scaled sequences of 

wet deposition. The absolute wet deposition outputs for the period 1850 to 1990 from each site 

modeled in ASTRAP were normalized using their year 1990 values, converting them into scaled 

sequences. It was then necessary to couple these historical scaled wet deposition sequences from 

the year 1990 to the MAGIC Reference Year 2002. This coupling was accomplished using the 

observed changes in wet deposition for the period 1983 to 2002 derived from the Grimm model. 

With these site-specific deposition magnitudes and rates of change, the normalized ASTRAP 

values were converted back into concentrations, though, now scaled to the deposition at the 

MAGIC sites. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 6 
Aquatic Acidification Case Study 

Because DDF is much less sensitive to location, the actual (nonscaled) estimates from 

ASTRAP were used at the MAGIC sites. The value of DDF for the year 1990 was used as the 

value of DDF for the Reference Year (i.e., no change was assumed for DDF for the period 1990 

to 2002). The resulting time series of DDF values for the period 1900 to 2002 for each ASTRAP 

site were normalized to the year 2002 values to provide historical scaled sequences of DDF at 

each ASTRAP site. 

1.1.6 Protocol for MAGIC Calibration and Simulation at Individual Sites 

The aggregated nature of the MAGIC model requires that it be calibrated with observed 

data from a system before it can be used to forecast potential system response to changes in 

deposition. Calibration is achieved by specifying values of certain parameters within the model 

that can be directly measured or observed in the system of interest (called fixed parameters). The 

model is then run (using observed and/or assumed atmospheric and hydrologic inputs), and the 

outputs (streamwater and soil chemical variables called criterion variables) are compared with 

observed values of these variables. If the observed and simulated values differ, the values of 

another set of parameters in the model (called optimized parameters) are adjusted to improve the 

fit. After a number of iterations adjusting the optimized parameters, the simulated-minusobserved values of the criterion variables usually converge to zero (within some specified 

tolerance, or uncertainty). The model is then considered calibrated. 

There are eight observed fixed parameters that are used to drive the estimate (i.e., current 

soil exchangeable pool size and current output flux of each of the four base cations), and there 

are eight parameters to be optimized in this procedure (i.e., the weathering and the selectivity 

coefficient of each of the four base cations). If new assumptions or new values for any of the 

observed fixed parameters or inputs to the model are adopted, the model must be recalibrated by 

readjusting the optimized parameters until the simulated-minus-observed values of the criterion 

variables again fall within the specified tolerance. 

Estimates of the fixed parameters, the deposition inputs, and the target variable values to 

which the model is calibrated all contain uncertainties. A “fuzzy optimization” procedure was 

used for these case study sites to provide explicit estimates of the effects of these uncertainties. 

The procedure consists of performing multiple calibrations at each site using random values of 

the fixed parameters drawn from a range of fixed parameter values (representing uncertainty in 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 7 
Aquatic Acidification Case Study 

knowledge of these parameters) and random values of Reference Year deposition drawn from a 

range of total deposition estimates (representing uncertainty in these inputs). The final 

convergence (i.e., completion) of the calibration is determined when the simulated values of the 

criterion variables are within a specified “acceptable window” around the nominal observed 

value. This acceptable window represents uncertainty in the target variable values being used to 

calibrate the site. 

Each of the multiple calibrations at a site begins with (1) a random selection of values of 

fixed parameters and deposition, and (2) a random selection of the starting values of the 

adjustable parameters. The adjustable parameters are then optimized using an algorithm seeking 

to minimize errors between simulated and observed criterion variables. Calibration success is 

judged when all criterion values simultaneously are within their specified acceptable windows, 

(which may occur before the absolute possible minimum error is achieved). This procedure is 

repeated 10 times for each site. 

For this project, the acceptable windows for base cation concentrations in streams were 

taken as +/- 2 microequivalents per liter (μeq/L) around the observed values. Acceptable 

windows for soil exchangeable base cations were taken as +/- 0.2% around the observed values. 

Fixed parameter uncertainty in soil depth, bulk density, cation exchange capacity, stream 

discharge, and stream area were assumed to be +/- 10% of the estimated values. Uncertainty in 

total deposition was +/- 10% for all ions. 

The final calibrated model at the site is represented by the ensemble of parameter values 

of all of the successful calibrations at the site. When performing a simulation of the site, each of 

the calibrated parameter sets are run for a given historical or future scenario, generating an 

ensemble of results. The results include multiple simulated values of each variable for each year, 

all of which are acceptable in the sense of the calibration constraints applied in the fuzzy 

optimization procedure. The median of all the simulated values within a year is taken to be “the 

most likely” response for the site in that year. For this project, whenever single values for a site 

are presented or used in an analysis, these values are the median of the ensemble values derived 

from running each of the parameter sets for the site. 

An estimate of the uncertainty (or reliability) of a simulated response to a given scenario 

can also be derived from the multiple simulated values within a year resulting from the ensemble 

simulations. For any year in a given scenario, the largest and smallest values of a simulated 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 8 
Aquatic Acidification Case Study 

variable define the upper and lower 95% confidence bounds for that site’s response for the 

scenario under consideration. Thus, for all variables and all years of the scenario, a band of 

simulated values can be produced from the ensemble simulations at a site that encompasses the 

likely response (and provides an estimate of the simulation uncertainty) for any point in the 

scenario. For these case study areas, whenever uncertainty estimates are presented, the estimate 

is based on the range of values from the ensemble simulations for each of its sites. 

In addition, uncertainty estimates for three classes of the major inputs of the model were 

made through a sensitivity study, examining response of parameters and ability of the model to 

attain calibration in response to variation in the following inputs: 

 Soils data for calibration 

 Stream water data calibration 

 Deposition data calibration. 

1.1.7 Combined Model Calibration and Simulation Uncertainty 

The sensitivity analyses described above were designed to address specific assumptions 

or decisions that had to be made to assemble the data for the 44 or 60 modeled sites in a form 

that could be used for calibration of the model. In all cases, the above analyses address the 

questions of what the effect would have been if alternate available choices had been taken. These 

analyses were undertaken for a subset of sites for which the alternate choices were available at 

the same sites. As such, the analyses above are informative, but they provide no direct 

information about the uncertainty in calibration or simulation arising from the choices that were 

incorporated into the final modeling protocol for all sites. That is, having made the choices about 

soils assignments, high elevation deposition, and stream samples for calibration (and provided an 

estimate of their inherent uncertainties), the need arises for a procedure for estimating 

uncertainty at each and all of the individual sites using the final selected calibration and 

simulation protocol. 

These simulation uncertainty estimates were derived from the multiple calibrations at 

each site provided by the “fuzzy optimization” procedure employed in this project. For each of 

the modeled sites, 10 distinct calibrations were performed with the target values, parameter 

values, and deposition inputs for each calibration, reflecting the uncertainty inherent in the 

observed data for the individual site. The effects of the uncertainty in the assumptions made in 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 9 
Aquatic Acidification Case Study 

calibrating the model (and the inherent uncertainties in the data available) can be assessed by 

using all successful calibrations for a site when simulating the response to different scenarios of 

future deposition. The model then produces an ensemble of simulated values for each site. The 

median of all simulated values in a year is considered the most likely response of the site. The 

simulated values in the ensemble can also be used to estimate the magnitude of the uncertainty in 

the projection. Specifically, the difference in any year between the maximum and minimum 

simulated values from the ensemble of calibrated parameter sets can be used to define an 

“uncertainty” (or a “confidence”) width for the simulation at any point in time. All 10 of the 

successful model calibrations will lie within this range of values. These uncertainty widths can 

be produced for any variable and any year to monitor model performance. 

Direct comparison of simulated versus observed water chemistry values were compared 

to determine the uncertainty and variability in the MAGIC model output. Average water 

chemistry (SO4

2-, NO3-

, and ANC) simulated versus observed values during the calibration 

period (i.e., reference year) were compared for all modeled sites. In addition, simulated versus 

observed average yearly values for ANC for the period of 1980 to 2007 for 4 sites were 

completed. The observed water chemistry data were from the, ALTM-LTM, VTSSS-LTM, and 

TIME water quality measurement programs and represent annual average concentrations. The 

statistic of Root Mean Squared Error (RMSE) were also calculated for predicted versus observed 

values for both the calibration period and the period of 1980 to 2007. RMSE is a frequently used 

measure of the differences between values predicted by a model or an estimator and the values 

actually observed from the thing being modeled or estimated. The RMSE was based on an 

annual average ANC over a 5-year period. 

1.1.8 Results of the Uncertainty Analysis 

Based on the MAGIC model simulations, the 95% confidence interval for the preacidification and current average ANC concentrations of the 44 modeled lakes was 106.8 to 

134.0 and 50.5 to 81.8 μeq/L, respectively, which is on average a 15 μeq/L difference in ANC 

concentrations, or 10%. The 95% confidence interval for pre-acidification and current average 

ANC concentrations of the 60 modeled streams was 91.9 to 110.9 and 53.4 to 62.4 μeq/L, 

respectively, which is on average 8 μeq/L difference in ANC concentration, or 5%.

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 10 
Aquatic Acidification Case Study 

These direct comparisons show good agreement between simulated and observed water 

quality values. Results of predicted versus observed average water chemistry during the 

calibration period (i.e., reference year) are shown in Figures 1.1-1 and 1.1-2 for MAGIC 

modeling. The model showed close agreement with measured values at all sites for the 1-year 

comparison of modeled values. For all sites’ SO4

2-, NO3-

, and ANC simulations, the RMSE for 

predicted versus observed values were 0.1 μeq/L, 0.05 μeq/L, and 3.5 μeq/L for lakes in the 

Adirondacks Case Study Area and 1.0 μeq/L, 0.06 μeq/L, and 1.0 μeq/L for streams in the 

Shenandoah Case Study Area. Plots of simulated and observed ANC values for the period of 

1980 to 2007 are graphed in Figures 1.1-3 and 1.1-4 for two lakes in the Adirondacks Case 

Study Area and for two streams Shenandoah Case Study Area. The RMSE of ANC was 11.8 

μeq/L and 4.0 μeq/L for the two lakes in the Adirondacks Case Study Area and was 7.8 μeq/L 

and 5.1 μeq/L for the two streams in Shenandoah Case Study Area. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 11 
Aquatic Acidification Case Study 

0

50

100

150

0 50 100 150

Observed Chemistry

Simulated Chemistry

SO4

2- μeq/L 1:1

-100

0

100

200

300

-100 0 100 200 300

Observed Chemistry

Simulated Chemistry

1:1

ANC μeq/L

0

5

10

15

20

0 5 10 15 20

Observed Chemistry

Simulated Chemistry

1:1 NO3- μeq/L

4

5

6

7

8

45678

Observed Chemistry

Simulated Chemistry

1:1 pH μeq/L

Figure 1.1-1. Simulated versus observed annual average surface water SO4

2-, NO3-

. 

ANC, and pH during the model calibration period for each of the 44 lakes in the 

Adirondacks Case Study Area. The black line is the 1:1 line. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 12 
Aquatic Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 13 

0

50

100

150

0 50 100 150

Observed Chemistry

Simulated Chemistry

SO4

2- μeq/L 1:1

-100

0

100

200

300

-100 0 100 200 300

Observed Chemistry

Simulated Chemistry

ANC μeq/L

0

10

20

30

40

50

0 10 20 30 40 5

Observed Chemistry

Simulated Chemistry

1:1

0

NO3- μeq/L

4

5

6

7

8

4567

Observed Chemistry

Simulated Chemistr

8

y

1:1 pH μeq/L

Figure 1.1-2. Simulated versus observed annual average surface water SO4

2-, NO3-

. 

ANC, and pH during the model calibration period for each of the 60 streams in the 

Shenandoah Case Study Area. The black line is the 1:1 line. 
Aquatic Acidification Case Study 

-25

0

25

50

ANC (μeq/L)

Observed

Simulated

Indian Lake

-50

-25

0

25

50

1975 1980 1985 1990 1995 2000 2005 2010

Years

ANC (μeq/L)

Dismal Pond

Figure 1.1-3. MAGIC simulated and observed values of ANC for two lakes in the 

Shenandoah Case Study Area. Red points are observed data and the simulated values are 

the line. The Root Mean Squared Error (RMSE) for ANC was 11.8 μeq/L for Helton 

Creek and 4.0 μeq/L for Nobusiness Creek. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 14 
Aquatic Acidification Case Study 

0

50

100

150

200

ANC (μeq/L)

Observed

Simulated

Helton Creek

-25

0

25

50

1975 1980 1985 1990 1995 2000 2005 2010

Years

ANC (μeq/L)

Nobusiness Creek

Figure 1.1-4. MAGIC simulated and observed values of ANC for two lakes in the 

Shenandoah Case Study Area. Red points are observed data and the simulated values are 

the line. The Root Mean Squared Error (RMSE) for ANC was 11.8 μeq/L for Helton 

Creek and 4.0 μeq/L for Nobusiness Creek. 

1.2 Critical Loads: Steady-State Water Chemistry Models 

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) is 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. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 15 
Aquatic Acidification Case Study 

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

-

), 

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. 

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 and 

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

-

 

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

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 16 
Aquatic Acidification Case Study 

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 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) 

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. 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 was 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 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 17 
Aquatic Acidification Case Study 

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 

Section 1.2.5, below). 

For the assessment of current condition in both case study areas, 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 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. 

1.2.1 Preindustrial Base Cation Concentration 

Present-day surface water concentrations of base cations are elevated above their steadystate preindustrial concentrations because of base cation leaching through ion exchange in the 

soil due to anthropogenic inputs of SO4

2- to the watershed. For this reason, present-day surface 

water base cation concentrations are higher than natural or preindustrial levels, which, if not 

corrected for, would result in critical load values not in steady-state condition. To estimate the 

preacidification flux of base cations, the present flux of base cations was estimated, BC*

t, given 

by 

 BC*

t = BC*dep + BCw – Bcu +BCexc, (7) 

where 

 BCexc = the release of base cations due to ion-exchange processes. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 18 
Aquatic Acidification Case Study 

Assuming that deposition, weathering rate, and net uptake have not changed over time, 

BCexc can be obtained by subtracting Equation 5 from Equation 7: 

 BCexc = BC*

t – BC*0 (8) 

This present-day excess production of base cations in the catchment was related to the 

long-term changes in inputs of nonmarine acid anions (∆SO*

2 + ∆NO3) by the F-factor (see 

below): 

 BCexc = F (∆SO*

2 + ∆NO3) (9) 

For the preacidification base cation flux, solving Equation 5 for BC*

0 and then 

substituting Equation 8 for BCexc and explicitly describing the long-term changes in nonmarine 

acid ion inputs: 

 BC*

0 = BC*t – F (SO*4,t - SO*4,0 + NO*3,t - NO*3,0) (10) 

The preacidification NO3

-

 concentration, NO*

3,0, was assumed to be zero. 

1.2.2 F-factor 

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

concentrations for lakes in the Adirondack Case Study Area. 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 

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

1990): 

 F =([BC*

]t - [BC*]0)/([SO4

*

]t - [SO4

*

]0 + [NO3

*

]t - [NO3

*

]0), (12) 

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]) (13) 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 19 
Aquatic Acidification Case Study 

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 SO4

2- 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 SO4

2- 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* (14) 

1.2.3 ANC Limits 

Four classes of ANC limits were estimated: Suitable ANC >50 μeq/L, Indeterminate 

ANC 20 to 50 μeq/L, Marginal ANC 0 to 20 μeq/L, and Unsuitable ANC <0 μeq/L. 

1.2.4 Sea Salt Corrections 

The model applies a sea salt correction to the water chemistry concentrations. The 

equations below were applied to all lakes and streams, and to all the New England states and 

eastern Canadian provinces for the New England Governors and Eastern Canadian Premier 

assessment. The equations correct for sea salt. An asterisk (*) indicates the value has been 

corrected for sea salt. Units are in μeq/L. 

 Ca*

 = (Ca – (CL × 0.0213)) (14) 

 Mg* = (Mg – (CL × 0.0669)) (15) 

 Na* = (Na – (CL × 0.557)) (16) 

 K*

 = (K – (CL × 0.0.0206)) (17) 

 SO4

*

 = (SO4 – (CL × 0.14)) (18) 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 20 
Aquatic Acidification Case Study 

1.2.5 Critical load exceedance 

It is not possible to define a maximal loading for a single total of acidity (i.e., both 

nitrogen and sulfur deposition) because the acid anions sulfate and nitrate behave differently in 

the way they are transported with hydrogen ions; one unit of deposition of sulfur will not have 

the same net effect on surface water ANC as an equivalent unit of nitrogen deposition. However, 

the individual maximum and minimum critical loads for nitrogen and sulfur are defined when 

nitrogen or sulfur do not contribute to the acidity in the water. The maximum critical load for 

sulfur (DLmax(S)) is the following: 

 CLmax(S) = [( [BC]0

*

 - [ANClevel])Q] (3) 

when nitrogen deposition does not contribute to the acidity balance. Given the assumption that 

the long-term sinks of sulfate in the catchment soils are negligible, the amount of sulfur entering 

the catchment is equal to the amount loaded to the surface water. For this reason, the minimal 

amount of sulfur is equal to zero: 

 CLmin(S) = 0 (4) 

In the case of nitrogen, CLmin(N) is the minimum amount of deposition of total nitrogen (NHx + 

NOx) that catchment processes can effectively remove (e.g., Nupt + Nimm + Nden +Nret) without 

contributing to the acidic balance: 

 CLmin(N) = fNupt + (1-r)( Nimm + Nden) (5) 

The CLmax(N) is the load for total nitrogen deposition when sulfur deposition is equal to 

zero: 

 CLmax(N) = fNupt + (1-r)( Nimm + Nden) + [( [BC]0

*

 - [ANClevel])Q]. (6) 

In reality, neither nitrogen nor sulfur deposition will ever be zero, so the depositional load for the 

deposition of one is fixed by the deposition of the other, according to the line defining in 

Figure 1.2-1. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 21 
Aquatic Acidification Case Study 

N Deposition 

DLmax(N)

S Deposition 

DLmin(N)

DLmax(s)

N Deposition 

DLmax(N)

S Deposition 

DLmin(N)

DLmax(s)

Figure 1.2-1. The depositional load function defined by the model. 

The thick lines indicate all possible combinations of depositional loads of nitrogen and 

sulfur acidity that a catchment can receive and still maintain an ANC concentration equal to its 

ANClimit. Note that in the above formulation, individual depositional loads of nitrogen and sulfur 

are not specified; each combinations of depositions (Sdep and Ndep) fulfilling Equations 2 through 

6. 

Finally, the calculated exceedances of the critical load of acidity Ex(A), when the Nleach is 

considered the contribution of acid anions acidification is the following: 

Ex(A) = S*

dep + Nleach – CL(A), (11) 

where S*

dep is the amount of sulfur deposited in the catchment (assuming that all SO4

2- deposited 

leaches into the waterbody) and Nleach is the amount of deposited nitrogen, Ndep, that moves into 

the water. 

While SO4

2- is assumed to be a mobile anion (Sleach = S*

dep), nitrogen is to a large extent 

retained in the catchment by various processes; therefore, Ndep cannot be used directly in the 

exceedances calculation. Only present-day exceedances can be calculated from the leaching of 

nitrogen, Nleach, which is determined from the sum of measured concentrations of NO3

-

 and 

ammonia in the stream chemistry. No nitrogen deposition data are required for exceedance 

calculations; however, Ex(A) quantifies only the exceedances at present rates of retention of 

nitrogen in the catchment. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 22 
Aquatic Acidification Case Study 

1.2.6 Lake-to-Catchment-Area Ratios 

Lake and catchment areas data are from the Environmental Monitoring and Assessment Program 

(EMAP) (see Section 4.1.2.1 of Chapter 4). 

1.2.7 Denitrification and N Immobilization in Soils 

It was assumed that the denitrification fractions are related to the soil types in the 

catchments. In deeply drained podzolic soils, denitrification values are generally low. However, 

high values may occur in areas with peatsoils (Klemedtsson and Svensson, 1988; De Vries et al., 

1994). Therefore, the average denitrification fraction for each catchment was approximated by 

the following linear relationship: 

 fde = 0.1 + 0.7fpeat (12) 

where fpeat is the fraction of peatlands in the catchment area. 

For the long-term immobilization of nitrogen in forest soils (Nimm), a constant value of 2 

kg N/ha/yr was used. This value represents the lower end of the range suggested for European 

critical load calculations (Posch et al., 1997). 

1.2.8 Uncertainty and Variability 

There is uncertainty associated with the parameters in the steady-state critical load model 

used to estimate aquatic critical loads. The strength of the critical load estimate and the 

exceedance calculation relies on the ability to estimate the catchment-average base cation supply 

(i.e., input of base cations from weathering of bedrock and soils and air), runoff, and surface 

water chemistry. The uncertainty associated with runoff and surface water measurements is fairly 

well known. However, the ability to accurately estimate the catchment supply of base cations to 

a waterbody is still poorly known. This is important because the catchment supply of base 

cations from the weathering of bedrock and soils is the factor that has the most influence on the 

critical load calculation and also has the largest uncertainty (Li and McNulty, 2007). Although 

the approach to estimate base cation supply in the case study areas (e.g., F-factor) approach has 

been widely published and analyzed in Canada and Europe, and has been applied in the United 

States (e.g., Dupont et al., 2005), the uncertainty in this estimate is unclear and is likely large. 

For this reason, an uncertainty analysis of the state-steady critical load model was completed to 

evaluate the uncertainty in the critical load and exceedances estimations. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 23 
Aquatic Acidification Case Study 

A probabilistic analysis using a range of parameter uncertainties was used to assess: (1) 

the degree of confidence in the exceedance values and (2) the coefficient of variation (CV) of the 

critical load and exceedance values. The probabilistic framework is Monte Carlo, whereby each 

steady-state input parameter varies according to specified probability distributions and their 

range of uncertainty (Table 1.2-1). The purpose of the Monte Carlo methods was to propagate 

the uncertainty in the model parameters in the steady-state critical load model. 

Table 1.2-1. Parameters Used and their Uncertainty Range. The Range of Surface Water 

Parameters (e.g., CA, MG, CL, NA, NO3, SO4) were Determined from Surface Water Chemistry 

Data for the Period from 1992 to 2006 from the LTM-TIME Monitoring Network. Runoff(Q) 

and Acidic Deposition were Set at 50% and 25% 

Parameter Units Uncertainty range Distribution 

Q μeq/L 50% Normal 

CA μeq/L 65% Normal 

MG μeq/L 64% Normal 

CL μeq/L 52% Normal 

NA μeq/L 58% Normal 

NO3 μeq/L 30% Normal 

SO4 μeq/L 57% Normal 

Acidic Deposition 

(NOx & SO4) 

meq/L 25% Lognormal 

Within the Monte Carlo analysis, model calculations were run a sufficient number of 

times (i.e., 1,000 times) to capture the range of behaviors represented by all variables. The 

analysis tabulated the number of lakes where the confidence interval is entirely below the critical 

load, the confidence interval is entirely above the critical load, and the confidence interval 

straddles zero. Similar results are given for the number of sites with all realizations above the 

critical load, all realizations below the critical load, and some realizations above and some below 

the critical load. An inverse cumulative distribution function for exceedances was constructed 

from the 1000 model runs for each site, which describes the probability of a site to exceed its 

critical load. For each site, the probability of exceeding its critical load (i.e. probability of 

exceedance) is determined at the percent of the cumulative frequency distribution that lies above 

zero. The probability of exceedance, where the percentage of the cumulative frequency 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 24 
Aquatic Acidification Case Study 

distribution lies above zero, was calculated for all sites and assigned to one of the following five 

classes: 

 0-5% probability: unlikely to be exceeded; 

 5-25% probability: relatively low risk of exceedance; 

 25-75% probability: potential risk of exceedance; 

 75-95% probability: relatively high risk of exceedance; 

 > 95% probability: highly likely to be exceeded. 

This gives us a measure of the degree of confidence in whether the site exceeds its critical 

load. The CDF for Little Hope Pond is shown in Figure 1.2-2. 

The coefficient of variation (CV) was also calculated on each site for both the critical 

load and exceedance calculations. The coefficient of variation represents the ratio of the standard 

deviation to the mean, and it is a useful statistic for comparing the degree of variation in the data. 

The coefficient of variation allows a determination of how much uncertainty (risk) comparison to 

its mean. 

Probability of 

exceedence, where 

the % of the 

cumulative 

frequency chart lies 

above zero 

N=1000 

Figure 1.2-2. The inverse cumulative frequency distribution for Little Hope Pond. The xaxis shows critical load exceedance in meq/ha/yr and y-axis is the probability. The 

dashed lines represent zero exceedance. In the case of Little Hope Pond, the dash line 

divides mostly the probability distribution on the left hand side, indicating Little Hope 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 25 
Aquatic Acidification Case Study 

Pond has a relative low probability of being exceeded (0.3). Critical load and 

exceedances values were based on a critical level of protection of ANC = 50 μeq/L. 

1.2.8.1 Results of the uncertainty analysis

The means and coefficients of variation for critical load (CL(A)) and exceedances 

(EX(A)) values are shown for all sites in Table 1.2-2 for four ANC limits (0, 20, 50, 100 μeq/L. 

The average coefficients of variation for the various critical load values for all sites are 

remarkably low except for those calculated using a critical limit of 100 μeq/L. It is noticeable 

that though all the relevant input parameters have spreads of 25% to 65%, the CVs for CL(A) are 

only 4%, 5%, 9%, and 100% for critical load limits of 0%, 20%, 50%, and 100% μeq/L, 

respectively. In the case of the absolute value of the exceedances (EX(A)), the average CVs for 

all sites are higher, but still relatively low at 18%, 17%, 25%, and 33%. The individual 

coefficients of variation for each sites and an ANC limit of 50 μeq/L are shown in Figure 1.2-3. 

Although the average CV is relatively small for the population of sites modeled, individual site 

CV can varies from 1% to 45% for CL(A) and to 5% to over 100% for EX(A). This difference is 

due to the high degree of uncertainty in site specific parameters for particular sites and a low 

degree of confidence in the exceedance value itself for these sites. In addition, when the mean 

value is near zero, as is the case for exceedance values, the coefficient of variation is sensitive to 

small changes in the mean, which likely explains why some sites have high CV compare to 

others. 

Table 1.2-2. Means and Coefficients of Variation of Critical Loads and Exceedances for Surface 

Water. 

Parameter Critical load Limit 

(μeq/L) 

Mean 

(meq/L) 

Coefficient of 

variation (%) 

CL(A) 0 247.8 4 

20 227.0 5 

50 196.7 9 

100 140.3 100 

EX(A) 0 -178.3 18 

20 -157.6 17 

50 -127.2 25 

100 -75.0 33 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 26 
Aquatic Acidification Case Study 

0

20

40

60

80

100

120

CV (%)

CL(A) EX(A)

Figure 1.2-3. Coefficients of variation of surface water critical load for acidity CL(A) 

and exceedances (EX(A)). Critical load and exceedances values were based on a critical 

level of protection of ANC = 50 μeq/L. 

The probability of exceedance results, where the percentage of the cumulative frequency 

distribution lies above zero, are shown in Figure 1.2-4. Those areas that have less than 5% 

probability of exceedance are those with a high degree of confidence that the critical loads are 

not exceeded; conversely, areas with more than a 95% probability of exceedance are the most 

certain to be exceeded. 

For the sites in the aquatic case study areas, the probability of exceeding the critical load 

at an ANC limit of 0, 20, 50, and 100 μeq/L were relatively high. The waterbodies that exceeded 

their critical loads had a greater than 80% probability of doing so. The range of probability of 

exceedance was from 80% to 98%, indicating a relatively high confidence that these sites 

exceeded their critical load. The results suggest a relatively robust estimate of critical loads and 

exceedance rates for the case study areas. It is important to note that this analysis may understate 

the actual uncertainty because some of the range and distribution types of parameters are not 

well known for the United States at this time. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 27 
Aquatic Acidification Case Study 

Figure 1.2-4. Probability of exceedance of critical load for acidity for 2002. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment A - 28 
Aquatic Acidification Case Study 

ATTACHMENT B 

1. EMAP/TIME/LTM PROGRAMS 

The EPA Environmental Monitoring and Assessment Program (EMAP) began regional 

surveys of the nation’s surface waters in 1991 with a survey of northeastern United States lakes. 

Since then, EMAP and Regional-EMAP (REMAP) surveys have been conducted on lakes and 

streams throughout the country. The objective of these EMAP surveys is to characterize 

ecological condition across populations of surface waters. EMAP surveys are probability surveys 

where sites are picked using a spatially balanced systematic randomized sample so that the 

results can be used to make estimates of regional extent of condition (e.g., number of lakes, 

length of stream). EMAP sampling typically consists of measures of aquatic biota (e.g., fish, 

macroinvertebrates, zooplankton, periphyton), water chemistry, and physical habitat. Of 

particular interest with respect to acidifying deposition effects were two EMAP surveys 

conducted in the 1990s, the Northeastern Lake Survey and the Mid-Atlantic Highlands 

Assessment of streams (MAHA). The Northeastern Lake Survey was conducted in summer from 

1991 to 1994 and consisted of 345 randomly selected lakes in New York, New Jersey, Vermont, 

New Hampshire, Maine, Rhode Island, Connecticut, and Massachusetts (Whittier et al., 2002). 

To make more precise estimates of the effects of acidic deposition, the sampling grid was 

intensified to increase the sample site density in the Adirondack Mountains and New England 

Uplands areas known to be susceptible to acidic deposition. The MAHA study was conducted on 

503 stream sites from 1993 to 1995 in the states of West Virginia, Virginia, Pennsylvania, 

Maryland, Delaware, and the Catskill Mountain region of New York (Herlihy et al., 2000). 

Sampling was done during spring baseflow. Sample sites were restricted to first through third 

order streams as depicted on the USGS 1:100,000 digital maps used in site selection. To make 

more precise estimates of the effects of acidic deposition, the sampling grid was intensified to 

increase the sample site density in the Blue Ridge, Appalachian Plateau, and Ridge section of the 

Valley and Ridge ecoregions. Results from both of these surveys were used to develop and select 

the sampling sites for the Temporally Integrated Monitoring of Ecosystems (TIME) program, 

which is described below. 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment B - 1 
Aquatic Acidification Case Study 

2. TEMPORALLY INTEGRATED MONITORING OF ECOSYSTEMS 

AND LONG-TERM MONITORING PROGRAMS 

There are two surface water chemistry monitoring programs, administered by EPA, that 

are especially important to inform the assessment of aquatic ecosystem responses to changes in 

atmospheric deposition. These are the TIME program (Stoddard et al., 2003) and the Long-term 

Monitoring (LTM) project (Ford et al., 1993; Stoddard et al., 1998). These efforts focus on 

portions of the United States most affected by the acidifying influence of sulfur and nitrogen 

deposition, including lakes in the Adirondack Mountains of New York and in New England, and 

streams in the Northern Appalachian Plateau and Blue Ridge in Virginia and West Virginia. 

Both projects are operated cooperatively with numerous collaborators in state agencies, academic 

institutions, and other federal agencies. The TIME program and LTM project have slightly 

different objectives and structures, which are outlined below. Stoddard et al. (2003) conducted a 

thorough trends analysis of the TIME and LTM data. 

2.1 TIME Program 

At the core of the TIME project is the concept of probability sampling, whereby each 

sampling site is chosen statistically from a predefined target population. Collectively, the 

monitoring data collected at the sites are representative of the target population of lakes or 

streams in each study region. The target populations in these regions include lakes and streams 

likely to be responsive to changes in acidifying deposition, defined in terms of acid neutralizing 

capacity (ANC), which represents an estimate of the ability of water to buffer acid. Measurement 

of Gran ANC uses the Gran technique to find the inflection point in an acid-base titration of a 

water sample (Gran, 1952). In the Northeast, the TIME target population consists of lakes with a 

Gran ANC <100 microequivalents per liter (μeq/L). In the mid-Atlantic, the target population is 

upland streams with Gran ANC <100 μeq/L. In both regions, the sample sites selected for future 

monitoring were selected from the EMAP survey sites in the region (Section AX3.2.1.1) that met 

the TIME target population definition. Each lake or stream is sampled annually (in summer for 

lakes; in spring for streams), and results are extrapolated with known confidence to the target 

population(s) as a whole using the EMAP site population expansion factors or weights (Larsen 

and Urquhart, 1993; Larsen et al., 1994; Stoddard et al., 1996; Urquhart et al., 1998). TIME sites 

were selected using the methods developed by the EMAP (Herlihy et al., 2000; Paulsen et al., 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment B - 2 
Aquatic Acidification Case Study 

1991;). The TIME program began sampling northeastern lakes in 1991. Data from 43 lakes in the 

Adirondack Mountains can be extrapolated to the target population of low ANC lakes in that 

region. There are about 1,000 low-ANC Adirondack lakes, out of a total population of 1,842 

lakes with surface area greater than 1 hectare (ha). Data from 30 lakes (representing about 1,500 

low-ANC lakes, out of a total population of 6,800) form the basis for TIME monitoring in New 

England. Probability monitoring of mid-Atlantic streams began in 1993. Stoddard et al. (2003) 

analyzed data from 30 low-ANC streams in the Northern Appalachian Plateau (representing 

about 24,000 kilometer (km) of low-ANC stream length out of a total stream length of 42,000 

km). 

The initial 1993 to 1995 EMAP-MAHA sample in the mid-Atlantic was not dense 

enough to obtain enough sites in the TIME target population in the Blue Ridge and Valley and 

Ridge ecoregions. In 1998, another denser random sample was conducted in these ecoregions to 

identify more TIME sites. After pooling TIME target sites taken from both MAHA and the 1998 

survey, there are now 21 TIME sites in the Blue Ridge and Ridge and Valley that can be used for 

trend detection in this aggregate ecoregion in the mid-Atlantic in addition to the northern 

Appalachian Plateau ecoregion. 

2.2 LTM Program 

As a complement to the statistical lake and stream sampling in TIME, the LTM Program 

samples a subset of generally acid-sensitive lakes and streams that have long-term data, many 

dating back to the early 1980s. These sites are sampled 3 to 15 times per year. This information 

is used to characterize how some of the most sensitive aquatic systems in each region are 

responding to changing deposition, as well as giving information on seasonal variation in water 

chemistry. In most regions, a small number of higher-ANC (e.g., Gran ANC >100 μeq/L) sites 

are also sampled, and these help to separate temporal changes due to acidifying deposition from 

those attributable to other disturbances (e.g., climate, land use change). Because of the 

availability of long-term records (i.e., more than two decades) at many LTM sites, their trends 

can also be placed in a better historical context than those of the TIME sites, where data are only 

available starting in the 1990s. Monitored water chemistry variables include pH, ANC, major 

anions and cations, monomeric aluminum (Al), silicon (Si), specific conductance, dissolved 

organic carbon, and dissolved inorganic carbon. The field protocols, laboratory methods, and 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment B - 3 
Aquatic Acidification Case Study 

quality assurance procedures are specific to each team of investigators. This information is 

contained in the cited publications of each research group. The EMAP and TIME protocols and 

quality assurance methods are generally consistent with those of the LTM cooperators. Details of 

LTM data from each region are given below. 

New England lakes: The LTM project collects quarterly data from lakes in Maine 

(sampled by the University of Maine) (Kahl et al., 1991; Kahl et al., 1993) and Vermont (data 

collected by the Vermont Department of Environmental Conservation) (Stoddard and Kellogg, 

1993; Stoddard et al., 1998). Data from 24 New England lakes were available for the trend 

analysis reported by Stoddard et al. (2003) for the period 1990 to 2000. In addition to quarterly 

samples, a subset of these lakes have outlet samples collected on a weekly basis during the 

snowmelt season; these data are used to characterize variation in spring chemistry. The majority 

of New England LTM lakes have mean Gran ANC values ranging from 20 to 100 μeq/L; two 

higher ANC lakes (i.e., Gran ANC between 100 and 200 μeq/L) are also monitored. 

Adirondack lakes: The trend analysis of Stoddard et al. (2003) included data from 48 

Adirondack lakes, sampled monthly by the Adirondack Lake Survey Corporation (Driscoll and 

Van Dreason, 1993; Driscoll et al., 1995); a subset of these lakes are sampled weekly during 

spring snowmelt to help characterize spring season variability. Sixteen of the lakes have been 

monitored since the early 1980s; the others were added to the program in the 1990s. The 

Adirondack LTM dataset includes both seepage and drainage lakes, most with Gran ANC values 

in the range –50 to 100 μeq/L; three lakes with Gran ANC between 100 μeq/L and 200 μeq/L are 

also monitored. 

Appalachian Plateau streams: Stream sampling in the Northern Appalachian Plateau is 

conducted about 15 times per year, with the samples spread evenly between baseflow (e.g., 

summer and fall) and high flow (e.g., spring) seasons. Data from four streams in the Catskill 

Mountains (collected by the U.S. Geological Survey) (Murdoch and Stoddard, 1993), and five 

streams in Pennsylvania (collected by Pennsylvania State University) (DeWalle and Swistock, 

1994) were analyzed by Stoddard et al. (2003). All of the northern Appalachian LTM streams 

have mean Gran ANC values in the range 25 to 50 μeq/L. 

Upper Midwest lakes: Forty lakes in the Upper Midwest were originally included in the 

LTM project, but funding in this region was terminated in 1995. The Wisconsin Department of 

Natural Resources (funded by the Wisconsin Acid Deposition Research Council, the Wisconsin 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment B - 4 
Aquatic Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 4, Attachment B - 5 

Utilities Association, the Electric Power Research Institute, and the Wisconsin Department of 

Natural Resources) has continued limited sampling of a subset of these lakes, as well as carrying 

out additional sampling of an independent subset of seepage lakes in the state. The data reported 

by Stoddard et al. (2003) included 16 lakes (both drainage and seepage) sampled quarterly 

(Webster et al., 1993), and 22 seepage lakes sampled annually in the 1990s. All of the Upper 

Midwest LTM lakes exhibit mean Gran ANC values from 30 to 80 μeq/L. 

Ridge/Blue Ridge streams: Data from the Ridge and Blue Ridge provinces consist of a 

large number of streams sampled quarterly throughout the 1990s as part of the Virginia Trout 

Stream Sensitivity Study (Webb et al., 1989), and a small number of streams sampled more 

intensively (as in the Northern Appalachian Plateau). A total of 69 streams, all located in the 

Ridge section of the Ridge and Valley province, or within the Blue Ridge province, and all 

within the state of Virginia, had sufficient data for the trend analyses by Stoddard et al. (2003). 

The data are collected cooperatively with the University of Virginia and the National Park 

Service. Mean Gran ANC values for the Ridge and Blue Ridge data range from 15 to 200 μeq/L, 

with 7 of the 69 sites exhibiting mean Gran ANC >100 μeq/L. 

September 2009 

Appendix 5 

Terrestrial Acidification Case Study 

Final 

EPA Contract Number EP-D-06-003 

Work Assignment 3-62 

Project Number 0209897.003.062 

Prepared for 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27709 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709-2194 

Terrestrial Acidification Case Study 

TABLE OF CONTENTS 

Acronyms and Abbreviations ........................................................................................................ xi

1.0 Background ............................................................................................................................... 1

1.1 Indicators, Ecological Endpoints, and Ecosystem Services ......................................................... 2

1.1.1 Indicators.............................................................................................................. 2

1.1.2 Ecological Endpoints ........................................................................................... 7

1.1.3 Ecosystem Services ............................................................................................ 10

1.2 Case Study Areas ........................................................................................................................ 11

1.2.1 GIS Analysis of National Sensitivity ................................................................. 11

1.2.2 Selection of Case Study Areas ........................................................................... 12

1.2.3 Sugar Maple ....................................................................................................... 17

1.2.3.1 Kane Experimental Forest ................................................................... 17

1.2.3.2 Plot Selection for Kane Experimental Forest Case Study Area .......... 18

1.2.4 Red Spruce ......................................................................................................... 20

1.2.4.1 Hubbard Brook Experimental Forest .................................................. 20

1.2.4.2 Plot Selection for Hubbard Brook Experimental Forest Case 

Study Area .......................................................................................... 22

2.0 Approach and Methods ........................................................................................................... 26

2.1 Chosen Method ........................................................................................................................... 30

2.1.1 Critical Load Equations and Calculations .......................................................... 31

2.1.1.1 Simple Mass Balance Calculations ..................................................... 31

2.1.1.2 Deposition Relative to Critical Load Calculations ............................. 37

2.1.1.3 Critical Load Function ........................................................................ 38

2.1.2 Critical Load Data Requirements....................................................................... 39

2.1.2.1 Data Requirements and Sources ......................................................... 39

2.1.2.2 Selection of Indicator Values .............................................................. 42

2.1.2.3 Case Study Input Data ........................................................................ 45

2.2 Critical Load Function Response Curves Associated with the Three Levels of 

Protection .................................................................................................................................... 49

3.0 Results ..................................................................................................................................... 50

3.1 Critical Load Estimates .............................................................................................................. 50

3.1.1 Sugar Maple ....................................................................................................... 50

3.1.2 Red Spruce ......................................................................................................... 58

3.2 Recommended Parameter Values and Critical Loads ................................................................. 62

3.3 Current Conditions ..................................................................................................................... 63

4.0 Expansion of Critical Load Assessments for Sugar Maple and Red Spruce .......................... 75

4.1 Critical Load Assessments .......................................................................................................... 75

4.2 Relationship between Atmospheric Nitrogen and Sulfur Deposition and Tree Growth ............. 91

5.0 Uncertainty Analysis ............................................................................................................... 92

5.1 Kane Experimental Forest and Hubbard Brook Experimental Forest Case Study Areas ........... 92

5.2 Expansion of Critical Load Assessments ................................................................................... 93

6.0 References ............................................................................................................................... 96

Final Risk and Exposure Assessment September 2009 

Appendix 5 - i 
Terrestrial Acidification Case Study 

Attachment A: Relationship Between Atmospheric Nitrogen and Sulfur Deposition and 

Sugar Maple and Red Spruce Tree Growth .............................................................................. 1

1. Introduction .................................................................................................................................. 1

2. Source of Data for Analyses ......................................................................................................... 2

3. Regression Analyses Methodology and Results ........................................................................... 7

4. Additional Sources of Variability Influencing the Relationships between Tree Volume 

Growth and Nitrogen Deposition and Critical Load Exceedance............................................... 18

4.1 State-Specific Variables ..................................................................................... 18

4.2 Dead Trees ......................................................................................................... 19

4.3 Other Factors ...................................................................................................... 19

5. Conclusions ................................................................................................................................ 21

LIST OF FIGURES 

Figure 1.1-1. Conceptual impacts of acidifying deposition on soil Ca2+ depletion, tree 

physiology, and forest ecosystem health and sustainability (recreated from 

DeHayes et al., 1999). ............................................................................................. 6

Figure 1.1-2. Areal coverages of red spruce and sugar maple tree species within the 

continental United States (USFS, 2006). .............................................................. 10

Figure 1.2-1. Map of areas of potential sensitivity of red spruce and sugar maple to 

acidification in the United States (see Table 1.2-1 for listing of data 

sources to produce this map). ................................................................................ 12

Figure 1.2-2. Location of the Kane Experimental Forest (Horsley et al., 2000). ......................... 17

Figure 1.2-3. The seven plots used to evaluate critical loads of acidity in the Kane 

Experimental Forest. ............................................................................................. 19

Figure 1.2-4. Location of the Hubbard Brook Experimental Forest. ............................................ 21

Figure 1.2-5. Vegetation cover (NLCD, 2001) and location of Watershed 6 of Hubbard 

Brook Experimental Forest. .................................................................................. 24

Figure 1.2-6. Grid units within Watershed 6 of Hubbard Brook Experimental Forest. The 

red outline delineates the spruce-fir forest type. The dotted grid cell areas 

indicate the grid units with high proportions of red spruce and represents 

the composite plot area for the Hubbard Brook Experimental Forest Case 

Study Area. ........................................................................................................... 25

Figure 1.2-7. Location of case study plots within Watershed 6 of Hubbard Brook 

Experimental Forest. ............................................................................................. 26

Figure 2.1-1. The critical load function created from the calculated maximum and 

minimum levels of total nitrogen and sulfur deposition (eq/ha/yr). The 

grey areas show deposition levels less than the established critical loads. 

The red line is the maximum critical level of sulfur deposition (valid only 

when nitrogen deposition is less than the minimum critical level of 

nitrogen deposition [blue dotted line]). The flat line portion of the curves 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - ii 
Terrestrial Acidification Case Study 

indicates nitrogen deposition corresponding to the CLmin(N) (i.e., nitrogen 

absorbed by nitrogen sinks within the system). .................................................... 39

Figure 2.1-2. The relationship between the Bc/Al ratio in soil solution and the percentage 

of tree species (found growing in North America) exhibiting a 20% 

reduction in growth relative to controls (after Sverdrup and Warfvinge, 

1993b). .................................................................................................................. 44

Figure 2.1-3. The relationship between soil solution Bc/Al ratio and stem or root growth 

in sugar maple (from Sverdrup and Warfvinge, 1993b). ...................................... 44

Figure 2.1-4. The relationship between soil solution Bc/Al ratio and biomass or root 

growth in red spruce (from Sverdrup and Warfvinge, 1993b). ............................. 45

Figure 2.2-1. An example of the critical load function response curves associated with the 

three (Bc/Al)crit ratios and the associated levels of protection of tree health. 

The flat line portion of the curves indicates total nitrogen deposition 

corresponding to the CLmin (nitrogen absorbed by nitrogen sinks within the 

system). ................................................................................................................. 50

Figure 3.1-1. The critical load function response curves detailing the lowest critical load 

estimates for Plot 1 of the Kane Experimental Forest (refer to Table 3.1-1 

for the parameters corresponding to each of the curves). The flat line 

portion of the curves indicates total nitrogen deposition corresponding to 

the CLmin(N) (nitrogen absorbed by nitrogen sinks within the system). ............... 56

Figure 3.1-2. The critical load function response curves detailing the highest critical load 

estimates for Plot 1 of the Kane Experimental Forest (refer to Table 3.1-1 

for the parameters corresponding to each of the curves). The flat line 

portion of the curves indicates total nitrogen deposition corresponding to 

the CLmin(N) (nitrogen absorbed by nitrogen sinks within the system. ................ 57

Figure 3.1-3. The critical load function response curves detailing the lowest critical load 

estimates for the Hubbard Brook Experimental Forest Case Study Area 

(refer to Table 3.1-10 for the parameters corresponding to each of the 

curves). The flat line portion of the curves indicates total nitrogen 

deposition corresponding to the CLmin(N) (nitrogen absorbed by nitrogen 

sinks within the system). ....................................................................................... 60

Figure 3.1-4. The critical load function response curves detailing the highest critical load 

estimates for the Hubbard Brook Experimental Forest Case Study Area 

(refer to Table 3.1-10 for the parameters corresponding to each of the 

curves). The flat line portion of the curves indicates total nitrogen 

deposition corresponding to the CLmin(N) (nitrogen absorbed by nitrogen 

sinks within the system). ....................................................................................... 60

Figure 3.3-1. Plot 1 Kane Experimental Forest critical load function response curves, 

detailing the lowest critical load estimates for Kane Experimental Forest 

Case Study Area (refer to Table 3.1-1 for the parameters corresponding to 

each of the curves). The 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels ((N+S)comb) were greater than the critical loads of 

nitrogen and sulfur at all levels of protection ((Bc/Al)crit = 0.6, 1.2, and 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - iii 
Terrestrial Acidification Case Study 

10.0). The flat line portion of the curves indicates total nitrogen deposition 

corresponding to the CLmin (N) (nitrogen absorbed by nitrogen sinks 

within the system). ................................................................................................ 68

Figure 3.3-2. Plot 1 critical load function response curves, detailing the highest maximum 

deposition load estimates for Kane Experimental Forest Case Study Area 

(refer to Table 3.1-1 for the parameters corresponding to each of the 

curves). The 2002 CMAQ/NADP total nitrogen and sulfur deposition 

levels ((N+S)comb) were greater than the critical load of total nitrogen and 

sulfur deposition calculated with the highest level of protection (Bc/Al)crit 

= 10.0). The flat line portion of the curves indicates total nitrogen 

deposition corresponding to the CLmin(N) (nitrogen absorbed by nitrogen 

sinks within the system). ....................................................................................... 69

Figure 3.3-3. Critical load function response curves, detailing the lowest critical load 

estimates for the Hubbard Brook Experimental Forest Case Study Area 

(refer to Table 3.1-10 for the parameters corresponding to each of the 

curves). The 2002 CMAQ/NADP total nitrogen and sulfur deposition 

levels ((N+S)comb) were greater than the critical load of total nitrogen and 

sulfur calculated with the highest and the intermediate levels of protection 

((Bc/Al)crit = 1.2 and 10.0). The flat line portion of the curves indicates 

total nitrogen deposition corresponding to the CLmin(N) (nitrogen absorbed 

by nitrogen sinks within the system). ................................................................... 70

Figure 3.3-4. Critical load function response curves, detailing the highest critical load 

estimates for the Hubbard Brook Experimental Forest Case Study Area 

(refer to Table 3.1-10 for the parameters corresponding to each of the 

curves). The 2002 CMAQ/NADP total nitrogen and sulfur deposition 

levels ((N+S)comb) were less than the critical loads associated with all three 

(Bc/Al)crit ratios. The flat line portion of the curves indicates total nitrogen 

deposition corresponding to the CLmin(N) (nitrogen absorbed by nitrogen 

sinks within the system). ....................................................................................... 71

Figure 3.3-5. Critical load function response curves for the three selected critical loads 

conditions (corresponding to the three levels of protection) for the Kane 

Experimental Forest Case Study Area. The 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels ((N+S)comb) were greater than the 

highest and intermediate level of protection critical loads. The flat line 

portion of the curves indicates total nitrogen deposition corresponding to 

the CLmin(N) (nitrogen absorbed by nitrogen sinks within the system). ............... 72

Figure 3.3-6. Critical load function response curves for the three selected critical loads 

conditions (corresponding to the three levels of protection) for the 

Hubbard Brook Experimental Forest Case Study Area. The 2002 

CMAQ/NADP total nitrogen and sulfur deposition levels ((N+S)comb) were 

greater than the highest level of protection critical loads. The flat line 

portion of the curves indicates total nitrogen deposition corresponding to 

the CLmin(N) (nitrogen absorbed by nitrogen sinks within the system). ............... 73

Final Risk and Exposure Assessment September 2009 

Appendix 5 - iv 
Terrestrial Acidification Case Study 

Figure 3.3-7. The influence of the 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels (NHx-N) on the critical load function response curve, and 

in turn, the maximum critical loads of sulfur (CLmax(S)) and oxidized 

nitrogen (NOx-N) for the selected highest protection critical load for the 

Kane Experimental Forest Case Study Area. The critical load of oxidized 

nitrogen (NOx-N) is 661 eq/ha/yr (910 − 249 eq/ha/yr). The CLmin(N) 

(nitrogen absorbed by nitrogen sinks within the system) corresponds to the 

value depicted in Figure 3.3-5. ............................................................................. 74

Figure 3.3-8. The influence of the 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels (NHx-N) on the critical load function response curve and, 

in turn, the maximum critical loads of sulfur (CLmax(S)) and oxidized 

nitrogen (NOx-N) for the selected highest protection critical load for the 

Hubbard Brook Experimental Forest Case Study Area. The critical load of 

oxidized nitrogen (NOx-N) is 328 eq/ha/yr (487 − 159 eq/ha/yr). The 

CLmin(N) (nitrogen absorbed by nitrogen sinks within the system) 

corresponds to the value depicted in Figure 3.3-6. ............................................... 75

Figure 4.1-1. States where sugar maple is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the lowest protection 

critical load (Bc/Al(crit) = 0.6) in the following: none of the sugar maple 

plots, <50% of the sugar maple plots, and >50% of the sugar maple plots. ......... 86

Figure 4.1-2. States where sugar maple is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the intermediate protection 

critical load (Bc/Al(crit) = 1.2) in the following: none of the sugar maple 

plots, <50% of the sugar maple plots, and >50% of the sugar maple plots. ......... 87

Figure 4.1-3. States where sugar maple is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the highest protection 

critical load (Bc/Al(crit) = 10.0) in the following: none of the sugar maple 

plots, <50% of the sugar maple plots, and >50% of the sugar maple plots. ......... 88

Figure 4.1-4. States where red spruce is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the lowest protection 

critical load (Bc/Al(crit) = 0.6) in the following: none of the red spruce 

plots, <50% of the red spruce plots, and >50% of the red spruce plots. ............... 89

Figure 4.1-5. States where red spruce is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the intermediate protection 

critical load (Bc/Al(crit) = 1.2) in the following: none of the red spruce 

plots, <50% of the red spruce plots, and >50% of the red spruce plots. ............... 90

Figure 4.1-6. States where red spruce is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the highest protection 

critical load (Bc/Al(crit) = 10.0) in the following: none of the red spruce 

plots, <50% of the red spruce plots, and >50% of the red spruce plots. ............... 91

Final Risk and Exposure Assessment September 2009 

Appendix 5 - v 
Terrestrial Acidification Case Study 

ATTACHMENT FIGURES 

Figure 1-1. Hypothesized relationships between tree growth and nitrogen deposition and 

critical load exceedance. When deposition does not exceed the critical 

load, growth is stimulated by nitrogen deposition. When deposition 

exceeds the critical load (deposition is greater than the critical load), 

growth is reduced. (This figure is a modification of a curve describing 

forest productivity as a function of long-term chronic nitrogen additions 

outlined in Aber et al., 1995). ............................................................................. A-2

Figure 3-1. Areas of the continental United States that were covered during the last 

glacial event (~ 20,000 ybp) (Reed and Bush, 2005). ...................................... A-13

LIST OF TABLES 

Table 1.1-1. Literature Support for Selected Indicators of Acidification ....................................... 2

Table 1.1-2. Key Indicators of Acidification Due to Nitrogen and Sulfur ..................................... 3

Table 1.1-3. Summary of Linkages between Acidifying Deposition, Biogeochemical 

Processes That Affect Ca2+, Physiological Processes That Are Influenced 

by Ca2+, and Effect on Forest Function .................................................................. 8

Table 1.2-1. Summary of Mapping Layers, Selected Indicator, and Selected Ecological 

Endpoint for the Terrestrial Acidification Case Study ......................................... 12

Table 1.2-2. Compilation of Potential Areas for the Terrestrial Acidification Case Study 

(i.e., for Studying Red Spruce) as Identified in the Literature .............................. 14

Table 1.2-3. Major Studies at the Kane Experimental Forest ....................................................... 18

Table 1.2-4. Characteristics of the Case Study Plots in the Kane Experimental Forest ............... 20

Table 1.2-5. Major Studies at the Hubbard Brook Experimental Forest ...................................... 22

Table 2.1-1. Soil Texture Classes as a Function of Clay and Sand Content ................................. 34

Table 2.1-2. Parent Material Classes for Common FAO Soil Types ............................................ 34

Table 2.1-3. Weathering Rate Classes as a Function of Texture and Parent Material 

Classes................................................................................................................... 35

Table 2.1-4. Data Requirements and Sources for Calculating Critical Loads for Total 

Nitrogen and Sulfur Deposition in Hubbard Brook Experimental Forest 

and Kane Experimental Forest .............................................................................. 40

Table 2.1-5. The Three Indicator (Bc/Al)crit Soil Solution Ratios Used in This Case Study 

and the Corresponding Levels of Protection to Tree Health and Critical 

Loads ..................................................................................................................... 43

Table 2.1-6. Input Values for the Calculation of Critical Load in Hubbard Brook 

Experimental Forest and Kane Experimental Forest ............................................ 47

Final Risk and Exposure Assessment September 2009 

Appendix 5 - vi 
Terrestrial Acidification Case Study 

Table 2.1-7. Soil Characteristics in the Seven Plots of the Kane Experimental Forest Case 

Study Area for the Calculation of the Base Cation Weathering Rate 

Parameters ............................................................................................................. 48

Table 2.1-8. Annual Volume Growth by Tree Species in Each of the Seven Plots of the 

Kane Experimental Forest Case Study Area for the Calculation of Nutrient 

Uptake (Bcu and Nu) .............................................................................................. 48

Table 2.1-9. Specific Gravity and Nutrient Concentrations by Biomass Component (Bark 

and Bole Wood) and by Tree Species for the Calculation of Nutrient 

Uptake (Bcu and Nu) in the Kane Experimental Forest Case Study Area ............. 49

Table 3.1-1. Critical Loads Calculated with the Different Base Cation Weathering, 

Gibbsite Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values in Plot 1 of the Kane Experimental Forest 

Case Study Area .................................................................................................... 51

Table 3.1-2. Critical Loads Calculated with the Different Base Cation Weathering, 

Gibbsite Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values in Plot 2 of the Kane Experimental Forest 

Case Study Area .................................................................................................... 52

Table 3.1-3. Critical Loads Calculated with the Different Base Cation Weathering, 

Gibbsite Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values in Plot 3 of the Kane Experimental Forest 

Case Study Area .................................................................................................... 52

Table 3.1-4. Critical Loads Calculated with the Different Base Cation Weathering, 

Gibbsite Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values in Plot 4 of the Kane Experimental Forest 

Case Study Area .................................................................................................... 53

Table 3.1-5. Critical Loads Calculated with the Different Base Cation Weathering, 

Gibbsite Equilibrium Constant (Kgibb) and Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values in Plot 5 of the Kane Experimental Forest 

Case Study Area .................................................................................................... 53

Table 3.1-6. Critical Loads Calculated with the Different Base Cation Weathering, 

Gibbsite Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values in Plot 6 of the Kane Experimental Forest 

Case Study Area .................................................................................................... 54

Table 3.1-7. Critical Loads Calculated with the Different Base Cation Weathering, 

Gibbsite Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values in Plot 7 of the Kane Experimental Forest 

Case Study Area .................................................................................................... 54

Table 3.1-8. Ranges of Critical Load Values (eq/ha/yr) (with and without the Influence of 

Nutrient Uptake and Removal with Tree Harvest) for the Seven Plots of 

the Kane Experimental Forest Case Study Area (Both Kgibb values and 

methods to estimate BCw were used in these calculations to present the 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - vii 
Terrestrial Acidification Case Study 

range of critical loads estimated using all combinations of the parameter 

values.) .................................................................................................................. 55

Table 3.1-9. Comparison of the Critical Load Values Determined in This Case Study and 

the Critical Load Values Determined by McNulty et al. (2007) for the 

Seven Plots in the Kane Experimental Forest Case Study Area ........................... 58

Table 3.1-10. Critical Load Calculated with the Different Base Cation Weathering and 

Gibbsite Equilibrium Constant (Kgibb) Parameter Values in the Hubbard 

Brook Experimental Forest Case Study Area ....................................................... 59

Table 3.1-11. Summary of the Critical Load Values Determined by Other Studies 

Conducted in the Hubbard Brook Experimental Forest (negative values are 

equal to 0 eq/ha/yr) ............................................................................................... 61

Table 3.2-1. Critical Loads Selected to Represent the Three Levels of Protection in the 

Kane Experimental Forest and Hubbard Brook Experimental Forest Case 

Study Areas ........................................................................................................... 62

Table 3.3-1. Ranges of Differences between the 2002 CMAQ/NADP Total Nitrogen and 

Sulfur Deposition Levels ((N+S)comb) and the Estimated Critical Load 

Values (with and without the Influence of Nutrient Uptake and Removal 

[Nu and Bcu]) for the Seven Plots of the Kane Experimental Forest Case 

Study Area ............................................................................................................ 64

Table 3.3-2. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Critical Load Values (with Two 

Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium 

Constants [Kgibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake 

Parameter Values) in Plot 1 of the Kane Experimental Forest Case Study 

Area ....................................................................................................................... 64

Table 3.3-3. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Critical Load Values (with Two 

Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium 

Constants [Kgibb], and Two Base Cation (Bcu) and Nitrogen (Nu) Uptake 

Parameter Values) in Plot 2 of the Kane Experimental Forest Case Study 

Area ....................................................................................................................... 65

Table 3.3-4. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Critical Load Values (with Two 

Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium 

Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen (Nu ) Uptake 

Parameter Values) in Plot 3 of the Kane Experimental Forest Case Study 

Area ....................................................................................................................... 65

Table 3.3-5. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Critical Load Values (with Two 

Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium 

Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen (Nu ) Uptake 

Parameter Values) in Plot 4 of the Kane Experimental Forest Case Study 

Area ....................................................................................................................... 66

Final Risk and Exposure Assessment September 2009 

Appendix 5 - viii 
Terrestrial Acidification Case Study 

Table 3.3-6. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Critical Load Values (with Two 

Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium 

Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen (Nu ) Uptake 

Parameter Values) in Plot 5 of the Kane Experimental Forest Case Study 

Area ....................................................................................................................... 66

Table 3.3-7. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Critical Load Values (with Two 

Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium 

Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen (Nu ) Uptake 

Parameter Values) in Plot 6 of the Kane Experimental Forest Case Study 

Area ....................................................................................................................... 67

Table 3.3-8. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Critical Load Values (with Two 

Base Cation Weathering Estimation Methods, Two Gibbsite Equilibrium 

Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen (Nu ) Uptake 

Parameter Values) in Plot 7 of the Kane Experimental Forest Case Study 

Area ....................................................................................................................... 67

Table 3.3-9. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Critical Load Values (with Two 

Base Cation Weathering Estimation Methods and Two Gibbsite 

Equilibrium Constants [Kgibb]) in the Hubbard Brook Experimental Forest 

Case Study Area .................................................................................................... 68

Table 4.1-1. Number and Location of USFS FIA Permanent Sampling Plots (each plot is 

0.07 ha) Used in the Analysis of Critical Loads For the Full Geographic 

Ranges of Sugar Maple and Red Spruce............................................................... 76

Table 4.1-2. Gibbsite Equilibrium (Kgibb) Determined by Percentage of Soil Organic 

Matter .................................................................................................................... 79

Table 4.1-3. Parent Material Acidity Classifications for Base Cation (BCw) Estimations ........... 79

Table 4.1-4. Parent Material and Descriptive Modifier Characteristics (within the 

SSURGO Soils [USDA-NRCS, 2008c] and USGS Geology [USGS, 

2009b] Databases) Used to Classify Parent Material Acidity .............................. 80

Table 4.1-5. Ranges of Critical Load Values by Level of Protection (Bc/Al(crit) = 0.6, 1.2, 

and 10.0) and by State for the Full Distribution Ranges of Sugar Maple 

and Red Spruce ..................................................................................................... 83

Table 4.1-6. Percentages of Plots, by Protection Level (Bc/Al(crit) = 0.6, 1.2, and 10.0) and 

by State, where 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels Were Greater Than the Critical Loads for Sugar Maple and Red 

Spruce ................................................................................................................... 85

Table 5.2-1. Differences and Percentage Differences in Plot-Level Critical Load 

Estimates Associated with the Misclassification of Parent Material Acidity 

for the Full Range Assessment of Sugar Maple .................................................... 94

Final Risk and Exposure Assessment September 2009 

Appendix 5 - ix 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - x 

Table 5.2-2. Differences and Percentage Differences in Plot-Level Critical Load 

Estimates Associated with the Misclassification of Parent Material Acidity 

for the Full Range Assessment of Red Spruce...................................................... 95

ATTACHMENT TABLES 

Table 2-1. Summary of Plot-Level Data Used in the Regression Analyses for Sugar 

Maple Volume and Growth, Nitrogen Deposition, and Critical Load 

Exceedances (based on critical loads calculated with Bc/Al = 10.0) ................. A-4

Table 2-2. Summary of Plot-Level Data Used in the Regression Analyses for Red Spruce 

Volume and Growth, Nitrogen Deposition, and Critical Load Exceedances 

(based on critical loads calculated with Bc/Al = 10.0) ....................................... A-6

Table 3-1a. Results from the Multivariate Ordinary Least Squares Linear Regression 

Analyses of Sugar Maple Tree Growth and Nitrogen Deposition (for plots 

where deposition did not exceed critical loads calculated with Bc/Al = 

10.0) .................................................................................................................... A-8

Table 3-1b. Results from the Multivariate Ordinary Least Squares Linear Regression 

Analyses of Red Spruce Tree Growth and Nitrogen Deposition (for plots 

where deposition did not exceed critical loads calculated with Bc/Al = 

10.0) .................................................................................................................... A-9

Table 3-2a. Results from the Multivariate Ordinary Least Squares Linear Regression 

Analyses of Sugar Maple Tree Growth and Critical Load Exceedance (for 

plots where nitrogen and sulfur deposition was greater than critical loads 

calculated with Bc/Al = 10.0) ............................................................................. A-9

Table 3-2b. Results from the Multivariate Ordinary Least Squares Linear Regression 

Analyses of Red Spruce Tree Growth and Critical Load Exceedance (for 

plots where nitrogen and sulfur deposition was greater than critical loads 

calculated with Bc/Al = 10.0) ........................................................................... A-10

Table 3-3. Summary of Plot-Level Data for Sugar Maple Volume and Growth and 

Critical Load Exceedances North of the Glaciation Line (for plots where 

nitrogen and sulfur deposition exceeded critical loads calculated with 

Bc/Al = 10.0) .................................................................................................... A-14

Table 3-4. Summary of Plot-Level Data for Red Spruce Volume and Growth and Critical 

Load Exceedances North of the Glaciation Line (for plots where nitrogen 

and sulfur deposition exceeded critical loads calculated with Bc/Al = 10.0) ... A-16

Table 3-5. Results from the Multivariate Ordinary Least Squares Linear Regression 

Analyses of Sugar Maple Tree Growth and Critical Load Exceedance 

North of the Glaciation Line (for plots where deposition exceeded critical 

loads calculated with Bc/Al = 10.0) .................................................................. A-17

Table 3-6. Results from the Multivariate Ordinary Least Squares Linear Regression 

Analyses of Red Spruce Tree Growth and Critical Load Exceedance, 

North of the Glaciation Line (for plots where deposition exceeded critical 

loads calculated with Bc/Al = 10.0) .................................................................. A-18
Terrestrial Acidification Case Study 

ACRONYMS AND ABBREVIATIONS 

Al aluminum2+,3+

Al3+ trivalent aluminum 

ANC acid neutralizing capacity 

ANCle,crit forest soil acid neutralizing capacity of critical load leaching (calculated 

value)

Bc base cation (Ca2+ + K+ + Mg2+) 

(Bc/Al)crit base cation (Ca2+ + K+ + Mg2+) to aluminum ratio (selected indicator value) 

BC base cation (Ca2+ + K+ + Mg2+ + Na+) 

BCdep base cation (Ca2+ + K+ + Mg2+ + Na+) deposition 

Bcu uptake of base cations (Ca2+ + K+ + Mg2+) by trees 

BCw base cation (Ca2+ + K+ + Mg2+ + Na+) weathering 

Ca2+ calcium 

Cl-

 chloride 

Cldep chloride deposition 

CLmax(N) maximum critical load of nitrogen 

CLmax(S) maximum critical load of sulfur 

CLmin minimum critical load 

CLmin(N) minimum critical load of nitrogen 

CLF critical load function 

CLRTAP Convention on Long-Range Transboundary Air Pollution 

cm centimeter 

CMAQ Community Multiscale Air Quality 

eq/ha/yr equivalents per hectare per year 

FAO Food and Agriculture Organization 

Fe3+ trivalent (ferrous) iron 

FGROWCFAL Net annual sound cubic-foot growth of a live tree on forest land 

FIA Forest Inventory and Analysis National Program 

ft3

 cubic feet 

GIS geographic information systems 

ha hectare 

HBEF Hubbard Brook Experimental Forest 

HNO3 nitric acid 

ICP International Cooperative Programme 

ISA Integrated Science Assessment 

K+

 potassium 

Kgibb the gibbsite equilibrium constant 

KEF Kane Experimental Forest 

kg kilogram 

km kilometer 

m meter 

Mg2+ magnesium 

mm millimeter 

Mn manganese2+,4+ 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - xi 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - xii 

mol mole 

N nitrogen 

Nde denitrification 

Ni nitrogen immobilization 

Nu uptake of nitrogen by trees 

(N+S)comb combined nitrogen and sulfur deposition 

Na+

 sodium 

NADP National Atmospheric Deposition Program 

NEG/ECP Conference of New England Governors and Eastern Canadian Premiers 

NH4

+

 ammonium 

NHx total reduced nitrogen 

NO3

-

 nitrate 

NOx nitrogen oxides 

NOy oxidized nitrogen 

NRCS Natural Resources Conservation Service 

OLS ordinary least squares 

S sulfur 

SMB Simple Mass Balance 

SO2 sulfur dioxide 

SO4

2- sulfate 

SOx sulfur oxides 

SSURGO Soil Survey Geographic Database 

STATSGO State Soil Geographic Database 

UNECE United Nations Economic Commission for Europe 

USDA U.S. Department of Agriculture 

USFS U.S. Forest Service 

USGS U.S. Geological Survey 

VOLCFNET total net volume per tree 

ybp years before present 
Terrestrial Acidification Case Study 

1.0 BACKGROUND 

The selection and performance of case studies represent Steps 3 and 4, respectively, of 

the 7-step approach to planning and implementing a Risk and Exposure Assessment of total 

nitrogen, nitrogen oxides (NOx) (as a component of total nitrogen), and sulfur oxides (SOx) 

deposition on ecosystems, as presented in the April 2008 Scope and Methods Plan for Risk 

Exposure Assessment (U.S. EPA, 2008a). Step 4 entails evaluating the current Community 

Multiscale Air Quality Model (CMAQ) modeling results for 2002 and the 2002 National 

Atmospheric Deposition Program (NADP) monitoring data for total nitrogen and sulfur 

deposition loads on, and effects to, a chosen case study assessment area, including ecosystem 

services. This case study evaluates the current wet and dry atmospheric nitrogen and sulfur 

deposition load to terrestrial ecosystems and the role atmospheric deposition can play in the 

acidification of a terrestrial ecosystem. 

Deposition of NOx and SOx can result in acidification of certain terrestrial ecosystems. 

Because ecosystems and species may respond differently, case studies have been used to 

illustrate potential effects of acidification on sensitive species. This report presents the 

quantitative approach used to analyze the acidification effects of total nitrogen, NOx (as a 

component of total nitrogen), and SOx deposition on red spruce and sugar maple. 

Acidification 

Acidification is the process of increasing the acidity of a system (e.g., lake, stream, forest 

soil). Within soils, acidification occurs through increases in hydrogen ions or protons. Terrestrial 

acidification occurs as a result of both natural biogeochemical processes and acidifying 

deposition where strong acids are deposited into the soil. Acidifying deposition increases 

concentrations of nitrogen and sulfur in the soil, which accelerates the leaching of sulfate (SO4

2-) 

and nitrate (NO3

-

) from the soil to drainage water. Under natural conditions (i.e., low 

atmospheric deposition of nitrogen and sulfur), the limited mobility of anions in the soil controls 

the rate of base cation leaching. However, acidifying deposition of nitrogen and sulfur species 

can significantly increase the concentration of anions in the soil, leading to an accelerated rate of 

base cation leaching, particularly the leaching of calcium (Ca2+) and magnesium (Mg2+) cations. 

If soil base saturation (i.e., the concentration of exchangeable base cations as a percentage of the 

total cation exchange capacity. Cation exchange capacity, the sum total of exchangeable cations 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 1 
Terrestrial Acidification Case Study 

that a soil can absorb, is 20% to 25%, or lower, inorganic aluminum2+,3+ (Al) can become 

mobilized, leading to the leaching of Al into soil waters and surface waters (Reuss and Johnson, 

1985). This is an important effect of acidifying deposition because inorganic Al is toxic to tree 

roots, fish, algae, and aquatic invertebrates (U.S. EPA, 2008c, Sections 3.2.1.5, 3.2.2.1, and 

3.2.3). 

1.1 INDICATORS, ECOLOGICAL ENDPOINTS, AND ECOSYSTEM 

SERVICES 

1.1.1 Indicators 

The Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur–Ecological 

Criteria (Final Report) (ISA) (U.S. EPA, 2008c) identified a variety of indicators supported by 

the literature that can be used to measure the effects of acidification in soils (Table 1.1-1). Table 

1.1-2 provides a general summary of these indicators by indicator groups. 

Table 1.1-1. Literature Support for Selected Indicators of Acidification 

Citation Main Finding 

Soil Base Saturation 

Reuss, 1983 When base saturation less than 15% to 20%, then 

exchange ion chemistry is dominated by inorganic Al. 

Cronan and Grigal, 1995 When base saturation below about 15% in the soil, Bhorizon could lead to impacts from Al stress. 

Lawrence et al., 2005 When base saturation declines from 30% to 20% in the 

upper soil, B-horizon showed decreases in diameter 

growth of Norway spruce. 

Bailey et al., 2004 Sugar maple mortality found at Ca2+ saturation less 

than 2% and Mg2+ saturation less than 0.5% in the 

upper soil B-horizon. 

Johnson et al., 1991; Joslin and Wolfe, 1992 When base saturation below about 20%, base cation 

reserves are so low that Al exchange dominates. 

Al Concentrations 

Johnson et al., 1991; Joslin and Wolfe, 1992 When base saturation below about 20%, base cation 

reserves are so low that Al exchange dominates. 

Cronan and Grigal, 1995; Eagar et al., 1996 There is a 50% chance of negative effects on tree 

growth if the molar ratio of Ca2+/Al in soil solution is 

as low as 1.0. There is a 100% chance for negative 

effects on growth at molar ratio value below 0.2. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 2 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 3 

Citation Main Finding 

Johnson et al., 1994a, b Ca2+/Al ratios above 1.0 were found in a forestland 

experiencing high mortality over the course of 4 years. 

DeWitt et al., 2001 Ca2+/Al ratios of Norway spruce stand below 0.5 

showed reduced Mg2+ concentrations in needles in the 

third year. 

Carbon/Nitrogen Ratio 

Aber et al., 2003; Ross et al., 2004 Increased effects of nitrification occur only in soil with 

carbon/nitrogen ratio below about 20 to 25. 

Source: U.S. EPA 2008c, Section 3.2.2.1. 

Table 1.1-2. Key Indicators of Acidification Due to Nitrogen and Sulfur 

Key Indicator Group Examples of Indicators Description 

Acid anions SO4

2-, NO3- Trends in these concentrations reflect recent 

trends in atmospheric deposition (especially 

SO4

2-) and in ecosystem responses to long-term 

deposition (notably NO3

-

 and desorbed SO4

2-). 

Base cations Ca2+, Mg2+, BC (sum of 

Ca2+, Mg2+, K+

 and Na+), 

Bc (sum of Ca2+, Mg2+, and 

K+

) 

These cations are mobilized by weathering 

reactions and cation exchange. They respond 

indirectly to decreases in SO4

2- and NO3- 

because a reduced input of acids will lead to a 

reduction of neutralizing processes in the soil, 

thereby reducing the release of base cations to 

soil water and runoff water. (Base saturation is 

included within this category.) 

Acidity pH, acid neutralizing 

capacity 

These indicators reflect the outcomes of 

interactions between the changing 

concentration of acid anions and base cations. 

Carbon Carbon/nitrogen ratio The carbon/nitrogen ratio of soil indicates 

alterations to the nitrogen biogeochemical 

cycle. 

Metals Al3+, Fe3+ These metals are mobilized as a response to the 

deposition of SO4

2- and NO3-

. 

Biological Tree health, community 

structure, species 

composition, taxonomic 

richness, Index of Biotic 

Integrity 

Ecological effects occur at four levels: 

individual, population, community, and 

ecosystem. Metrics have been developed for 

each level to assess the negative effects of 

acidification. 

Note: BC = base cation (Ca2+ + K+ + Mg2+ + Na+); Bc = base cation (Ca2+ + K+ + Mg2+); K+ = potassium; 

Na+

 = sodium; Al3+ = trivalent aluminum; Fe3+ = trivalent (ferrous) iron.

Much of the literature discussing terrestrial acidification focuses on Ca2+ and Al as the 

primary indicators of detrimental effects for trees and other terrestrial vegetation. As such, this 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 4 

discussion of indicators of terrestrial acidification focuses on these two parameters and the 

interaction between them. The use of these indicators—in combination and through the 

evaluation framework that will be described within this case study—ultimately combines all 

indicator categories described in Table 1.1-1 except the carbon category. Ca2+ and Al are the 

focus of the analysis because both of these indicators are strongly influenced by soil acidification 

and both have been shown to have quantitative links to tree health, including aluminum’s 

interference with Ca2+ uptake and Al toxicity to roots. 

A detailed description of the influences of Al on Ca2+ is provided by Schaberg et al. 

(2001)1: 

Decreases in concentrations of exchangeable calcium are generally attributed to 

displacement by hydrogen ions, which can originate from either acidifying 

deposition or uptake of cations by roots (Johnson et al., 1994a; Richter et al., 

1994). A regional survey of soils in northeastern red spruce forests in 1992-93 

(fig. 2)2 has revealed that decreases in exchangeable calcium concentrations in the 

Oa horizon (a layer within the forest floor, where uptake of nutrients is greatest) 

can also result from increased concentrations of exchangeable aluminum, which 

originated in the underlying mineral soil (Lawrence et al., 1995). By lowering the 

pH of the aluminum-rich mineral soil, acid deposition can increase aluminum 

concentrations in soil water through dissolution and ion-exchange processes. 

Once in solution, the aluminum (although not a nutrient) is taken up by roots and 

transported through the trees to be eventually deposited on the forest floor in 

leaves and branches. 

A continued buildup of aluminum in the Oa horizon can (1) decrease the 

availability of calcium for roots (Lawrence et al., 1995), (2) lower the efficiency 

of calcium uptake because aluminum is more readily taken up than calcium when 

the ratio of calcium to aluminum in soil water is less than 1 (Cronan and Grigal, 

1995), and (3) be toxic to roots at high concentrations (Lawrence et al., 1995). 

The relationship between Ca2+ and Al and tree health is summarized in the ISA (U.S. 

EPA, 2008c, Section 3.2.2.1), as excerpted below3: 

Al may be toxic to tree roots. Plants [exposed to] high Al concentration in soil 

solution often have reduced root growth, which restricts the ability of the plant to 

take up water and nutrients, especially Ca (Parker et al., 1989) (Figure 3-5 [of 

U.S. EPA, 2008c]). Ca is well known as an ameliorant for Al toxicity to roots in 

soil solution, as well as to fish in a stream. However, because inorganic Al tends 

 

1 References contained within this quotation are not included in the References section of this case study report. 

2 Figure 2 is not included in this case study report. 

3 References contained within this quotation are not included in the References section of this case study report. 
Terrestrial Acidification Case Study 

to be increasingly mobilized as soil Ca is depleted, elevated concentrations of 

inorganic Al tend to occur with low levels of Ca in surface waters. Mg, and to a 

lesser extent Na and K, have also been associated with reduced Al toxicity. 

Dissolved Al concentrations in soil solution at spruce-fir study sites in the 

southern Appalachian Mountains frequently exceed 50 μM and sometimes exceed 

100 μM (Eagar et al., 1996; Johnson et al., 1991; Joslin and Wolfe, 1992a). All 

studies reviewed by Eagar [et al.] (1996) showed a strong correlation between Al 

concentrations and NO3

−

 concentrations in soil solution. They surmised that the 

occurrence of periodic large pulses of NO3

−

 in solution were important in 

determining Al chemistry in the soils of southern Appalachian Mountain sprucefir forests. 

The negative effect of Al mobilization on Ca uptake by tree roots was proposed 

by Shortle and Smith (1988). Substantial evidence of this relationship has 

accumulated over the past two decades through field studies (Kobe et al., 2002; 

McLaughlin and Tjoelker, 1992; Minocha et al., 1997; Schlegel et al., 1992; 

Shortle et al., 1997) and laboratory studies (see review by Cronan and Grigal, 

1995; Sverdrup and Warfvinge, 1993). Based on these studies, it is clear that high 

inorganic Al concentration in soil water can be toxic to plant roots. The toxic 

response is often related to the concentration of inorganic Al relative to the 

concentration of Ca, expressed as the molar ratio of Ca to inorganic Al in soil 

solution. As a result, considerable effort has been focused on determining a 

threshold value for the ratio of Ca to Al that could be used to identify soil 

conditions that put trees under physiological stress. 

Building on the explanation of the relationship between Ca2+, Al, and tree health, a figure 

developed by DeHayes et al. (1999), clearly shows the connections between nitrogen and sulfur 

acidifying deposition and Ca2+ within an ecosystem (Figure 1.1-1). The authors used solid lines 

to denote known connections and dotted lines to present potential impacts. While the authors did 

not specify that increases in Al within the soils would occur with reductions in biologically 

available Ca2+ pools, this impact is expected as detailed in the previous paragraphs. The final 

process represented in Figure 1.1-1 completes the linkage from the indicator of Ca2+ (and 

therefore Al) to the effects on the ecosystem services for the terrestrial area. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 5 
Terrestrial Acidification Case Study 

Acid Rain

Air pollutants

Temperature perturbation 

(high, low, variable)

Insects

Pathogens

Drought

Heavy metals

Disruptions in Forest Ecosystem Health/Stability

Predisposition to Stress-induced Injury

Membrane 

Disruption

Reductions in Biologically 

Important Calcium Pools

Soil Calcium 

Depletion

Disruptions in Stress 

Response Systems

Potential Secondary 

Environmental Stresses

 

Figure 1.1-1. Conceptual impacts of acidifying deposition on soil Ca2+ depletion, 

tree physiology, and forest ecosystem health and sustainability (recreated from 

DeHayes et al., 1999). 

In summary, based on the ISA (U.S. EPA, 2008c) and supporting literature, soil Ca2+ and 

Al are suitable chemical indicators to represent the impacts of acidification in soils and to 

provide a linkage between soil acidification and tree health. Therefore, the Ca/Al ratio in soil 

solution was selected as the basis for the indicator in this case study to evaluate critical loads of 

acidity in terrestrial systems. Within the calculations of critical loads, the base cation (Bc) to Al 

ratio (Bc/Al), consisting of molar equivalents of Ca2+, Mg2+, and K+

, was used to represent the 

Ca/Al indicator. This Bc/Al ratio was selected because it is the most commonly used indicator or 

critical ratio (Bc/Al(crit)) in the Simple Mass Balance (SMB) model used to estimate critical acid 

loads in the European Union, Canada, and the United States (McNulty et al., 2007; Ouimet et al., 

2006; UNECE, 2004), and the SMB model was applied to this case study (see Section 2.1 for 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 6 
Terrestrial Acidification Case Study 

description of model). In addition, tree species show similar sensitivities to Ca/Al and Bc/Al soil 

solution ratios. Therefore, the Bc/Al ratio represents a good indicator of the negative impacts of 

soil acidification on terrestrial vegetation. Sverdrup and Warfvinge (1993b), in a metadata 

analysis of laboratory and field studies, reported that the critical Bc/Al ratios for a large variety 

of tree species ranged from 0.2 to 0.8. This range is similar to that described by Cronan and 

Grigal (1995) for Ca/Al. In their metadata assessment of studies examining sensitivities to the 

Ca/Al ratio, plant toxicity or nutrient antagonism was reported to occur at Ca/Al ratios ranging 

from 0.2 to 2.5. 

1.1.2 Ecological Endpoints 

The tree species most commonly associated with the impacts of acidification due to 

atmospheric nitrogen and sulfur deposition include red spruce (Picea rubens), a coniferous tree 

species, and sugar maple (Acer saccharum), a deciduous tree species. Both species are found in 

the eastern United States, and soil acidification is widespread throughout this area (Warby et al., 

2009). 

Red spruce is found scattered throughout high-elevation sites in the Appalachian 

Mountains, including the southern peaks (Figure 1.1-2). Noticeable levels of the canopy red 

spruce died within the Adirondack, Green, and White mountains in the 1970s and 1980s. 

Although a variety of conditions, such as changes in climate and exposure to ozone, may impact 

the growth of red spruce (Fincher et al., 1989; Johnson et al., 1988), acidifying deposition has 

been implicated as one of the main factors causing this decline. Based on the research conducted 

to date, acidic deposition can cause a depletion of base cations in upper soil horizons, Al toxicity 

to tree roots, and accelerated leaching of base cations from foliage (U.S. EPA, 2008c, Section 

3.2.2.3). Such nutrient imbalances and deficiencies can reduce the ability of trees to respond to 

stresses, such as insect defoliation, drought, and cold weather damage (DeHayes et al., 1999; 

Driscoll et al., 2001), thereby decreasing tree health and increasing mortality. Additional 

linkages between acidifying deposition and red spruce physiological responses are indicated in 

Table 1.1-3. Within the southeastern United States, periods of red spruce decline slowed after 

the 1980s, when a corresponding decrease in SO2 emissions, and therefore acidic deposition, was 

recorded (Webster et al., 2004). 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 7 
Terrestrial Acidification Case Study 

Sugar maple is found throughout the northeastern United States and the central 

Appalachian Mountain region (Figure 1.1-2). This species has been declining in the eastern 

United States since the 1950s. Studies on sugar maple have found that one source of this decline 

in growth is related to both acidifying deposition and base-poor soils on geologies dominated by 

sandstone or other base-poor substrates (Bailey et al., 2004; Horsley et al., 2000). These site 

conditions are representative of the conditions expected to be most susceptible to impacts of 

acidifying deposition because of probable low initial base cation pools and high base cation 

leaching losses (U.S. EPA, 2008c, Section 3.2.2.3). The probability of a decrease in crown vigor 

or an increase in tree mortality has been noted to increase at sites with low Ca2+ and Mg2+ as a 

result of leaching caused by acidifying deposition (Drohan and Sharpe, 1997). Low levels of 

Ca2+ in leaves and soils were related to lower rates of photosynthesis and higher antioxidant 

enzyme activity in sugar maple stands in Pennsylvania (St. Clair et al., 2005). Additionally, plots 

of sugar maples in decline were found to have Ca2+/Al ratios less than 1, as well as lower base 

cation concentrations and pH values compared to plots of healthy sugar maples (Drohan et al., 

2002). Sugar maple regeneration has also been noted to be restricted under conditions of low soil 

Ca2+ levels (Juice et al., 2006). These indicators have been shown to be related to the deposition 

of atmospheric nitrogen and sulfur. Additional linkages between acidifying deposition and sugar 

maple physiological responses are indicated in Table 1.1-3. 

Table 1.1-3. Summary of Linkages between Acidifying Deposition, Biogeochemical Processes 

That Affect Ca2+, Physiological Processes That Are Influenced by Ca2+, and Effect on Forest 

Function 

Biogeochemical Response to 

Acidifying Deposition Physiological Response Effect on Forest Function 

Leach Ca2+ from leaf membrane Decrease the cold tolerance of 

needles in red spruce 

Loss of current-year needles in 

red spruce 

Reduce the ratio of Ca2+/Al in 

soil and soil solutions 

Dysfunction in fine roots of red 

spruce blocks uptake of Ca2+

Decreased growth and increased 

susceptibility to stress in red 

spruce 

Reduce the ratio of Ca2+/Al in 

soil and soil solutions 

More energy is used to acquire 

Ca2+ in soils with low Ca2+/Al 

ratios 

Decreased growth and increased 

photosynthetic allocation to red 

spruce roots 

Reduce the availability of 

nutrient cations in marginal soils 

Sugar maples on drought-prone 

or nutrient-poor soils are less 

able to withstand stresses 

Episodic dieback and growth 

impairment in sugar maple 

Source: Fenn et al., 2006a. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 8 
Terrestrial Acidification Case Study 

Although the main focus of the Terrestrial Acidification Case Study is an evaluation of 

the negative impacts of nitrogen and sulfur deposition on soil acidification and tree health, it 

should be recognized that, under certain conditions, nitrogen and sulfur deposition can have a 

positive impact on tree health. Nitrogen limits the growth of many forests (Chapin et al., 1993; 

Killam, 1994; Miller, 1988); therefore, in such forests, nitrogen deposition may act as a fertilizer 

and stimulate growth. Forests where critical acid loads are not exceeded by nitrogen and sulfur 

deposition could potentially be included within this group of forests that respond positively to 

deposition. These potential positive growth impacts of nitrogen and sulfur deposition are 

discussed further, and the results of analyses are presented in Attachment A at the end of this 

Appendix. 

In summary, among the potential influencing 

factors, including elevated ozone levels and changes in 

climate, the acidification of soils is one of the factors that 

can negatively impact the health of red spruce and sugar 

maple. Mortality and susceptibility to disease and injury can be increased and growth decreased 

with acidifying deposition. Therefore, the health of sugar maple and red spruce was used as the 

endpoints (ecological responses) to evaluate acidification in terrestrial systems. “Health,” in the 

context of this case study, is defined as the physiological condition of a tree, which impacts 

growth and/or mortality. 

End Point: The health of sugar 

maple and red spruce was 

selected as the endpoints to 

estimate critical deposition loads of 

acidity in this case study. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 9 
Terrestrial Acidification Case Study 

Figure 1.1-2. Areal coverages of red spruce and sugar maple tree species 

within the continental United States (USFS, 2006).

1.1.3 Ecosystem Services 

Ecosystem services are generally defined as the benefits individuals and organizations 

obtain from ecosystems. In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem 

services are classified into four main categories: 

 Provisioning—includes products obtained from ecosystems 

 Regulating—includes benefits obtained from the regulation of ecosystem processes 

 Cultural—includes the nonmaterial benefits people obtain from ecosystems through 

spiritual enrichment, cognitive development, reflection, recreation, and aesthetic 

experiences 

 Supporting—includes those services necessary for the production of all other ecosystem 

services (MEA, 2005). 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 10 
Terrestrial Acidification Case Study 

A number of impacts on the ecological endpoints of forest health, water quality, and 

habitat exist, including the following: 

 Decline in forest aesthetics—cultural 

 Decline in forest productivity—provisioning 

 Increases in forest soil erosion and reductions in water retention—cultural and regulating. 

Recognizing that many ecosystem services have not been adequately studied, the 

ecosystem services highlighted in this case study will include economic values associated with 

red spruce and sugar maple wood volume production. 

1.2 CASE STUDY AREAS 

The selection of case study areas to evaluate terrestrial acidification was based on 

geographic information systems (GIS) mapping (locations recommended by the ISA [U.S. EPA, 

2008c; Sections 3.2, 4.2, and Annex B]), and the availability of data for the selected indicators 

and ecological endpoints, as presented in relevant literature and databases. 

1.2.1 GIS Analysis of National Sensitivity 

A GIS analysis was performed on datasets and datalayers of physical, chemical, and 

biological properties to map areas of potential sensitivity to acidification in the United States 

(Table 1.2-1). Ranges of sugar maple and red spruce were mapped by extracting counties with 

plots that contained either sugar maple or red spruce from the U.S. Forest Service (USFS) Forest 

Inventory and Analysis (FIA) database (http://fia.fs.fed.us/tools-data/). To characterize soil 

acidity, soil pH was mapped with State Soil Geographic Database (STATSGO) soils 

(http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov) and USFS Forest Soils 

datalayers. Soil thickness was also extracted from the STATSGO soils data. Areas with bedrock 

with high acid neutralizing capacity (ANC) were determined by using the karst topography 

dataset from the National Atlas of the United States (Tobin and Weary, 2005). Karst topography 

is a landscape formed by the dissolution of soluble rock (e.g., limestone, dolomite); caves, 

springs, and sinkholes are common features of this type of landscape (USGS, 2009a). Locations 

with sugar maple or red spruce, soil pH ≤5.0, soils ≤51 centimeters (cm) in depth, and low ANC 

bedrock (not dominated by carbonate rocks) were selected to represent areas with potential 

sensitivity to acidification (Figure 1.2-1). 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 11 
Terrestrial Acidification Case Study 

Table 1.2-1. Summary of Mapping Layers, Selected Indicator, and Selected Ecological Endpoint 

for the Terrestrial Acidification Case Study

Targeted 

Ecosystem 

Effect 

Selected 

Indicator 

Selected 

Biological 

Endpoint Mapping Layers 

Terrestrial 

acidification 

due to 

nitrogen and 

sulfur 

Ca2+/Al 

ratio* 

Reduced 

health of 

red spruce 

and sugar 

maple 

Sugar maple and red spruce coverages (USFS, 2006) 

Soil pH (USDA, 1994; USFS, 2008, Dr. Charles Perry, 

personal communication) 

Soil depth (USDA, 1994) 

Karst topography (Tobin and Weary, 2005)

* The Bc/Al (Bc = Ca2+, Mg2+, and K+

) is used to represent the Ca2+/Al ratio indicator in the acid load 

calculations (described further at the end of Section 1.1.1). 

Figure 1.2-1. Map of areas of potential sensitivity of red spruce and sugar maple to 

acidification in the United States (see Table 1.2-1 for listing of data sources to produce 

this map).

1.2.2 Selection of Case Study Areas 

Following the identification of regions of potential sensitivity to acidification, Risk and 

Exposure Assessment sites recommended by the Science Advisory Board—Ecological Effects 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 12 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 13 

Subcommittee (U.S. EPA, 2005) and found in the ISA (U.S. EPA, 2008c, Appendix A) and in 

the body of published and unpublished literature were reviewed to determine the most suitable 

locations for the Hubbard Brook Experimental Forest (HBEF) and Kane Experimental Forest 

(KEF) case study areas. 

Selection of a location for studying the sugar maple focused on the Allegheny Plateau 

region in Pennsylvania, where a large proportion of published and unpublished research has been 

focused. A significant amount of the research work in the Allegheny Plateau region has been 

sponsored by the USFS and has produced extensive datasets of soil and tree characteristics 

(Horsley et al., 2000; Bailey et al., 2004; Hallett et al., 2006). The USFS-designated KEF was 

selected as the area for studying the sugar maple as part of the Terrestrial Acidification Case 

Study. The KEF has been the focus of several long-term studies since the 1930s. 

Selection of a case study area for studying the red spruce involved the review of a variety 

of regions. Four studies that examined the relationship between the Ca2+/Al soil solution ratio 

and tree health were identified, and relevant soil and tree information for each of the study 

regions was compiled (Table 1.2-2). A review of this information led to the selection of the 

HBEF in New Hampshire’s White Mountains as the area for the study of red spruce in the 

Terrestrial Acidification Case Study. The HBEF was also recommended by the ISA (U.S. EPA, 

2008c, Appendix A) as a good location for the Risk and Exposure Assessment. This forest has 

experienced high atmospheric nitrogen and sulfur deposition levels and low Ca2+/Al soil solution 

ratios. It has been the subject of extensive nutrient investigations and has provided a large dataset 

from which to work on the case study. 
Terrestrial Ac

Table 1.2-2. Compilation of Potential Areas for the Terrestrial Acidification Case Study (i.e., for Studying Red Sin the Literature

Site Name 

Elevation 

(m) 

Size of Tree 

Population 

Availability of 

Field Data 

Ecological 

Importance Reported Impacts 

Ca2+:Al and 

Al/Ca2+

Ratios 

Dep

L

(kg

Balsam High Top, NC 

1,641 Spruce-fir foreste University study Great Smoky 

Mountains National Park 

Nearly 100% chance of negative forest health effects 

0.094a Un

Clingman’s Dome, TN 

2,020 Spruce-fir foreste University study Great Smoky 

Mountains National Park 

Nearly 100% chance of negative forest health effects 

0.084a Un

Double Spring Gap, TN 

1,678 Spruce-fir foreste University study Great Smoky 

Mountains National Park 

Nearly 100% chance of negative forest health effects 

0.053a Un

Mount LeConte, TN 

2,010 Spruce-fir foreste University study Great Smoky 

Mountains National Park 

75% chance of a negative forest health effects 

0.567a Un

Mount Sterling, TN 

1,772 Spruce-fir foreste University study Great Smoky 

Mountains National Park 

Nearly 100% chance of negative forest health effects 

0.07a Un

Richland Balsam Mountain, NC 

1,941 Spruce-fir foreste University study Blue Ridge 

Parkway 

Nearly 100% chance of negative forest health effects 

0.07a Un

Spruce Mountain, NC 

1,695 Spruce-fir foreste University study Great Smoky 

Mountains National Park 

Nearly 100% chance of negative forest health effects 

0.128a Un

Sleepers River, VT 

NA Red spruce 

dominated with low exchangeable Al/Ca2+ ratios 

Not selected in studies 

— Site did not contain 

sufficient number of healthy, mature red spruce for study 

NA Un

Groton, VT 520 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS study location 

— No specific references at 

this time, but disturbances are known to have occurred 

0.3b 

Final Risk and Exposure Assessment 

Appendix 5 - 14 
Terrestrial Ac

Final Risk and Exposure Assessment 

Appendix 5 - 15 

Site Name 

Elevation 

(m) 

Size of Tree 

Population 

Availability of 

Field Data 

Ecological 

Importance Reported Impacts 

Ca2+:Al and 

Al/Ca2+

Ratios 

Dep

L

(kg

Howland, ME 60 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS study location 

— No specific references at 

this time, but disturbances are known to have occurred 

0.4b 

Bartlett, NH 525 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS Experimental Forest (1,052 ha); red spruce covers highest slopes 

Within the White Mountains 

No specific references at this time, but disturbances are known to have occurred 

0.8b 

Kossuth, ME 100 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS study location 

— No specific references at 

this time, but disturbances are known to have occurred 

0.8b 

Hubbard Brook, NH 

755 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS Experimental Forest (3,138 ha); red spruce abundant at higher elevations and on rock outcrops 

Within the White Mountains 

Acid-extractable Al in the forest floor increased over the past two decades at the HBEF, and ratios of Al to Ca2+ in mineral 

soil solutions (but not forest floor solutions) were strongly correlated with exchangeable Al content in the forest floor.

0.8b 

Whiteface Mountain, NY 

950 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS study location 

— Contained neither 

evidence of unusual mortality or current tree decline; winter injury events reported (Lazarus et al., 2004) 

0.8b 

Crawford Notch, NH 

670 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS study location 

Within the White Mountains 

50% chance of negative forest health effects; mortality of red spruce was significant, but most of the remaining trees were in good to fair health 

1.1b 
Terrestrial Ac

Final Risk and Exposure Assessment 

Appendix 5 - 16 

Site Name 

Elevation 

(m) 

Size of Tree 

Population 

Availability of 

Field Data 

Ecological 

Importance Reported Impacts 

Ca2+:Al and 

Al/Ca2+

Ratios 

Dep

L

(kg

Big Moose Lake, NY 

550 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS study location 

— 50% chance of negative 

forest health effects 

1.2b 

Bear Brook, ME 400 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS study location 

— 75% chance of negative 

forest health effects 

1.9b 

Cone Pond, NH 610 Red spruce 

dominated with a gradient of forest floor exchangeable Al/Ca2+ ratios 

USFS study location 

Within the White Mountains 

Nearly 100% chance of negative forest health effects 

5.2b 

Mt. Abraham, VT 

NA Red spruce 

dominated with a high exchangeable Al/Ca2+ ratios 

Not selected in studies 

Within the Green Mountains 

Site did not contain sufficient number of healthy, mature red spruce for study; forest floor solution Al/ Ca2+ 

ratio above the 50% chance level 

7.1b 

Mt. Ascutney, VT 

762 Series of high 

elevation spruce-fir forest nitrogen addition plots f

USFS study location 

Nitrogen additions to system 

Reduction in live basal area on the high nitrogen addition plots versus control plots 

NA Ad

a Molar Ca2+/Al ratio (Bintz and Butcher, 2007). b Oa horizon Al/Ca2+ ratios (Wargo et al., 2003). c Estimated wet nitrogen deposition (Lilleskov et al., 2008). d In addition to ambient total nitrogen deposition, paired plots each received 15.7 kilograms (kg) N/ha/yr (low nitrogen addition), 31.4 

addition) or no nitrogen addition (control) from 1988 to 2002. 

e High elevation sites in the Southern Appalachians—The sites are located in the Great Smoky Mountains National Park and Richland 

Blue Ridge Parkway. Sites were selected because of the presence of a spruce-fir forest with a northwest slope aspect within 10 km of 

between 1,650 and 2,025 meters (m). 

f Red spruce grew in large patches (>1 ha) at elevations above 725 m. Red spruce comprised > 80% of the total basal area in all plots; 

tree species were divided among balsam fir, red maple, mountain maple, and birch. 

NA= Not available
Terrestrial Acidification Case Study 

1.2.3 Sugar Maple

1.2.3.1 Kane Experimental Forest 

The KEF (USFS, 1999, 2008b) is located on the eastern edge of the Allegheny National 

Forest, 5.6 kilometers (km) south of Kane, PA (Figure 1.2-2). It is comprised of 703 hectare (ha) 

of forestland and ranges in elevation from about 550 to 640 meters (m) above sea level, primarily 

on flat to gently sloping land. The climate of the KEF is humid temperate; the average annual 

temperature is 6°C. The forest receives approximately 110 cm of precipitation per year, mostly 

as rain, including 10 cm/month during the growing season. 

Figure 1.2-2. Location of the Kane Experimental Forest (Horsley et al., 2000).

The forest soils on the Allegheny Plateau are derived from shales and sandstones. In 

general, these soils are very stony and exist as extremely stony loams and sandy loams. They are 

strongly acidic. The major soil series are the well-drained Hazelton series, the moderately welldrained to somewhat poorly drained Cookport series, and the somewhat poorly drained Cavode 

series. 

The forest stands on the KEF are typical of the Allegheny Plateau. They resulted from a 

series of cuttings made in the original hemlock-beech-maple stands starting as early as the mid1880s. Currently, the KEF contains second-growth stands ranging from 60 to about 100 years of 

age, several third-rotation stands 20 or 40 years old, and one tract with remnant old growth. Most 

stands are even-aged, with black cherry, maples, and beech being the main overstory species. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 17 
Terrestrial Acidification Case Study 

The KEF was formally established in 1932, although research there began as early as 

1927 or 1928. The forest’s primary mission has been forest management research, and the 

current research focus is centered on three topics: regeneration and forest renewal stand 

dynamics, silviculture, and sugar maple decline. Table 1.2-3 summarizes major studies at the 

KEF related to the sugar maple and chemical criterion that can be used in calculating critical 

loads of atmospheric nitrogen and sulfur deposition. 

Table 1.2-3. Major Studies at the Kane Experimental Forest

Authors Year Title Key Finding 

Horsley.et al. 2000 Factors Associated 

with the DeclineDisease of Sugar 

Maple on the 

Allegheny Plateau 

The most important factors determining sugar maple 

health were foliar levels of Mg2+ and Mn and 

defoliation history. The decline disease of sugar 

maple appears to be the result of an interaction 

between Mg2+ (and perhaps Mn) nutrition and stress 

caused by defoliation. 

Bailey et al. 2005 Thirty Years of 

Change in Forest 

Soils of the 

Allegheny Plateau, 

Pennsylvania 

Between 1967 and 1997, there were significant 

decreases in exchangeable Ca2+ and Mg2+

concentrations and pH at all soil depths. 

Exchangeable Al concentrations increased at all 

depths at all sites; however, increases were only 

significant in upper soil horizons. At most of the 

sites, losses of Ca2+ and Mg2+ on a pool basis were 

much larger than could be accounted for in biomass 

accumulation, suggesting the leaching of nutrients 

off site. 

Note: Mn = manganese. 

1.2.3.2 Plot Selection for Kane Experimental Forest Case Study Area 

Plots were selected for the KEF Case Study Area based on the location of permanent 

sampling plots and the presence of sugar maple trees. Permanent sampling plots are 0.1 acres 

(0.04 ha) in size and are evenly distributed and spaced (200 m east-west between plots) 

throughout the forest. Only plots that were not located within an active research study and 

contained a basal area of at least 20% sugar maple were selected (Table 1.2-4). A total of seven 

plots (0.28 ha total) were identified based on these criteria and were used for the KEF Case 

Study Area (Figure 1.2-3). Site, stand, and soil characteristics of these seven plots are presented 

in Table 1.2-4. 

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Appendix 5 - 18 
Terrestrial Acidification Case Study 

Figure 1.2-3. The seven plots used to evaluate critical loads of acidity in the Kane 

Experimental Forest.

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 19 
Terrestrial Acidification Case Study 

Table 1.2-4. Characteristics of the Case Study Plots in the Kane Experimental Forest 

Plot 

Number 

KEF Plot 

ID Location 

Elevation 

(m) 

Stand 

Basal Area 

(m2/ha) 

Representation 

of Sugar Maple 

in Stand (% of 

Basal Area) 

Soil Type 

(Soil Series)

1 2,920 78°47'35"W 

41°35'41"N 

583.9 62.8 32% CpB 

(Cockport) 

2 2,150 78°46'33"W 

41°36'5"N 

616.5 44.8 23% HaC 

(Harleton) 

3 950 78°44'50"W 

41°35'50"N 

530.2 28.8 36% BxD 

(Buchanan) 

4 850 78°44'40"W 

41°35'51"N 

548.9 22.4 44% HaF 

(Harleton) 

5 760 78°44'33"W 

41°36'01"N 

589.0 24.0 28% CpD 

(Cockport) 

6 650 78°44'24"W 

41°35'56"N 

530.6 30.7 35% HaF 

(Harleton) 

7 560 78°44'16"W 

41°35'59"N 

527.7 23.63 33% HaF 

(Harleton) 

Note: W = west; N = north. 

1.2.4 Red Spruce 

1.2.4.1 Hubbard Brook Experimental Forest 

The HBEF is located in the southern part of the White Mountain National Forest in 

Grafton County, central New Hampshire (Figure 1.2-4). The experimental forest consists of an 

oblong basin approximately about 8-km long by 5-km wide, and covers 3,138 ha. Hubbard 

Brook is the single major stream draining the basin. Elevations within the HBEF range from 222 

to 10,015 m. The climate of HBEF is predominantly continental, with a January temperature 

average of −9oC and an average July temperature of 18oC. Annual precipitation at the HBEF 

averages about 1,400 millimeters (mm), with one-third to one-quarter as snow. 

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Appendix 5 - 20 
Terrestrial Acidification Case Study 

Figure 1.2-4. Location of the Hubbard Brook Experimental Forest. 

Soils at the HBEF are predominantly well-drained Spodosols (Typic Haplorthods) 

derived from glacial till, with sandy loam textures. Principal soil series are the sandy loams of 

the Berkshire series, along with the Skerry, Becket, and Lyman series. These soils are acidic (i.e., 

pH about 4.5 or less) and relatively infertile (i.e., base saturation of mineral soil ~ 10%). 

Although highly variable, soil depths, including unweathered till, average about 2.0 m from 

surface to bedrock. 

The HBEF is entirely forested, mainly with deciduous northern hardwoods. Red spruce is 

abundant at higher elevations and on rock outcrops. Logging in the area began in the late 1880s 

and ended around 1917. The present second-growth forest is even-aged and composed of about 

80% to 90% hardwoods and 10% to 20% conifers. 

The HBEF was established in 1955 as a major center for hydrologic research in New 

England, and in 1963, the Hubbard Brook Ecosystem Study was founded. In 1988, the HBEF 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 21 
Terrestrial Acidification Case Study 

was designated as a Long-Term Ecological Research site. Research at the HBEF has been in 

progress for more than 50 years and has focused on hydrometeorological monitoring, 

biogeochemical nutrient cycling, and stand dynamics. Table 1.2-5 summarizes major studies that 

were related to red spruce and calculated critical loads of nitrogen and sulfur at the HBEF 

(HBES, 2008b; Pardo and Driscoll, 1996; USFS, 2008a). 

Table 1.2-5. Major Studies at the Hubbard Brook Experimental Forest

Authors Year Title Key Finding 

Driscoll et al. 1989 Changes in the chemistry of 

surface waters: 25-year results at 

the HBEF 

A decline in the sum of base cations in 

surface water paralleled the sulfate 

decline in atmospheric deposition, 

preventing any long-term decrease in 

stream acidity. There have been no 

significant long-term trends in 

precipitation inputs or stream outflow 

of NO3

-

. 

Pardo and 

Driscoll 

1996 Critical loads for nitrogen 

deposition: case studies at two 

northern hardwood forests 

Critical loads for nitrogen deposition 

with respect to acidity ranged from 0 to 

630 eq/ha/yr; critical loads with respect 

to effects of elevated nitrogen 

(eutrophication and nutrient 

imbalances) ranged from 0 to1,450 

eq/ha/yr. 

Palmer et al. 2004 Long-term trends in soil solution 

and stream water chemistry at 

the HBEF; relationship with 

landscape position 

Significant declines in strong acid 

anion concentrations were accompanied 

by declines in base cation 

concentrations in soil solutions draining 

the Oa and Bs soil horizons at all 

elevations. Persistently low Ca2+/Ali

ratios (<1) in Bs-horizon soil solutions 

at these sites may be evidence of 

continuing Al stress to trees. 

Siccama et al. 2007 Population and biomass 

dynamics of trees in a northern 

hardwood forest at HBEF 

Tree data from 1991 to 2001, including 

total aboveground biomass, in-growth 

of ≥10 cm DBH trees, mortality, 

biomass by type, aboveground net 

primary productivity, and net 

ecosystem productivity. 

Note: eq/ha/yr = equivalents per hectare per year; DBH = diameter at breast height. 

1.2.4.2 Plot Selection for Hubbard Brook Experimental Forest Case Study Area 

Selection of plots for the HBEF Case Study Area was restricted to Watershed 6 (Figure 

1.2-5). This watershed is 13.2 ha and is maintained as the biogeochemical control watershed for 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 22 
Terrestrial Acidification Case Study 

research studies. It consists of typical northern hardwood species (e.g., sugar maple, beech, 

yellow birch) on the lower 90% of its area and by a montane boreal transition forest of red 

spruce, balsam fir, and white birch (e.g., spruce-fir forest type) on the highest 10% of its area. 

The watershed is divided into 208 25x25-m2

 grid cells. This grid system and the 2002 Forest 

Inventory for the watershed were used to identify the nine grid units (units 9, 14, 15, 21 to 24, 

32, and 33) within the northeast portion of the watershed that contain large portions of red spruce 

trees (Figure 1.2-6). These nine grid cells were combined into a 0.56-ha plot for the HBEF Case 

Study Area (Figure 1.2-7). This case study plot is located at 43°57'N, 71°44'W and is 762.0 

to769.3 m in elevation. Soils within the plot are from the Tunbridge-Lyman soil association and 

consist of Tunbridge and Lyman soil series with smaller inclusions of Marlowe and Peru soils. 

Red spruce accounts for 18.8% of the total basal area (131.3 m2

/ha) in the plot area. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 23 
Terrestrial Acidification Case Study 

Figure 1.2-5. Vegetation cover (NLCD, 2001) and location of Watershed 6 of Hubbard 

Brook Experimental Forest. 

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Appendix 5 - 24 
Terrestrial Acidification Case Study 

Figure 1.2-6. Grid units within Watershed 6 of Hubbard Brook Experimental Forest. The 

red outline delineates the spruce-fir forest type. The dotted grid cell areas indicate the 

grid units with high proportions of red spruce and represents the composite plot area for 

the Hubbard Brook Experimental Forest Case Study Area.

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 25 
Terrestrial Acidification Case Study 

Figure 1.2-7. Location of case study plots within Watershed 6 of Hubbard Brook 

Experimental Forest. 

2.0 APPROACH AND METHODS 

The ISA (U.S. EPA, 2008c, Section 3.1.1) identified critical load assessments as a 

suitable approach to evaluate the potential impacts of anthropogenic pollution on biological end 

points and ecosystem impairment. A critical load is “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). Critical loads of acidity from atmospheric nitrogen and sulfur deposition for an ecosystem 

have been specifically defined as “the highest deposition of acidifying compounds that will not 

cause chemical changes leading to long-term harmful effects on ecosystem structure and 

function” (Nilsson and Grennfelt, 1988). “The basic idea of the critical load concept is to balance 

the depositions that an ecosystem is exposed to with the capacity of this ecosystem to buffer the 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 26 
Terrestrial Acidification Case Study 

input (e.g., the acidity input buffered by the weathering rate), or to remove it from the system 

(e.g., nitrogen by harvest) without harmful effects within or outside the system” (UNECE, 2004). 

European countries have been using critical load assessments for many years to 

determine the impacts of atmospheric nitrogen and sulfur deposition in forest ecosystems. These 

studies have served as the platform for informing policy related to the control and reduction of 

emissions of acidifying pollutants. The International Cooperative Programme (ICP) on 

Modelling and Mapping Critical Loads and Levels and Air Pollution Effects, Risks and Trends 

has published a series of manuals (the most recent in 2004) to provide guidance on calculating 

and mapping critical loads. These manuals helped parties to the United Nations Economic 

Commission for Europe (UNECE) Convention on Long-Range Transboundary Air Pollution 

(CLRTAP) meet their obligations and conduct effects and risk assessments (UNECE, 2004). 

Canada has also completed critical load evaluations in support of efforts to design emissionreduction programs (Jeffries and Lam, 1993; RMCC, 1990). Critical load modeling was included 

in the 1997 Canadian Acid Rain Assessment (Jeffries, 1997) for several regions in eastern 

Canada. 

The establishment and analysis of critical loads within the United States is relatively new. 

The Conference of New England Governors and Eastern Canadian Premiers (NEG/ECP) funded 

studies that used critical load–based methods to estimate sustainable acidifying deposition rates 

and exceedances for upland forests representative of the New England states and the eastern 

Canadian Provinces in 2000 to 2001 (NEG/ECP Forest Mapping Group, 2001). More recently, 

McNulty et al. (2007) completed a national critical load assessment for U.S. forest soils at a 1-

km2

 scale. 

Within the ISA (U.S. EPA, 2008c, Section D.2.2), EPA detailed an 8-step protocol to 

define the basic critical load question in any analysis. Those steps are repeated here: 

1. Identify the ecosystem disturbance that is occurring (e.g., acidification, eutrophication). 

Not all disturbances will occur in all regions or at all sites, and the degree of disturbance 

may vary across landscape areas within a given region or site. 

2. Identify the landscape receptors that are subjected to the disturbance (e.g., forests, 

surface waters, crops). Receptor sensitivity may vary locally and/or regionally, and the 

hierarchy of those receptors that are most sensitive to a particular kind of disturbance 

may vary as well. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 27 
Terrestrial Acidification Case Study 

3. Identify the biological indicators within each receptor that are affected by atmospheric 

deposition (i.e., individual organism, species, population, or community characteristics). 

Indicators will vary geographically and perhaps locally within a given receptor type. 

4. Establish the critical biological responses that define “significant harm” to the biological 

indicators (e.g., presence/absence, loss of condition, reduced productivity, species shifts). 

Significant harm may be defined differently for biological indicators that are already at 

risk from other stressors or for indicators that are perceived as “more valued.” 

5. Identify the chemical indicators or variables that produce or are otherwise associated 

with the harmful responses of the biological indicators (e.g., streamwater pH, lake Al 

concentration, soil base saturation). In some cases, the use of relatively easily measured 

chemical indicators (e.g., surface water pH or acid neutralizing capacity [ANC]) may be 

used as a surrogate for chemical indicators that are more difficult to measure (e.g., Al 

concentration). 

6. Determine the critical chemical limits for the chemical indicators at which the harmful 

responses to the biological indicators occur (e.g., pH < 5, base saturation < 5%, inorganic 

Al concentration greater than 2 μmol). Critical limits may be thresholds for indicator 

responses, such as presence/absence, or may take on a continuous range of values for 

continuous indicator responses, such as productivity or species richness. Critical limits 

may vary regionally or locally depending on factors such as temperature, existence of 

refugia, or compensatory factors (e.g., high Ca2+ concentration mitigates the toxicity of 

Al to fish and plant roots). 

7. Identify the atmospheric pollutants that control (affect) the pertinent chemical indicators 

(e.g., deposition of SO4

2-, NO3-

, ammonium [NH4

+

], nitric acid [HNO3]). Multiple 

pollutants can affect the same chemical variable. The relative importance of each 

pollutant in producing a given chemical response can vary spatially and temporally. 

8. Determine the critical pollutant loads (e.g., kg/ha/yr total deposition of sulfur or 

nitrogen) at which the chemical indicators reach their critical limits. Critical pollutant 

loads usually include both wet and dry forms of pollutant deposition. The critical 

pollutant load may vary regionally within a receptor or locally within a site (e.g., as 

factors such as elevation or soil depth vary) and may vary temporally at the same location 

(e.g., as accumulated deposition alters chemical responses). 

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Appendix 5 - 28 
Terrestrial Acidification Case Study 

As shown in the eight steps above, a variety of indicators and responses can be 

incorporated into the estimation of a critical load, the point at which ecological impacts occur. 

Varying any one of these will result in a different critical load estimate. As a result, there is no 

single definitive critical load for an ecosystem. In this case study, terrestrial acidification was 

evaluated using the chemical indicator of the Bc/Al ratio in the soil solution (as a surrogate for 

Ca2+/Al—discussed earlier at the end of Section 1.1.1) and biological indicators (ecological 

endpoints) of red spruce and sugar maple tree health and growth. The critical chemical limits 

discussed above allow for the calculation of multiple critical loads, depending on the level of 

protection of interest. Three base cation to aluminum ratio – critical load (Bc/Al)crit ratio values 

were applied in this case study to provide a range of protection (i.e., low, intermediate, high) to 

tree health and growth, and these values (Bc/Al)crit ratios) are detailed in Section 2.1.2.2. 

Several methodological approaches can be taken to estimate critical loads in terrestrial 

ecosystems. Three of the most commonly used methods are empirically derived estimations, 

steady-state mass-balance model estimations, and dynamic model estimations (Bull et al., 2001; 

Bobbink et al., 2003; Jenkins et al., 2003; McNulty et al., 2007; UNECE, 2004). 

The UNECE CLRTAP has used the empirically-derived estimation approach within their 

mapping framework. Empirically derived critical load estimates of atmospheric nitrogen 

deposition for specific receptor groups within natural and seminatural terrestrial ecosystems and 

wetland ecosystems were first presented in a background document for the 1992 workshop on 

critical loads held under the UNECE CLRTAP Convention at Lökeberg (Sweden) (Bobbink et 

al., 1992). Updates to the empirically derived loads were completed for a 2007 update to the 

2004 Manual on Methodologies and Criteria for Modeling and Mapping Critical Loads and 

Levels and Air Pollution Effects, Risks, and Trends (henceforth referred to as the ICP Mapping 

and Modeling Manual) (UNECE, 2004). Empirically derived critical loads can provide good 

estimates of the impacts of acidifying deposition on terrestrial systems. However, they require 

data from studies that establish the impacts of varying loads (e.g., amount and duration) on 

ecosystem processes and attributes and have a limited ability to extrapolate to other systems with 

different characteristics. 

The mass-balance model estimation method to calculate critical loads consists of simple 

models that relate chemical indicators (e.g., related to or indicative of biological impact of 

acidifying deposition) to the deposition levels observed in an ecosystem. The chemical indicator 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 29 
Terrestrial Acidification Case Study 

used in the mass balance calculations must have a proven relationship to the biological indicator. 

With the mass-balance approach, critical loads are calculated by relating the flow of acidifying 

agents (i.e., base cations and other ions) and nutrients into, out of, and within an ecosystem. 

These mass-balance models are steady state and offer estimates of critical loads for time frames 

based on the data used to evaluate the mass balance (UNECE, 2004). To accurately characterize 

the steady-state ecosystem condition and impacts of acidifying deposition, it is important to use 

long-term averages of input fluxes in the mass-balance calculations. Benefits of the simple massbalance approach are its ease of use, moderate data requirements, and applicability over a large 

area (Pardo and Driscoll, 1996). Disadvantages, however, include an inability to incorporate 

changes or ecosystem responses into the modeled critical load estimates. 

Dynamic-model estimation methods simulate the processes of pollutant fate and transport 

into, out of, and within a system on a temporally varying basis. They are more data intensive 

than mass-balance models and require the modeling of temporal rates and processes in addition 

to the mass balance of acidifying agents, base cations, and nutrients. Some dynamic models 

involve the integration of hydrologic, geochemical, and biological processes, but such models 

are still of limited use in determining critical loads (Pardo and Driscoll, 1996). An advantage of 

dynamic models is that they allow for an estimation or prediction of ecosystem response over 

time and under different acidifying deposition scenarios (Pardo and Duarte, 2007). 

2.1 CHOSEN METHOD 

The Simple Mass Balance (SMB) model, outlined in the ICP Mapping and Modeling 

Manual (UNECE, 2004) to determine terrestrial critical loads, was used to estimate the critical 

loads of acidifying nitrogen and sulfur deposition in the KEF and HBEF (i.e., for sugar maple 

and red spruce, respectively) case study areas. This model is currently the most commonly used 

approach to estimate critical loads and has been widely applied in Europe (Sverdrup and de 

Vries, 1994), the United States (McNulty et al., 2007; Pardo and Duarte, 2007), and Canada 

(Watmough et al., 2006; Ouimet et al., 2006). Although a limitation of the SMB model is that it 

is a steady-state model, as stated by the UNECE (2004), “Since critical loads are steady-state 

quantities, the use of dynamic models for the sole purpose of deriving critical loads is somewhat 

inadequate.” In addition, if dynamic models are “used to simulate the transition to a steady state 

for the comparison with critical loads, care has to be taken that the steady-state version of the 

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Appendix 5 - 30 
Terrestrial Acidification Case Study 

dynamic model is compatible with the critical load model” (UNECE, 2004). Therefore, the 

selection of the SMB model is the most suitable approach for this case study examining critical 

loads for sugar maple and red spruce. 

The SMB model examines a long-term, steady-state balance of base cation, chloride, and 

nutrient inputs, “sinks,” and outputs within an ecosystem. With this model, base cation 

equilibrium is assumed to equal the system’s critical load. It is a single-layer model, where 

assumptions stipulate that the soil layer is a homogeneous unit at least as deep as the rooting 

zone, so that the nutrient cycle can be ignored. This allows the model to focus directly on growth 

and uptake processes. There are several additional assumptions that are included with application 

of the SMB model (UNECE, 2004): 

 All evapotranspiration occurs on the top of the soil profile 

 Percolation is constant through the soil profile and occurs only vertically 

 Physico-chemical constants are assumed to be uniform throughout the whole soil profile 

 Internal fluxes (e.g., weathering rates, nitrogen immobilization) are independent of soil 

chemical conditions (e.g., pH). 

The SMB model relates atmospheric nitrogen and sulfur deposition to a critical load by 

incorporating mass balances for nitrogen and sulfur within the soils with the charge balance of 

ions in the soil leaching flux. This model accounts for the processes that add and remove 

nitrogen and sulfur, as well as base cations and other charged elemental species, from the soil. 

Although this model analyzes both total nitrogen and sulfur deposition loads, it does not 

allow for the analysis of the specific effects of the different total reactive nitrogen species. 

However, as stated in Chapter 5 of the ICP Mapping and Modeling Manual, “the possible 

differential effects of the deposited nitrogen species (oxidized nitrogen [NOy] or reduced 

nitrogen [NHx]) are insufficiently known to make a differentiation between these nitrogen 

species for critical load establishment” (UNECE, 2004). Therefore, attempting an analysis of the 

impacts of different nitrogen species was not seen as necessary. 

2.1.1 Critical Load Equations and Calculations 

2.1.1.1 Simple Mass Balance Calculations 

The SMB model used to estimate critical loads of acidity in this case study is presented in 

Equation 1. The full derivation of this equation is detailed in the ICP Mapping and Modeling 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 31 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 32 

Manual (UNECE, 2004). Unless otherwise stated, all variables are expressed in units of eq/ha/yr. 

Equivalent, or “eq,” is a unit that removes the influence of molecular weight and is equivalent to 

“mole.” For example, 1 g of Ca is equal to 0.25 eq (1 g/the molecular weight of Ca = 40.08). 

dep −=+ dep + − + + + deuiuw − ANC NNNBcBCClBCN)CL(S critle, (1) 

where 

 CL(S+N) = forest soil critical load for combined nitrogen and sulfur acidifying 

deposition ((N+S)comb) 

 BCdep = base cation (Ca2+ + K+

 + Mg2+ + Na+) deposition4

 Cldep = chloride deposition 

 BCw = base cation (Ca2+ + K+ + Mg2+ + Na+

) weathering 

 Bcu = uptake of base cations (Ca2+ + K+

 + Mg2+) by trees 

 Ni = nitrogen immobilization 

 Nu = uptake of nitrogen by trees 

 Nde = denitrification 

 ANCle,crit = forest soil acid neutralizing capacity of critical load leaching 

NOTE: There is a distinction between the base cation variables base cation (BC) and Bc. BC 

includes all four base cations (Ca2+ + K+

 + Mg2+ and K+), whereas Bc only includes three 

cations—those that are taken up by vegetation (Ca2+ + K+

 + Mg2+) (UNECE, 2004). Terms in the 

SMB equations that are directly related to or impact vegetation use the Bc variable. 

Some of these parameters had defined or selected input values (BCdep, Cldep, Ni, Nu and 

Nde), while four of these parameters, including BCw, Bcu, Nu and ANCle,crit, required calculation. 

Two methods were used to calculate BCw in this case study; the clay-substrate method 

and the soil type–texture approximation method. The clay-substrate method has been used by 

many researchers in North America (Ouimet et al., 2006; Watmough et al., 2006; McNulty et al., 

2007; Pardo and Duarte 2007), and the soil type–texture approximation is one of the methods 

outlined in the ICP Mapping and Modeling Manual (UNECE, 2004). Base cation weathering is 

 

4 The ICP Mapping and Modeling Manual (UNECE, 2004) recommends that wet deposition be corrected for sea salt 

on sites within 70 km of the coast. Both the HBEF and KEF case study areas are greater than 70 km from the 

coast, so this correction was not used. 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 33 

the most influential and most difficult-to-estimate parameter within the SMB model (Whitfield et 

al., 2006; Li and McNulty, 2007). Therefore, these two methods were chosen to provide a range 

of BCw estimates within which the correct value probably lies (discussed further in Section 5).

Base cation weathering was calculated with the clay-substrate method using equations 

outlined by McNulty et al. (2007) (Equations 1 to 3). This method relies on a combination of 

parent material and clay percentage to determine the soil weathering rate. Parent material acidity 

was determined by silica content (see Table 3 in McNulty et al., 2007). 

 Acid Substrate: ( ) ( ( ) ) 2

e ×−×= clay%32.0clay%7.56BC (2) 

 Intermediate Substrate: ( ) ( ( ) ) 2

e ×−×+= clay%18.0clay%6.53500BC (3) 

 Basic Substrate: 500BC ( %clay2.59 ) e = + × (4) 

where 

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

) weathering rate 

(eq/ha/yr) 

 % clay = the percentage of clay within the soil. 

The empirical base cation weathering rate was corrected for soil temperature and depth of 

the rooting zone soil (Sverdrup and de Vries, 1994; Hodson and Langan, 1999; van der Salm and 

de Vries, 2001; UNECE, 2004; Watmough et al., 2006; Whitfield et al., 2006; Pardo and Duarte 

2007; NEG/ECP Forest Mapping Group, 2001) to determine the final BCw as outlined in 

Equations 5 and 6. 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ ⎟−

⎠

⎞ ⎜

⎝

⎛

+ ×=

T273 m

A

2736.2

A

c e BC eBC (5) 

BCBC depth w = c × (6) 

where 

 BCc = base cation (Ca2+ + K+ + Mg2+ + Na+

) weathering rate corrected for 

temperature (eq/ha/yr/m) 

 A = Arrhenius constant (3,600 kelvin [K]) 
Terrestrial Acidification Case Study 

 Tm = mean annual soil (or air) temperature (oC) 

 Depth = the depth of rooting zone mineral soil (m). 

Base cation weathering was calculated with the soil type–texture approximation method 

using Tables 2.1-1, 2.1-2, and 2.1-3 and Equation 7 (UNECE, 2004). Similar to the claysubstrate method, the soil type–texture approximation method also requires a combination of soil 

texture and parent material acidity to calculate base cation weathering. The soil texture class was 

determined by percentages of clay and sand (Table 2.1-1), and the parent material acidity was 

classified according to Food and Agriculture Organization (FAO) soil types (Table 2.1-2). Soil 

texture class and parent material acidity was then combined to determine the weathering rate 

class (Table 2.1-3). 

Table 2.1-1. Soil Texture Classes as a Function of Clay and Sand Content 

Texture Class Name Definition 

1 Coarse clay <18% and sand >65% 

2 Medium clay <35% and sand >15%, but 

clay >18% if sand >65% 

3 Medium fine clay <35% and sand <15% 

4 Fine 35% <clay <60% 

5 Very fine clay >60% 

Source: UNECE, 2004. 

Table 2.1-2. Parent Material Classes for Common FAO Soil Types 

Parent Material FAO Soil Type 

Acidic Ah, Ao, Ap, B, Ba, Bd, Be, Bf, Bh, Bm, Bx, D, Dd, De, Dg, Gx, I, Id, Ie, Jd, P, 

Pf, Pg, Ph, Pl, Po, Pp, Q, Qa, Qc, Qh, Ql, Rd, Rx, U, Ud, Wd 

Intermediate A, Af, Ag, Bv, C, Cg, Ch, Cl, G, Gd, Ge, Gf, Gh, Gi, Gl, Gm, Gs, Gt, H, Hg, Hh, 

Hl, J, Je, Jm, Jt, L, La, Ld, Lf, Lg, Lh, Lo, Lp, Mo, R, Re, V, Vg, Vp, W, We 

Basic F, T, Th, Tm, To, Tv 

Organic O, Od, Oe, Ox 

Source: UNECE, 2004. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 34 
Terrestrial Acidification Case Study 

Table 2.1-3. Weathering Rate Classes as a Function of Texture and Parent Material Classes 

Parent 

Material 

Texture Class 

1 2 3 4 5 

Acidic 1 3 3 6 6 

Intermediate 2 4 4 6 6 

Basic 2 5 5 6 6 

Organic Class 6 for Oe and class 1 for other organic soils 

Source: UNECE, 2004. 

( ) ⎟

⎠

⎞ ⎜

⎝

⎛

+ −

⋅−⋅⋅= T

AA

w zBC WRc e 273281 500 5.0 (7) 

where 

z = rooting zone soil depth (m) 

WRc = weathering rate class 

T = average annual soil temperature (°C) 

A = Arrhenius constant (3,600 K) 

Base cation (Bcu) and nitrogen (Nu) uptake were calculated for this case study using the 

equation outlined by McNulty et al. (2007) (Equation 8). These terms represent nutrients that are 

taken up from the soil and used to support tree growth and maintenance but are eventually 

returned to the system through litter senescence and decay. In a forest stand that does not 

experience biomass removal, these nutrients are internally cycled and not lost from the system. 

Under this scenario, both Bcu and Nu would be given values of 0 equivalents per hectare per year 

(eq/ha/yr) in the SMB calculations. However, in a managed stand that is harvested, base cations 

and nitrogen taken up by the trees are removed from the forest system with tree harvesting and 

are, therefore, considered a loss or output from the system within the SMB calculations. 

Watershed 6 in HBEF is a reference watershed and is not harvested. Therefore, for the 

HBEF Case Study Area, the Bcu and Nu variables were assumed to have a value of 0 eq/ha/yr 

because biomass and nutrients are not removed from these plots. In contrast, most of the stands 

in the KEF are harvested, and therefore, as discussed further in Section 3.1.1 Bcu and Nu were 

estimated for the seven plots in the KEF Case Study Area. Equation 8 was modified, as 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 35 
Terrestrial Acidification Case Study 

necessary, to estimate uptake in the bark and bole for nitrogen, Ca2+, Mg2+, and K+

. These 

calculations were conducted for each species on each plot. 

Uptake (eq/ha/yr) = × × × × 0.65bark %SGNCAVI (8) 

where 

AVI = average forest volume increment (m3

/ha/yr) 

NC = base cation ((Ca2+ + K+ + Mg2+ ) or nitrogen nutrient concentration in bark and 

bole (%) 

SG = specific gravity of bark and bole wood (g/cm3

) 

% bark = percentage of volume growth that is allotted to bark 

65% = average aboveground tree volume that is removed from the site (Birdsey, 

1992; Hall et al., 1998; Martin et al., 1998). 

Acid neutralizing capacity (ANC(le,crit)) represents the buffering or acid neutralizing 

capacity of the soil, and the selection of the chemical indicator for the effects on the biological 

receptor or ecological endpoint occurs within the calculation of ANC(le,crit). Several formulations 

for ANC(le,crit) exist, depending on which indicator is being used to examine the critical load for 

the biological receptor (endpoint), sensitivity to pH conditions, or sensitivity to the toxic effects 

of Al. A large proportion of the research indicates Al toxicity in relation to Ca2+ depletion as the 

main indicator of red spruce and sugar maple mortality and decline. Therefore, for the estimates 

of critical loads for these two species at HBEF or KEF, Ca2+ and Al concentrations applied 

through the base cation to aluminum (Bc/Al)crit indicator ratio were used in the ANC(le,crit)

calculations according to Equation 9. As outlined in the end of Section 1.1.1, the Bc/Al ratio is a 

good surrogate for the Ca2+/Al indicator and is the most commonly used indicator (Bc/Al(crit)) in 

estimations of acid load (McNulty et al., 2007; Ouimet et al., 2006; UNECE, 2004). 

crit

dep w u

1/3

crit

gibb

2/3 dep w u

crit)(le,

Al

Bc

BcBcBc

1.5

Al

Bc

BcBcBc

ANC 1.5

⎟

⎠

⎞ ⎜

⎝

⎛

−+

×−

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛ ×

−+

××−=

K

Q (9) 

where 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 36 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 37 

Q = annual runoff in m3/ha/yr 

Bcdep = base cation (Ca2+ + K+ + Mg2+ ) deposition5 

Bcw = soil base cation (Ca2++ K+

 + Mg2+) weathering6

Bcu = base cation (Ca2++ K+

 + Mg2+) uptake by trees 

Kgibb = the gibbsite equilibrium constant (a function of forest soil organic matter 

content that affects Al solubility) (UNECE, 2004) 

(Bc/Al)crit = the base cation to aluminum ratio (indicator) 

Base cation weathering (Bcw) in the ANC(le,crit) parameter was calculated using the two 

methods described earlier: the clay-substrate and soil type–texture approximation methods. 

However, sodium (Na+

) typically accounts for 10% to 30% of base cation weathering (BCw) 

(Sverdrup and deVries 1994), and therefore, the Bcw, which only consists of Ca2+, K+

, and Mg2+, 

was determined by multiplying BCw by 0.80, the mid-range of the Na+

 proportional content. 

2.1.1.2 Deposition Relative to Critical Load Calculations 

If total nitrogen and sulfur deposition (combined) ((N+S)comb) is greater than the 

calculated critical load for acidity, the soil is no longer able to neutralize the acidifying 

deposition, and there is increased likelihood of environmental harm (McNulty et al., 2007). 

Deposition of (N+S)comb (expressed as eq/ha/yr) that is greater than the critical load was 

calculated in this case study by comparing the SMB estimated critical load to the CMAQ/NADP 

total nitrogen and sulfur deposition levels as outlined in Equation 3. 

CL(SNSN)Ex(S N) + dep = dep + dep − + (10) 

where 

Ex = exceedance of the forest soil critical nitrogen and sulfur loads 

(S+N)dep = the deposition of sulfur and nitrogen. 

 

5 Bcdep is not the same as BCdep used in Equation 1. BCdep includes Ca2+, K+

, Mg2+, and Na+, whereas Bcdep includes 

base cations that are taken up by vegetation (i.e., only includes Ca2+, K+

, and Mg2+), 

6 Bcw is not the same as BCw used in Equation 1. BCw includes Ca2+, K+

, Mg2+, and Na+, whereas Bcw includes base 

cations that are taken up by vegetation (i.e., only includes Ca2+, K+

, and Mg2+). 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 38 

2.1.1.3 Critical Load Function 

The critical load function (CLF) expresses the relationship between all combinations of 

total nitrogen and sulfur deposition ((N+S)comb) and the critical load of an ecosystem. To define 

the CLF, minimum and maximum critical load levels for both total nitrogen and sulfur 

deposition must be determined (UNECE, 2004). These maximum and minimum levels were 

calculated in this case study using Equations 11 through 13 (UNECE, 2004). 

( ) max = dep − dep + w − u − ANCBCBCClBCSCL critle, (11) 

min = + + NNN(N)CL deui (12) 

de

max

max min f1

(S)CL (N)CL(N)CL − = + (13) 

where 

fde = denitrification fraction (0 <fde <1); unitless 

There is no minimum critical load of sulfur deposition because sulfur cycling processes 

(e.g., immobilization, uptake, reduction of sulfur) do not significantly contribute to the cycling of 

nutrients within forests (Johnson, 1984). 

The maximum critical load of sulfur (CLmax(S)) (Equation 11) occurs when nitrogen 

deposition does not exceed the nitrogen sinks (Ni + Nu + Nde) within the ecosystem. These 

nitrogen sinks are accounted for by the minimum critical load of nitrogen (CLmin(N) (Equation 

12). Above this CLmin(N) level, nitrogen deposition can no longer be absorbed by the system and 

acidification effects can occur. The maximum critical load level for nitrogen (CLmax(N)) occurs 

when there is no sulfur deposition and all acidity due to deposition comes from nitrogen. 

Translated into an equation (Equation 13), this critical load can be calculated as the sum of 

CLmin(N) and CLmax(S) (corrected for denitrification). 

An example of a CLF is depicted in Figure 2.1-1. All combinations of total nitrogen and 

sulfur deposition that fall on the black line representing the CLF are at the critical load level. 

Any deposition combination that falls below the line or within the grey area is below the critical 

load level. All combinations of total nitrogen and sulfur deposition that are located above the line 

or within the white area are greater than the critical load. 
Terrestrial Acidification Case Study 

CLmin(N)

N Deposition 

S De

position 

CLmax(N)

CLmax(S)

Figure 2.1-1. The critical load function created from the calculated maximum and 

minimum levels of total nitrogen and sulfur deposition (eq/ha/yr). The grey areas show 

deposition levels less than the established critical loads. The red line is the maximum 

critical level of sulfur deposition (valid only when nitrogen deposition is less than the 

minimum critical level of nitrogen deposition [blue dotted line]). The flat line portion of 

the curves indicates nitrogen deposition corresponding to the CLmin(N) (i.e., nitrogen 

absorbed by nitrogen sinks within the system). 

2.1.2 Critical Load Data Requirements 

2.1.2.1 Data Requirements and Sources 

Atmospheric, hydrologic, soil, bedrock geology, and tree measurement data are necessary 

to evaluate critical loads associated with total nitrogen and sulfur deposition. The specific data 

requirements to satisfy Equations 1 through 13 and calculate critical loads and CLF for this case 

study are presented in Table 2.1-4. This table also outlines the sources of these data specific to 

the two case study areas. Cloud deposition of nitrogen was not included in the critical load 

calculations because of the lack of available data. However, it should be noted that cloud 

deposition coupled with wet and dry deposition can result in 6 to 20 times greater total nitrogen 

deposition at high elevation relative to low elevation sites (Baumgardner et al., 2003). Therefore, 

total nitrogen deposition and the degree to which total nitrogen deposition exceeds the critical 

load at the HBEF Case Study Area may be underestimated. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 39 
Terrestrial Acidification Case Study 

Table 2.1-4. Data Requirements and Sources for Calculating Critical Loads for Total Nitrogen 

and Sulfur Deposition in Hubbard Brook Experimental Forest and Kane Experimental Forest

DATA 

DATA NAME AND TYPE DATA SOURCE 

Name Type 

Hubbard Brook 

Experimental Forest 

Kane Experimental 

Forest 

Total nitrogen 

and sulfur 

deposition— wet 

and dry 

CMAQ/ 

NADP 

GIS 

datalayers 

Provided by U.S. 

Environmental Protection 

Agency (EPA)/NADP, 

2003a, e, h 

Provided by EPA/ 

NADP, 2003a, e, h 

Base cation 

(Ca2+, Mg2+, 

Na+

, K+) 

deposition— wet 

NADP GIS datalayer NADP, 2003b, d, f, g NADP, 2003b, d, f, g 

Base cation 

(Ca2+, Mg2+, 

Na+

, K+) 

deposition— dry 

CASTNET GIS datalayer U.S. EPA, 2008b U.S. EPA, 2008b 

Chlorine (Cl-) 

deposition— wet 

NADP GIS datalayer NADP, 2003c NADP, 2003c 

Chlorine (Cl-) 

deposition— dry 

CASTNET GIS datalayer U.S. EPA, 2008b U.S. EPA, 2008b 

Runoff Annual runoff (1: 

7,500,000 

scale) 

GIS datalayer Gebert et al., 1987 Gebert et al., 1987 

Mean annual 

soil temperature 

Soil 

temperature 

data (HBEF/ 

KEF) 

Database 

(HBEF)/ 

Peerreviewed 

journal 

articles 

(KEF) 

HBES, 2008c Carter and Ciolkosz, 

1980 

Soil horizon 

depth 

SSURGO GIS datalayer USDA-NRCS, 2008b USDA-NRCS, 2008a 

Percentage of 

clay by soil 

horizon 

SSURGO GIS datalayer USDA-NRCS, 2008b USDA-NRCS, 2008a 

Percentage of 

sand by soil 

horizon 

SSURGO GIS datalayer USDA-NRCS, 2008b USDA-NRCS, 2008a 

Percentage of 

organic matter 

by soil horizon 

SSURGO GIS datalayer USDA-NRCS, 2008b USDA-NRCS, 2008a 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 40 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 41 

DATA 

DATA NAME AND TYPE DATA SOURCE 

Name Type 

Hubbard Brook 

Experimental Forest 

Kane Experimental 

Forest 

Gibbsite 

equilibrium 

constant (Kgibb) 

Selected Kgibb

values 

Peerreviewed 

journal 

articles and 

literature 

Ouimet et al., 2006; 

Watmough et al., 2006; 

UNECE, 2004 

Ouimet et al., 2006; 

Watmough et al., 2006; 

UNECE, 2004 

Parent material/ 

bedrock 

Map of 

bedrock 

geology 

GIS datalayer USGS, 2000 PA DCNR, 2001 

 

Food and 

Agriculture 

Organization 

(FAO) soil type 

Map of 

dominant soil 

types 

GIS datalayer FAO, 2007 FAO, 2007 

Nitrogen 

immobilization 

(Ni) 

Selected Ni

value 

Peerreviewed 

journal 

article and 

literature 

McNulty et al., 2007; 

UNECE, 2004 

McNulty et al., 2007; 

UNECE, 2004 

Denitrification 

(Nde) 

Selected Nde

value 

Peerreviewed 

journal 

articles 

McNulty et al., 2007; 

Ouimet et al., 2006; 

Watmough et al., 2006 

McNulty et al., 2007; 

Ouimet et al., 2006; 

Watmough et al., 2006 

Stand 

composition 

Forest 

inventory 

(HBEF)/ 

SILVAH 

(KEF) 

Database 

(HBEF)/ 

mensuration 

model (KEF) 

HBES, 2008a Thomasma et al., 2008 

Annual volume 

increment (AVI) 

by species 

Forest 

inventory 

(HBEF) / 

SILVAH 

(KEF) 

Database 

(HBEF)/ 

mensuration 

model (KEF) 

HBES, 2008a Thomasma et al., 2008 

Percentage 

allocation of 

growth to bark 

by species 

Selected % 

allocation 

values 

Peerreviewed 

journal 

article 

McNulty et al., 2007 McNulty et al., 2007 

Specific gravity 

(bark and bole 

wood) by 

species 

Selected 

specific 

gravity 

values 

Peerreviewed 

journal 

article 

Jenkins et al., 2001 Jenkins et al., 2001 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 42 

DATA 

DATA NAME AND TYPE DATA SOURCE 

Name Type 

Hubbard Brook 

Experimental Forest 

Kane Experimental 

Forest 

Nutrient 

concentration 

(bark and bole 

wood) by 

species— 

nitrogen, Ca2+, 

Mg2+, K+

Nitrogen, 

Ca2+, K+

, 

Mg2+

concentrations in bark 

and bole 

wood by 

species 

Forest 

Service 

Report 

Pardo et al., 2004 Pardo et al., 2004 

Percentage 

biomass (bark 

and bole) 

removal during 

harvest 

Selected 

value 

Peer 

reviewed 

journal 

article 

McNulty et al., 2007 McNulty et al., 2007 

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

Program; CASTNET = Clean Air Status and Trends Network; GIS = Geographic Information System; 

SSURGO = Soil Survey Geographic Database; SILVAH = Silviculture of Allegheny Hardwoods 

2.1.2.2 Selection of Indicator Values 

As described at the end of Section 1.1.1, the Bc/Al ratio ((Bc/Al)crit) in the soil solution 

was selected as the indicator for the calculation of critical loads in this case study. The (Bc/Al)crit

connects the acid-influenced chemical status of the soil with the tree response: as the ratio 

decreases, tree health and growth can be impaired because of reduced uptake of base cations and 

increased Al toxicity. Most studies that calculate critical loads of acidity set the (Bc/Al)crit ratio 

to 1.0 or 10.0 (McNulty et al., 2007; NEG/ECP Forest Mapping Group, 2001; Pardo and Duarte, 

2007; UNECE, 2004). The (Bc/Al)crit ratio of 1.0 is a common default value in European forests 

(UNECE, 2004) and has been applied to coniferous forests in the United States (McNulty et al., 

2007). A (Bc/Al)crit ratio of 10.0 is a more conservative ratio and has been applied to hardwood 

forests in the United States (McNulty et al., 2007), in Canadian forests (Ouimet et al., 2006; 

Watmough et al., 2006), and in systems where maintained tree health is required (NEG/ECP 

Forest Mapping Group, 2001). Soil solution Bc/Al ratios of 10.0 are less likely to reduce soil 

base saturation and are not known to impair tree vigor or growth. 

Cronan and Grigal (1995) conducted a meta-analysis of research investigating the 

relationship between soil solution Ca2+/Al ratio and growth of 18 tree species. They found a 50% 

chance of negative impacts on tree growth or nutrition when the soil solution Ca2+/Al ratio was 
Terrestrial Acidification Case Study 

as low as 1.0, a 75% chance when the soil solution ratio was as low as 0.5, and nearly a 100% 

chance of impaired tree growth or nutrition when the soil solution Ca2+/Al molar ratio was as low 

as 0.2. In a similar meta-analysis of studies that explored the relationship between Bc/Al and tree 

growth, Sverdrup and Warfvinge (1993b) reported the Bc/Al ratio at which growth was reduced 

by 20% relative to control trees. Figure 2.1-2 presents the findings of Sverdrup and Warfvinge 

(1993b) based on 46 of the tree species that grow in North America. This summary indicates that 

there is a 50% chance of negative tree response (i.e., greater than 20% reduced growth) at a soil 

solution Bc/Al ratio of 1.2. Sverdrup and Warfvinge (1993b) also presented the results of studies 

conducted on individual tree species. Figures 2.1-3 and 2.1-4 show growth in sugar maple and 

red spruce, respectively. According to these figures, sugar maple growth was reduced by 20% 

and red spruce growth was reduced by 35% (relative to controls) at a Bc/Al ratio of 0.6. 

Three Bc/Al ratio ((Bc/Al)crit) values were used in this case study to evaluate different 

levels of protection associated with total nitrogen and sulfur deposition: 0.6, 1.2, and 10 (Table 

2.1-5). The (Bc/Al)crit ratio of 0.6 represents the highest level of impact (lowest level of 

protection) to tree health and growth; as much as 75% of 46 tree species found in North America 

experience reduced growth at this ratio (Sverdrup and Warfvinge, 1993b). Both red spruce and 

sugar maple show at least a 20% reduction in growth at the 0.6 (Bc/Al)crit ratio. The (Bc/Al)crit

ratio of 1.2 is considered to represent a moderate level of impact; the growth of 50% of tree 

species (found growing in North America) were negatively impacted at this soil solution ratio 

(Figure 2.1-4). The (Bc/Al)crit ratio of 10.0 was selected to represent the lowest level of impact 

(greatest level of protection) to tree growth; it is the most conservative value used in studies that 

have calculated critical loads in the United States and Canada (NEG/ECP Forest Mapping 

Group, 2001; McNulty et al., 2007; Watmough et al., 2004). 

Table 2.1-5. The Three Indicator (Bc/Al)crit Soil Solution Ratios Used in This Case Study and the 

Corresponding Levels of Protection to Tree Health and Critical Loads

Indicator (Bc/Al)crit Soil 

Solution Ratio 

Level of Protection to Tree 

Health 

Critical Load 

0.6 Low High 

1.2 Intermediate Intermediate 

10.0 High Low 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 43 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 44 

0.01

0.1

1

10

0 25 50 75

Cumulative % of Species Exhibiting Reduced Growth Response

Bc/Al Ratio in Soil Solution 

100

(Bc/Al)crit = 1.2

(Bc/Al)crit = 0.6

0.01

0.1

1

10

0 25 50 75

Cumulative % of Species Exhibiting Reduced Growth Response

Bc/Al Ratio in Soil Solution 

100

(Bc/Al)crit = 1.2

(Bc/Al)crit = 0.6

Figure 2.1-2. The relationship between the Bc/Al ratio in soil solution and the percentage 

of tree species (found growing in North America) exhibiting a 20% reduction in growth 

relative to controls (after Sverdrup and Warfvinge, 1993b).

Figure 2.1-3. The relationship between soil solution Bc/Al ratio and stem or root growth 

in sugar maple (from Sverdrup and Warfvinge, 1993b). 
Terrestrial Acidification Case Study 

Figure 2.1-4. The relationship between soil solution Bc/Al ratio and biomass or root 

growth in red spruce (from Sverdrup and Warfvinge, 1993b). 

2.1.2.3 Case Study Input Data 

The data used to calculate critical loads for sugar maple and red spruce in the KEF and 

HBEF of this case study are presented in Tables 2.1-5, 2.1-6, 2.1-7, and 2.1-8. The majority of 

the data was specific to the case study areas and was compiled from published research studies 

and models, site-specific databases, or spatially-explicit GIS datalayers. However, several of the 

parameters, including denitrification (Nde), nitrogen immobilization (Ni), the gibbsite equilibrium 

constant (Kgibb), and rooting zone soil depth required the use of default values or values used in 

published critical load assessments. Denitrification loss of nitrogen (Nde) was assumed to be 0.0 

eq/ha/yr because both the KEF and HBEF study plots are upland forests and denitrification is 

considered negligible in such forests (McNulty et al., 2007; Ouimet et al., 2006; Watmough et 

al., 2006). The ICP Mapping and Modeling Manual (UNECE, 2004) reported values of Ni in the 

soil, ranging from 14.3 to 35.7 eq/ha/yr in colder climates and up to 71.4 eq/ha/yr in warmer 

climates. Nitrogen immobilization (Ni) was set to 42.86 eq/ha/yr (the average of the colder and 

warmer climate immobilization rates) for both forests in this case study. This approach and value 

was also used by McNulty et al. (2007) for forests in the United States. Two values of the Kgibb, 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 45 
Terrestrial Acidification Case Study 

300 and 3,000 m6

/eq2, were used in the calculations of critical loads because the 300 m6/eq2

value is a widely used default value (UNECE, 2004; McNulty et al., 2007), and the 3,000 m6

/eq2

value has been used to map critical loads in Canada (Ouimet et al., 2006; Watmough et al., 

2006). The 3,000 m6

/eq2 constant is also the highest Kgibb value associated with soils with low 

organic matter contents (UNECE, 2004). Fifty cm (0.5 m) was selected to represent the depth of 

the rooting zone layer in this case study. Fine roots, which are responsible for the vast majority 

of nutrient uptake, are typically concentrated in the upper 10 to 20 cm of soil (van der Salm and 

de Vries, 2001). These roots are most susceptible to the impacts of acidification. Therefore, a 0.5 

m depth has been suggested as a suitable rooting zone depth in the calculation of critical loads 

for forest soils (Sverdrup and de Vries, 1994; Hodson and Langan, 1999). 

As detailed in the preceding section, the three (Bc/Al)crit ratio values, associated with 

three levels of forest protection, were used in the critical load calculations for this case study. 

The 0.6, 1.2, and 10.0 (Bc/Al)crit ratios were applied to both the KEF and HBEF case study areas. 

As outlined earlier, base cation weathering rates were calculated using two methods; the 

clay-substrate method and the soil type–texture association method. The data presented in 

Tables 2.1-6 and 2.1-7 were used for these calculations. Similarly, base cation (Bcu) and 

nitrogen (Nu) uptake values were calculated in two different ways for the two case study areas. In 

HBEF, Bcu and Nu were assumed to be 0 eq/ha/yr because Watershed 6 is a reference watershed 

and does not have a history or future of harvesting. Biomass (and the nutrients contained therein) 

would, therefore, not have been removed from site. In KEF, two sets of values were used to 

model two scenarios and estimate Bcu and Nu in the SMB model calculations. In the first 

scenario, it was assumed that the tree biomass was not harvested. Therefore, Nu and Bcw, in this 

scenario, were set to 0 eq/ha/yr. In the second scenario, the case study plots were assumed to be 

managed and harvested on a regular basis. Values of Bcu and Nu for this scenario were therefore 

calculated using tree data (Tables 2.1-6, 2.1-8, and 2.1-9) and Equation 8 in Section 2.1.1.1. The 

calculation of critical loads with the different Bcu and Nu values allowed for a comparison of the 

influence of forest harvesting on the estimates of critical loads. The removal of nitrogen and base 

cations with harvesting can significantly reduce the critical load of total nitrogen and sulfur 

acidifying deposition in an ecosystem; the uptake and removal of base cations reduces the 

capacity of the system to neutralize acidifying deposition. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 46 
Terrestrial Acidification Case Study 

Table 2.1-6. Input Values for the Calculation of Critical Load in Hubbard Brook Experimental 

Forest and Kane Experimental Forest

DATA 

CASE STUDY AREA 

Hubbard Brook 

Experimental Forest Kane Experimental Forest 

2002 CMAQ/NADP total nitrogen and 

sulfur deposition levels—wet and dry 

(eq/ha) 

Nitrogen = 601.07, 

Sulfur = 233.0811 

Nitrogen = 967.54, 

Sulfur = 646.37 

Average annual (2000 to 2007) base 

cation (Ca2+, Mg2+, Na+

, K+) 

deposition—wet (eq/ha) 

Ca2+ = 15.87, Mg2+ = 5.69, K+

 = 

4.27, Na+

 = 35.78 

Ca2+ = 33.03, Mg2+ = 7.96, K+

 = 

6.33, Na+

 = 18.31 

Average annual (2000 to 2007) base 

cation (Ca2+, Mg2+, Na+

, K+) 

deposition— dry (eq/ha) 

Ca2+ = 0.29, Mg2+ = 0.14, 

K+ = 0.18, Na+ = 0.83 

Ca2+ = 1.08, Mg2+ = 0.38, 

K+ = 0.34, Na+ = 0.82 

Average annual (2000 to 2007) 

chloride (Cl-

) deposition—wet (eq/ha) 

45.48 35.36 

Average annual chloride (Cl-) 

deposition—dry (eq/ha) 

(2004 to 2007) 0.37 (2003 to 2007) 0.17 

Runoff (m3/ha/yr) 7,620 6,350 

Average annual soil temperature (to 

0.5 m) (°C) 

(1989 to 1998) 7.29 (1976 to 1979) 7.90 

Rooting Zone Soil depth (m) 0.5 0.5 

Percentage clay (in top 0.5 m of soil) a 6.4 See Table 2.1-7

Percentage sand (in top 0.5 m of soil) a 57.4 See Table 2.1-7

Percentage organic matter (in top 0.5 m 

of soil) a

3.6 See Table 2.1-7

Gibbsite equilibrium constant (Kgibb) 

(m6

/eq2) 

300 and 3,000 300 and 3,000 

Parent material/bedrockb Quartz, mica, schist, and 

quartzite 

Sandstone, conglomerate, and 

shale 

Food and Agriculture Organization 

(FAO) soil type 

Orthic Podzol (Po) Dystric Cambisol (Bd) 

Nitrogen immobilization (Ni) 

(eq/ha/yr) 

42.86 42.86 

Denitrification (Nde) (eq/ha/yr) 0 0 

Stand composition (in common name 

alphabetical order) 

American Beech (Fagus 

grandifolia), Balsam Fir (Abies 

balsamea), Birch spp. (Betula 

spp.), Mountain Ash (Sorbus 

americana), Red Maple (Acer 

rubrum), Red Spruce, Striped 

Maple (Acer pensylvanicum)

and Sugar Maple 

American Beech (Fagus 

grandifolia), Birch spp. (Betula 

spp.), Black cherry (Prunus 

serotina), Cucumber Tree 

(Magnolia acuminata), Eastern 

Hemlock (Tsuga canadensis), 

Red Maple (Acer rubrum), and 

Sugar Maple

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 47 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 48 

DATA 

CASE STUDY AREA 

Hubbard Brook 

Experimental Forest Kane Experimental Forest 

Annual volume increment (AVI) by 

species (m3

/ha/yr) 

— See Table 2.1-8

Percentage allocation of growth to bark 

by species 

— 11%—coniferous species / 

15%—deciduous species 

Specific gravity (bark and bole wood) 

by species (g/cm3

) 

— 

See Table 2.1-9

Nutrient concentration (% nitrogen, 

Ca2+, Mg2+, K+

) (bark and bole wood) 

by tree species 

— See Table 2.1-9

Percentage biomass (bark and bole) 

removal during harvest 

— 65 

a

 Determined by weighted average by horizon depth and soil series coverage 

b

 Based on dominant mineralogy

Table 2.1-7. Soil Characteristics in the Seven Plots of the Kane Experimental Forest Case Study 

Area for the Calculation of the Base Cation Weathering Rate Parameters

Soil Attribute (in 

top 0.5 m of soil) 

PLOT 

1 2 3 4 5 6 7 

Percentage Clay 23.3 20.1 21.3 20.1 23.3 20.1 20.1 

Percentage Sand 39.9 27.0 26.2 27.0 39.9 27.0 27.0 

Percentage Organic 

Matter 1.4 0.7 1.1 0.7 1.4 0.7 0.7 

Table 2.1-8. Annual Volume Growth by Tree Species in Each of the Seven Plots of the Kane 

Experimental Forest Case Study Area for the Calculation of Nutrient Uptake (Bcu and Nu)

PLOT 

TREE SPECIES ANNUAL VOLUME GROWTH (m3/ha/yr) 

American 

Beech 

Birch 

spp. 

Black 

Cherry 

Cucumber 

Tree 

Eastern 

Hemlock 

Red 

Maple 

Sugar 

Maple 

1 NA NA 3.3 NA NA 0.8 2.8 

2 NA NA 3.0 NA NA NA 1.4 

3 0.8 0.2 1.3 NA 0.1 0.3 1.3 

4 0.9 NA NA NA NA 1.1 0.4 

5 0.9 3.0 NA 0 NA NA 0.6 

6 NA NA 1.8 NA NA 0.1 1.3 

7 0.6 0.1 0.8 NA 0.1 NA 0.7 

NA = Not applicable. 
Terrestrial Acidification Case Study 

Table 2.1-9. Specific Gravity and Nutrient Concentrations by Biomass Component (Bark and 

Bole Wood) and by Tree Species for the Calculation of Nutrient Uptake (Bcu and Nu) in the Kane 

Experimental Forest Case Study Area 

SPECIES 

BARK BOLE 

Specific 

Gravity 

(g/cm3) 

% N % K+ % 

Mg2+ % Ca2+

Specific 

Gravity 

(g/cm3) 

% N % K+ % 

Mg2+

% 

Ca2+

American 

Beech 0.50 0.75 0.22 0.05 2.81 0.56 0.11 0.07 0.02 0.07 

Birch spp 0.61 0.40 0.12 0.04 0.78 0.61 0.09 0.05 0.02 0.08 

Black 

Cherry 0.50 0.00 0.00 0.00 2.69 0.47 0.00 0.00 0.00 0.08 

Cucumber 

Tree 0.50 0.54 0.21 0.06 2.15 0.52 0.10 0.09 0.02 0.11 

Eastern 

Hemlock 0.34 0.27 0.15 0.03 0.74 0.38 0.08 0.09 0.01 0.07 

Red Maple 0.55 0.43 0.20 0.05 1.30 0.49 0.09 0.08 0.02 0.11 

Sugar 

Maple 0.54 0.51 0.31 0.06 2.23 0.56 0.10 0.07 0.02 0.13 

Note: N = nitrogen. 

2.2 CRITICAL LOAD FUNCTION RESPONSE CURVES ASSOCIATED 

WITH THE THREE LEVELS OF PROTECTION 

The three (Bc/Al)crit ratio values (0.6, 1.2, and 10.0) used to evaluate level of protection 

to tree health and growth correspond to three different critical load values of total nitrogen and 

sulfur deposition ((N+S)comb). A critical load based on 0.6 (Bc/Al)crit ratio is the least stringent 

load and offers the least protection to forests of the three critical loads. The 1.2 (Bc/Al)crit ratio 

results in an intermediate critical load of total nitrogen and sulfur deposition with moderate 

protection to tree health. A critical load based on a 10.0 (Bc/Al)crit ratio would be the most 

stringent load and offers the greatest protection to the health of trees. 

As outlined in Section 2.1.1.3, critical loads of acidity can be translated into CLF 

relationships; the CLF defines the combinations of (N+S)comb that are equal to the calculated 

combined critical load of an ecosystem. Therefore, to provide an indication of all combinations 

of total nitrogen and sulfur deposition associated with the three different levels of protection, 

CLF response curves were produced with the three (Bc/Al)crit ratios. From the 0.6, 1.2, and 10.0 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 49 
Terrestrial Acidification Case Study 

(Bc/Al)crit ratios, the critical loads of (N+S)comb and corresponding CLmax(N), CLmin(N) and 

CLmax(S) values were calculated to generate CLF response curves for each case study area. 

Figure 2.2-1 provides an example of the CLF response curves associated with the three levels of 

protection. 

N Deposition 

S De

position 

CLF based on lowest 

protection level 

(Bc/Al(crit)) = 0.6 

CLF based on 

intermediate protection 

level (Bc/Al(crit)) = 1.2 

CLF based on highest 

protection level 

(Bc/Al(crit)) = 10.0 

Figure 2.2-1. An example of the critical load function response curves associated with 

the three (Bc/Al)crit ratios and the associated levels of protection of tree health. The flat 

line portion of the curves indicates total nitrogen deposition corresponding to the CLmin 

(nitrogen absorbed by nitrogen sinks within the system). 

3.0 RESULTS 

3.1 CRITICAL LOAD ESTIMATES 

3.1.1 Sugar Maple 

The estimates of critical loads for sugar maple in the seven plots in KEF are presented in 

Tables 3.1-1 through 3.1-7. The critical load estimates for all seven plots are summarized in 

Table 3.1-8 and ranged from 728 eq/ha/yr to 2,998 eq/ha/yr of combined total nitrogen and 

sulfur deposition ((N+S)comb). The ranges of critical loads associated with the three (Bc/Al)crit 

ratios differed by level of impact to tree health, but there was some overlap between the ranges 

of values. The lowest level of protection, (Bc/Al)crit ratio = 0.6, had the highest critical loads that 

ranged from 1,132 eq/ha/yr to 2,998 eq/ha/yr of (N+S)comb. The intermediate level of protection, 

(Bc/Al)crit ratio = 1.2, had critical loads ranging from 1,033 eq/ha/yr to 2,079 eq/ha/yr.

. The 

(Bc/Al)crit ratio of 10.0, corresponding to the most protective level for tree health, had critical 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 50 
Terrestrial Acidification Case Study 

load values ranging from 728 eq/ha/yr to 1,139 eq/ha/yr of (N+S)comb. Plot location, the two 

different methods to estimate base cation weathering, the two Kgibb values, and the inclusion of 

the influence of nutrient uptake and removal (Bcu and Nu greater than 0 eq/ha/yr) in the critical 

load calculations all influenced the critical loads for the three (Bc/Al)crit ratio values. In general, 

Plot 1 had both the lowest and highest critical load values. This was largely due to the method 

used to calculate BCw (clay-substrate method) and the relative high percentages of clay and 

organic matter in the soil, which accounted for the high critical load values. The low critical load 

values in Plot 1 were caused by the comparatively high amount of base cation and nitrogen 

uptake by the trees. The Kgibb constant also influenced the critical load values, with 300 m6

/eq2

causing higher critical loads than 3000 m6

/eq2. Similarly, the clay-substrate method to estimate 

BCw produced higher critical load values than did the soil type–texture approximation method. 

The inclusion of the influence of nutrient uptake and removal by trees (Bcu and Nu greater than 0 

eq/ha/yr) in the calculations of critical loads resulted in a large decrease in the critical values, 

especially for the (Bc/Al)crit ratio of 0.6, which offers the lowest level of protection to tree health. 

Table 3.1-1. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite 

Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values 

in Plot 1 of the Kane Experimental Forest Case Study Area

Nutrient 

Uptake in 

Critical 

Load 

(Bc/Al)crit

Ratio 

Critical Load (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 2,998 2,678 2,633 2,328 

1.2 2,079 1,825 1,832 1,591 

10.0 1,139 1,014 1,002 883 

Bcu and Nu

Included

0.6 1,940 1,692 1,553 1,332 

1.2 1,473 1,276 1,208 1,033 

10.0 960 862 814 728 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 51 
Terrestrial Acidification Case Study 

Table 3.1-2. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite 

Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values 

in Plot 2 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

(Bc/Al)crit

Ratio 

Critical Load (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 2,711 2,403 2,633 2,328 

1.2 1,885 1,641 1,832 1,591 

10.0 1,032 911 1,002 883 

Bcu and Nu

Included

0.6 2,009 1,749 1,928 1,673 

1.2 1,481 1,275 1,426 1,223 

10.0 910 808 879 779 

Table 3.1-3. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite 

Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values 

in Plot 3 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

Bc/Al)crit

Ratio 

Critical Load (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 2,817 2,504 2,633 2,328 

1.2 1,957 1,709 1,832 1,591 

10.0 1,071 949 1,002 883 

Bcu and Nu

Included

0.6 2,228 1,954 2,039 1,776 

1.2 1,626 1,408 1,497 1,288 

10.0 983 875 912 809 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 52 
Terrestrial Acidification Case Study 

Table 3.1-4. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite 

Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values 

in Plot 4 of the Kane Experimental Forest Case Study Area

Nutrient 

Uptake in 

Critical 

Load 

(Bc/Al)crit

Ratio 

Critical Load (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 2,711 2,403 2,633 2,328 

1.2 1,885 1,641 1,832 1,591 

10.0 1,032 911 1,002 883 

Bcu and Nu

Included

0.6 2,388 2,101 2,308 2,025 

1.2 1,707 1,479 1,653 1,429 

10.0 990 878 960 850 

Table 3.1-5. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite 

Equilibrium Constant (Kgibb) and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values 

in Plot 5 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

(Bc/Al)crit

Ratio 

Critical Load (eq/ha/yr ) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 2,998 2,678 2,633 2,328 

1.2 2,079 1,825 1,832 1,591 

10.0 1,139 1,014 1,002 883 

Bcu and Nu

Included

0.6 2,545 2,256 2,173 1,903 

1.2 1,849 1,620 1,596 1,382 

10.0 1,118 1,004 978 873 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 53 
Terrestrial Acidification Case Study 

Table 3.1-6. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite 

Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values 

in Plot 6 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

(Bc/Al)crit

Ratio 

Critical Load (eq/ha/yr ) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 2,711 2,403 2,633 2,328 

1.2 1,885 1,641 1,832 1,591 

10.0 1,032 911 1,002 883 

Bcu and Nu

Included

0.6 2,212 1,936 2,132 1,861 

1.2 1,599 1,380 1,545 1,329 

10.0 946 838 916 810 

Table 3.1-7. Critical Loads Calculated with the Different Base Cation Weathering, Gibbsite 

Equilibrium Constant (Kgibb), and Base Cation (Bcu) and Nitrogen (Nu) Uptake Parameter Values 

in Plot 7 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

(Bc/Al)crit

Ratio 

Critical Load (eq/ha/yr ) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 2,711 2,403 2,633 2,328 

1.2 1,885 1,641 1,832 1,591 

10.0 1,032 911 1,002 883 

Bcu and Nu

Included

0.6 2,368 2,082 2,289 2,007 

1.2 1,693 1,466 1,639 1,415 

10.0 981 869 951 840 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 54 
Terrestrial Acidification Case Study 

Table 3.1-8. Ranges of Critical Load Values (eq/ha/yr) (with and without the Influence of 

Nutrient Uptake and Removal with Tree Harvest) for the Seven Plots of the Kane Experimental 

Forest Case Study Area (Both Kgibb values and methods to estimate BCw were used in these 

calculations to present the range of critical loads estimated using all combinations of the 

parameter values.) 

Nutrient 

Uptake in 

Critical 

Load 

(Bc/Al)crit

Ratio 

Critical Loads (eq/ha/yr ) 

Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 Plot 6 Plot 7 

Bcu and 

Nu NOT 

Included

0.6 2,328 to 

2,998 

2,328 to 

2,711 

2,328 to 

2,817 

2,328 to 

2,711 

2,328 to 

2,998 

2,328 to 

2,711 

2,328 to 

2,711 

1.2 1,591 to 

2,079 

1,591 to 

1,885 

1,591 to 

1,957 

1,591 to 

1,885 

1,591 to 

2,079 

1,591 to 

1,885 

1,591 to 

1,885 

10.0 883 to 

1,139 

883 to 

1,032 

883 to 

1,071 

883 to 

1,032 

883 to 

1,139 

883 to 

1,032 

883 to 

1,032 

Bcu and 

Nu

Included

0.6 1,332 to 

1,940 

1,673 to 

2,009 

1,776 to 

2,228 

2,025 to 

2,388 

1,903 to 

2,545 

1,861 to 

2,212 

2,007 to 

2,368 

1.2 1,033 to 

1,473 

1,223 to 

1,481 

1,288 to 

1,626 

1,429 to 

1,707 

1,382 to 

1,849 

1,329 to 

1,599 

1,415 to 

1,693 

10.0 728 to 

960 

779 to 

910 

809 to 

983 

850 to 

990 

873 to 

1,118 

810 to 

946 

840 to 

981 

Two series of CLF response curves corresponding to the three (Bc/Al)crit ratio values (i.e., 

0.6, 1.2, and 10.0) for Plot 1 of the KEF Case Study Area are shown in Figures 3.1-1 and 3.1-2. 

Plot 1 had the highest and lowest critical load estimates. Therefore, the extreme values of this 

plot were graphed to capture the range of critical loads associated with the three levels of 

protection in the KEF Case Study Area. Figure 3.1-1 presents the CLF response curves 

associated with the lowest critical load estimates. This scenario corresponded to the BCw 

calculated using the soil type–texture approximation method, the 3000 m6/eq2 Kgibb constant, and 

the inclusion of the influence of nutrient uptake and removal (Bcu and Nu greater than 0 eq/ha/yr) 

in the calculation of critical load. Nitrogen uptake (Nu) substantially increased the minimum 

critical load of nitrogen (CLmin(N)) in this CLF relationship. Figure 3.1-2 shows the CLF 

response curves associated with the highest critical load estimates in Plot 1. These estimates 

occurred with BCw calculated using the clay-substrate method and the 300 m6

/eq2 Kgibb constant. 

Nutrient uptake (i.e., Bcu and Nu) was set to 0 eq/ha/yr in this calculation of critical load. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 55 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 56 

 1026

1332

727

306 1033

0

3500

0 3500

N Deposition

(eq/ha/yr)

S Deposition

(eq/ha/yr)

422

728

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

Figure 3.1-1. The critical load function response curves detailing the lowest critical load 

estimates for Plot 1 of the Kane Experimental Forest (refer to Table 3.1-1 for the 

parameters corresponding to each of the curves). The flat line portion of the curves 

indicates total nitrogen deposition corresponding to the CLmin(N) (nitrogen absorbed by 

nitrogen sinks within the system). 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 57 

2998

2955

2079

2037

43

0

3500

0 3500

N Deposition

(eq/ha/yr)

S Deposition

(eq/ha/yr)

1139

1096

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

Figure 3.1-2. The critical load function response curves detailing the highest critical load 

estimates for Plot 1 of the Kane Experimental Forest (refer to Table 3.1-1 for the 

parameters corresponding to each of the curves). The flat line portion of the curves 

indicates total nitrogen deposition corresponding to the CLmin(N) (nitrogen absorbed by 

nitrogen sinks within the system. 

The critical loads calculated for the KEF Case Study Area are consistent with critical 

loads determined by other studies conducted on forests in the Allegheny Plateau. McNulty et al. 

(2007), in their evaluation of critical loads across the United States, calculated loads of 1,061 to 

1,146 eq/ha/yr , corresponding to the location of the seven case study plots in KEF (Table 3.1-9). 

These values are very similar to the ranges (910 to 1,139 eq/ha/yr) determined in this case study, 

using similar parameter values. McNulty et al. (2007) used the SMB model to calculate critical 

load, the clay-substrate method to estimate BCw and the indicator value of 10.0 for (Bc/Al)crit for 

hardwood tree species. It is not known which Kgibb constant was used or if nutrient uptake and 

removal (Bcu and Nu greater than 0 eq/ha/yr) was included in their calculations. 
Terrestrial Acidification Case Study 

Table 3.1-9. Comparison of the Critical Load Values Determined in This Case Study and the 

Critical Load Values Determined by McNulty et al. (2007) for the Seven Plots in the Kane 

Experimental Forest Case Study Area 

Case Study 

Plot 

Critical Load (eq/ha/yr) 

Case Study* 

McNulty et al. (2007) 

Nutrient Uptake (Bcu

and Nu) NOT Included 

Nutrient Uptake (Bcu

and Nu) Included 

1 1,139 960 1,146 

2 1,032 910 1,144 

3 1,071 983 1,061 

4 1,032 990 1,061 

5 1,139 1,118 1,061 

6 1,032 946 1,061 

7 1,032 981 1,064 *

 The case study values in this table are those calculated with Kgibb = 300 m6/eq2 and (Bc/Al)crit = 10.0, 

and the clay-substrate method to estimate base cation weathering. 

3.1.2 Red Spruce 

The estimates of critical loads of acidity for red spruce in the HBEF Case Study Area are 

presented in Table 3.1-10. The critical load estimates for this case study area were lower than 

those for KEF, and ranged from 391 eq/ha/yr to 2,568 eq/ha/yr of combined total nitrogen and 

sulfur deposition ((N+S)comb). Similar to the KEF Case Study Area, the ranges of critical loads 

associated with the three (Bc/Al)crit ratios differed by level of protection to tree health. The least 

stringent, least protective level, (Bc/Al)crit ratio = 0.6, had the highest critical loads that ranged 

from 991 eq/ha/yr to 2,568 eq/ha/yr of (N+S)comb The intermediate level of protection, (Bc/Al)crit 

ratio = 1.2, had critical loads ranging from 697 eq/ha/yr to 1,801 eq/ha/yr. The (Bc/Al)crit ratio of 

10.0, corresponding to the most stringent, most protective level for tree protection, had critical 

load values ranging from 391 eq/ha/yr to 987 eq/ha/yr of (N+S)comb. The Kgibb and method to 

calculate BCw also influenced the critical load estimates for the HBEF Case Study Area, with the 

Kgibb value of 300 m6/eq2 resulting in higher critical load values than 3000 m6/eq2. In contrast, 

the soil type–texture approximation method to estimate BCw caused higher critical load values in 

the HBEF Case Study Area. These trends in the results were largely due to the relatively low 

clay and organic matter concentrations in the soils in HBEF Case Study Area compared to the 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 58 
Terrestrial Acidification Case Study 

KEF Case Study Area; this lower clay content resulted in much lower BCw rates using the claysubstrate compared to soil type–texture method. 

Table 3.1-10. Critical Load Calculated with the Different Base Cation Weathering and Gibbsite 

Equilibrium Constant (Kgibb) Parameter Values in the Hubbard Brook Experimental Forest Case 

Study Area 

(Bc/Al)crit Ratio 

Critical Load (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture Approximation 

Method 

Kgibb = 300 m6 

eq-2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

0.6 1,237 991 2,568 2,232 

1.2 892 697 1,801 1,534 

10.0 487 391 987 856 

Two series of CLF response curves that indicate the combined total nitrogen and sulfur 

deposition ((N+S)comb) levels for the three (Bc/Al)crit ratios (i.e., 0.6, 1.2, and 10.0) for the HBEF 

Case Study Area are shown in Figures 3.1-3 and 3.1-4. These two sets of critical load estimates 

were selected to provide an indication of the range of critical loads associated with the three 

levels of protection for red spruce health in the HBEF Case Study Area. 

Figure 3.1-3 shows the CLF response curves corresponding to the lowest critical loads. 

This scenario occurred with the BCw calculated using the clay-substrate method and the 300 

m6

/eq2 Kgibb constant. Figure 3.1-4 shows the CLF response curves associated with the highest 

critical load estimates in the HBEF Case Study Area. These estimates were calculated with BCw 

estimated using the soil type–texture approximation method and the 3,000 m6/eq2 Kgibb constant. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 59 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 60 

991

948

697

654

391

348

43

0

3000

0 3000

N Deposition 

(eq/ha/yr)

S Deposition

(eq/ha/yr)

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

Figure 3.1-3. The critical load function response curves detailing the lowest critical load 

estimates for the Hubbard Brook Experimental Forest Case Study Area (refer to Table 

3.1-10 for the parameters corresponding to each of the curves). The flat line portion of 

the curves indicates total nitrogen deposition corresponding to the CLmin(N) (nitrogen 

absorbed by nitrogen sinks within the system). 

2568

2525

1801

1758

987

944

43

0

3000

0 3000

N Deposition

(eq/ha/yr)

S Deposition

(eq/ha/yr)

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

Figure 3.1-4. The critical load function response curves detailing the highest critical load 

estimates for the Hubbard Brook Experimental Forest Case Study Area (refer to Table 

3.1-10 for the parameters corresponding to each of the curves). The flat line portion of 

the curves indicates total nitrogen deposition corresponding to the CLmin(N) (nitrogen 

absorbed by nitrogen sinks within the system). 
Terrestrial Acidification Case Study 

The HBEF Case Study Area has been the site of extensive research, and several studies 

have estimated critical loads of acidity for the experimental forest (Table 3.1-11). Using the 

SMB model and parameter values similar or equivalent to those used in this case study, McNulty 

et al. (2007) calculated a critical load of 516 eq/ha/yr for the location of the case study plot. The 

NEG/ECP Forest Mapping Group (2005) that conducted a detailed assessment of critical loads in 

several of the northeastern states and the eastern provinces of Canada determined critical load 

values around 1,500 eq/ha/yr for the area of the case study plot in HBEF (NEG/ECP Forest 

Mapping Group, 2005). This group used a modified version of the SMB model to estimate 

critical loads. Pardo and Driscoll (1996) also calculated critical loads for the HBEF. They 

evaluated four charge- and mass-balance models (steady-state water chemistry, nitrogen mass 

balance, base cation mass balance, and steady-state mass balance models) over three different 

time periods (between 1965 and 1988) and found values ranging from -433 to 1,452 eq/ha/yr 

(negative values are equal to 0 eq/ha/yr). Therefore, the critical load values of 391 to 2,181 

eq/ha/yr calculated in this case study are consistent with those from earlier studies that used 

similar and different methods or models to estimate critical loads. The higher estimates (i.e., 

2,568 eq/ha/yr) in this case study can be attributed to the soil type–texture approximation method 

of calculating BCw and the lowest (Bc/Al)crit (0.6) value. 

Table 3.1-11. Summary of the Critical Load Values Determined by Other Studies Conducted in 

the Hubbard Brook Experimental Forest (negative values are equal to 0 eq/ha/yr) 

Critical Load Model 

Used for Estimation 

Critical Load Values for Each Study (eq/ha/yr) 

Pardo and 

Driscoll (1996) 

McNulty et 

al. (2007) 

NEG/ECP Forest 

Mapping Group 

(2005) Case Study 

Simple Mass Balance – 516 ~1,500 391 to 2,568 

Steady-State Water 

Chemistry 

−4 to 11 – – – 

Nitrogen Mass Balance 133 to 1,452 – – – 

Basic Cation Mass 

Balance 

62 to 133 – – – 

Modified Basic Cation 

Mass Balance 

0 to 1,405 – – – 

Steady-State Balance −433 to 630 – – – 

Source: Pardo and Driscoll, 1996. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 61 
Terrestrial Acidification Case Study 

3.2 RECOMMENDED PARAMETER VALUES AND CRITICAL LOADS 

Within the ranges of critical loads estimated for the KEF and HBEF case study areas, 

three critical loads were selected to represent the conditions associated with the three levels of 

protection (Bc/Al(crit) = 0.6, 1.2, and 10.0) for sugar maple in KEF and for red spruce in HBEF 

(Table 3.2-1). For the KEF Case Study Area, these critical load values, in order of lowest to 

highest level of protection were 2,009, 1,481 and 910 eq/ha/yr (for Bc/Al(crit) = 0.6, 1.2, and 10.0, 

respectively). For the HBEF Case Study Area, these values, in order of lowest to highest level of 

protection, were 1,237, 892, and 487 eq/ha/yr (for Bc/Al(crit) = 0.6, 1.2, and 10.0, respectively). 

These critical load estimates were derived using the clay-substrate method to estimate 

BCw and a Kgibb of 300 m6

/eq2. For the KEF Case Study Area, nutrient uptake and removal with 

tree harvest (Bcu and Nu) was also included in the critical load estimates, and within the 

constraints of the selected parameters, the plot with the most conservative (i.e., lowest critical 

load) was selected to represent the full KEF Case Study Area. The parameter values were set to 0 

eq/ha/yr in the HBEF Case Study Area because the study plots in this experimental forest are not 

actively managed or harvested. The selection of these parameters and methods was based on the 

best available recommendations of scientists and research efforts, to date. When field 

assessments and measurements are not possible, the clay-substrate method is one of the most 

commonly used methods to estimate base cation weathering in North America (Ouimet et al., 

2006; Watmough et al., 2006; McNulty et al., 2007; Pardo and Duarte 2007), and the 300 m6

/eq2

value of the Kgibb is a recommended default value (UNECE, 2004). For the KEF Case Study 

Area, the influence of nutrient uptake and removal (i.e., Bcu and Nu greater than 0 eq/ha/yr) was 

included because the forest has been and will likely continue to be actively harvested (USFS, 

1999). 

Table 3.2-1. Critical Loads Selected to Represent the Three Levels of Protection in the Kane 

Experimental Forest and Hubbard Brook Experimental Forest Case Study Areas

Protection Level (Bc/Al(crit) ratio) 

Critical Load Values for Each Case Study Area (eq/ha/yr) 

KEF HBEF 

Low (Bc/Al(crit) = 0.6) 2,009 1,237 

Medium (Bc/Al(crit) = 1.2) 1,481 892 

High (Bc/Al(crit) = 10.0) 910 487 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 62 
Terrestrial Acidification Case Study 

3.3 CURRENT CONDITIONS 

This section discusses the impact of the 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels relative to the critical loads estimated for the KEF and HBEF case study areas. 

The atmospheric deposition of total nitrogen and sulfur ((N+S)comb) in both the HBEF and KEF 

case study areas was elevated. According to 2002 CMAQ output, the KEF Case Study Area 

received 13.6 kilograms (kg) N/ha (967.5 eq/ha) and 20.7 kg S/ha (646.4 eq/ha), and the HBEF 

Case Study Area experienced 8.4 kg N/ha (601.1 eq/ha) and 7.5 kg S/ha (233.1 eq/ha). When 

these deposition levels were compared to the critical loads calculated using the three (Bc/Al)crit

ratio values, the CMAQ-modeled (N+S)comb deposition loads were found to be both greater than 

and less than the three critical loads for the two case study areas (Tables 3.3-1 to 3.3-9, Figures 

3.3-1 to 3.3-4) . In all plots of the KEF Case Study Area, the 2002 CMAQ/NADP total nitrogen 

and sulfur deposition levels ((N+S)comb) were greater than the range of total nitrogen and sulfur 

allowable for the most stringent critical load (where (Bc/Al)crit = 10.0). Similarly, in the HBEF 

Case Study Area, the modeled (N+S)comb deposition was greater than the critical loads estimated 

using the (Bc/Al)crit = 10.0 ratio and the clay-substrate method to estimate BCw,. However, 

combined 2002 CMAQ/NADP total nitrogen and sulfur deposition levels were less than the 

critical loads estimated with (Bc/Al)crit = 10.0 and BCw determined by the soil type–texture 

approximation. The 2002 CMAQ/NADP total nitrogen and sulfur deposition levels ((N+S)comb) 

were less than the ranges of total nitrogen and sulfur allowable for the least stringent critical load 

(where (Bc/Al)crit = 0.6) for both the HBEF Case Study Area and all plots of the KEF Case Study 

Area. The only exception to this trend was in Plot 1 of the KEF Case Study Area, where the 

removal of base cations and nitrogen with harvesting (Bcu and Nu greater than 0 eq/ha/yr) were 

included in the load calculations. The variability in the results comparing 2002 CMAQ/NADP 

total nitrogen and sulfur deposition levels to calculated acid loads shows the strong influence of 

the Bc/Al(crit) indicator ratio (reflecting the level of tree protection) in critical load estimates. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 63 
Terrestrial Acidification Case Study 

Table 3.3-1. Ranges of Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur 

Deposition Levels ((N+S)comb) and the Estimated Critical Load Values (with and without the 

Influence of Nutrient Uptake and Removal [Nu and Bcu]) for the Seven Plots of the Kane 

Experimental Forest Case Study Area

Nutrient 

Uptake in 

Critical 

Load 

Estimate 

(Bc/Al)crit

Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and Estimated 

Critical Loads (eq/ha/yr) 

Plot 1 Plot 2 Plot 3 Plot 4 Plot 5 Plot 6 Plot 7 

Bcu and Nu

NOT 

Included

0.6 −1,384 

to −714 

−1,098 

to −714 

−1,203 

to −714 

−1,098 

to −714 

−1,384 

to −714 

−1,098 

to −714 

1,098 to 

−714 

1.2 −465 to 

23* 

−271 to 

23* 

−343 to 

23* 

−271 to 

23* 

−465 to 

23* 

−271 to 

23* 

−271 to 

23* 

10.0 475* to 

731* 

582* to 

731* 

543* to 

731* 

582* to 

731* 

475* to 

731* 

582* to 

731* 

582* to 

731* 

Bcu and Nu

Included

0.6 −327 to 

282* 

−395 to 

−59 

−614 to 

−162 

−774 to 

−411 

−931 to 

−289 

−598 to 

−247 

−755 to 

−393 

1.2 141* to 

581* 

132* to 

391* 

−12 to 

326* 

−93 to 

185* 

−235 to 

231* 

15* to 

285* 

−79 to 

199* 

10.0 654* to 

886* 

704* to 

835* 

631* to 

805* 

624* to 

764* 

496* to 

741* 

668* to 

804* 

633* to 

774* 

* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 

(N+S)comb deposition. 

Table 3.3-2. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation 

Methods, Two Gibbsite Equilibrium Constants [Kgibb], and Two Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values) in Plot 1 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

Estimate 

(Bc/Al)crit

Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and 

Estimated Critical Deposition Loads (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 Kgibb = 3,000 Kgibb = 300 Kgibb = 3,000 

Bcu and Nu

NOT 

Included

0.6 −1,384 −1,064 −1,019 −714 

1.2 −465 −211 −218 23* 

10.0 475* 600* 612* 731* 

Bcu and Nu

Included

0.6 −327 −78 61* 282* 

1.2 141* 338* 406* 581* 

10.0 654* 751* 800* 886* 

* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 

(N+S)comb deposition. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 64 
Terrestrial Acidification Case Study 

Table 3.3-3. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation 

Methods, Two Gibbsite Equilibrium Constants [Kgibb], and Two Base Cation (Bcu) and Nitrogen 

(Nu) Uptake Parameter Values) in Plot 2 of the Kane Experimental Forest Case Study Area

Nutrient 

Uptake in 

Critical 

Load 

Estimate 

(Bc/Al)crit

Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and 

Estimated Critical Loads ( eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 Kgibb = 3,000 Kgibb = 300 Kgibb = 3,000 

Bcu and Nu

NOT 

Included

0.6 −1,098 −789 −1,019 −714 

1.2 −271 −27 −218 23* 

10.0 582* 703* 612* 731* 

Bcu and Nu

Included

0.6 −395 −135 −314 −59 

1.2 132* 339* 188* 391* 

10.0 704* 806* 735* 835* 

* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 

(N+S)comb deposition. 

Table 3.3-4. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation 

Methods, Two Gibbsite Equilibrium Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen 

(Nu ) Uptake Parameter Values) in Plot 3 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

Estimate 

(Bc/Al)crit

Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and 

Estimated Critical Loads (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 −1,203 −890 −1,019 −714 

1.2 −343 −95 −218 23* 

10.0 543* 665* 612* 731* 

Bcu and Nu

Included

0.6 −614 −340 −425 −162 

1.2 −12 206* 117* 326* 

10.0 631* 738* 702* 805* 

* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 

(N+S)comb deposition. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 65 
Terrestrial Acidification Case Study 

Table 3.3-5. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation 

Methods, Two Gibbsite Equilibrium Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen 

(Nu ) Uptake Parameter Values) in Plot 4 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

Estimate 

(Bc/Al)crit

Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and 

Estimated Critical Loads (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 −1,098 −789 −1,019 −714 

1.2 −271 −27 −218 23* 

10.0 582* 703* 612* 731* 

Bcu and Nu

Included

0.6 −774 −487 −694 −411 

1.2 −93 134* −39 185* 

10.0 624* 736* 654* 764* 

* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 

(N+S)comb deposition. 

Table 3.3-6. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation 

Methods, Two Gibbsite Equilibrium Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen 

(Nu ) Uptake Parameter Values) in Plot 5 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

Estimate 

(Bc/Al)crit

Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and 

Estimated Critical Loads (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 −1,384 −1,064 −1,019 −714 

1.2 −465 −211 −218 23* 

10.0 475* 600* 612* 731* 

Bcu and Nu

Included

0.6 −931 −642 −559 −289 

1.2 −235 −6 18* 231* 

10.0 496* 609* 636* 741* 

* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 

(N+S)comb deposition. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 66 
Terrestrial Acidification Case Study 

Table 3.3-7. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation 

Methods, Two Gibbsite Equilibrium Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen 

(Nu ) Uptake Parameter Values) in Plot 6 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

Estimate 

(Bc/Al)crit

Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and 

Estimated Critical Loads (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 −1,098 −789 −1,019 −714 

1.2 −271 −27 −218 23* 

10.0 582* 703* 612* 731* 

Bcu and Nu

Included

0.6 −598 −322 −518 −247 

1.2 15* 234* 69* 285* 

10.0 668* 776* 698* 804* 

* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 

(N+S)comb deposition. 

Table 3.3-8. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation 

Methods, Two Gibbsite Equilibrium Constants [Kgibb], and Two Base Cation (Bcu ) and Nitrogen 

(Nu ) Uptake Parameter Values) in Plot 7 of the Kane Experimental Forest Case Study Area 

Nutrient 

Uptake in 

Critical 

Load 

Estimate 

(Bc/Al)crit

Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and 

Estimated Critical Loads (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture 

Approximation Method 

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

Bcu and Nu

NOT 

Included

0.6 −1,098 −789 −1,019 −714 

1.2 −271 −27 −218 23* 

10.0 582* 703* 612* 731* 

Bcu and Nu

Included

0.6 −755 −469 −675 −393 

1.2 −79 148* −25 199* 

10.0 633* 745* 663* 774* 

* Indicates a positive value or deposition greater than the critical load based on CMAQ-modeled 2002 

(N+S)comb deposition. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 67 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 68 

Table 3.3-9. Differences between the 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition 

Levels ((N+S)comb) and the Critical Load Values (with Two Base Cation Weathering Estimation 

Methods and Two Gibbsite Equilibrium Constants [Kgibb]) in the Hubbard Brook Experimental 

Forest Case Study Area 

(Bc/Al)crit Ratio 

Difference between CMAQ/NADP (N+S)comb Deposition and Estimated 

Critical Loads (eq/ha/yr) 

Clay-Substrate Method 

Soil Type–Texture Approximation 

Method 

Kgibb = 300 m6 

eq-2

Kgibb = 3,000 

m6/eq2

Kgibb = 300 

m6/eq2

Kgibb = 3,000 

m6/eq2

0.6 −403 −157 −1,734 −1,398 

1.2 −58 137* −967 −700 

10.0 347* 443* −153 −21 

 1026

1332

727

306 1033

0

3500

0 3500

N Deposition

(eq/ha/yr)

S Deposition

(eq/ha/yr)

422

728

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

2002 CMAQ N and S Deposition

Figure 3.3-1. Plot 1 Kane Experimental Forest critical load function response curves, 

detailing the lowest critical load estimates for Kane Experimental Forest Case Study Area 

(refer to Table 3.1-1 for the parameters corresponding to each of the curves). The 2002 

CMAQ/NADP total nitrogen and sulfur deposition levels ((N+S)comb) were greater than 

the critical loads of nitrogen and sulfur at all levels of protection ((Bc/Al)crit = 0.6, 1.2, 

and 10.0). The flat line portion of the curves indicates total nitrogen deposition 

corresponding to the CLmin (N) (nitrogen absorbed by nitrogen sinks within the system). 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 69 

2955

2998

2037

43 2079

0

3500

0 3500

N Deposition

(eq/ha/yr)

S Deposition

(eq/ha/yr)

1096

1139

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

2002 CMAQ N and S Deposition

Figure 3.3-2. Plot 1 critical load function response curves, detailing the highest 

maximum deposition load estimates for Kane Experimental Forest Case Study Area 

(refer to Table 3.1-1 for the parameters corresponding to each of the curves). The 2002 

CMAQ/NADP total nitrogen and sulfur deposition levels ((N+S)comb) were greater than 

the critical load of total nitrogen and sulfur deposition calculated with the highest level of 

protection (Bc/Al)crit = 10.0). The flat line portion of the curves indicates total nitrogen 

deposition corresponding to the CLmin(N) (nitrogen absorbed by nitrogen sinks within the 

system). 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 70 

948

991

654

43 697

0

3000

0 3000

N Deposition 

(eq/ha/yr)

S Deposition

(eq/ha/yr)

348

391

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

2002 CMAQ N and S Deposition

Figure 3.3-3. Critical load function response curves, detailing the lowest critical load 

estimates for the Hubbard Brook Experimental Forest Case Study Area (refer to Table 

3.1-10 for the parameters corresponding to each of the curves). The 2002 CMAQ/NADP 

total nitrogen and sulfur deposition levels ((N+S)comb) were greater than the critical load 

of total nitrogen and sulfur calculated with the highest and the intermediate levels of 

protection ((Bc/Al)crit = 1.2 and 10.0). The flat line portion of the curves indicates total 

nitrogen deposition corresponding to the CLmin(N) (nitrogen absorbed by nitrogen sinks 

within the system). 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 71 

2568

2525

1801

1758

987

944

43

0

3000

0 3000

N Deposition

(eq/ha/yr)

S Deposition

(eq/ha/yr)

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

2002 CMAQ N and S Deposition

Figure 3.3-4. Critical load function response curves, detailing the highest critical load 

estimates for the Hubbard Brook Experimental Forest Case Study Area (refer to Table 

3.1-10 for the parameters corresponding to each of the curves). The 2002 CMAQ/NADP 

total nitrogen and sulfur deposition levels ((N+S)comb) were less than the critical loads 

associated with all three (Bc/Al)crit ratios. The flat line portion of the curves indicates total 

nitrogen deposition corresponding to the CLmin(N) (nitrogen absorbed by nitrogen sinks 

within the system). 

As outlined in Section 3.2, critical loads of 2,009, 1,481 and 910 eq/ha/yr were selected 

to represent the three levels of increasing protection for the KEF Case Study Area, and 1,237, 

892 and 487 eq/ha/yr were the critical loads selected for the HBEF Case Study Area. These 

estimates are based on the critical load parameters suggested and most frequently used by 

scientists and previous research. When compared to the 2002 CMAQ-modeled deposition levels, 

it was evident that the deposition levels were greater than the most protective critical load 

(Bc/Al(crit) = 10.0) for both case study areas and also greater than the intermediate protection 

critical load (Bc/Al(crit) = 1.2) for KEF (Figures 3.3-5 and 3.3-6)). In these comparisons, 2002 

CMAQ/NADP total nitrogen and sulfur deposition levels exceeded the KEF Case Study Area 

critical load by 132 to 704 eq/ha/yr and exceeded the HBEF Case Study Area’s critical load by 

347 eq/ha/yr. Similar results have been reported in other studies which have assessed the two 

case study areas. McNulty et al. (2007) and Pardo and Driscoll (1996) found that deposition 

levels were greater than the estimated critical loads in the HBEF area. McNulty et al. (2007) also 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 72 

reported that 2002 CMAQ/NADP total nitrogen and sulfur deposition levels in the KEF 

exceeded the calculated critical loads for the KEF Case Study Area. These results suggest that 

the health of red spruce at HBEF and sugar maple at KEF may have been compromised by the 

acidifying nitrogen and sulfur deposition received in 2002. 

2009

1796

1481

1268

910

697

213

0

3500

0 3500

N Deposition 

(eq/ha/yr)

S Depositio

n

(eq/ha/yr)

Low Protection (Bc/Al = 0.6)

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

2002 CMAQ N and S Deposition

Figure 3.3-5. Critical load function response curves for the three selected critical loads 

conditions (corresponding to the three levels of protection) for the Kane Experimental 

Forest Case Study Area. The 2002 CMAQ/NADP total nitrogen and sulfur deposition 

levels ((N+S)comb) were greater than the highest and intermediate level of protection 

critical loads. The flat line portion of the curves indicates total nitrogen deposition 

corresponding to the CLmin(N) (nitrogen absorbed by nitrogen sinks within the system). 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 73 

1237

1194

892

849

487

444

43

0

3000

0 3000

N Deposition 

 (eq/ha/yr)

S Depositio

n

 (eq/ha/yr)

Low Protection (Bc/Al = 0.6) 

Intermediate Protection (Bc/Al = 1.2)

High Protection (Bc/Al = 10.0)

CLmin(N)

2002 CMAQ N and S Deposition

Figure 3.3-6. Critical load function response curves for the three selected critical loads 

conditions (corresponding to the three levels of protection) for the Hubbard Brook 

Experimental Forest Case Study Area. The 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels ((N+S)comb) were greater than the highest level of protection critical 

loads. The flat line portion of the curves indicates total nitrogen deposition corresponding 

to the CLmin(N) (nitrogen absorbed by nitrogen sinks within the system). 

Acidifying nitrogen deposition consists of both reduced (NHx) and oxidized (NOx) forms 

of nitrogen. However, only NOx is currently regulated as a criteria pollutant. Therefore, to gain 

an understanding of the relationship between the two states (i.e., reduced and oxidized) of total 

nitrogen deposition and the critical loads for the KEF and HBEF case study areas, total nitrogen 

deposition must be separated into NHx-N and NOx-N. Examples of the separation of nitrogen 

species are presented in Figure 3.3-7 and Figure 3.3-8 which indicate the CLF response curves

for the highest level of protection critical load condition selected for the KEF and HBEF case 

study areas, respectively. In these relationships, the CLF function has been modified by 

maintaining NHx-N deposition at the 2002 CMAQ-modeled deposition level; only sulfur and 

NOx-N deposition levels vary to indicate the combined critical load. Based on 2002 

CMAQ/NADP total nitrogen and sulfur deposition levels, NHx-N accounted for 25.7% (249 

eq/ha) and 26.4% (159 eq/ha) of total nitrogen deposition in KEF and HBEF case study areas, 

respectively. These fixed amounts of NHx-N influenced the highest protection CLF response 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 74 

curves for both areas. For both case studies, the maximum sulfur critical load (CLmax(S)) and the 

maximum nitrogen critical load (CLmax(N)), as NOx, were lowered.. In the calculations for the 

KEF Case Study Area, the CLmax(S) was reduced by 5% to 661 eq/ha/yr, and in the HBEF Case 

Study Area calculations, the CLmax(S) was reduced by 26% to 328 eq/ha/yr. Similarly, the 

CLmax(N) (as NOx) for KEF was reduced by 27% to 661 eq/ha/yr, and the CLmax(N) (as NOx) for 

HBEF was reduced by 33% to 328 eq/ha/yr. 

CLmax(S)

1200

0

0 249 910 1200

NHx-N deposition (fixed amount)

NOx-N deposition

2002 CMAQ N and S deposition

CLmin(N)

N Deposition 

(eq/ha/yr)

Adj. CLmax(S) 661

CLmin(N)

213

Figure 3.3-7. The influence of the 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels (NHx-N) on the critical load function response curve, and in turn, the 

maximum critical loads of sulfur (CLmax(S)) and oxidized nitrogen (NOx-N) for the 

selected highest protection critical load for the Kane Experimental Forest Case Study 

Area. The critical load of oxidized nitrogen (NOx-N) is 661 eq/ha/yr (910 − 249 

eq/ha/yr). The CLmin(N) (nitrogen absorbed by nitrogen sinks within the system) 

corresponds to the value depicted in Figure 3.3-5. 
Terrestrial Acidification Case Study 

CLmax(S)

750

0

0 159 487 750

Adj. CLmax(S) 328

NHx-N deposition (fixed amount)

NOx-N deposition

2002 CMAQ N and S deposition

CLmin(N)

S Deposition 

(eq/ha/yr)

N Deposition 

(eq/ha/yr)

43

CLmin(N)

Figure 3.3-8. The influence of the 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels (NHx-N) on the critical load function response curve and, in turn, the 

maximum critical loads of sulfur (CLmax(S)) and oxidized nitrogen (NOx-N) for the 

selected highest protection critical load for the Hubbard Brook Experimental Forest Case 

Study Area. The critical load of oxidized nitrogen (NOx-N) is 328 eq/ha/yr (487 − 159 

eq/ha/yr). The CLmin(N) (nitrogen absorbed by nitrogen sinks within the system) 

corresponds to the value depicted in Figure 3.3-6. 

4.0 EXPANSION OF CRITICAL LOAD ASSESSMENTS FOR 

SUGAR MAPLE AND RED SPRUCE 

4.1 CRITICAL LOAD ASSESSMENTS 

Although the KEF and HBEF case studies estimated critical loads for red spruce and 

sugar maple in two locations and established that the 2002 CMAQ/NADP total nitrogen and 

sulfur deposition levels were greater than the calculated loads, these results cannot be 

extrapolated directly to represent the critical load condition for the full distribution ranges of the 

two tree species. Critical loads are largely determined by soil characteristics, and these 

characteristics vary by location. Therefore, to gain an understanding of the range of critical load 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 75 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 76 

values experienced by sugar maple and red spruce, it is necessary to calculate critical loads in 

multiple locations throughout the ranges of the two species. 

Critical load calculations were applied to multiple locations within 24 states for sugar 

maple and in 8 states for red spruce. Individual site locations within each State were determined 

by the USFS FIA database permanent sampling plots’ locations on forestland7 (timberland8 for 

New York, Arkansas, Kentucky and North Carolina), each covering 0.07 ha. Only database 

information for nonunique9, permanent sampling plots that supported the growth of sugar maple 

or red spruce and had the necessary soil, parent material, atmospheric deposition, and runoff data 

were included in the analyses. With these restrictions, 4,992 of the 14,669 sugar maple plots and 

763 of the 2,875 red spruce plots were included in the calculations of the plot-specific critical 

loads (Table 4.1-1). Although only a subset of the total sugar maple and red spruce plots were 

included in the analyses, the results are thought to capture accurately the range and trends of 

critical loads of the two species. Due to the randomness of the plot restrictions, it is unlikely that 

a bias was incorporated into the analyses. 

Table 4.1-1. Number and Location of USFS FIA Permanent Sampling Plots (each plot is 0.07 

ha) Used in the Analysis of Critical Loads For the Full Geographic Ranges of Sugar Maple and 

Red Spruce

State Sugar Maple Red Spruce 

Alabama 13 – 

Arkansas 10 – 

Connecticut 35 – 

Illinois 29 – 

Indiana 306 – 

Iowa 13 – 

Kansas NA – 

 

7 Forestland is defined as, “land at least 10 percent stocked by forest trees of any size, or formerly having such tree 

cover, and not currently developed for non-forest uses, with a minimum area classification of 1 acre.” (USFS, 

2002a). 

8 Timberland is defined as, “forest land capable of producing in excess of 20 cubic feet per acre per year and not 

legally withdrawn from timber production, with a minimum area classification of 1 acre.” (USFS, 2002b). 

9 Nonunique permanent sampling plot locations are those that have critical load attribute values (e.g., soils, runoff, 

and atmospheric deposition) that are not distinct and are repeated within a 250-acre area of the plot location. This 

“confidentiality” filter is a requirement of the USFS to prevent the disclosure of data that can be directly linked to 

a location on private land. To comply with the necessary “confidentiality,” full coverages of the data required for 

the critical load deposition calculations were given to the USFS, and the USFS matched and provided the data to 

each nonunique, permanent sampling plot. 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 77 

State Sugar Maple Red Spruce 

Kentucky 14 – 

Maine 271 560 

Maryland 4 – 

Massachusetts 33 3 

Michigan 633 – 

Minnesota 289 – 

Missouri 147 – 

New Hampshire 82 55 

New Jersey 6 – 

New York 485 52 

North Carolina 17 1 

Ohio 374 – 

Pennsylvania 285 NA 

Rhode Island NA – 

South Carolina NA – 

Tennessee 319 1 

Vermont 114 11 

Virginia 175 NA 

West Virginia 378 7 

Wisconsin 960 – 

TOTAL 4,992 763 

Note: NA = data not available for State; “–” = tree species not present on 

forestland in State. 

The parameter values selected for the SMB calculations of critical loads for all plots 

within the ranges of sugar maple and red spruce included wet deposition of base cations (Na+

, 

Ca+2, Mg+2, and K+

) and chlorine, the clay-substrate method to estimate BCw, three levels of 

protection ((Bc/Al)crit ratio = 0.6, 1.2 and 10.0), a 0.5 m rooting zone soil depth, and the Ni

(42.86 eq/ha/yr) and Nde (0 eq/ha/yr) default values used for the HBEF and KEF case study 

areas. The Kgibb constant ranged from 100 to 950 m6

/eq2, and was determined by average organic 

matter content, as outlined by McNulty et al. (2007) (Table 4.1-2). Nutrient (Nu) and base cation 

(BCu) uptake were not included in the SMB calculations because it was not possible to determine 

the harvesting status of the individual sampling plots. Corrections for sea salt influence were not 

applied to the wet deposition because such corrections were found to over-correct deposition 

estimates (McNulty et al., 2007). 
Terrestrial Acidification Case Study 

Similar to the KEF and HBEF case studies, the U.S. Department of Agriculture- Natural 

Resources Conservation Service (USDA-NRCS) SSURGO soils database (USDA-NRCS, 

2008c) was the main source used to estimate BCw in the calculations of critical loads for the full 

distribution ranges of sugar maple and red spruce. The U.S. Geological Survey (USGS) statelevel integrated map database for the United States (USGS, 2009b) was used as a secondary 

source of information, when necessary. Parent material acidity was inferred from the parent 

material attribute in the SSURGO soils database. The estimated total silica and ferromagnesium 

content, relative to the mineral assemblage typical of the rock or sediment type, were used to 

classify parent material as acidic, intermediate, or basic, according to the classification table 

(Table 4.1-3) outlined by McNulty et al. (2007) from Gray and Murphy (1999). When possible, 

classification of the parent material silica content was determined by the range of rock types 

provided as examples in Table 4.1-3. When rock types were not clearly indicated in the parent 

material attribute, parent material acidity was classified using a systematic protocol involving the 

consideration of descriptive modifiers that suggest a probable range of silica or ferromagnesium 

content (Table 4.1-4). In cases where the parent material attribute in the SSURGO soils database 

was not populated or was too nondescriptive to classify, acidity rating of parent material was 

inferred from the USGS state-level spatial geology databases. The criteria applied to the soils 

data were also used in interpretation of these USGS spatial datasets, along with general 

observation related to spatial patterns of local and regional geologic settings that suggested 

characteristics of igneous and metamorphic petrogenesis and implied sedimentary depositional 

mechanisms and environments. If parent material acidity was classified as “organic” or “other” 

or could not be determined by either the SSURGO or USGS geology databases, the critical load 

was not estimated for the location. The BCw values in the critical load assessments were not 

corrected for temperature because the soil temperature attribute in the SSURGO soils database 

was missing data for most of the plot locations. Average air temperature was not used as a 

substitute because McNulty et al. (2007) determined that corrections for air temperatures were 

more suitable for northern climates, presumably where the temperature corrections were derived 

(i.e., Scandinavia). 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 78 
Terrestrial Acidification Case Study 

Table 4.1-2. Gibbsite Equilibrium (Kgibb) Determined by Percentage of Soil Organic Matter 

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 

Source: Modification of table from McNulty et al. (2007). 

Table 4.1-3. Parent Material Acidity Classifications for Base Cation (BCw) Estimations 

Parent 

Material 

Classification 

Parent 

Material 

Category 

Silica 

Content 

CalciumFerromagnesium 

Content 

Examples 

Acidic 

Extremely 

siliceous >90% Extremely low (generally <3%) 

Quartz sands (i.e., beach, 

alluvial, or Aeolian), chert, 

quartzite, quartz reefs, and 

silicified rocks 

Highly 

siliceous 

72% to 

90% 

Low 

(generally 3% to 

7%) 

Granite, rhyolite, adamellite, 

dellenite, quartz sandstone, 

and siliceous tuff 

Intermediate 

Transitional 

siliceous/ 

Intermediate 

62% to 

72% 

Moderately low 

(generally 7% to 

14%) 

Granodiorite, dacite, 

trachyte, syenite, most 

greywacke, most lithic 

sandstone, most argillaceous 

rocks, and 

siliceous/intermediate tuff 

Intermediate 52% to 

62% 

Moderate 

(generally 14% to 

20%) 

Monzonite, trachy-andesite, 

diorite, andesite, 

intermediate tuff, as well as 

some greywacke, lithic 

sandstone, and argillaceous 

rock 

Basic 

Mafic 45% to 

52% 

High 

(generally 20% to 

30%) 

Gabbro, dolerite, basalt, and 

mafic tuff (uncommon) 

Ultramafic <45% Very high 

(generally >30%) 

Serpentinite, dunite, 

peridotite, amphibolite, and 

tremolite-chlorite-talc schists 

Calcareous Low* CaCO3 dominate 

other bases variable 

Limestone, dolomite, 

calcareous shale, and 

calcareous sands 

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Appendix 5 - 79 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 80 

Parent 

Material 

Classification 

Parent 

Material 

Category 

Silica 

Content 

CalciumFerromagnesium 

Content 

Examples 

Organic Organic Low*

Organic matter 

dominates bases 

variable 

Peat, coal, and humified 

vegetative matter 

Other 

Alluvial Variable* Variable Variable 

Sesquioxide Variable* 

Variable, 

dominated by 

sesquioxides 

Laterite, bauxite, ferruginous 

sandstone, and ironstone 

* Category not defined by silica content 

Source: Modified from McNulty et al. (2007). 

Table 4.1-4. Parent Material and Descriptive Modifier Characteristics (within the SSURGO 

Soils [USDA-NRCS, 2008c] and USGS Geology [USGS, 2009b] Databases) Used to Classify 

Parent Material Acidity 

Parent Material or 

Modifier Characteristic Acidity Classification Rational 

Glacial deposits, till, 

outwash, or drift (without 

modifiers) 

Intermediate Probable mixture of different mineralogies 

Glacial lacustrine (without 

modifiers) Intermediate Probable mixture of different mineralogies 

Glaciomarine (without 

modifiers) Intermediate Probable mixture of different mineralogies 

Marine deposits (without 

modifiers) Intermediate Probable mixture of different mineralogies 

Loess and eolian (without 

modifiers) Intermediate Probable mixture of different mineralogies 

Alluvium (without 

modifiers) Acidic Probable composition, predominantly quartz 

Residuum (without 

modifier) Not able to classify Mineralogy unknown 

Colluvium (without 

modifiers) Intermediate Probable mixture of different mineralogies located close to source area 

Deposits, till, or outwash 

(without modifiers) Not able to classify Mineralogy unknown 

Saprolite (without 

modifiers) Not able to classify Mineralogy unknown 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 81 

Parent Material or 

Modifier Characteristic Acidity Classification Rational 

Sandy modifier Acidic Probable high silica content 

Loamy modifier Intermediate 

Equal contributions of chemical properties 

(intermediate ion exchange capacity) from 

the three soil textures (i.e., sand, silt, clay) 

Skeletal modifier 

Classification based on 

top two layer 

descriptions 

Skeletal layer is very thin and spotty, so top 

two layers considered main source of 

parent material 

Red, brown, ferric, iron 

modifier Basic 

Probable high iron content (associated with 

potentially higher) cation ion-exchange 

capacity 

Opposing mineralogies Intermediate Considered average of two mineralogies 

Multiple layers described Classification based on 

top layer description 

Top layer considered main source of soil 

parent material 

The calculated critical loads for the three levels of protection (Bc/Al(crit) = 0.6, 1.2 and 

10.0) were compared to 2002 CMAQ/NADP total nitrogen and sulfur deposition levels to 

determine which plots with sugar maple and/or red spruce experienced deposition levels greater 

than the critical load values. 

Based on the SMB calculations for the three levels of protection (Bc/Al(crit) = 0.6, 1.2 and 

10.0), critical loads of acidifying deposition for sugar maple in the 24 states were found to range 

from 107 to 6,008 eq/ha/yr(Table 4.1-5). Critical loads for red spruce in the 8 states ranged from 

180 to 4,278 eq/ha/yr. In a comparison of the 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels and calculated critical loads, 3% to 75% of all sugar maple plots and 3% to 

36% of all red spruce plots were found to have 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels greater than the critical loads; the highest protection critical loads (Bc/Al(crit) = 

10.0) had the highest frequency of exceedance (Table 4.1-6). Aggregated by State, a large 

proportion of the sugar maple and red spruce plots showed high levels of critical load 

exceedance for the highest protection level (Bc/Al(crit) = 10.0) and comparatively lower 

exceedance frequency at the lowest protection level ((Bc/Al(crit) = 0.6)) (Table 4.1-6, Figures 

4.1-1 to 4.1-6). In general, New Hampshire displayed the greatest degree of critical load 

exceedance at all protection levels for both species. 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 82 

Collectively, given the limitations and uncertainties associated with the SMB model to 

estimate critical acid loads (see Section 4.3.9 in Chapter 4 and Section 5 in Appendix 5 for 

further description), these results suggest that the health of at least a portion of the sugar maple 

and red spruce growing in the United States may have been compromised with the 2002 

CMAQ/NADP total nitrogen and sulfur deposition levels; even with the lowest level of 

protection, half the states contained sugar maple and red spruce stands that were negatively 

impacted by acidifying deposition. At the highest level of protection (Bc/Al(crit) = 10.0), the 

apparent impact of the 2002 CMAQ/NADP total nitrogen and sulfur deposition levels was much 

greater. A large proportion of sugar maple (>80% of plots in 13 of 24 states) and the majority of 

red spruce (100% of plots in 5 of 8 states) experienced deposition levels that exceeded the 

critical loads. If this high protection critical load accurately represents the conditions of the two 

species, a large proportion of both sugar maple and red spruce, throughout their ranges, were 

most likely negatively impacted under 2002 CMAQ/NADP total nitrogen and sulfur deposition 

levels.
Terrestrial Ac

Table 4.1-5. Ranges of Critical Load Values by Level of Protection (Bc/Al(crit) = 0.6, 1.2, and 10.0) and b

Distribution Ranges of Sugar Maple and Red Spruce 

State 

Ranges of Critical Load Values (eq/ha/yr) 

Sugar Maple Red Spruce 

Bc/Al = 0.6 Bc/Al = 1.2 Bc/Al = 10.0 Bc/Al = 0.6 Bc/Al = 1.2 Bc/A

Alabama 1,592 to 5,337 1,114 to 3,638 617 to 2,015 – – Arkansas 2,239 to 4,290 1,536 to 2,913 857 to 1,623 – – Connecticut 1,519 to 2,468 1,058 to 1,702 581 to 941 – – Illinois 2,543 to 3,671 1,730 to 2,485 965 to 1,390 – – Indiana 1,478 to 5,859 1,020 to 3,971 573 to 2,214 – – Iowa 2,260 to 3,791 1,533 to 2,560 854 to 1,424 – – Kansas NA NA NA – – Kentucky 2,044 to 3,994 1,390 to 2,707 749 to 1,497 – – Maine 746 to 4,284 535 to 2,983 295 to 1,620 599 to 4,278 439 to 2,979 249 tMaryland 2,066 to 3,090 1,417 to 2,122 929 to 1,178 – – Massachusetts 791 to 2,414 566 to 1,661 319 to 919 1,706 to 1,736 1,191 to 1,213 656 Michigan 400 to 6,008 294 to 4,070 169 to 2,269 – – Minnesota 220 to 4,916 166 to 3,318 107 to 1,861 – – Missouri 978 to 4,891 681 to 3,304 377 to 1,843 – – New Hampshire 580 to 1,994 419 to 1,439 236 to 780 418 to 1,994 324 to 1,439 180 New Jersey 1,452 to 2,651 1,029 to 1,824 566 to 1,012 – – New York 503 to 4,467 370 to 3,039 209 to 1,686 526 to 3,146 386 to 2,156 217 tNorth Carolina 1,415 to 3,444 1,010 to 2,426 558 to 1,319 1256 926 Ohio 1,226 to 4,986 855 to 3,366 469 to 1,877 – – Pennsylvania 1,026 to 4,047 723 to 2,752 402 to 1,530 NA NA 

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Appendix 5 - 83 
Terrestrial Ac

Final Risk and Exposure Assessment 

Appendix 5 - 84 

State 

Ranges of Critical Load Values (eq/ha/yr) 

Sugar Maple Red Spruce 

Bc/Al = 0.6 Bc/Al = 1.2 Bc/Al = 10.0 Bc/Al = 0.6 Bc/Al = 1.2 Bc/

Rhode Island NA NA NA – – South Carolina NA NA NA – – Tennessee 921 to 5,755 653 to 3,901 351 to 2,175 2,065 1,433 Vermont 479 to 5,660 351 to 3,846 201 to 2,142 846 to 2,305 6,016 to 1,648 33Virginia 1,036 to 5,852 726 to 3,968 410 to 2,208 NA NA West Virginia 369 to 4,134 270 to 2,819 152 to 1,560 2,300 to 3,634 1,610 to 2,533 884 Wisconsin 400 to 5,031 290 to 3,393 166 to 1,898 – – Combined (all plots) 220 to 6,008 166 to 4,070 107 to 2,269 418 to 4,278 324 to 2,979 180 Note: NA = data not available for state; “-” = tree species not present on forestland in state. 
Terrestrial Acidification Case Study 

Table 4.1-6. Percentages of Plots, by Protection Level (Bc/Al(crit) = 0.6, 1.2, and 10.0) and by 

State, where 2002 CMAQ/NADP Total Nitrogen and Sulfur Deposition Levels Were Greater 

Than the Critical Loads for Sugar Maple and Red Spruce 

State 

Percentage of Plots Where Critical Load is Exceeded (%) 

Sugar Maple Red Spruce 

Bc/Al = 

0.6 Bc/Al = 1.2 Bc/Al = 10.0 Bc/Al = 0.6 Bc/Al = 1.2 Bc/Al = 10.0 

Alabama 0 23 31 – – – 

Arkansas 0 0 10 – – – 

Connecticut 0 23 100 – – – 

Illinois 0 0 66 – – – 

Indiana 0.3 12 87 – – – 

Iowa 0 0 23 – – – 

Kansas NA NA NA – – – 

Kentucky 0 0 86 – – – 

Maine 0 0.7 20 0.2 0.5 16 

Maryland 0 25 100 – – – 

Massachusetts 6 33 100 0 100 100 

Michigan 6 14 70 – – – 

Minnesota 2 7 30 – – – 

Missouri 0.7 2 46 – – – 

New Hampshire 29 38 84 27 38 78 

New Jersey 0 67 100 – – – 

New York 6 20 95 14 15 79 

North Carolina 0 6 71 0 0 100 

Ohio 1 16 95 – – – 

Pennsylvania 7 22 98 NA NA NA 

Rhode Island NA NA NA – – – 

South Carolina NA NA NA – – – 

Tennessee 0.3 3 50 0 0 100 

Vermont 2 7 99 2 6 100 

Virginia 2 9 59 NA NA NA 

West Virginia 2 8 95 0 0 100 

Wisconsin 2 10 82 – – – 

Combined 

(all plots) 3 12 75 3 5 36 

Note: NA = data not available for state; “−” = tree species not present on forestland in state 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 85 
Terrestrial Acidification Case Study 

Figure 4.1-1. States where sugar maple is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the lowest protection critical load 

(Bc/Al(crit) = 0.6) in the following: none of the sugar maple plots, <50% of the sugar 

maple plots, and >50% of the sugar maple plots. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 86 
Terrestrial Acidification Case Study 

Figure 4.1-2. States where sugar maple is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the intermediate protection critical load 

(Bc/Al(crit) = 1.2) in the following: none of the sugar maple plots, <50% of the sugar 

maple plots, and >50% of the sugar maple plots. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 87 
Terrestrial Acidification Case Study 

Figure 4.1-3. States where sugar maple is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the highest protection critical load 

(Bc/Al(crit) = 10.0) in the following: none of the sugar maple plots, <50% of the sugar 

maple plots, and >50% of the sugar maple plots. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 88 
Terrestrial Acidification Case Study 

Figure 4.1-4. States where red spruce is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the lowest protection critical load 

(Bc/Al(crit) = 0.6) in the following: none of the red spruce plots, <50% of the red spruce 

plots, and >50% of the red spruce plots. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 89 
Terrestrial Acidification Case Study 

Figure 4.1-5. States where red spruce is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the intermediate protection critical load 

(Bc/Al(crit) = 1.2) in the following: none of the red spruce plots, <50% of the red spruce 

plots, and >50% of the red spruce plots. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 90 
Terrestrial Acidification Case Study 

Figure 4.1-6. States where red spruce is found and where 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels exceeded the highest protection critical load 

(Bc/Al(crit) = 10.0) in the following: none of the red spruce plots, <50% of the red spruce 

plots, and >50% of the red spruce plots. 

4.2 RELATIONSHIP BETWEEN ATMOSPHERIC NITROGEN AND 

SULFUR DEPOSITION AND TREE GROWTH 

The impacts of the 2002 CMAQ/NADP total nitrogen and sulfur deposition and critical 

load exceedances on sugar maple and red spruce growth throughout the full ranges of the two 

species is presented and discussed in Attachment A. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 91 
Terrestrial Acidification Case Study 

5.0 UNCERTAINTY ANALYSIS 

5.1 KANE EXPERIMENTAL FOREST AND HUBBARD BROOK 

EXPERIMENTAL FOREST CASE STUDY AREAS 

Despite the extensive use of the SMB model to estimate critical loads, there is uncertainty 

regarding the output from the model and calculations. To a large degree, this uncertainty comes 

from the dependence of the SMB calculations on assumptions made by the researcher and the 

use of default values. Parameters including base cation weathering (BCw and Bcw), ANCle,crit, 

Kgibb, Nu, Ni, Nde, and Bcu are rarely measured at each location and must be selected based on the 

literature or on other calculations and models. In an analysis conducted by Li and McNulty 

(2007), it was determined that BCw and ANCle,crit were the main sources of uncertainty, with 

each respectively contributing 49% and 46% to the total variability in critical load estimates. It 

has, therefore, been suggested that the calculation of critical loads using a relevant range of 

parameter values can provide the foundation for an uncertainty analysis (Li and McNulty, 2007; 

Hall et al., 2001; Hodson and Langan 1999); it is likely that the correct critical load of a system 

will be contained within the range of load estimates from such an approach. If all or a large 

majority of estimates indicate that the critical load of a system is exceeded with 2002 

CMAQ/NADP total nitrogen and sulfur deposition levels, it is likely that deposition is greater 

than the critical load and that the trees and vegetation in that system are being negatively 

impacted by acidification. Conversely, if deposition is not greater than the majority of critical 

load estimates, there can be greater confidence that acidifying deposition is not impacting the 

system. Under a scenario of a near equal number of estimates indicating exceedance and 

nonexceedance, it is not possible to determine the actual acidification status of a system with 

confidence. Nonetheless, such results do suggest that the system is near the critical load level and 

should be monitored or assessed more thoroughly. 

In this case study, multiple values were used for several parameters in the SMB 

calculations for KEF and HBEF; BCw was calculated with two methods, two values of Kgibb

constant were used, and three indicator values of (Bc/Al)crit were evaluated. Therefore, it was 

possible to use the range of output values from the calculations to access the certainty of the 

acidification status of the HBEF and KEF case study areas. For both sugar maple and red spruce, 

a similar number of estimates indicated deposition levels greater than the critical loads; the 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 92 
Terrestrial Acidification Case Study 

critical loads associated with the most stringent, most protective Bc/Alcrit ratio indicator (Bc/Alcrit

= 10.0) were frequently lower than the 2002 CMAQ/NADP total nitrogen and sulfur deposition 

levels. Conversely, the critical loads calculated with the (Bc/Al)crit ratio indicative of a high risk 

to tree health (Bc/Alcrit = 0.6) were higher than the 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels. The intermediate indicator ratio ((Bc/Al)crit) had critical load estimates that 

were either exceeded or not exceeded by 2002 CMAQ/NADP total nitrogen and sulfur 

deposition levels . The patterning of the results suggests that the 2002 CMAQ/NADP total 

nitrogen and sulfur deposition levels were very close to, if not greater than, the critical loads of 

the two case study areas, and both ecosystems are likely to be sensitive to any future changes in 

the levels of nitrogen and sulfur acidifying deposition. 

A more thorough, quantified uncertainty analysis of the parameters that are selected for 

the SMB method calculations of critical acid loads is recommended for future analyses. 

5.2 EXPANSION OF CRITICAL LOAD ASSESSMENTS 

Critical load estimates for individual plots within the distribution ranges of sugar maple 

and red spruce were calculated using the clay-substrate method to estimate BCw. As discussed 

earlier, the BCw term within the SMB model is one of the most influential terms in the 

calculation of a critical load, and the determination of this BCw value is strongly influenced by 

the classified acidity of the soil parent material. In large-scale analyses, descriptions of the 

mineralogy of parent material underlying the soil may be missing, nondescriptive, only 

suggestive of mineralogy, or may only represent the dominant mineralogy in a large area (and 

therefore not accurately capture the smaller-scale variation in mineralogy). Therefore, it is 

possible to misclassify the parent material acidity in the BCw term. 

In the analyses of critical loads for the full distribution ranges of sugar maple and red 

spruce in this report, two fine-scale databases (i.e., SSURGO soils [USDA-NRCS, 2008c] and 

USGS state-level geology [USGS, 2009b] databases) were used as the sources for parent 

material mineralogy to allow for location-specific mineralogy descriptions. In addition, a 

systematic protocol similar to that used in Europe (UNECE, 2004) and Australia (Gray and 

Murphy, 1999), and based on known and probable silica and ferromagnesium content, spatial 

patterns of local and geologic settings, and implied depositional mechanisms and environments 

was used to determine the parent material acidity classifications. Therefore, steps were taken to 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 93 
Terrestrial Acidification Case Study 

determine accurate, location-specific acidity classifications. Nonetheless, parent material in some 

of the plots may have been misclassified. 

To evaluate the degree to which critical load estimates could change with a 

misclassification of parent material acidity, a simple analysis of absolute (eq/ha/yr) and 

percentage change associated with misclassifications of parent materials was conducted, using 

the critical loads associated with the three levels of protection ((Bc/Al)crit = 0.6. 1.2 and 10. 0) for 

sugar maple and red spruce. The differences between all combinations of critical loads calculated 

with basic, intermediate, and acidic parent materials were determined, and these difference 

values were expressed as a percentage of the original critical load estimates (Tables 5.2-1 and

5.2-2). For example, the percentage difference associated with the misclassification of an 

intermediate parent material as acidic would be calculated as the absolute value of (CLintermediate −

CLacid)/ CLintermediate. 

Table 5.2-1. Differences and Percentage Differences in Plot-Level Critical Load Estimates 

Associated with the Misclassification of Parent Material Acidity for the Full Range Assessment 

of Sugar Maple 

Bc/Al(crit)

Ratio 

Misclassification of 

Parent Material 

Difference between Critical 

Loads (eq/ha/yr) 

% Difference between 

Critical Loads (%) 

Range of 

Values Average Median Range of Values Average Median 

0.6 

Acidic as Intermediate 737 to1,559 797 784 25 to 492 58 51 

Acidic as Basic 784 to 3,631 1,018 936 42 to 492 70 61 

Intermediate as Basic 0 to 2,072 222 156 0 to 36 8 7 

Intermediate as Acidic 737 to 1,559 797 784 20 to 83 35 34 

Basic as Acidic 784 to 3,631 1,018 936 30 to 83 40 38 

Basic as Intermediate 0 to 2,072 222 156 0 to 26 7 7 

1.2 

Acidic as Intermediate 493 to 1,045 537 529 24 to 428 56 50 

Acidic as Basic 527 to 2,433 686 634 40 to 428 67 60 

Intermediate as Basic 0 to 1,388 149 105 0 to 36 8 7 

Intermediate as Acidic 493 to 1,045 537 529 19 to 81 34 33 

Basic as Acidic 527 to 2,433 686 634 29 to 81 39 37 

Basic as Intermediate 0 to 1,388 149 105 0 to 26 7 6 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 94 
Terrestrial Acidification Case Study 

Bc/Al(crit)

Ratio Bc/Al(crit) Ratio 

Misclassification of Parent 

Material 

Difference between Critical 

Loads (eq/ha/yr) 

Range of 

Values 

Range 

of 

Values 

Averag

e 

Median 

Range 

of 

Values 

Averag

e 

10.0 

Acidic as Intermediate 275 to 583 298 294 24 to 376 56 50 

Acidic as Basic 293 to 1,357 381 351 41 to 376 67 60 

Intermediate as Basic 0 to 774 83 58 0 to 36 8 7 

Intermediate as Acidic 275 to 583 298 294 20 to 79 34 33 

Basic as Acidic 293 to 1,357 381 351 29 to 79 39 37 

Basic as Intermediate 0 to 774 83 58 0 to 26 7 6 

Table 5.2-2. Differences and Percentage Differences in Plot-Level Critical Load Estimates 

Associated with the Misclassification of Parent Material Acidity for the Full Range Assessment 

of Red Spruce 

Bc/Al(crit)

Ratio 

Misclassification of 

Parent Material 

Difference between Critical 

Loads (eq/ha/yr) 

% Difference between 

Critical Loads (%) 

Range of 

Values Average Median Range of Values Average Median 

0.6 

Acidic as Intermediate 772 to 1,340 831 829 27 to 453 69 67 

Acidic as Basic 853 to 1,584 965 932 44 to 453 78 74 

Intermediate as Basic 0 to 655 134 99 0 to 16 6 5 

Intermediate as Acidic 772 to 1,340 831 829 22 to 82 40 40 

Basic as Acidic 853 to 1,584 965 932 31 to 82 43 43 

Basic as Intermediate 0 to 655 134 99 0 to 14 5 5 

1.2 

Acidic as Intermediate 521 to 969 567 566 27 to 392 66 64 

Acidic as Basic 578 to 1,075 658 637 43 to 392 74 71 

Intermediate as Basic 0 to 444 91 67 0 to 16 5 5 

Intermediate as Acidic 521 to 969 567 566 21 to 80 38 39 

Basic as Acidic 578 to 1,075 658 637 30 to 80 42 42 

Basic as Intermediate 0 to 444 91 67 0 to 14 5 4 

10.0 

Acidic as Intermediate 289 to 511 312 311 27 to 345 66 65 

Acidic as Basic 320 to 594 362 350 45 to 345 75 73 

Intermediate as Basic 0 to 246 50 37 0 to 16 5 5 

Intermediate as Acidic 289 to 511 312 311 21 to 78 39 39 

Basic as Acidic 320 to 594 362 350 31 to 78 42 42 

Basic as Intermediate 0 to 246 50 37 0 to 14 5 5 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 95 
Terrestrial Acidification Case Study 

The comparisons of critical loads revealed that changes in critical load values could range 

from 0 to 3,631 eq/ha/yr for sugar maple and 0 to 1,584 eq/ha/yr for red spruce with the 

misclassification of parent material acidity. These ranges correspond to percentage differences 

ranging from 0% to 492% and 0% to 453% for sugar maple and red spruce, respectively. The 

results also indicate that the biggest impacts of a misclassification on critical load estimates 

would occur with an acidic parent material being misclassified as basic; the average percentage 

changes in the estimated critical loads, in such a scenario, were 67% to 70% for sugar maple and 

74% to 78% for red spruce, and the median percentage changes were 60% to 61% and 71% to 

74% for the two species, respectively. In contrast, the smallest impacts on critical load estimates 

would occur when a basic parent material was incorrectly classified as intermediate and vice 

versa. In this scenario, the average and median percentage changes in critical load estimates were 

only 7% to 8% and 6% to 7% for sugar maple and 5% to 6% and 4% to 5% for red spruce. Given 

the potential significant impacts of a misclassification of parent material acidity on critical load 

estimates, this potential source of error should be considered in the accuracy and application of 

the critical load estimates. 

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St. Clair, S.B., J.E. Carlson, and J.P. Lynch. 2005. Evidence for oxidative stress in sugar maple 

stands growing on acidic, nutrient imbalanced forest soils. Oecologia 145:258–369. 

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Stone, D.M. 1986. Effect of thinning and nitrogen fertilization on diameter growth of pole-size 

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geochemical model PROFILE. Applied Geochemistry 8:273–283. 

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Environmental Engineering 2. Lund University, Department of Chemical Engineering, 

Lund, Sweden. 

Sverdrup, H., and W. de Vries. 1994. Calculating critical loads for acidity with the simple mass 

balance equation. Water, Air, and Soil Pollution 72:143–162. 

Thomasma, S., P. Knopp, and S. Stout. 2008. SILVAH Version 5.6. Online computer tool. U.S. 

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Irvine, PA. Available at: http://www.nrs.fs.fed.us/tools/silvah/ (accessed September 9, 

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Washington, DC. June 23. 

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Service). 2008b. Soil Survey Geographic (SSURGO) Database for Grafton County Area, 

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New Hampshire. GIS datalayer. U.S. Department of Agriculture, Natural Resources 

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Appendix 5 - 110 
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soil Al/Ca ratios in the northeastern United States. Canadian Journal of Forest Research 

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of the Critical Load with Biological Effects at Ontario Forests. Report 2. Environmental 

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development of critical loads for sulphur and nitrogen. Pp. 33–38 in Monitoring Science 

and Technology Symposium: Unifying Knowledge for Sustainability in the Western 

Hemisphere Proceedings RMRS-P-42CD. Edited by C. Aguirre-Bravo, P.J. Pellicane, 

D.P. Burns, and S. Draggan. U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO. 

Webster, K.L., I.F. Creed, N.S. Nicholas, and H.V. Miegroet. 2004. Exploring interactions 

between pollutant emissions and climatic variability in growth of red spruce in the Great 

Smoky Mountains National Park. Water, Air, and Soil Pollution 159:225–248. 

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rates for acid-sensitive catchments in Nova Scotia, Canada and their impact on critical 

load calculations. Geoderma 136: 899-911. 

Final Risk and Exposure Assessment September 2009 

Appendix 5 - 111 
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Final Risk and Exposure Assessment September 2009 

Appendix 5 - 112 
Terrestrial Acidification Case Study 

ATTACHMENT A: 

RELATIONSHIP BETWEEN ATMOSPHERIC 

NITROGEN AND SULFUR DEPOSITION AND SUGAR 

MAPLE AND RED SPRUCE TREE GROWTH 

1. INTRODUCTION 

Nitrogen and sulfur deposition in forest systems can have either positive or negative 

impacts on tree growth. The growth of many forests in North America is limited by nitrogen 

availability (Chapin et al., 1993; Killam, 1994; Miller, 1988), and nitrogen fertilization is often a 

key component of forest management (Allen, 2001). Therefore, in such nitrogen-limited systems, 

nitrogen deposition may stimulate tree growth. In contrast, nitrogen additions in some systems 

can sometimes be greater than what trees require and can negatively impact tree health and 

growth (Aber et al., 1995; McNulty et al., 2005). Forests where atmospheric deposition of 

nitrogen and sulfur is greater than the critical load may be examples of such a condition. When 

critical loads are exceeded, tree health and growth may be compromised both directly and 

indirectly because of soil nutrient deficiencies and imbalances caused by acidic deposition and 

the leaching of base cations from the soil. Tree growth may be reduced and/or trees may have an 

increased susceptibility to drought and pest damage, aluminum (Al) toxicity in roots, reduced 

tolerance to cold, and a greater propensity to frost injury (Driscoll et al., 2001; DeHayes et al., 

1999; Fenn et al., 2006b; McNulty et al., 2005; Ouimet et al., 2008). In the context of acidifying 

deposition of nitrogen and sulfur, the positive versus negative impact of deposition on tree 

growth may depend largely upon whether the critical load is exceeded by the deposition level 

and may follow a modified version of the inverted U-shape relationship hypothesized by Aber et 

al. (1995) for forest systems that receive chronic, long-term nitrogen additions (Figure 1-1). If 

nitrogen and sulfur deposition is less than the critical load, tree growth may be stimulated 

because of a fertilizer effect of nitrogen deposition. Conversely, when the deposition is greater 

than the critical load, tree vigor and growth may be reduced because of the negative impacts of 

soil acidification. The transition point between growth stimulation and impairment would occur 

when deposition is equal to the critical load. 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 1 
Terrestrial Acidification Case Study 

Tree Growth 

Nitrogen Deposition Critical Load Exceedance

Figure 1-1. Hypothesized relationships between tree growth and nitrogen deposition and 

critical load exceedance. When deposition does not exceed the critical load, growth is 

stimulated by nitrogen deposition. When deposition exceeds the critical load (deposition 

is greater than the critical load), growth is reduced. (This figure is a modification of a 

curve describing forest productivity as a function of long-term chronic nitrogen additions 

outlined in Aber et al., 1995). 

To assess the impacts of nitrogen and sulfur deposition on sugar maple and red spruce in 

the Terrestrial Acidification Case Study, the relationships between net annual volume growth 

and (1) nitrogen deposition and (2) critical load exceedance were examined empirically across 

the geographical ranges of the two species. 

2. SOURCE OF DATA FOR ANALYSES 

Data from plots in the U.S. Forest Service (USFS) Forest Inventory and Analysis (FIA) 

database used in Section 4 of the Terrestrial Acidification Case Study (Appendix 5) were applied 

to this assessment. Highest protection level critical loads (Bc/Al = 10.0), 2002 CMAQ/NADP 

nitrogen and sulfur deposition, and USFS FIA database net annual individual tree volume 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 2 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 3 

 

growth10 and tree volume11 for all live sugar maple and red spruce trees in each plot were used in 

the analyses. Critical load exceedances and nitrogen deposition were determined using the 2002 

CMAQ/NADP modeled nitrogen and sulfur deposition estimates. The tree volumes and annual 

growth values were from the most recent FIA measurement period, and the interval between 

measurements (to determine growth rates) for the plots ranged from 1 to 11 years. Trees with 

negative growth values (but that were not dead) were included in the analyses to account for the 

potential indirect impacts of nitrogen and sulfur deposition. All trees that had “0” volume values 

were excluded from the analyses. Given these data restrictions, a total of 1,059 sugar maple and 

419 red spruce plots were included in the tree volume growth–nitrogen deposition regression 

analyses, and a total of 2,988 sugar maple and 194 red spruce plots were included in the tree 

growth–critical load exceedance analyses. Volumes and volume growth for the sugar maple and 

red spruce trees in each plot were averaged to produce single values of each parameter for each 

species. Tables 2-1 and 2-2 summarize the plot-level FIA sugar maple and red spruce data used 

to model the relationships between tree growth and nitrogen deposition and critical load 

exceedance. 

10 FGROWCFAL or GROWCFAL (i.e., the net annual sound cubic-foot growth of a live tree (of trees >12.7 cm 

diameter). Growth values may be negative because of loss of volume due to death or damage, rot, broken top, or 

other natural causes. Source: FIA database (USFS, 2008c). 

11 VOLCFNET (i.e., the net cubic foot volume of wood in the central stem of a tree, beginning at 12.7 cm in 

diameter or larger up the stem to a minimum 10.16 cm top outer-bark diameter. This measurement does not 

include rotten, missing, and form cull. Source: FIA database (USFS, 2008c). 
Terrestrial Ac

Table 2-1. Summary of Plot-Level Data Used in the Regression Analyses for Sugar Maple Volume and Growth, 

and Critical Load Exceedances (based on critical loads calculated with Bc/Al = 10.0) 

State 

Total 

Number 

of Plots 

NITROGEN DEPOSITION REGRESSION 

ANALYSIS 

CRITICAL LOAD EXC

ANA

Number of 

Plots with 

N+S 

Deposition 

Lower Than 

CL 

Average 

Nitrogen 

Deposition

(eq/ha/yr) 

Average 

Tree 

Volume Growth

(m3/yr) 

Average 

Tree 

Volume

(m3) 

Number of 

Plots with 

N+S 

Deposition 

Greater 

Than CL 

Average 

Critical 

Load 

Exceedanc

(eq/ha/yr) 

Alabama 12 9 821.9 0.0111 0.5254 3 434Arkansas 8 7 676.3 0.0098 0.3469 1 105Connecticut 33 – – – – 33 487Illinois 25 8 796.1 0.0093 0.2967 17 145Indiana 266 31 899.0 0.0177 0.5649 235 439Iowa 8 6 881.3 0.0114 0.2329 2 48Kentucky 14 2 815.2 0.0129 0.4126 12 412Maine 242 191 393.9 0.0079 0.2696 51 130Maryland 4 – – – – 4 599Massachusetts 27 – – – – 27 473Michigan 596 178 563.2 0.0055 0.2995 418 242Minnesota 257 178 618.8 0.0074 0.2482 79 156Missouri 122 64 717.9 0.0072 0.2422 58 171New Hampshire 72 12 457.1 0.0053 0.1963 60 378New Jersey 6 – – – – 6 601New York 280 16 677.1 0.0097 0.5243 264 437North Carolina 13 4 663.7 –0.0038 0.6127 9 299Ohio 55 1 846.0 0.0033 0.0892 54 554

Final Risk and Exposure Assessment 

Appendix 5, Attachment A - 4 
Terrestrial Ac

State 

Total 

Number 

of Plots 

NITROGEN DEPOSITION REGRESSION 

ANALYSIS 

CRITICAL LOA

REGRESSIO

Number of 

Plots with 

N+S 

Deposition 

Lower 

Than CL 

Average 

Nitrogen 

Depositio

n 

(eq/ha/yr)

Average 

Tree 

Volume 

Growth

(m3/yr) 

Average 

Tree 

Volume

(m3) 

Number of 

Plots with 

N+S 

Deposition 

Greater 

Than CL 

Average 

Critical 

Load 

Exceedan

e 

(eq/ha/yr

Pennsylvania 270 7 707.7 0.0185 0.3778 263 557Tennessee 264 132 770.4 0.0111 0.3179 132 161Vermont 162 2 743.4 0.0092 0.2935 160 301Virginia 104 41 712.2 0.0130 0.2827 63 291West Virginia 337 19 768.2 0.0148 0.2824 318 352Wisconsin 870 151 733.0 0.0107 0.3078 719 185Total Observations (used in calculations) 4,047 1,059 1,059 1,059 1,059 2,988 2,98

Final Risk and Exposure Assessment 

Appendix 5, Attachment A - 5 
Terrestrial Ac

Final Risk and Exposure Assessment 

Appendix 5, Attachment A - 6 

Table 2-2. Summary of Plot-Level Data Used in the Regression Analyses for Red Spruce Volume and Growth, N

and Critical Load Exceedances (based on critical loads calculated with Bc/Al = 10.0) 

State 

Total 

Number 

of Plots 

NITROGEN DEPOSITION REGRESSION 

ANALYSIS 

CRITICAL LOAD EXC

ANA

Number of 

Plots with 

N+S 

Deposition 

Lower 

Than CL 

Average 

Nitrogen 

Deposition

 (eq/ha/yr) 

Average 

Tree 

Volume Growth

(m3/yr) 

Average 

Tree 

Volume

(m3) 

Number of 

Plots with 

N+S 

Deposition 

Greater 

Than CL 

Average 

Critical 

Load 

Exceedanc

(eq/ha/yr) 

Maine 483 405 390.3 0.0075 0.2487 78 133Massachusetts 3 – – – – 3 628New Hampshire 42 10 450.1 0.0060 0.1826 32 369New York 18 4 615.0 0.0066 0.6146 14 282Tennessee 1 – – – – 1 420Vermont 60 – – – – 60 292West Virginia 6 – – – – 6 257Total Observations (used in calculations) 613 419 419 419 419 194 19
Terrestrial Acidification Case Study 

3. REGRESSION ANALYSES METHODOLOGY AND RESULTS 

The impacts of nitrogen deposition and critical load exceedance on individual tree 

volume growth of sugar maple and red spruce were examined empirically using multivariate 

ordinary least squares (OLS) linear regression analyses, testing the positive and negative linear 

relationships represented in Figure 1-1. Plots where 2002 nitrogen and sulfur deposition did not 

exceed the critical load (based on Bc/Al = 10.0 critical load estimates) were used in the tree 

volume growth–nitrogen deposition analyses, and plots where deposition exceeded the critical 

load were used in the regression analyses comparing critical load exceedance and volume growth 

(Tables 2-1 and 2-2). In both sets of analyses for both species, the explanatory variables 

included the linear term of nitrogen deposition or critical load exceedance (both expressed as 

equivalents per hectare per year [eq/ha/year]) for each plot, linear and squared terms of average 

tree volumes in cubic meters (m3

), and a categorical (dummy) variable for each State (with 

Arkansas and Connecticut arbitrarily selected as the reference categories for sugar maple for the 

nitrogen deposition and critical load exceedance regressions, respectively. For red spruce, New 

York was selected for the nitrogen deposition regression, and Vermont was selected for the 

critical load exceedance regression). Tree volume was included as an explanatory variable of tree 

growth because tree age, the preferred explanatory variable, was not available for this dataset. 

Tree age and tree volume are highly correlated, and volume growth is influenced by tree size, so 

tree volume was seen as an appropriate surrogate explanatory variable. The State variables were 

included in the analyses to control for unobserved sources of variation in tree growth related to a 

plot’s general geographic location. Examples of potential unobserved factors include differences 

in data collection methods and measurements across reporting State, climatic factors, and 

geological characteristics. 

The results of the linear regression analyses to test the influences of nitrogen deposition 

and critical load exceedance on sugar maple and red spruce volume growth are presented in 

Tables 3-1a–b and 3-2a–b, respectively. 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 7 
Terrestrial Acidification Case Study 

Table 3-1a. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses 

of Sugar Maple Tree Growth and Nitrogen Deposition (for plots where deposition did not exceed 

critical loads calculated with Bc/Al = 10.0) 

Explanatory Variables 

Dependent Variable: Average Tree Growth (m3/yr) 

Coefficient t-statistic p-value 

Intercept −0.00278 -0.53 0.5965

Nitrogen Deposition 1.22 x 10-5 3.22 0.0013

Average Tree Volume 0.01296 6.44 <0.0001

Square of Average Tree Volume −8.32 x 10-4 −1.39 0.1645

Alabama −0.00242 −0.39 0.6941

Illinois −0.00131 −0.21 0.8350

Indiana 0.00338 0.65 0.5139

Iowa 5.19 x 10-4 0.08 0.9392

Kentucky 6.37 x 10-4 0.07 0.9479

Maine 0.00249 0.52 0.6034

Michigan −0.00238 −0.51 0.6134

Minnesota −4.20 x 10-4 −0.09 0.9286

Missouri −0.00167 −0.34 0.7302

New Hampshire −3.34 x 10-6 0.00 0.9995

New York −0.00190 −0.35 0.7301

North Carolina −0.01658 −2.17 0.0299

Ohio −0.00535 −0.41 0.6812

Pennsylvania 0.00790 1.22 0.2241

Tennessee 5.46 x 10-4 0.12 0.9080

Vermont −7.88 x 10-4 −0.08 0.9356

Virginia 0.00360 0.72 0.4693

West Virginia 0.00468 0.87 0.3847

Wisconsin 6.76 x 10-4 0.14 0.8858

Number of Observations 1,059 

Adjusted R2 0.1175 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 8 
Terrestrial Acidification Case Study 

Table 3-1b. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses 

of Red Spruce Tree Growth and Nitrogen Deposition (for plots where deposition did not exceed 

critical loads calculated with Bc/Al = 10.0) 

Explanatory Variables 

Dependent Variable: Average Tree Growth (m3/yr) 

Coefficient t-statistic p-value 

Intercept 0.00704 1.57 0.1166

Nitrogen Deposition –6.69 x 10-6 −1.12 0.2679

Tree Volume 0.00955 3.72 0.0002

Square of Tree Volume -0.00528 −2.31 0.0215

Maine 0.00121 0.44 0.6626

New Hampshire 4.5 x 10-4 0.15 0.8825

Number of Observations 419 

Adjusted R2 0.0351 

Table 3-2a. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses 

of Sugar Maple Tree Growth and Critical Load Exceedance (for plots where nitrogen and sulfur 

deposition was greater than critical loads calculated with Bc/Al = 10.0) 

Explanatory Variables 

Dependent Variable: Average Tree Growth 

(m3/yr) 

Coefficient t-statistic p-value 

Intercept 0.00430 1.35 0.1787

Critical Load Exceedance −1.43 x 10-6 −0.92 0.3571

Average Tree Volume 0.02001 10.81 <0.0001

Square of Average Tree Volume 2.58 x 10-4 0.34 0.7365

Alabama 0.00341 0.32 0.7523

Illinois −0.00112 −0.21 0.8347

Indiana 0.00544 1.65 0.0991

Iowa −0.00142 −0.11 0.9135

Kentucky 0.00449 0.74 0.4564

Maine −1.12 x 10-4 −0.03 0.9776

Maryland −7.89 x 10-4 −0.08 0.9337

Massachusetts −0.00814 −1.76 0.0786

Michigan 7.77 x 10-4 0.24 0.8095

Minnesota 8.48 x 10-4 0.23 0.8194

Missouri −0.00389 −1.00 0.3192

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 9 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 10 

Explanatory Variables 

Dependent Variable: Average Tree Growth 

(m3/yr) 

Coefficient t-statistic p-value 

New Hampshire −0.00120 −0.31 0.7556

New Jersey 0.00190 0.24 0.8108

New York −5.42 x 10-4 −0.17 0.8685

North Carolina 0.00431 0.64 0.5226

Ohio 1.85 x 10-4 0.05 0.9626

Pennsylvania 8.33 x 10-4 0.25 0.7994

Tennessee 0.00172 0.49 0.6225

Vermont −0.00448 −1.32 0.1879

Virginia 0.00261 0.68 0.4957

West Virginia 9.14 x 10-4 0.28 0.7783

Wisconsin −0.00151 −0.48 0.6347

Number of Observations 2,988 

Adjusted R2 0.1329 

Table 3-2b. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses 

of Red Spruce Tree Growth and Critical Load Exceedance (for plots where nitrogen and sulfur 

deposition was greater than critical loads calculated with Bc/Al = 10.0) 

Explanatory Variables 

Dependent Variable: Average Tree Growth 

(m3/yr) 

Coefficient t-statistic p-value 

Intercept 0.00628 5.15 <0.0001 

Critical Load Exceedance −5.62 x 10-6 −2.30 0.0223 

Tree Volume 0.00611 1.38 0.1685 

Square of Tree Volume 0.00463 1.11 0.2705 

Maine 1.26 x 10-5 0.01 0.9890 

Massachusetts −1.73 x 10-4 −0.06 0.9524 

New Hampshire 2.68 x 10-4 0.25 0.7993 

New York −0.00200 −1.43 0.1549 

West Virginia 0.00331 1.64 0.1020 

Number of Observations 194 

Adjusted R2 0.2009 
Terrestrial Acidification Case Study 

The results of the linear regressions analyzing the relationships between nitrogen 

deposition and tree volume growth differed by species. For sugar maple, nitrogen deposition had 

a positive and significant (at the p<0.05 level, p=0.00013) impact on the aboveground volume 

growth of individual trees (Table 3-1a). These results suggest that the 2002 nitrogen additions, 

that ranged from 332 to 1,146 eq/ha/yr (4.7 to 16.1 kg N/ha/yr), acted as a fertilizer and 

stimulated growth on sites where nitrogen and sulfur deposition did not exceed the critical load. 

On these sites, nitrogen most likely posed a limitation to sugar maple growth. There are few 

studies that have demonstrated a positive growth response of sugar maple to nitrogen additions. 

Stone (1986) found that sugar maple diameter growth was not impacted by nitrogen 100 to 300 

kg N/ha fertilizers applied three years before or after thinning, and similar results have been 

determined in other studies (see references in Stone, 1986). In contrast to sugar maple, the red 

spruce in the tree growth–nitrogen deposition analyses displayed a negative, nonsignificant (at 

the p<0.05 level, p=0.2679) relationship between deposition and growth (Table 3-1b). These 

results suggest that red spruce growth may not have been limited by nitrogen on sites where the 

critical load was not exceeded by nitrogen and sulfur deposition; nitrogen deposition in the range 

of 255 to 667 eq/ha/yr (3.6 to 9.3 kg N/ha/yr) did not stimulate the growth of red spruce. Briggs 

et al. (2000) reported that diameter growth of red spruce was unchanged by the application of 

200 kg N/ha on pre-commercially thinned spruce-fir stands in Maine. McNulty et al. (2005) 

found that although ambient nitrogen deposition of 5.4 kg N/ha/yr over the course of 14 years 

was associated with a 20% increase in basal area of a red spruce–dominated spruce-fir forest, 

supplemental additions of 15.4 kg N/ha/yr resulted in a 20% decrease in live basal area. 

Therefore, the documented response of red spruce to nitrogen additions is mixed. 

The results of the analyses determining the relationships between critical load exceedance 

and tree growth showed similar trends for both sugar maple and red spruce; growth declined with 

increasing levels of critical load exceedance. For red spruce, the relationship was significant at 

the 5% level (p=0.0223) and negative, indicating that red spruce growth may be negatively 

impacted by deposition levels that exceed the critical load (Table 3-2b). Decreased basal area of 

red spruce in response to chronic additions of nitrogen have been reported in several other 

studies (Aber et al., 1995; McNulty et al., 2005), and authors of these studies speculated that 

these negative growth responses were due to sites reaching a level of nitrogen saturation. 

Although the tree growth–critical load exceedance relationship was also negative for sugar 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 11 
Terrestrial Acidification Case Study 

maple, it was not statistically significant at the 5% level (p=0.3571), suggesting that sugar maple 

may not be negatively impacted by nitrogen and sulfur deposition levels greater than critical 

loads (Table 3-2a). However, shortcomings of at least one of the variables used within the SMB 

calculations of critical loads may, in part, have contributed to the lack of a significant 

relationship between tree growth and critical load exceedance. As discussed in Appendix 5, the 

base cation weathering (BCw) term that accounts for the contributions of base cations from the 

weathering of soil minerals and parent material is one of the most influential terms in the SMB 

model. Li and McNulty (2007) determined that 49% of the variability in critical load estimates 

was due to this term. Within the United States and Canada, BCw for critical load assessments is 

commonly estimated using the clay-substrate model (Ouimet et al., 2006; Watmough et al., 

2006; McNulty et al., 2007; Pardo and Duarte, 2007), and this model was also used in the 

Terrestrial Acidification Case Study. Critical load experts from both the United States and 

Canada have commented that the clay-substrate model method performs well in young soils that 

have formed since the last glaciation (20,000 years before present (ybp)), but may not be suitable 

for older, more weathered soils south of the most recent glacial advancement. Therefore, to 

account for the potential influence of a poorer estimation of BCw in plots south of the glaciation 

line (Figure 3-1), a second set of multivariate, OLS linear regression analyses were conducted, 

using the same specifications (i.e., plots where nitrogen and sulfur deposition exceeded the 

critical load calculated with Bc/Al=10.0), as described above. However, these analyses were 

restricted to data from plots that had been covered by the last glaciation (i.e., north of the 

glaciation line). Limiting the analysis to plots north of the glaciation line led to analyzing 4% 

fewer red spruce plots and 26% fewer sugar maple plots (Tables 3-3 and 3-4). 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 12 
Terrestrial Acidification Case Study 

Figure 3-1. Areas of the continental United States that were covered during the last 

glacial event (~ 20,000 ybp) (Reed and Bush, 2005).

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 13 
Terrestrial Acidification Case Study 

Table 3-3. Summary of Plot-Level Data for Sugar Maple Volume and Growth and Critical Load 

Exceedances North of the Glaciation Line (for plots where nitrogen and sulfur deposition 

exceeded critical loads calculated with Bc/Al = 10.0) 

State 

Total 

Number of 

Plots with 

N+S 

Deposition 

Greater 

Than CL 

Number of 

Plots North 

of the 

Glaciation 

Line with 

N+S 

Deposition 

Greater 

Than CL 

Average CL 

Exceedance 

 (eq/ha/yr) 

Average 

Tree Volume 

Growth

(m3/yr) 

Average 

Tree Volume 

(m3) 

Alabama 3 0 NA NA NA

Arkansas 1 0 NA NA NA

Connecticut 33 33 487.76 0.009 0.279

Illinois 17 12 117.17 0.007 0.227

Indiana 235 204 390.90 0.018 0.397

Iowa 2 2 48.07 0.005 0.123

Kentucky 12 0 NA NA NA

Maine 51 51 130.42 0.011 0.323

Maryland 4 0 NA NA NA

Massachusetts 27 27 473.31 0.003 0.366

Michigan 418 418 242.17 0.011 0.307

Minnesota 79 79 156.06 0.010 0.256

Missouri 58 18 84.02 0.012 0.246

New Hampshire 60 60 378.40 0.009 0.304

New Jersey 6 6 601.39 0.013 0.357

New York 264 264 437.94 0.010 0.344

North Carolina 9 0 NA NA NA

Ohio 54 26 452.60 0.013 0.545

Pennsylvania 263 126 387.35 0.011 0.366

Tennessee 132 0 NA NA NA

Vermont 160 160 301.67 0.008 0.411

Virginia 63 0 NA NA NA

West Virginia 318 0 NA NA NA

Wisconsin 719 719 185.16 0.009 0.304

Total Observations 

(used in calculations) 2,988 2,205 2,205 2,205 2,205

NA = not applicable. Average Critical Load Exceedance, Average Tree Volume Growth, and Average 

Tree Volume values could not be determined because there were no sugar maple plots north of the 

glaciation line. 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 14 
Terrestrial Acidification Case Study 

State 

Total 

Number of 

Plots with 

N+S 

Deposition 

Greater 

than CL 

Number of 

Plots with 

N+S 

Deposition 

Greater than 

CL North of 

Glaciation 

Line 

Average CL 

Exceedance 

 (eq/ha/yr) 

Average 

Tree 

Volume 

Growth

(m3/yr) 

Average 

Tree 

Volume (m3)

Alabama 3 0 NA NA NA

Arkansas 1 0 NA NA NA

Connecticut 33 33 487.76 0.009 0.279

Illinois 17 12 117.17 0.007 0.227

Indiana 235 204 390.90 0.018 0.397

Iowa 2 2 48.07 0.005 0.123

Kentucky 12 0 NA NA NA

Maine 51 51 130.42 0.011 0.323

Maryland 4 0 NA NA NA

Massachusetts 27 27 473.31 0.003 0.366

Michigan 418 418 242.17 0.011 0.307

Minnesota 79 79 156.06 0.010 0.256

Missouri 58 18 84.02 0.012 0.246

New Hampshire 60 60 378.40 0.009 0.304

New Jersey 6 6 601.39 0.013 0.357

New York 264 264 437.94 0.010 0.344

North Carolina 9 0 NA NA NA

Ohio 54 26 452.60 0.013 0.545

Pennsylvania 263 126 387.35 0.011 0.366

Tennessee 132 0 NA NA NA

Vermont 160 160 301.67 0.008 0.411

Virginia 63 0 NA NA NA

West Virginia 318 0 NA NA NA

Wisconsin 719 719 185.16 0.009 0.304

TOTAL Observations 

(used in calculations) 2988 2205 2205 2205 2205

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 15 
Terrestrial Acidification Case Study 

Table 3-4. Summary of Plot-Level Data for Red Spruce Volume and Growth and Critical Load 

Exceedances North of the Glaciation Line (for plots where nitrogen and sulfur deposition 

exceeded critical loads calculated with Bc/Al = 10.0) 

State 

Total 

Number of 

Plots with 

N+S 

Deposition 

Greater 

Than CL 

Number of 

Plots with 

N+S 

Deposition 

Greater 

Than CL 

North of 

Glaciation 

Line 

Average CL 

Exceedance

 (eq/ha/yr) 

Average 

Tree 

Volume 

Growth

(m3/yr) 

Average 

Tree 

Volume 

(m3) 

Maine 78 78 133.1048 0.007 0.245

Massachusetts 3 3 628.5439 0.004 0.203

New Hampshire 32 32 368.9527 0.006 0.245

New York 14 14 282.5433 0.004 0.221

Tennessee 1 0 NA NA NA

Vermont 60 60 292.4329 0.007 0.328

West Virginia 6 0 NA NA NA

Total Observations 

(used in 

calculations) 194 187 187 187 187

NA = not applicable. Average Critical Load Exceedance, Average Tree Volume Growth, and Average 

Tree Volume values could not be determined because there were no red spruce plots north of the 

glaciation line. 

The results from the linear regression analyses for sugar maple and red spruce, north of 

the glaciation line, are presented in Tables 3-5 and 3-6, respectively. Similar to the first linear 

regression analyses that included all critical load exceedance plots, the relationship between tree 

growth and critical load exceedance was negative for both species and was statistically 

significant at the 5% level (p-value of 0.0354) for red spruce. However, in contrast to the first 

linear regression analyses, the relationship for the sugar maple plots north of the glaciation line 

was significant at the 10% level (p-value of 0.1008), and occurred despite the 26% reduction of 

plots used in the analysis. These results suggest that a larger portion of the variation in sugar 

maple growth could be accounted for by critical load exceedance when the analysis was 

restricted to plots north of the glaciation line and lend support to the hypothesis that the claysubstrate model to estimate BCw may not be suitable for older soils. Future assessment of critical 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 16 
Terrestrial Acidification Case Study 

loads may, therefore, want to consider exploring other BCw models to estimate critical loads of 

atmospheric nitrogen and sulfur deposition. Models such as PROFILE (Sverdrup and Warfvinge, 

1993a), which are based on soil mineralogy, may provide better estimates of the contribution of 

base cations from soil weathering. 

Table 3-5. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses of 

Sugar Maple Tree Growth and Critical Load Exceedance North of the Glaciation Line (for plots 

where deposition exceeded critical loads calculated with Bc/Al = 10.0) 

Explanatory Variables 

Dependent Variable: Average Tree Growth (m3/yr) 

Coefficient t-statistic p-value 

Intercept 0.004875 1.48 0.1385 

Critical Load Exceedance −3.344 x 10-6 −1.64 0.1008 

Average Tree Volume 0.021150 10.12 <.0001 

Square of Average Tree Volume 8.944 x 10-4 1.1 0.27 

Illinois −0.001884 −0.31 0.755 

Indiana 0.005452 1.63 0.1029 

Iowa −0.002052 −0.16 0.8743 

Maine −0.000895 −0.22 0.8245 

Massachusetts −0.008403 −1.82 0.0685 

Michigan 0.000222 0.07 0.9456 

Minnesota 0.000210 0.06 0.9553 

Missouri 0.001850 0.35 0.7255 

New Hampshire −0.001647 −0.43 0.6696 

New Jersey 0.001956 0.25 0.8042 

New York −0.000817 −0.25 0.8035 

Ohio −0.002104 −0.45 0.6522 

Pennsylvania −0.000803 −0.23 0.8177 

Vermont −0.005168 −1.51 0.131 

Wisconsin −0.002195 −0.68 0.4958 

Number of Observations 2,205 

Adjusted R2 0.1722 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 17 
Terrestrial Acidification Case Study 

Table 3-6. Results from the Multivariate Ordinary Least Squares Linear Regression Analyses of 

Red Spruce Tree Growth and Critical Load Exceedance, North of the Glaciation Line (for plots 

where deposition exceeded critical loads calculated with Bc/Al = 10.0) 

Explanatory Variables 

Dependent Variable: Average Tree Growth (m3/yr) 

Coefficient t-statistic p-value 

Intercept 0.006034 4.96 <.0001 

Critical Load Exceedance −5.162 x 10-6 −2.12 0.0354 

Tree Volume 0.005590 1.26 0.2093 

Square of Tree Volume 5.100 x 10-3 1.23 0.2218 

Maine 0.000285 0.32 0.7489 

Massachusetts −0.000132 −0.05 0.9629 

New Hampshire 0.000435 0.42 0.6736 

New York −0.001805 −1.32 0.1897 

Number of Observations 187 

Adjusted R2 0.1963 

4. ADDITIONAL SOURCES OF VARIABILITY INFLUENCING THE 

RELATIONSHIPS BETWEEN TREE VOLUME GROWTH AND 

NITROGEN DEPOSITION AND CRITICAL LOAD EXCEEDANCE 

In addition to potential inaccuracy in the BCw variable in the estimation of critical loads, 

there are additional sources of variation that may have influenced the relationships between 

critical load exceedance and the growth of red spruce and sugar maple. These sources of 

variability may have also influenced the relationships between tree growth and nitrogen 

deposition. 

4.1 State-Specific Variables 

Although State dummy variables and current tree volume were included as covariates in 

the regression analyses to account for the influences of location and tree size on tree growth, 

additional factors could be included in future regression analyses. For example, incorporating 

latitude/longitude and elevation in analyses could remove the influence of location on tree 

growth. Similarly, site index could remove the influence of site quality on the growth of red 

spruce and sugar maple. Total basal area of the stand would remove the influence of stand 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 18 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 19 

density and site occupancy. Measurement year and time between measurements could help 

remove the influence of year-to-year variation in conditions and measurement methodology on 

the growth data. Climatic variation (e.g., rainfall, temperature) could be included to account for 

the influence of drought and frost conditions (McNulty and Boggs, in press) on tree growth. It is 

recommended that future analyses comparing tree growth and nitrogen deposition or critical load 

exceedance take additional sources of variability into account. Much of this data is included in 

the FIA database.

4.2 Dead Trees 

The regression relationship between tree growth and nitrogen deposition or critical load 

exceedance may also be improved with the inclusion of dead trees in the analyses. In the FIA 

database, when a tree is recorded as dead for the first time, the total volume of that tree is 

considered negative volume growth over the most recent measurement period.12 As described 

earlier, atmospheric deposition of nitrogen and sulfur can indirectly result in tree mortality. 

Therefore, it may be appropriate to include tree mortality in an evaluation of the relationship 

between tree growth and nitrogen deposition or critical load exceedance. However, the validity 

of including dead tree negative volume growth measurements (as calculated by the FIA 

database) in the regression analyses was uncertain, and, therefore, the inclusion of dead trees was 

not pursued in the analyses reported in this Attachment. 

4.3 Other Factors 

The FIA sugar maple and red spruce tree data used in the analyses may have also 

introduced variability and a source of error in the analyses. As discussed in Appendix 5, due to 

restriction factors, not all sugar maple and red spruce plots were included in the analyses. It is 

uncertain to what degree, if any, these restrictions may have biased the results. The influence of 

 

12 Dead tree volume growth is calculated as a difference in volumes (v2–v1) divided by the time between sequential 

measurement period (t2–t1). When a tree is recorded as dead, it is assigned a v2 value of “0.” Therefore, the 

associated volume growth is equal to the entire tree volume divided by the difference in the number of years 

between the current and last measurement cycle. 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 20 

tree ingress13 may also not have been completely accounted for in the analyses and may have 

introduced another source of error. According to USFS FIA database methodology, trees must be 

at least 12.7 centimeters (cm) in diameter to be included in the VOLCFNET (total net volume 

per tree) tree volume table. When they reach this size, the full volume of the stem is incorporated 

into the volume growth measurements, and, in many cases, these measurements would be larger 

than the actual annual growth rates. Trees with 0 m3

 VOLCFNET volume values were excluded 

from the analyses to at least partially account for this influence. With the data that were made 

available for the analyses, it was not possible to determine other possible ingress trees. The use 

of VOLCFNET tree volume as a covariate of FGROWCFAL (net annual sound cubic-foot 

growth of a live tree on forest land) tree growth may have also introduced a small source of 

error. VOLCFNET is based on merchantable volume (e.g., pulp and sawlog), whereas 

FGROWCFAL is based on growth of sound wood. The difference between the two 

measurements is cull wood that is sound but not merchantable due to circumstances such as the 

location on the tree or tree branchiness. The removal of trees with 0 m3

 VOLCFNET volume 

from the analyses removed at least a portion of the trees that would increase the influence of cull 

wood on the covariate relationship between the two variables. Measurement error may also have 

introduced another source of error to the analyses. Tree volumes and volume growth are based 

on the measurements conducted on the main stem of the tree. Slight differences in measurements 

conducted by different crews and in different years could have introduced some error to the 

volume and growth estimates. Based on the FIA data provided by the USFS, it was not possible 

to determine the degree to which each of these various source of error influenced the data, nor 

was it possible to determine if reduction or elimination of these sources of error would change 

and/or improve the regression analyses. Attempts to minimize these potential sources of error are 

recommended in future analyses of the relationships between tree growth and nitrogen deposition 

and critical load exceedance. 

 

13 “Ingress” refers to trees that appear that are included in the dataset for the first time. Usually ingress is a result of 

trees reaching a certain size, but occasionally, ingress can also occur when new trees are included in the 

measurements. Steps are taken to identify these new trees and estimate previous size and growth, but sometimes 

they may be missed. 
Terrestrial Acidification Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 5, Attachment A - 21 

5. CONCLUSIONS 

In conclusion, the results from these analyses do suggest that nitrogen and sulfur 

deposition can result in either a positive or negative impact of tree volume growth. When 

deposition levels do not exceed the critical load of a site, tree growth may be stimulated by the 

nitrogen additions, as was seen in the sugar maple analyses. Conversely, when critical loads are 

exceeded by nitrogen and sulfur deposition, tree growth may be negatively impacted. This 

negative growth trend was seen in both the red spruce and sugar maple (restricted to plots North 

of the glaciation line). The trends suggested by these results indicate that large-scale analyses 

using the USFS FIA database may provide a useful tool with which to examine both the positive 

and negative impacts of nitrogen and sulfur deposition on tree species in the United States. 

September 2009 

Appendix 6 

Aquatic Nutrient Enrichment Case Study 

Final 

EPA Contract Number EP-D-06-003 

Work Assignment 3-62 

Project Number 0209897.003.062 

Prepared for 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27709 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709-2194 

Aquatic Nutrient Enrichment Case Study 

TABLE OF CONTENTS 

Acronyms and Abbreviations ......................................................................................................... v

1.0 Background ............................................................................................................................... 1

1.1 Indicators, Ecological endpoints, and Ecosystem Services ............................................. 4

1.2 Case Studies ..................................................................................................................... 9

1.2.1 National Overview of Sensitive Areas ................................................................. 9

1.2.2 Use of ISA Information and Rationale for Site Selection .................................. 12

1.2.3 Potomac River and Potomac Estuary ................................................................. 17

1.2.4 Neuse River and Neuse River Estuary ............................................................... 20

2.0 Approach and Methods ........................................................................................................... 26

2.1 Modeling ........................................................................................................................ 27

2.2 Chosen Method .............................................................................................................. 30

2.2.1 SPARROW ........................................................................................................ 31

2.2.1.1 Background and Description............................................................... 31

2.2.1.2 Key Definitions for Understanding SPARROW Modeling ................ 36

2.2.1.3 Concepts of Importance to Case Study—SPARROW 

Application .......................................................................................... 38

2.2.2 ASSETS Eutrophication Index .......................................................................... 39

2.2.2.1 Background and Description............................................................... 39

2.2.2.2 Applications and Updates ................................................................... 46

2.2.3 Assessments Using Linked SPARROW and ASSETS EI ................................. 46

2.2.3.1 Back Calculation Method ................................................................... 52

2.2.3.2 Uncertainty Bounds on TNs, OEC Min/Max Values .......................... 53

3.0 Results ..................................................................................................................................... 59

3.1 Current Conditions ......................................................................................................... 59

3.1.1 Summary of Results for the Potomac River/Potomac Estuary Case Study 

Area .................................................................................................................... 59

3.1.1.1 SPARROW Assessment ..................................................................... 60

3.1.1.2 ASSETS EI Assessment ..................................................................... 66

3.1.2 Summary of Results for the Neuse River/Neuse River Estuary Case 

Study Area ......................................................................................................... 70

3.1.2.1 SPARROW Assessment ..................................................................... 70

3.1.2.2 ASSETS EI Assessment ..................................................................... 77

3.2 Alternative Effects Levels .............................................................................................. 80

3.2.1 Potomac River Watershed.................................................................................. 80

3.2.2 Neuse River Watershed ...................................................................................... 89

4.0 Implications for Other Systems .............................................................................................. 98

5.0 Uncertainty ............................................................................................................................ 101

6.0 Conclusions ........................................................................................................................... 105

7.0 References ............................................................................................................................. 105

Final Risk and Exposure Assessment September 2009 

Appendix 6 - i 
Aquatic Nutrient Enrichment Case Study 

LIST OF FIGURES 

Figure 1.1-1. Descriptions of the five eutrophication indicators used in NOAA’s NEEA 

(Bricker et al., 2007a). ............................................................................................ 6

Figure 1.1-2. A simplified schematic of eutrophication effects on an aquatic ecosystem. ............. 7

Figure 1.1-3. An illustrated representation of eutrophication measures through the use of 

indicators and influencing factors from NOAA’s NEEA (Bricker et al., 

2007a). .................................................................................................................... 7

Figure 1.2-1. The relationship between mean dissolved inorganic nitrogen concentration 

and mean wet inorganic nitrogen in unproductive lakes in different regions 

in North America and Europe (Bergström and Jansson, 2006). ........................... 11

Figure 1.2-2. Areas potentially sensitive to aquatic nutrient enrichment. .................................... 12

Figure 1.2-3. The Potomac River Watershed and Potomac Estuary. ............................................ 18

Figure 1.2-4. The Neuse River Watershed and Neuse River Estuary. .......................................... 23

Figure 2.2-1. Modeling methodology for case study. ................................................................... 31

Figure 2.2-2. Mass balance description applied to the SPARROW model formulation. ............. 32

Figure 2.2-3. Conceptual illustration of a reach network. ............................................................ 33

Figure 2.2-4. SPARROW model components (Schwarz et al., 2006). ......................................... 35

Figure 2.2-5. Influencing factors/Overall Human Influence index description and decision 

matrix (Bricker et al., 2007a). ............................................................................... 40

Figure 2.2-6. Overall Eutrophic Condition index description and decision matrix (Bricker 

et al., 2007a). ......................................................................................................... 43

Figure 2.2-7. Detailed descriptions of primary and secondary indicators of eutrophication 

(Bricker et al., 2007a). .......................................................................................... 44

Figure 2.2-8. Determined Future Outlook index description and decision matrix (Bricker 

et al., 2007a). ......................................................................................................... 45

Figure 2.2-9. Example response curve of instream total nitrogen concentrations to 

atmospheric deposition loads. ............................................................................... 48

Figure 2.2-10. Example of response for case study analysis (Bricker et al., 2007b). ................... 49

Figure 2.2-11. ASSETS EI response curve. .................................................................................. 51

Figure 2.2-12a. Back calculation analysis scenario A: no uncertainty. ........................................ 55

Figure 2.2-12b. Back calculation analysis scenario B: uncertainty in ASSETS EI 

assessment. ............................................................................................................ 56

Figure 2.2-12c. Back calculation analysis scenario C: uncertainty in both ASSETS EI 

assessment and nitrogen loading assessment. ....................................................... 57

Figure 2.2-13. Example of an improvement by one ASSETS EI score category in a back 

calculation assessment. ......................................................................................... 58

Figure 2.2-14. Example for resulting change in atmospheric nitrogen loads due to 

improvement in ASSETS EI score in back calculation assessment. .................... 59

Figure 3.1-1a. Atmospheric deposition yields of oxidized nitrogen over the Potomac 

River and Potomac Estuary watershed. ................................................................ 62

Figure 3.1-1b. Atmospheric deposition yields of reduced nitrogen over the Potomac River 

and Potomac Estuary watershed. .......................................................................... 63

Figure 3.1-1c. Atmospheric deposition yields of total nitrogen over the Potomac River 

and Potomac Estuary watershed. .......................................................................... 64

Final Risk and Exposure Assessment September 2009 

Appendix 6 - ii 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-2. Total nitrogen yields from all sources as predicted using the Version 3 of 

the Chesapeake Bay SPARROW application with updated 2002 

atmospheric deposition inputs. .............................................................................. 65

Figure 3.1-3. Source contributions to Potomac Estuary nitrogen load. ........................................ 66

Figure 3.1-4. The ASSETS EI scores for the Potomac Estuary (Bricker et al., 2006). ................ 68

Figure 3.1-5a. Atmospheric deposition yields of oxidized nitrogen over the Neuse River 

and Neuse River Estuary watershed. .................................................................... 72

Figure 3.1-5b. Atmospheric deposition yields of reduced nitrogen over the Neuse River 

and Neuse River Estuary watershed. .................................................................... 73

Figure 3.1-5c. Atmospheric deposition yields of total nitrogen over the Neuse River and 

Neuse River Estuary watershed. ........................................................................... 74

Figure 3.1-6. Total nitrogen yields from all sources in the Neuse River watershed as 

predicted by a SPARROW modeling application for the Neuse, TarPamlico, and Cape Fear rivers’ watersheds with 2002 data inputs. ...................... 76

Figure 3.1-7. Source contributions to Neuse River Estuary total nitrogen load. .......................... 77

Figure 3.2-1. Response curve relating instream total nitrogen concentration to total 

nitrogen atmospheric deposition load for the Potomac River watershed. ............ 81

Figure 3.2-2. Fitted Overall Eutrophic Condition curve for target ASSETS EI=2, median 

TNatm*

i (i = run 280) .............................................................................................. 87

Figure 3.2-3. Response curve relating instream total nitrogen concentration to total 

nitrogen atmospheric deposition load for the Neuse River/Neuse River 

Estuary Case Study Area. ..................................................................................... 90

Figure 3.2-4. Fitted Overall Eutrophic Condition curve for target ASSETS EI=2, median 

TNatm*

i (i = run 287). ............................................................................................. 95

Figure 3.2-5. Theoretical SPARROW response curves demonstrating relative influence of 

sources on nitrogen loads to an estuary. ............................................................... 97

Figure 4-1. Preliminary classifications of estuary typology across the nation (modified 

from Bricker et al., 2007a). ................................................................................. 100

Final Risk and Exposure Assessment September 2009 

Appendix 6 - iii 
Aquatic Nutrient Enrichment Case Study 

LIST OF TABLES 

Table 1.1-1. Key Indicators of Nutrient Enrichment Due to Nr, Including NOx ............................ 5

Table 1.1-2. Assessment Ecological Endpoints for Nutrient Enrichment Due to 

Deposition of Total Reactive Nitrogen, Including NOx.......................................... 7

Table 1.1-3. Ecosystem Services for Aquatic Systems Affected by Nutrient Enrichment ............. 8

Table 1.2-1. Summary of Indicators, Mapping Layers, and Models for Targeted 

Ecosystems ............................................................................................................ 10

Table 1.2-2. Nitrogen Deposition Level vs. EPA Total Nitrogen Criteria for Lakes and 

Reservoirs ............................................................................................................. 11

Table 1.2-3. Science Advisory Board/Ecological Effects Subcommittee Listing of 

Potential Assessment Areas for Evaluation of Benefits of Decreases in 

Atmospheric Deposition with Respect to Aquatic Nutrient Enrichment .............. 14

Table 1.2-4. Potential Assessment Areas for Aquatic Nutrient Enrichment Identified in 

the ISA .................................................................................................................. 15

Table 1.2-5. Physical Characteristics of the Potomac Estuary ..................................................... 18

Table 1.2-6. Hydrological Characteristics of the Potomac Estuary .............................................. 19

Table 1.2-7. Physical Characteristics of the Neuse River Estuary ............................................... 23

Table 1.2-8. Neuse River Watershed Land Use and Population ................................................... 24

Table 1.2-9. Hydrological Characteristics of the Neuse River Estuary ........................................ 25

Table 2.1-1. Examples of SPARROW Applications .................................................................... 36

Table 3.1-1. Potomac Estuary Current Condition Overall Human Influence Index Score ........... 69

Table 3.1-2. Model Parameters for 2002 Current Condition SPARROW Application for 

the Neuse River Watershed ................................................................................... 74

Table 3.1-3. Model Evaluation Statistics for 2002 Current Condition SPARROW 

Application for the Neuse River Watershed ......................................................... 75

Table 3.1-4. Current Condition Overall Eutrophic Condition Index Score for the Neuse 

River/Neuse River Estuary Case Study Area........................................................ 79

Table 3.2-1. Potomac River Watershed Alternative Effects Levels ............................................. 81

Table 3.2-2. Historical Potomac River Total Nitrogen Loads and Concentrations ...................... 82

Table 3.2-3. Additional Potomac Estuary Overall Eutrophic Condition Index Scores for 

Alternative Effects Levels ..................................................................................... 83

Table 3.2-4. Summary Statistics for Target ASSETS EI Scenarios for the Potomac 

Estuary .................................................................................................................. 87

Table 3.2-5. Neuse River/Neuse River Estuary Case Study Area Alternative Effects 

Levels .................................................................................................................... 90

Table 3.2-6. Annual Average Instream Total Nitrogen Concentrations in the Neuse River ........ 91

Table 3.2-7. Additional Neuse River Estuary Overall Eutrophic Condition Index Scores 

for Alternative Effects Levels ............................................................................... 92

Table 3.2-8. Summary Statistics for Target ASSETS EI Scenarios for the Neuse 

River/Neuse River Estuary Case Study Area........................................................ 95

Table 4-1. Typology Group Categorizations ................................................................................ 99

Final Risk and Exposure Assessment September 2009 

Appendix 6 - iv 
Aquatic Nutrient Enrichment Case Study 

ACRONYMS AND ABBREVIATIONS 

ASSETS EI Assessment of Estuarine Trophic Status eutrophication index 

AQUATOX a simulation model for aquatic systems 

CASTNet Clean Air Status and Trends Network 

CBP Chesapeake Bay Program 

CE-QUAL a mathematical model of water quality 

CMAQ Community Multiscale Air Quality model. 

CO2 carbon dioxide 

DFO determined future outlook 

DIN dissolved inorganic nitrogen 

EDA Estuarine Drainage Areas 

EES Ecological Effects Subcommittee 

EMAP Environmental Monitoring and Assessment Program 

GIS geographic information systems 

GT/MEL Georgia Tech Hydrologic Model/Multiple Element Limitation 

HAB harmful algal bloom 

HNO3 nitric acid 

HSPF Hydrologic Simulation Program-FORTRAN 

INCA Integrated Nitrogen in Catchments 

ISA Integrated Science Assessment 

km kilometer 

km2 square kilometer 

LANDSCAN a worldwide population database 

m meter 

m

2

 square meter 

m3 cubic meter 

MAGIC Model of Acidification of Groundwater in Catchments 

MEA Millennium Ecosystem Assessment 

mg/L milligrams per liter 

mi2 square mile 

MODMON Modeling and Monitoring Project 

N2O nitrous oxide 

NADP National Atmospheric Deposition Program 

NCCR National Coastal Condition Report 

NC DWQ North Carolina Department of Water Quality 

NEEA National Estuarine Eutrophication Assessment 

NH3 ammonia gas 

NH4

+ ammonium 

NOAA National Oceanic and Atmospheric Administration 

NO nitric oxide 

NO2 nitrogen dioxide 

NO3

– nitrate 

NOx nitrogen oxides 

Nr reactive nitrogen 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - v 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - vi 

OEC Overall Eutrophic Condition 

OHI Overall Human Influence 

psu practical salinity unit 

PnET-BCG biogeochemical model 

QA quality assurance 

QC quality control 

QUAL2K Enhanced Stream Water Quality Model 

RCA/ECOMSED Row Column AESOP/Estuary and Coastal Ocean Model with Sediment 

Transport 

RF1 Reach File version 1 

RHESSys Regional Hydro-Economic Simulation System 

RTI RTI International 

SAGT Atlantic and the eastern Gulf of Mexico, as well as the Tennessee River 

basin 

SAV submerged aquatic vegetation 

SOx sulfur oxides 

SPARROW Spatially Referenced Regression on Watershed 

STORET STOrage and RETrieval 

TN total nitrogen 

TNatm total nitrogen atmospheric deposition load 

TNs instream total nitrogen concentration 

μg/L microgram per liter 

USGS U.S. Geological Survey 

VIF Variance Inflation Factor 

VIMS Virginia Institute of Marine Science 

WASP Water Quality Analysis Simulation Program 

WATERSN Watershed Assessment Tool for Evaluating Reduction Strategies for 

Nitrogen 
Aquatic Nutrient Enrichment Case Study 

1.0 BACKGROUND 

One classification of effects targeted for this Risk and Exposure Assessment is nitrogen 

and sulfur enrichment of ecosystems in response to deposition of nitrogen oxides (NOx) and 

sulfur oxides (SOx). Nutrient enrichment effects are caused by nitrogen or sulfur deposition, but 

are dominated by nitrogen deposition, which is the focus of this case study. Nutrient enrichment 

can result in eutrophication in aquatic systems (see Section 4.3 of the Integrated Science 

Assessment (ISA) for Oxides of Nitrogen and Sulfur–Ecological Criteria (Final Report) (ISA) 

(U.S. EPA, 2008a). 

Because ecosystems may respond differently to nutrient enrichment, it is necessary to 

first perform Risk and Exposure Assessment case studies unique to the effect and ecosystem 

type. The feasibility of consolidating the effects and/or ecosystems in the Risk and Exposure 

Assessment was assessed, and where feasible, a broader characterization was performed. 

However, some ecosystems and their effects may be too unique to consolidate into a broad 

characterization. 

Upon completion of all risk and exposure assessment case studies, the results of the 

assessments performed for unique combinations of effects and ecosystem types are presented 

together to facilitate decision making on the total effects of nitrogen and sulfur deposition. 

Ecosystem services that relate to the effects are identified and valued, if possible. Ecosystem 

services provide an additional way to compare effects across various ecosystems. 

The selection and performance of case studies represent Steps 3 and 4, respectively, of 

the seven-step approach to planning and implementing a risk and exposure assessment, as 

presented in the April 2008 Draft Scope and Methods Plan for Risk/Exposure Assessment: 

Secondary NAAQS Review for Oxides of Nitrogen and Oxides of Sulfur (U.S. EPA, 2008b). Step 

4 entails evaluating the current nitrogen and sulfur loads and effects to a chosen case study 

assessment area, including ecosystems services. This case study evaluates the current nitrogen 

deposition load to aquatic ecosystems; in particular, estuarine systems and the role atmospheric 

deposition can play in the eutrophication of an aquatic ecosystem. Note that volatilization of 

nitrogen from these systems is not considered within the case study because of the lack of 

quantitative data on denitrification and volatilization rates for estuarine systems across the 

United States (U.S. EPA, 2008a). 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 1 
Aquatic Nutrient Enrichment Case Study 

Eutrophication 

Eutrophication is the process whereby a body of water becomes over-enriched in 

nutrients, resulting in increased productivity. As productivity increases with concomitant 

increases in organic matter production, dissolved oxygen levels in the waterbody may decrease 

and result in hypoxia (i.e., low dissolved oxygen levels). Total reactive nitrogen (Nr) can 

promote eutrophication in inland freshwater ecosystems, as well as in estuarine and coastal 

marine ecosystems, ultimately reducing biodiversity because of the lack of available oxygen 

needed for the survival of many species of aquatic plants and animals. Total Nr includes all 

biologically, chemically, and radiatively active nitrogen compounds in the atmosphere and 

biosphere, such as ammonia gas (NH3), ammonium (NH4

+

), nitric oxide (NO), nitrogen dioxide 

(NO2), nitric acid (HNO3), nitrous oxide (N2O), nitrate (NO3

–

), and organic compounds (e.g., 

urea, amines, nucleic acids) (U.S. EPA, 2008b). 

Freshwater Aquatic Ecosystems 

A freshwater lake or stream must be nitrogen-limited to be sensitive to nitrogen-mediated 

eutrophication. Although conventional wisdom holds that most lakes and streams in the United 

States are limited by phosphorus, recent evidence illustrates examples of lakes and streams that 

are limited by nitrogen and show symptoms of eutrophication in response to nitrogen addition. 

For example, surveys of lake nitrogen concentrations and trophic status along gradients of 

nitrogen deposition show increased inorganic nitrogen concentrations and productivity to be 

correlated with atmospheric nitrogen deposition (Bergström and Jansson, 2006). Additional 

information supporting the connection between nitrogen loading and eutrophication in freshwater 

systems is provided in EPA’s ISA (U.S. EPA, 2008a, Sections 3.3.2.3 and 3.3.3.2). 

Estuarine and Coastal Marine Ecosystems 

Estuarine and coastal marine ecosystems are highly important to human and ecological 

welfare through the ecosystem services they provide (e.g., fisheries, recreation). “Because the 

productivity of estuarine and nearshore marine ecosystems is generally limited by the availability 

of Nr, an excessive contribution of Nr from sources of water and air pollution can contribute to 

eutrophication” (U.S. EPA, 2008a, Section 4.3.4.1). The National Oceanic and Atmospheric 

Administration’s (NOAA’s) National Estuarine Eutrophication Assessment (NEEA) examined 

more than 140 estuaries along the coasts of the conterminous United States. The assessment 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 2 
Aquatic Nutrient Enrichment Case Study 

examined a range of symptoms of eutrophication, including algal blooms, hypoxia, and 

vegetation growth. Findings from the study concluded that 65% of the assessed systems had 

moderate to high overall eutrophic conditions (OECs) (Bricker et al., 2007a). Increasingly, 

individual estuarine ecosystems have become the center of intensive studies on nutrient 

enrichment/eutrophication causes and effects. Within the Chesapeake Bay, studies of the 

frequency of phytoplankton blooms and the extent and severity of hypoxia revealed overall 

increases in these detrimental effects (Officer et al., 1984). Within the Pamlico Estuary in North 

Carolina, similar trends have been observed and studied by Paerl et al. (1998). Sources identified 

within these assessments range from atmospheric deposition to fertilizer applications and other 

land use-based applications. 

Estuarine and coastal marine ecosystems experience a range of ecological problems 

associated with nutrient enrichment. Because the productivity of estuarine and nearshore marine 

ecosystems is generally limited by the availability of Nr, an excessive contribution of Nr from 

sources of water and atmospheric pollution can contribute to eutrophication. Some of the most 

important environmental effects include increased algal blooms, the occurrence of bottom-water 

hypoxia, and decreases in fishery populations and the abundance of seagrass habitats (Boynton et 

al., 1995; Valiela and Costa, 1988; Howarth et al., 1996; Paerl, 1995, 1997; Valiela et al., 1990). 

There is broad scientific consensus that nitrogen-driven eutrophication in shallow U.S. 

estuaries has increased over the past several decades and that environmental degradation of 

coastal ecosystems is now a widespread occurrence (Paerl et al., 2001). For example, the 

frequency of phytoplankton blooms and the extent and severity of hypoxia have increased in the 

Chesapeake Bay (Officer et al., 1984), the Pamlico Estuary in North Carolina (Paerl et al., 1998), 

and along the continental shelf adjacent to the Mississippi and Atchafalaya river discharges to 

the Gulf of Mexico (Eadie et al., 1994). A recent national assessment of eutrophic conditions in 

estuaries found that 65% of the assessed systems had moderate to high OECs (Bricker et al., 

2007a). Estuaries with high OECs were generally those that received the greatest nitrogen loads 

from all sources, including atmospheric and land-based sources (Bricker et al., 2007a). 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 3 
Aquatic Nutrient Enrichment Case Study 

1.1 INDICATORS, ECOLOGICAL ENDPOINTS, AND ECOSYSTEM 

SERVICES 

Major indicators for nutrient enrichment to aquatic systems from atmospheric deposition 

of total Nr require measurements based on available monitoring stations for wet deposition 

(National Atmospheric Deposition Program [NADP]/National Trends Network) and limited 

networks for dry deposition (Clean Air Status and Trends Network [CASTNet]). Wet deposition 

monitoring stations can provide more information on an extensive range of nitrogen species than 

is possible for dry deposition monitoring stations. This creates complications in developing 

estimates for total nitrogen (TN) deposition levels because dry deposition data sources will likely 

be underestimated because of the use of fixed deposition velocities that do not reflect local 

conditions at the time of measurement, under-representation of monitoring sites in certain 

landscapes, and omission of some Nr species in the measurements (U.S. EPA, 2008a, Section 

2.5). 

For aquatic ecosystems, the indicators for “nutrient enrichment” effects reflect a 

combination of inputs from all media (e.g., air, discharges to water, diffuse runoff, groundwater 

inputs). Major aquatic system indicators include nutrient loadings (Heinz Center for Science, 

2007), excess algal standing crops, or in larger waterbodies, anoxia (i.e., absence of dissolved 

oxygen) and/or hypoxia in bottom waters (see Table 1.1-1). For nitrogen, loadings or 

concentration values related to total Nr (a combination of nitrates, nitrites, organic nitrogen, and 

total ammonia) are encouraged for inclusion in numeric criteria as part of EPA-approved state 

water quality standards (U.S. EPA, 2000). Given the nature of the major indicators for 

atmospheric deposition and indicators for aquatic and terrestrial ecological systems, a data-fusion 

approach that combines monitoring indicators with modeling inputs and outputs is often used 

(Howarth, 2007). 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 4 
Aquatic Nutrient Enrichment Case Study 

Table 1.1-1. Key Indicators of Nutrient Enrichment Due to Nr, Including NOx 

Key Indicator 

Group Examples of Indicators Description 

Nitrogen deposition Nitrate or ammonia From wet or dry deposition monitoring stations 

and networks 

Nitrogen throughfall 

deposition 

Nitrate, ammonia, organic 

nitrogen 

Special measurements in terrestrial ecosystem 

with corrections for nitrogen intercepted by 

plant canopies 

Nitrogen loadings 

and fluxes to 

receiving waters 

Total nitrogen or 

constituent species 

combined with flow data 

from gauged stations 

Reflects a combination of inputs from all media 

(e.g., air, discharges to water, diffuse runoff, 

and groundwater inputs); relative role of air 

deposition should ideally be compared with air 

deposition data and also with available 

(preferably multimedia) models 

Other indicators of 

aquatic system 

nutrient enrichment 

(eutrophication) 

Algal standing crop 

(plankton and periphyton); 

anoxia/hypoxia for 

estuaries and large rivers 

Reflects a combination of inputs from all media 

(e.g., air, discharges to water, diffuse runoff, 

and groundwater inputs); relative role of air 

deposition should ideally be compared with air 

deposition data and also with available 

(preferably multimedia) models 

Nitrogen is an essential nutrient for estuarine and marine ecosystem fertility and is often 

the algal growth-limiting nutrient (U.S. EPA, 2008a; Section 3.3.5.3). Excessive nitrogen 

contributions can cause habitat degradation, algal blooms, toxicity, hypoxia, anoxia, fish kills, 

and decreases in biodiversity (Paerl et al., 2002). To evaluate these impacts, five ecological 

indicators were used in NOAA’s recent NEEA of estuary trophic condition: chlorophyll a, 

macroalgae, dissolved oxygen, nuisance/toxic algal blooms, and submerged aquatic vegetation 

(SAV) (Bricker et al., 2007a). 

Figure 1.1-1, excerpted from the NOAA’s NEEA Update, provides a brief description of 

each of the indicators. Further interactions between the indicators are described in the following 

text. For greater detail on each of the indicators, including previous findings and study areas, 

refer to the ISA (U.S. EPA, 2008a, Sections 3.3.2, 3.3.3, 3.3.5, 3.3.8, 4.3.4, and C.5) and the 

NEEA Update (Bricker et al., 2007a). 

Figure 1.1-2 provides a simplified progression of the indicators as the estuarine waters 

become more eutrophic. In the NEEA Update (Bricker et al., 2007a), an illustrated relationship 

between the OEC, water quality and ecological indicators, and influencing factors (e.g., nitrogen 

loads) is presented (Figure 1.1-3). 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 5 
Aquatic Nutrient Enrichment Case Study 

Indicators of eutrophication do not provide a direct link to the ecological benefits of the 

ecosystem. Because of this, the eutrophication-impact ecological endpoints and the ecosystem 

services affected must be identified and related to the quantifiable indicators. Table 1.1-2

provides some examples of the ecological endpoints associated with the indicators of 

eutrophication. As described in the introduction, the ecological endpoints are ecological entities 

and their impacts. For instance, an indicator may be low dissolved oxygen, but the ecological 

endpoint or impact of having low dissolved oxygen is a decrease in the populations of fish that 

are highly sensitive to dissolved oxygen conditions. 

Figure 1.1-1. Descriptions of the five eutrophication indicators used in NOAA’s NEEA 

(Bricker et al., 2007a).

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 6 
Aquatic Nutrient Enrichment Case Study 

Nutrient inputs 

Excessive growth of phytoplankton and macroalgae (grazers cannot 

control growth) 

SAV inhibition Nuisance/toxic algal blooms 

Low dissolved oxygen/hypoxia 

Invertebrates and fish kills 

Decreased water clarity/decreased light penetration/decreased 

dissolved oxygen 

Figure 1.1-2. A simplified schematic of eutrophication effects on an aquatic ecosystem. 

Figure 1.1-3. An illustrated representation of eutrophication measures through the use of 

indicators and influencing factors from NOAA’s NEEA (Bricker et al., 2007a). 

Table 1.1-2. Assessment Ecological Endpoints for Nutrient Enrichment 

Due to Deposition of Total Reactive Nitrogen, Including NOx

Assessment Ecological Endpoint 

Fish abundance/population 

Water quality, color, clarity 

Species richness/community structure 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 7 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 8 

Assessment Ecological Endpoint 

Habitat quality, including benthos and shoreline 

Surface scum, odors 

Continuing to link the indicators and ecological endpoints to the ecological processes of 

value to society brings us to the ecosystem services related to eutrophication. Examples are 

provided in Table 1.1-3. The example of dissolved oxygen and the resulting decrease in fish 

population was used to identify the ecosystem services of fish catch rate and fish kills, which 

support both food and materials and recreational uses of the ecosystem. 

Table 1.1-3. Ecosystem Services for Aquatic 

Systems Affected by Nutrient Enrichment 

Ecosystem Service 

Fisheries 

 Fish catch rate 

 Fishable area 

 Size/extent of fish kills 

Recreation 

 Boating 

 Swimming 

 Beach conditions 

Tourism 

 Aesthetics 

Risk of illness 

 Drinking water quality 

 Contaminated fish 

The methods of connecting the ecological endpoints and ecosystem services related to 

eutrophication are beyond the scope of this case study, but they have been examined in another 

study (RTI, 2008). Rather, the remaining discussion focuses on determining and detailing the 

indicator measures as a function of the changing atmospheric deposition inputs of Nr, including 

NOx. 

Ecosystem services are generally defined as the benefits individuals and organizations 

obtain from ecosystems. In the Millennium Ecosystem Assessment (MEA), ecosystem services 

are classified into four main categories: 

 Provisioning. Includes products obtained from ecosystems. 

 Regulating. Includes benefits obtained from the regulation of ecosystem processes. 
Aquatic Nutrient Enrichment Case Study 

 Cultural. Includes the nonmaterial benefits people obtain from ecosystems through spiritual 

enrichment, cognitive development, reflection, recreation, and aesthetic experiences. 

 Supporting. Includes those services necessary for the production of all other ecosystem services 

(MEA, 2005). 

A number of impacts on the ecological endpoints of fish population, water quality, and 

habitat quality and the related ecosystem services exist, including the following: 

 Fish kills – provisioning and cultural 

 Surface scum – cultural 

 Fish/water contamination – provisioning and cultural 

 Decline in fish population – provisioning and cultural 

 Decline in shoreline quality (e.g., erosion) – cultural and regulating 

 Poor water clarity and color – cultural 

 Unpleasant odors – cultural. 

The goal of the Aquatic Nutrient Enrichment Case Study was to focus on fisheries, 

recreation, and tourism. Attempts have been made to link fisheries (e.g., closings, decreased 

species richness) quantitatively to eutrophication symptoms through monitoring data, and 

recreation activities qualitatively through user surveys. The symptoms of eutrophication defined 

by Bricker et al., (2007a) were pursued as the ecosystem ecological endpoints to link to these 

ecosystem services. 

1.2 CASE STUDIES 

1.2.1 National Overview of Sensitive Areas 

The selection of case study areas specific to eutrophication began with national 

geographic information systems (GIS) mapping. Spatial datasets were reviewed that included 

physical, chemical, and biological properties indicative of eutrophication potential in order to 

identify sensitive areas of the United States (Table 1.2-1). The analysis then led to combining 

the eutrophic estuaries from NOAA’s Coastal Assessment Framework, along with areas that 

exceed the nutrient criteria for lakes/reservoirs (U.S. EPA, 2002), as compared with wet nitrogen 

deposition, to define areas of national aquatic nutrient enrichment sensitivity. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 9 
Aquatic Nutrient Enrichment Case Study 

Table 1.2-1. Summary of Indicators, Mapping Layers, and Models for Targeted Ecosystems 

Targeted 

Ecosystem 

Effect Indicator(s) Mapping Layers Model(s) 

Aquatic 

nutrient 

enrichment and 

eutrophication 

 Nitrate and ammonia, total 

nitrogen (major reactive 

nitrogen species) 

 Al toxicity data 

 Chlorophyll a (e.g., algal 

standing crop) 

 Anoxia/hypoxia (e.g., 

primarily estuaries and tidal 

rivers) 

 Nitrogen loadings for subwatersheds or larger basins 

and EDAs 

 EPA NCCR Water Quality 

Index and NOAA Estuarine 

Coastal Eutrophication 

Index 

 Diatom data for nitrogenlimited systems 

 STORET retrievals 

 USGS National Water 

Quality Assessment Program 

information 

 USGS SPARROW attributes, 

information 

 Water quality standards 

nutrient criteria for rivers and 

lakes 

 EPA, NCCR, and NOAA 

estuarine eutrophication 

indicators 

 NOAA EDAs 

 EPA/NOAA airsheds for 

major Atlantic and Gulf 

estuaries CMAQ (e.g., 

nitrogen) by hydrological unit 

code 

 USGS 

SPARROW 

 PnET-BCG 

Note: EDAs = Estuarine Drainage Areas; NCCR = National Coastal Condition Report; STORET = 

STOrage and RETrieval; USGS = U.S. Geological Survey; SPARROW = Spatially Referenced 

Regression on Watershed; CMAQ = Community Multiscale Air Quality; PnET-BCG = a 

biogeochemical model 

Bergström and Jansson (2006) compiled dissolved inorganic nitrogen (DIN) data from 

4,296 lakes (i.e., 195 lakes from United States/Canada and 4,101 lakes from Europe). They 

found that the mean lake DIN concentrations were strongly correlated to the mean wet DIN 

deposition over large areas of Europe and North America (Figure 1.2-1). The equation for this 

correlation is: 

 logY = 1.34×logX-1.55 (r2

=0.70; P<0.001) (1) 

where 

 Y is lake water DIN (microgram per liter [μg/L]), and 

 X is wet deposition (kilograms [kg] N/km2

/yr). 

EPA recommended TN criteria for lakes and reservoirs for 12 aggregated ecoregions in 

2002 (U.S. EPA, 2002). 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 10 
Aquatic Nutrient Enrichment Case Study 

Based on equation (1), nitrogen deposition level (X: kg N/ha/yr) associated with EPA TN 

criteria (Y: μg/L) for each aggregated ecoregion can be calculated by equation (2), and the 

results are listed in Table 1.2-2. 

10 100/ (2) + 34.1/)55.1(log = Y X

Figure 1.2-1. The relationship between mean dissolved inorganic nitrogen concentration 

and mean wet inorganic nitrogen in unproductive lakes in different regions in North 

America and Europe (Bergström and Jansson, 2006).

Table 1.2-2. Nitrogen Deposition Level vs. EPA Total Nitrogen Criteria for Lakes and 

Reservoirs

Agg 

Ecor 

II 

Agg 

Ecor 

III 

Agg 

Ecor 

IV 

Agg 

Ecor 

V 

Agg 

Ecor 

VI 

Agg 

Ecor 

VII 

Agg 

Ecor 

VIII 

Agg 

Ecor 

IX 

Agg 

Ecor 

XI 

Agg 

Ecor 

XII 

Agg 

Ecor 

XIII 

Agg 

Ecor 

XIV 

TN EPA 

criteria 

(μg/L) 

100 400 440 560 780 660 240 360 460 520 1270 320 

N wet dep 

(kg 

N/ha/yr) 

4.46 12.55 13.47 16.13 20.65 18.23 8.57 11.60 13.93 15.26 29.72 10.62 

NADP 

Mean wet 

N dep (kg 

N/ha/yr) 

1.19 1.16 2.36 3.02 5.01 6.36 5.21 4.44 4.93 3.28 3.35 4.22 

Source: Prepared by Lingli Liu, U.S. EPA Office of Research and Development and transmitted in 

communication from Tara Graever, U.S. EPA Office of Research and Development, May 2008. 

(Comparable information was not available for rivers and streams.) 

Note: kg N/ha/yr = kilograms of nitrogen per hectare per year; μg/L = micrograms per liter. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 11 
Aquatic Nutrient Enrichment Case Study 

The resulting map reveals areas of highest potential sensitivity to nitrogen deposition as 

shown in Figure 1.2-2.These areas are identified in blue as nutrient sensitive estuaries contained 

in NOAA’s Coastal Assessment Framework, and in red in areas where deposition exceeds the 

nutrient criteria. Yellow areas indicate those areas that are below the nutrient criteria, but are 

within 5 kg/ha/yr of exceeding the criterion. White areas do not have an EPA nutrient criterion 

for lakes/reservoirs. 

Figure 1.2-2. Areas potentially sensitive to aquatic nutrient enrichment. 

1.2.2 Use of ISA Information and Rationale for Site Selection 

The potential case study areas identified by the Ecological Effects Subcommittee (EES) 

of the Advisory Council on Clean Air Compliance Analysis were considered for examining the 

ecological benefits of decreasing atmospheric deposition. Nutrient enrichment–relevant case 

study areas suggested by the EES (U.S. EPA, 2005) are reproduced in Table 1.2-3. The ISA 

(U.S. EPA, 2008b) also recommends case study areas as candidates for risk and exposure 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 12 
Aquatic Nutrient Enrichment Case Study 

assessments; Table 1.2-4 contains potential assessment areas for aquatic nutrient enrichment. 

Additionally, Howarth and Marino (2006) provide a comprehensive summary of the literature 

and scientific findings on eutrophication over the past 3 decades. This summary has led to the 

general consensus that freshwater lakes and estuaries differ in terms of nutrient limitation as the 

cause of eutrophication, and that nitrogen is the limiting element to primary production in coastal 

marine ecosystems in the temperate zone. 

For purposes of the Risk and Exposure Assessment, two regions were selected for case 

study analysis to which a common methodology could be applied—Chesapeake Bay and the 

Pamlico Sound. For aquatic nutrient enrichment, special emphasis was given to the Chesapeake 

Bay region because it has been the focus of many previous studies and modeling efforts, and it is 

currently one of the few systems within the United States in which economic-related ecosystem 

services studies have been conducted. The Pamlico Sound, an economically important estuary 

because of its fisheries, has been studied and modeled greatly by the universities and has also 

been known to exhibit symptoms of extreme eutrophication. The following factors were 

considered in choosing these case study areas: 

 Availability of atmospheric deposition data 

 Availability of existing water quality modeling that accounts for the role of atmospheric deposition 

 A large, mainstem river that feeds a system with adequate hydrologic unit code delineation and 

point- and nonpoint-source input data 

 Scientific stature of the case study area 

 Scalability and generalization opportunities for risk analysis results from the case studies. 

These estuarine ecosystems have been the subjects of extensive research that provides the 

data needed for a first phase of quantitative analysis of the role of nitrogen deposition in 

eutrophication. Other candidate estuarine systems could be evaluated for potential future 

analyses, whereas freshwater ecosystems in the western United States would most likely require 

a separate analysis. 

Because the Chesapeake Bay and Pamlico Sound are fed by multiple river systems, the 

case study was scaled to one main stem river and associated estuary for each system: the 

Potomac River and Potomac Estuary for the Chesapeake Bay and the Neuse River and Neuse 

River Estuary for the Pamlico Sound. Details on each estuarine system are provided below. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 13 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 14 

Table 1.2-3. Science Advisory Board/Ecological Effects Subcommittee Listing of Potential 

Assessment Areas for Evaluation of Benefits of Decreases in Atmospheric Deposition with 

Respect to Aquatic Nutrient Enrichment

Ecosystem/ 

Region 

Main CAA 

Pollutant(s) 

Percentage(s) 

Attributable to 

Atmospheric 

Deposition 

Quantitative 

Ecological and 

Economic 

Information EES Comments 

Coastal 

Waquoit Bay Nitrogen 30% Yes High priority. Higher loading 

from nondepositional sources 

may confound analysis. 

Chesapeake 

Bay 

Nitrogen 20% to 30% Yes High priority. Loading from 

diverse sources, particularly 

agricultural, may confound 

analysis. 

Long Island 

Sound 

Nitrogen; 

mercury 

Nitrogen = 23% 

to 35%; Mercury 

= ? 

Yes High priority. High nitrogen 

loading from wastewater 

treatment plants may 

confound analysis. 

Barnegat Bay Nitrogen 50% total; 

direct deposition 

30% to 39% 

Yes High priority. Direct linkage 

of ecological effects with 

atmospheric deposition; 

quantitative economic data 

exist. 

Tampa Bay Nitrogen; 

mercury 

Nitrogen = 25% 

to 30% 

Yes Medium priority. Examined in 

previous EPA efforts. 

Variability in loading data 

may confound analysis. 

Gulf of 

Maine 

Nitrogen Low ? Low priority. Linkage of 

nitrogen loadings and 

ecological impacts is not well 

established. Major source of 

nitrogen is open-ocean influx. 

Casco Bay Nitrogen; 

mercury 

Nitrogen = 30% 

to 40% 

Mercury = 84% to 

92% 

Yes Medium priority. Good data 

on ecological and economic 

impacts are available. 

Rocky 

Mountains 

Nitrogen Nearly 100% Yes Medium priority. Levels of 

nitrogen loading much lower 

than for northeastern 

locations. Economic data may 

be lacking. 
Aquatic Nutrient E

Table 1.2-4. Potential Assessment Areas for Aquatic Nutrient Enrichment Identified in the ISA 

Area Indicator 

Detailed 

Indicator Area Studies Models References in U.S. EPA, 2

Adirondack Mountains 

Aquatic nutrient enrichment; terrestrial nutrient enrichment; mercury methylation 

Foliar N concentration; NO3- leaching; 

C:N ratio; N mineralization; nitrification; denitrification 

PIRLA I and II; Adirondack Lakes Survey; Episodic Response Project; EMAP 

MAGIC; PnET-BGC 

Baker and Laflen, 1983; Baker et al.Baker et al., 1990c; Baker et al., 199al., 2003; Chen and Driscoll, 2004; C1983; Cumming et al., 1992; Driscol1987a; Driscoll et al., 1991; Driscoll Driscoll et al., 2001a; Driscoll et al., Driscoll et al., 2003b; Driscoll et al., Driscoll et al., 2007a; Driscoll et al., et al., 2007; GAO, 2000; Havens et aet al., 2002; Johnson et al., 1994b; L1988; Lawrence et al., 2007; NAPAPSiegfried et al., 1989; U.S. EPA, 200et al., 1990; Sullivan et al., 2006a; Su2006b; U.S. EPA, 1995b; Van Sickle Whittier et al., 2002; Wigington et alet al., 2007 

Chesapeake Bay 

Aquatic nutrient enrichment; aquatic nitrogenlimited eutrophication 

Watershed N sources; chlorophyll a; dissolved oxygen; submerged aquatic vegetation 

NA NA Bricker et al., 1999; Bricker et al., 20

et al., 2001; Boyer et al., 2002; BoyeHowarth, 2002; Cooper and Brush, 1and Oppenheimer, 1991; Harding an1997; Howarth, 2007; Kemp et al., 11991, 1992; Officer et al., 1984; Orth 1984; Twilley et al., 1985 

Alpine and subalpine communities of the eastern slope of the Rocky Mountains, CO 

Aquatic nutrient enrichment; terrestrial nutrient enrichment 

Biomass production; NO3-

leaching; species richness 

NA NA Baron et al., 1994; Baron et al., 2000

2006; Bowman, 2000; Bowman and 1998; Bowman et al., 1993; Bowman Bowman et al., 2006; Burns, 2004; F2003a; Fisk et al., 1998; Korb and RRueth et al., 2003; Seastedt and VaccSherrod and Seastedt, 2001; Steltzer Bowman, 1998; Suding et al., 2006; Tonnessen, 2000; Williams et al.,199al., 2001 

Final Risk and Exposure Assessment 

Appendix 6 - 15 
Aquatic Nutrient E

Final Risk and Exposure Assessment 

Appendix 6 - 16 

Area Indicator 

Detailed 

Indicator Area Studies Models References in U.S. EPA, 2

Beartooth Mountain, WY 

Aquatic nutrient enrichment 

Algae composition switch 

NA NA Saros et al., 2003 

Pamlico Estuary, NC 

Aquatic nitrogen limited eutrophication 

Hypoxia; phytoplankton bloom 

NA NA Paerl et al., 1998 

Rocky Mountain National Park, CO 

Aquatic nutrient enrichment 

Diatom shifts NA NA Interlandi and Kilham, 1998 

Lake Tahoe, CA 

Aquatic nutrient enrichment 

Primary productivity; chlorophyll a

NA NA Goldman, 1988; Jassby et al., 1994 

Source: U.S. EPA, 2008a Note: CAA = Clean Air Act; PIRLA = paleoecological investigation of recent lake acidification; EMAP = Environmental Monitoring 

Program; MAGIC = Model of Acidification of Groundwater in Catchments; PnET-BGC = a biogeochemical model; NO3- = nitrate; 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 17 

Atmospheric deposition accounts 

for between 5% and 15% to 20% 

of the Potomac River watershed’s 

total nitrogen load according to 

published research (U.S. EPA, 

2000; Boyer et al., 2002, 

respectively). Additional expert 

estimates put the contribution by 

atmospheric deposition at 30% to 

40% of the total load. 

1.2.3 Potomac River and Potomac Estuary 

The Chesapeake Bay is the largest of 130 estuaries in the United States. This commercial 

and recreational resource serves more than 15 million people who live in and near its watershed 

(i.e., drainage basin). The bay produces approximately 500 million pounds of oysters, crabs, and 

other seafood per year. The richness of its species can be seen in the value of the bay’s annual 

fish harvest, which is estimated at more than $100 million. The Chesapeake Bay estuary receives 

approximately 50% of its water from the Atlantic Ocean in the form of saltwater. The other half 

of the water (i.e., fresh water) drains into the bay from a large 165,800-square-kilometer (km2

) 

(64,000-square-mile [mi2

]) drainage watershed. Among the 150 major rivers and streams in the 

Chesapeake Bay drainage basin are the James, Potomac, York, Rappahannock, Patuxent, and 

Susquehanna rivers. The Potomac River watershed comprises about 22% of the land area and 

30% of the population of the total Chesapeake Bay watershed. As a result, pollution loads from 

the Potomac River have a significant impact on the health of the bay. The Chesapeake Bay 

contains on average more than 68 trillion liters (18 trillion gallons) of water (Atkins and 

Anderson, 2009). 

The Potomac River is approximately 413 miles (665 km) long, with a drainage area of 

approximately 14,670 mi2

 (38,000 km2) and a population of approximately 5,350,000 people. It 

begins at Fairfax Stone, WV, and runs to Point Lookout, MD. In terms of area, this makes the 

Potomac River the fourth largest river along the Atlantic Coast of the United States and the 

twenty-first largest in the United States as a whole (Fact-index.com, 2009). As shown in Figure 

1.2-3, as well as in Table 1.2.5 and Table 1.2-6, the Potomac River contains diverse watersheds 

in terms of topography, elevation (e.g., extending into the Shenandoah Mountains), and nutrient 

point and nonpoint sources (e.g., forestland, farmland, and the Washington, DC, metropolitan 

area). The Potomac River watershed lies in five geological 

provinces: the Appalachian Plateau, Ridge and Valley, Blue 

Ridge, Piedmont Plateau, and Coastal Plain. The watershed 

is approximately 12% urbanized, 36% agricultural use, and 

52% forested. Atmospheric deposition has also been reported 

to contribute from 5% to 15%–20% of the watershed’s TN 

load (U.S. EPA, 2000; Boyer et al., 2002, respectively). 
Aquatic Nutrient Enrichment Case Study 

Figure 1.2-3. The Potomac River Watershed and Potomac Estuary. 

Table 1.2-5. Physical Characteristics of the Potomac Estuary 

Parameter Value Metadata 

Estuary area (km2) 1,260 Estuary area, calculated from NOAA shapefiles 

Tidal fresh zone area (km2) 183 Tidal fresh area, calculated from NOAA shapefiles 

Mixing zone area (km2) 1,077 Mixing zone area, calculated from NOAA shapefiles 

Saltwater zone area (km2) 0 Saltwater area, calculated from NOAA shapefiles 

Estuary volume (m3) 6.46 × 109 Best estimate of volume from digital bathymetric chart 

if available; otherwise, NOAA planimetry 

Estuary depth (m) 5.13 From digital bathymetric chart if available; otherwise, 

NOAA planimetry 

Estuary perimeter (km) 1,350 Perimeter of estuary, based on shapefile; can be used to 

calculate various aspect ratios 

Percentage of estuary open (%) 1.33 Percentage of the perimeter that is the “open” (or 

oceanic) boundary; somewhat subjective 

Catchment area (km2) 36,804 Not available 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 18 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 19 

Parameter Value Metadata 

Catchment mean elevation (m) 330 Calculated from catchment shapefiles + Hydro1K (a 

global 1-km grid of elevation) 

Catchment maximum elevation 

(m) 

1,433 Calculated from catchment shapefiles + Hydro1K (a 

global 1-km grid of elevation) 

Catchment/estuary area ratio 29.2 Area ratio, based on catchment and area data given 

above 

Source: NEEA Estuaries Database 

Note: m = meter 

Table 1.2-6. Hydrological Characteristics of the Potomac Estuary 

Parameter Value Metadata 

Tide height (m) 0.55 NOAA estimate of tide height, back-calculated 

from tide volume; in some cases, guessed from 

nearby systems 

Tide volume (m3) 6.93 × 108 Tide height (m) × estuary area (km2) × 106

Tides/day (#) 2 NOAA designation 

Tide volume/day (m3.d-1) 1,339,130,435 Calculated from tide volume and tides per day 

Tide ratio 0.11 Tide height divided by estuary depth; a clean-up of 

a NOAA variable 

Stratification ratio 0.02649 Total freshwater flux per day divided by tide 

volume per day 

Percent freshwater (%) 14.5 Based on NOAA shapefiles of the three zones 

according to their designation 

Percent mixed water (%) 85.5 Based on NOAA shapefiles of the three zones 

according to their designation 

Percent seawater (%) 0 Based on NOAA shapefiles of the three zones 

according to their designation 

Average salinity (psu) 11 Based on NOAA estimate of freshwater volume, 

but scaled to “local coastal salinity,” below 

Tidal exchange (days) 121 Exchange time as (Est_V/net fw_V per d) * 

(coastal_sal - avg_sal)/coastal_sal); a salinitybased estimate of exchange 

Tidal freshwater flush (days) 36 NOAA-based calculation, using (daily tide + 

freshwater volume)/system volume 

Daily freshwater/estuary 

area (m.d-1) 

27.063 NOAA estimate of daily flow/estuary area 

Daily freshwater (m3/day) 

(best) 

34,100,000 NOAA estimate above or (if not available) NCPDI 

estimate 

Flow/estuary area (m/day) 

(best) 

27.063 Best estimate/estuary area 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 20 

Parameter Value Metadata 

Total freshwater Volume 

(1/day) 

0.00549 Best estimate/estuary volume (= hydraulic 

exchange rate) 

Daily precipitation 

(m3

/day) 

3.64 × 106 Direct precipitation on system, derived from 

PRISM Shapefile 

Daily evaporation (m3/day) 2.26 × 106 Direct evaporation from system, derived from 

LOICZ 0.5 degree database, originally from 

Wilmott 

Daily precipitation/estuary 

area (mm/day) 

2.889 Daily precipitation/estuary area 

Daily evaporation/estuary 

area (mm/day) 

1.794 Daily evaporation/estuary area 

Flow (m3/day) 2.33 × 107 NCPDI_1982–1991 

Source: NEEA Estuaries Database 

Note: m = meter; psu = practical salinity unit; NCPDI = National Coastal Pollution Discharge 

Inventory; PRISM = Parameter-elevation Regressions on Independent Slopes Model; LOICZ = 

Land-Ocean Interactions in the Coastal Zone; mm = millimeters. 

1.2.4 Neuse River and Neuse River Estuary 

The Neuse River is the longest river in North Carolina, and the Neuse River watershed is 

the third largest river watershed in the state (Figure 1.2-4). The Neuse River is a mainstem river 

to the Pamlico Sound—one of the two largest estuaries on the Atlantic Coast. The river 

originates in north-central North Carolina and flows southeasterly until it reaches tidal waters 

upstream of New Bern, NC. At New Bern, the river broadens dramatically and changes from a 

free-flowing river to a sound. While the Neuse River itself is 399 kilometers (km)_(248 miles) 

long, there are 5,628 freshwater stream kilometers (3,497 miles), 6,643 hectares (16,414 acres) 

of freshwater reservoirs and lakes, 149,724 estuarine hectares (369,977 acres), and 33.8 

kilometers (21 miles) of Atlantic coastline within the entire Neuse River watershed. The drainage 

area for the watershed is approximately 14,210 mi2

 (36,804 km2). There are 19 major reservoirs 

in the Neuse River watershed; most of these are located in the upper portion of the watershed. 

The watershed starts in the eastern Piedmont physiographic region, with approximately twothirds of the watershed located in the Coastal Plain (NC DENR, 2002). 

The Neuse River watershed encompasses all or portions of 18 counties and 74 

municipalities. The watershed has a population of approximately 1,320,379 according to the 

2000 census. Fifty-six percent of the land in the watershed is forested, and approximately 23% is 

in cultivated cropland. Only 8% of the land falls into the urban/built-up category. Despite the 
Aquatic Nutrient Enrichment Case Study 

large amount of cultivated cropland and the relatively small amount of urban area, the basin has 

seen a significant decrease (-72,800 hectares [-180,000 acres]) in cultivated cropland and forest 

and an increase (+91,900 hectares [+227,000 acres]) in developed areas over the past 15 years 

(NRCS, 2001). The Neuse River watershed is divided into 14 subbasins (6-digit North Carolina 

Division of Water Quality [NC DWQ] subbasins) (NC DENR, 2002). Table 1.2-7 through Table 

1.2-9, respectively, provide physical, land use and population, and hydrological characteristics of 

the Neuse River and Neuse River Estuary. 

There are 134,540 estuarine hectares (332,457 acres) classified for shellfish harvesting 

(Class SA [shellfishing]) in the Neuse River Estuary. The Neuse River Estuary is important to 

the commercial blue crab (Callinectes sapidus) fishery in the eastern United States and 

accounted for approximately one-quarter of the blue crab harvest from 1994 to 2002 (Smith and 

Crowder, 2005). Eutrophication became a water quality concern in the lower Neuse River and 

Neuse River Estuary in the late 1970s and early 1980s. Nuisance algal blooms prevalent in the 

upper estuary prompted investigations by the state. These investigations, as well as other studies, 

indicated that algal growth was being stimulated by excess nutrients entering the estuarine waters 

of the system. In 1988, a phosphate detergent ban was put in place, and the lower Neuse River 

and Neuse River Estuary received the supplemental classification of nutrient-sensitive waters. 

Phosphorus loading was greatly decreased, and algal blooms in the river and freshwater portions 

of the system were lowered as a result of this action. However, the 1993 Neuse River Basin-wide 

Water Quality Plan (NC DENR, 1993) recognized that eutrophication continued to be a water 

quality problem in the estuary below New Bern. Extensive fish kills in 1995 prompted further 

study of the problem. Low dissolved oxygen levels associated with algal blooms were 

determined to be a probable cause of many of the fish kills. The algal blooms and 

correspondingly high levels of chlorophyll a prompted the state to place the Neuse River and 

Neuse River Estuary on the 1994, 1996, 1998, and 2000 303(d) List of Impaired Waters. It was 

determined that control of nitrogen was needed to decrease the extent and duration of algal 

blooms. 

Atmospheric deposition is believed to play a role in nutrient loading to the Neuse River 

and Neuse River Estuary. As excerpted from Whitall and Paerl (2001), the following discusses 

the role of atmospheric deposition to nutrient loading for sensitive waterbodies: 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 21 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 22 

Excessive nitrogen loading to nitrogen-sensitive waters, such asthe Neuse River 

Estuary (North Carolina) has been shown to promote changes in microbial and 

algal community composition and function (harmful algal blooms), hypoxia and 

anoxia, and fish kills. Previous studies have estimated that wet atmospheric 

deposition of nitrogen (WAD-N), as deposition of dissolved inorganic nitrogen

(DIN: NO3, NH3/NH+

4) and dissolved organic nitrogen, may contribute at least 

15% of the total externally supplied or “new” nitrogen flux to the coastal waters 

of North Carolina. In a 3-year study from June 1996 to June 1999, Whitall and 

Paerl calculated the weekly wet deposition of inorganic and organic nitrogen at 11 

sites on a northwest–southeast transect in the watershed. The annual mean total 

(wet DIN + wet organics) WAD-N flux for the Neuse River watershed was 

calculated to be 956 mg N/m2

/yr (15,026 Mg N/yr). Seasonally, the spring

(March–May) and summer (June–August) months contain the highest total weekly 

nitrogen deposition; this pattern appearsto be driven by nitrogen concentration in 

precipitation. There is also spatial variability in WAD-N deposition; in general, 

the upper portion of the watershed receives the lowest annual deposition and the 

middle portion of the watershed receives the highest deposition. Based on a range 

of watershed nitrogen retention and in-streamriverine processing values, we 

estimate that this flux contributes approximately 24% of the total “new” nitrogen 

flux to the estuary (Whitall and Paerl, 2001). 

Of these atmospheric deposition measurements, it is 

expected that the contributions will be greater from reduced 

forms of nitrogen than from oxidized forms because of the large 

amounts of agriculture within the watershed. One of the reasons 

for selecting this case study area is to evaluate the impact of a 

NOx-based standard on an area dominated by reduced forms of 

nitrogen. 

According to Whitall and 

Paerl (2001), atmospheric 

deposition accounts for 

approximately 24% of the 

Neuse River watershed’s 

total nitrogen loading, with 

reduced forms of nitrogen 

making up a larger portion of 

the total than oxidized forms. 
Aquatic Nutrient Enrichment Case Study 

Figure 1.2-4. The Neuse River Watershed and Neuse River Estuary. 

Table 1.2-7. Physical Characteristics of the Neuse River Estuary 

Parameter Value Metadata 

Estuary area (km2) 456 Estuary area, calculated from NOAA shapefiles 

Tidal fresh zone area 

(km2

) 

5 Tidal fresh area, calculated from NOAA shapefiles 

Mixing zone area (km2) 451 Mixing zone area, calculated from NOAA shapefiles 

Saltwater zone area (km2) 0 Saltwater area, calculated from NOAA shapefiles 

Estuary volume (m3) 1.304 × 109 Best estimate of volume from digital bathymetric chart 

if available; otherwise, NOAA planimetry 

Estuary depth (m) 2.86 From digital bathymetric chart if available; otherwise, 

NOAA planimetry 

Estuary perimeter (km) 523 Perimeter of estuary, based on shapefile; can be used to 

calculate various aspect ratios 

Percentage estuary open 

(%) 

2.1 Percentage of the perimeter that is the “open” (or 

oceanic) boundary; somewhat subjective 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 23 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 24 

Parameter Value Metadata 

Catchment area (km2) 14,066 Not available 

Catchment mean 

elevation (m) 

56 Calculated from catchment shapefiles + Hydro1K (a 

global 1-km grid of elevation) 

Catchment maximum 

elevation (m) 

245 Calculated from catchment shapefiles + Hydro1K (a 

global 1-km grid of elevation) 

Catchment/estuary area 

ratio 

30.8 Area ratio, based on catchment and area data given 

above 

Source: NEEA Estuaries Database 

Note: m = meter 

Table 1.2-8. Neuse River Watershed Land Use and Population 

Parameter Value Metadata 

Urban (km2) 1,328.66 

(9.5%) 

USGS (LUDA) for entire watershed 1972 with census 

1990 information, base year early 1990s 

Agriculture (km2) 4,983.14 

(35.6%) 

USGS LUDA for entire watershed 1972 with census 

1990 information, base year early 1990s 

Forest (km2) 6,648.5 

(47.5%) 

USGS LUDA for entire watershed 1972 with census 

1990 information, base year early 1990s 

Wetland (km2) 1,020.46 

(7.3%) 

USGS LUDA for entire watershed 1972 with census 

1990 information, base year early 1990s 

Range (km2) 5.17998 (0%) USGS LUDA for entire watershed 1972 with census 

1990 information, base year early 1990s 

Total (km2) 13,985.93998 USGS LUDA for entire watershed 1972 with census 

1990 information, base year early 1990s 

Population (#) 1,015,059 Based on gridded (1-km) U.S. 1990 census data, 

corrected for catchments extending outside the United 

States (with LANDSCAN) 

Population/estuary area 

(#.km-2) 

2,226 Population based on gridded (1-km) U.S. 1990 census 

data, corrected for catchments extending outside the 

United States (with LANDSCAN). Estuary area, 

calculated from NOAA shapefiles. 

Source: NEEA Estuaries Database 

Note: USGS = U.S. Geological Survey; LUDA = Land Use and Land Cover; LANDSCAN = a global 

population database 
Aquatic Nutrient Enrichment Case Study 

Table 1.2-9. Hydrological Characteristics of the Neuse River Estuary 

Parameter Value Metadata 

Tide height (m) 0.15 NOAA estimate of tide height, back-calculated from tide 

volume; in some cases, guessed from nearby systems 

Tide volume (m3) 6.84 × 107 Tide height (m) × estuary area (km2) × 106

Tides/day (#) 2 NOAA designation 

Tide volume/day 

(m3

/day) 

132,173,913 Calculated from tide volume and tides per day 

Tide ratio 0.05 Tide height divided by estuary depth; a clean-up of a 

NOAA variable 

Stratification ratio 0.08318 Total freshwater flux per day divided by tide volume per 

day 

Percent freshwater (%) 1.1 Based on NOAA shape files of the three zones according 

to their designation 

Percent mixed water (%) 98.9 Based on NOAA shape files of the three zones according 

to their designation 

Percent seawater (%) 0 Based on NOAA shape files of the three zones according 

to their designation 

Average salinity (psu) 13 Based on NOAA estimate of freshwater volume, but 

scaled to “local coastal salinity,” below 

Tidal exchange (days) 74 Exchange time as (Est_V/net fw_V per d)*(coastal_sal - 

avg_sal)/coastal_sal); a salinity-based estimate of 

exchange 

Tidal freshwater flush 

(days) 

73 NOAA-based calculation, using (daily tide + freshwater 

volume)/system volume 

Daily freshwater/estuary 

area (m/day) 

22.368 NOAA estimate of daily flow/estuary area 

Daily freshwater 

(m3

/day) (best) 

10,200,000 NOAA estimate above or (if not available) NCPDI 

estimate 

Flow/estuary area 

(m/day) (best) 

22.368 Best estimate/estuary area 

Total freshwater volume 

(1/day) 

0.00843 Best estimate/estuary volume (= hydraulic exchange rate)

Daily precipitation 

(m3

/day) 

1.72 × 106 Direct precipitation on system, derived from PRISM 

shapefile 

Daily evaporation 

(m3

/day) 

926,000 Direct evaporation from system, derived from LOICZ 0.5 

degree database, originally from Wilmott 

Daily precipitation/ 

estuary area (mm/day) 

3.772 Daily precipitation/estuary area 

Daily evaporation/ estuary 

area (mm/day) 

2.031 Daily evaporation/estuary area 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 25 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 26 

Parameter Value Metadata 

Flow (m3/day) 7.95 × 106 NCPDI_1982–1991 

Source: NEEA Estuaries Database 

Note: m = meter; psu = practical salinity unit; NCPDI = National Coastal Pollution Discharge 

Inventory; PRISM = Parameter-elevation Regressions on Independent Slopes Model; LOICZ = LandOcean Interactions in the Coastal Zone; mm = millimeters 

Ammonia emissions from intensive livestock feeding operations are believed to 

contribute to nitrogen deposition in eastern North Carolina watersheds. During a 10-year 

legislative mandated moratorium on new operations, poultry populations increased in two Neuse 

River watershed counties, according to the U.S. Department of Agriculture’s 2002 Ag Census. 

Statewide, the census reported an increase in poultry farms from 5,094 in 1997 to 6,251 in 2002 

statewide (USDA, 2002). (As of this writing, the 2007 Ag Census is not complete.) The 

continued contribution of poultry operations’ growth to nitrogen deposition during the moratoria 

has not been assessed, particularly in terms of its deposition in the Neuse River watershed. 

2.0 APPROACH AND METHODS 

Since it was necessary for this case study to span both terrestrial and aquatic systems to 

accommodate indirect (i.e., to the watershed) and direct (i.e., to the water surface) deposition 

effects, as well as a variety of indicators, a modeling approach was necessary to examine the 

impacts due to aquatic nutrient enrichment from nitrogen deposition. 

There are several complicating factors to carrying out an analysis of eutrophication in 

waterbodies when one of the requirements is to include modeled output of atmospheric 

deposition from a high-level, detailed atmospheric model. This analysis is considered a 

multimedia analysis where the air, land, and water are involved. Typically, models or analysis 

methods existing in the literature focus on only one of those components. Links between the 

components with the desired output of eutrophication indicators are rare in the current literature 

or modeling environments. Additionally, the few instances that are available in the literature tend 

to focus on specific case study areas or on being highly empirical and difficult to scale or extend 

to alternate locations. All these facts must be considered when developing a method to examine 

the effects of Nr, including NOx, deposition on aquatic nutrient enrichment. 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 27 

2.1 MODELING 

There are four basic steps necessary to undertake a modeling effort to examine the effects 

of nitrogen deposition (RTI, 2007): 

1. Choose the specific question/problem to address. 

2. Choose the best models based on model formulation (e.g., are biological processes 

considered?), desired output, study area, data availability, and necessary 

uncertainty/sensitivity analyses for the models. 

3. Determine and set up any processes/algorithms necessary to match atmospheric modeling 

output (assumed to be from Community Multiscale Air Quality [CMAQ]) to the chosen 

receiving water or terrestrial/watershed model. 

4. Obtain the data needed for model parameterization. 

The problem to be addressed in this analysis is assessment of the effects of deposition of 

Nr, including NOx, on aquatic nutrient enrichment. The impacts of both direct (i.e., deposition on 

the waterbody surface) and indirect (i.e., deposition within the watershed and transport to the 

waterbody) deposition need to be identified. A method is needed to provide measures of the 

eutrophication indicators that were previously described in Section 1.1. 

A previous RTI International (RTI)1 report (RTI, 2007) detailed the difficulty, along with 

the desire, to utilize atmospheric modeling in combination with the receiving-water and 

terrestrial/watershed models for analyzing the effects of Nr, including NOx, deposition. The 

multimedia approach to modeling is still in development; therefore, at this time, not many 

models are set up to immediately accept the output from an atmospheric model such as CMAQ. 

In the previous model investigation, RTI examined 35 receiving-water and terrestrial/watershed 

models, which represent a wide diversity of types of ecosystems; history, location, and 

spatial/temporal scales of application; scientific acceptance levels and organizational and agency 

support; complexity and requirements; state variables and processes; and management uses. 

Several existing models accept atmospheric concentration or flux data, but the time-step, 

spatial resolution, and exact species required might all differ from the atmospheric model output. 

The RTI report (2007) provided a list of models that could fulfill the multimedia approach while 

using CMAQ output as input for the atmospheric component to the model. These models include 

the Hydrologic Simulation Program-FORTRAN (HSPF), Regional Hydeo-Economic Simulation 

System (RHESSys), the Georgia Tech Hydrologic Model/Multiple Element Limitation 

(GT/MEL), Model of Acidification of Groundwater in Catchments (MAGIC), PnET-BGC, 

 

1 RTI International is a trade name of Research Triangle Institute
Aquatic Nutrient Enrichment Case Study 

Integrated Nitrogen in Catchments (INCA), Spatially Referenced Regression on Watershed 

(SPARROW) attributes, AQUATOX, Water Quality Analysis Simulation Program (WASP), 

Enhanced Stream Water Quality Model (QUAL2K), CE-QUAL family of models, and Row 

Column AESOP/Estuary and Coastal Ocean Model with Sediment Transport 

(RCA/ECOMSED). These models are very different from one another in terms of the system 

components included, process representations, data requirements, and output parameters (for 

comprehensive details for each model, refer to the RTI report [2007]). 

After determining which models could utilize CMAQ data, the ecosystem component 

encompassed by the models was examined. The choice of case study areas that include estuaries 

dictated that the model chosen must provide nutrient loads to an estuary waterbody and examine 

the impacts of those loads within the estuary itself. Although AQUATOX and QUAL2K are 

receiving-water models, they do not function for estuaries, nor do they account for indirect 

deposition over the contributing watershed. The WASP, CE-QUAL family of models, and 

RCA/ECOMSED are receiving-water models, which can be parameterized for estuaries, but they 

do not simulate terrestrial processes. Several of the other models account for indirect deposition 

and are strictly terrestrial models. These models include Regional Hydro-Economic Simulation 

System (RHESSys) and GT/MEL. Other models include both the indirect deposition and direct 

deposition, but only over streams and lakes within the watershed. These models are HSPF, 

MAGIC, PnET-BGC, INCA, and SPARROW. 

From this analysis, it was apparent that a multiple step (i.e., linked processes or 

calculations) or model (i.e., separate but linked models) analysis would be optimal, including 

both a step/model to examine the indirect deposition and a step/model to examine the estuarine 

effects. The challenge was balancing analysis power against data, effort, and scalability 

requirements. Higher-level modeling approaches could be used to evaluate the eutrophication 

effects of interest if significant data resources, time, and expertise were available for a specific 

site. An approach of this kind would not be scalable or applicable to wider regions, but it would 

provide estimates with less uncertainty for a studied system. 

The list of models above was used to identify several models that could be used to 

produce nutrient loads to the estuary, the obvious critical component of an eutrophication 

analysis. The best model for determining nitrogen loading to the estuary would track the 

atmospheric deposition of nitrogen through the watershed and to the estuary. This requirement 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 28 
Aquatic Nutrient Enrichment Case Study 

eliminated models that did not provide stream networking (i.e., PnET-BGC, MAGIC) or that 

lumped land-use categories together (i.e., INCA). The remaining models of HSPF and 

SPARROW are very different. HSPF is a highly parameterized, dynamic model that requires 

extensive data inputs and calibration. SPARROW is a hybrid statistical and process-based, 

steady-state model that requires much less data for parameterization, but still includes spatial 

variation and source investigation. Therefore, the SPARROW model was chosen to estimate 

nitrogen loadings to the estuaries. 

Next, the most applicable method for examining eutrophication effects in an estuary was 

assessed. The three identified models that could represent estuarine processes (i.e., WASP, CEQUAL family of models, and RCA/ECOMSED) were systematically ruled out as possibilities. 

RCA/ECOMSED is a proprietary model with extensive data requirements and requires a high 

level of expertise. The CE-QUAL family of models has primarily been used by the U.S. Army 

Corps of Engineers. The various versions of CE-QUAL all have extensive data requirements, 

and no indications of model integration have been uncovered in the literature. WASP provides 

the output desired, but requires parameterization for each system of study. Considering that the 

SPARROW model will provide TN loads to the estuary and the fact that that the chosen method 

needs to be scalable and applicable to a variety of future case study areas, the SPARROW model 

was selected for this case study. 

With the elimination of the three identified dynamic modeling applications, a more 

descriptive method of evaluation was sought. The method developed by NOAA and used in their 

NEEA was identified as a likely candidate for eutrophication assessment. 

The screening process that led to the decision to use SPARROW and a more descriptive 

eutrophication evaluation technique considered the level of effort needed for an analysis of this 

scale in the time available. Additionally, as summarized in recent literature (Howarth and 

Marino, 2006), the complex processes that cause and express eutrophication within an estuary 

are not greatly understood and could lead to under- and mis-representation within dynamic 

models. The loss of a temporally varying analysis with the use of a steady-state or annual 

average model results in the loss in detailing seasonal changes and some of the intricate 

processes that may occur on a daily or even monthly time scale rather than over an entire year. 

This trade-off allows for development toward the ultimate goal of providing a scalable 

methodology that can be applied to various sites across the nation. With the acknowledged 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 29 
Aquatic Nutrient Enrichment Case Study 

uncertainties in eutrophication process details across different systems, a more screening-level, 

scalable approach was deemed appropriate for this initial study to link atmospheric nitrogen 

deposition to eutrophicatic conditions. 

2.2 CHOSEN METHOD 

After examining several estuarine assessment options, the most comprehensive 

evaluation technique that could be applied on a wide scale was revealed to be an assessment of 

eutrophication as conducted in NOAA’s NEEA. This assessment method is titled Assessment of 

Estuarine Trophic Status eutrophication index (ASSETS EI) (Bricker et al., 2007a). NOAA’s 

ASSETS EI results in an estimation of the likelihood that the estuary is experiencing 

eutrophication or will experience eutrophication in the future. 

The ASSETS EI incorporates indirect deposition over the watershed through the 

evaluation of nitrogen loading to the estuary. Thus, a decision was required on how to derive the 

nitrogen load to the estuary based on the 2002 CMAQ-modeled deposition data. Because the 

ASSETS EI is more of a screening-level approach to assessing eutrophication, the nitrogen load 

to the estuary is only required to be an annual estimate of TN loading. For these reasons, The 

SPARROW model was chosen to provide the estimates of nitrogen loading to the estuary. 

The combination of SPARROW modeling and the ASSETS method to developing an EI 

(Figure 2.2-1) provides a sound basis for conducting an eutrophication assessment. Both 

SPARROW and the ASSETS EI are supported by federal agencies and have been through 

several improvement iterations. As shown in the following sections, the method provides a 

screening-level approach that includes an appropriate level of detail for determining the impacts 

on the degree of eutrophication in an estuary based on changes in atmospheric deposition 

loadings. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 30 
Aquatic Nutrient Enrichment Case Study 

SPARROW Cumulative 

Nitrogen Load 

to Estuary

Nitrogen Load 

by Reach

Atmos 

Depo 

Load

Other 

Nonpoint 

Sources

Point 

Sources

Streams 

& Catchments

Land 

Use

Proportion of 

Load Due to 

Atmospheric 

Deposition by 

Reach

Proportion of 

Cumulative 

Load Due to 

Atmos. Depo. 

to Estuary

Influencing 

Factors

Overall 

Eutrophic 

Conditions 

 Future 

Outlook 

Susceptibility

Nutrient 

Inputs

Susceptibility

Primary 

Symptoms 

(Chlorophyll 

a, 

Macroalgae 

abundance)

ASSETS 

Score/

Eutrophication 

Index

Heuristic 

Estimate 

of 

Change

Secondary 

Symptoms 

(Low DO, 

Loss of SAV, 

NTB/HAB)

Figure 2.2-1. Modeling methodology for case study. 

Note: DO = dissolved oxygen; HAB = harmful algal bloom.

ASSETS EI scores were available for both the Potomac and Neuse River estuaries, and 

both estuaries were the subject of past and ongoing SPARROW modeling of point and nonpoint 

sources, including atmospheric deposition. 

2.2.1 SPARROW 

2.2.1.1 Background and Description 

SPARROW is a watershed modeling technique designed and supported by the U.S. 

Geological Survey (USGS). The model relies on a nonlinear regression formulation to relate 

water quality measurements throughout the watershed of interest to attributes of the watershed. 

Both point and diffuse sources within the watershed are considered along with nonconservative 

transport processes (i.e., loss and storage of contaminants within the watershed). SPARROW 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 31 
Aquatic Nutrient Enrichment Case Study 

follows the rules of mass balance while using a hybrid statistical and process-based approach 

(Figure 2.2-2). “Because the dependent variable in SPARROW models (i.e., the mass of 

contaminant that passes a specific stream location per unit time) is, in mathematical terms, 

linearly related to all sources of contaminant mass in the model, all accounting rules relating to 

the conservation of mass will apply” (Schwarz et al., 2006). Additionally, since SPARROW is a 

statistical model at its core, it provides measures of uncertainty in model coefficient and water 

quality predictions. Utilization of the SPARROW model results in estimates of long-term, 

steady-state water quality in a stream. In most applications, SPARROW estimates represent 

mean annual stream loadings of a contaminant. 

Load leaving the 

reach = 

Load generated within 

upstream reaches and 

transported to the reach via 

the stream network 

+ 

Load originating within the 

reach’s incremental watershed 

and delivered to the reach 

segment 

Figure 2.2-2. Mass balance description applied to the SPARROW model formulation. 

A key component of SPARROW is its reliance on the spatial distribution of watershed 

characteristics and sources. The stream reach network is spatially referenced against all 

monitoring stations, GIS data for watershed properties, and source information. This structure 

allows for the simulation of fate and transport of contaminants from sources to streams and 

downstream ecological endpoints. “Spatial referencing and the mechanistic structure in 

SPARROW have been shown to improve the accuracy and interpretability of model parameters 

and the predictions of pollutant loadings as compared to those estimated in conventional linear 

regression approaches” (e.g., Smith et al., 1997; Alexander et al., 2000) (Schwarz et al., 2006). 

This spatially distributed model structure based on a defined stream network allows separate 

statistical estimation of land and water parameters that quantify the rates of pollutant delivery 

from sources to streams and the transport of pollutants to downstream locations within the stream 

network (i.e., reaches, reservoirs, and estuaries) (Schwarz et al., 2006). Figure 2.2-3 shows how 

each watershed and stream reach within the stream network defined for the SPARROW 

application (represented by different colors in the figure) is processed separately and linked to 

derive a final loading at a downstream location (the star labeled X). The SPARROW model is 

calibrated at each monitoring station (represented by stars in Figure 2.2-3) by comparing the 

modeled loads (i.e., a total of loads from each watershed segment and any upstream loads from 

previous calibrations) against monitored data at the station. In this case, the modeled load at 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 32 
Aquatic Nutrient Enrichment Case Study 

downstream monitoring station X would include loads from upstream monitoring station Y and 

the five watershed segments between the two monitoring stations. 

Figure 2.2-3. Conceptual illustration of a reach network. 

Within this Aquatic Nutrient Enrichment Case Study, the mathematical formulation of 

the basic version of SPARROW presented by McMahon et al., (2003) is shown for consideration 

in Equations 3 to 5. “The additive contaminant source components and multiplicative land and 

water transport terms are conceptually consistent with the physical mechanisms that explain the 

supply and movement of contaminants in watersheds” (Schwarz et al., 2006). Preservation of 

mass, accounting for transport and decomposition at individual sources, is accomplished within 

SPARROW through the spatial referencing of all processes with respect to the stream network 

and the specific reach in which the process is carried out. Decomposition processes are 

represented through losses in delivery to the stream and within the stream reach itself (Equation 

4) or within a reservoir (Equation 5). 

∑ ∑ (3) = ∈

− =

N

n i Jj

i

R

ji

S

ji

Z Loadi jnn HHeS j

1 ) (

,,

)(

, β ε α

where 

 Load = Nitrogen load or flux in reach i, measured in metric tons 

 n, N = Source index where N is the total number of individual n sources 

 J(i) = Set of all reaches upstream, including reach i

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 33 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 34 

βn = Estimated source coefficient for source n 

 Sn.j = Nitrogen mass from source n drainage to reach j

α = Estimated vector of land to water delivery coefficients 

 Zj = Land-surface characteristics associated with drainage to reach j

 HS

i., j = Fraction of nutrient mass present in water body j transported to water 

body i as a function of first-order loss process associated with stream 

channels 

 HR

 ,j i = Fraction of nutrient mass present in water body j transported to water 

body i as a function of first-order loss process associated with lakes 

and reservoirs 

εi = Multiplicative error term assumed to be independent and identically 

distributed across separate subbasins defined by intervening drainage 

areas between monitoring stations. 

∏ −= S

m

H ji Lk mjim exp( ) , ,, (4) 

where 

 km = First-order loss coefficient (km-1) (A k value of 0.08, for example, 

indicates that nitrogen is removed at a rate of approximately 8% per 

km of channel length.) 

 m = Number of discrete flow classes 

 Li.j.m = Length of the stream channel between water bodies j and i in flow 

class m.

∏ − −= R 1

l

ji l H kq )exp( , (5) 

where 

 k = Estimated first-order loss rate (or settling velocity; units = m/yr)

 ql

-1 = Reciprocal areal hydraulic load of lake or reservoir (ratio of watersurface area to outflow discharge; units = yr/m) for each of the lakes 

and reservoirs (l) located between water bodies j and i. 

SPARROW has been designed to identify and quantify pollution sources that contribute 

to the water quality conditions predicted by the model. Several different types of sources may be 

examined, and sources may be for an individual stream location or summarized for a grouping of 

stream locations. Examples of sources modeled within SPARROW include atmospheric 

deposition, point sources, animal agriculture, or land use–based supply of contamination. “The 
Aquatic Nutrient Enrichment Case Study 

ability to develop quantitative information on pollution sources in SPARROW models stems 

from the ability to trace, for each contaminant category, the predicted in-stream flux through a 

given stream reach to the individual sources in each of the upstream reach watersheds 

contributing contamination to that reach” (Schwarz et al., 2006). Figure 2.2-4 highlights some of 

these sources in a conceptualization of the SPARROW model process. 

Figure 2.2-4. SPARROW model components (Schwarz et al., 2006). 

Complete procedures, such as calculation of monitoring station flux estimation (Figure 

2.2-4) and details on data formatting, will not be discussed here. The reader is pointed to the 

documentation for the recently released SAS version of the SPARROW model available from 

the USGS SPARROW Web site (http://water.usgs.gov/nawqa/sparrow/sparrow-mod.html) for 

full details on the model. The reader may also review some of the previous SPARROW 

applications presented in Table 2.1-1. The following sections describing SPARROW provide 

basic definitions of terms that aid in understanding SPARROW inputs and outputs and discuss 

some details that pertain to an application focused on atmospheric deposition inputs. Finally, an 

alternate formulation of SPARROW is described that highlights contributions of ammonia to the 

total Nr load for use in the Neuse River watershed. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 35 
Aquatic Nutrient Enrichment Case Study 

Table 2.1-1. Examples of SPARROW Applications 

Location Citation 

National Smith and Alexander, 2000 

Major estuaries of the United States Alexander et al., 2001 

Chesapeake Bay Preston and Brakebill, 1999; Brakebill and Preston, 

2004 

State of Kansas waters Kansas Department of Health and Environment, 2004 

Connecticut River Basin NEIWPCC, 2004 

State of New Jersey waters Smith et al., 1994 

New England waters Moore et al., 2004 

New Zealand river basins Alexander et al., 2002; Elliott et al., 2005 

North Carolina coastal watersheds McMahon et al., 2003 

Tennessee and Kentucky watersheds Hoos, 2005 

2.2.1.2 Key Definitions for Understanding SPARROW Modeling 

The following definitions have been summarized from the documentation accompanying 

the SAS application of the SPARROW model available from the USGS (Schwarz et al., 2006). 

Additional references are noted when used. 

 Bootstrapping. This is the practice of estimating the properties associated with the model 

coefficients by estimating those properties when sampling from a specified distribution using 

replacement (e.g., the model coefficients are estimated a number of times until the best evaluation 

properties of the coefficients are found).

 Delivered Yield (load per area). This is the amount of nutrients generated locally for each stream 

reach and weighted by the amount of in-stream loss that would occur with transport from the reach 

to the receiving water. The cumulative loss of nutrients from generation to delivery to the receiving 

water is dependent on the travel time and in-stream loss rate of each individual reach (Preston and 

Brakebill, 1999). 

 Incremental Yield (load per area). This yield represents the local generation of nutrients. It is the 

amount of nutrients generated locally (independent of upstream load) and contributed to the 

downstream end of each stream reach. Each stream reach and associated watershed is treated as an 

independent unit, quantifying the amount of nutrient generated (Preston and Brakebill, 1999). 

 In-Stream Loss. This refers to stream attenuation processes that act on contaminant flux as it 

travels along stream reaches. A first-order decay process implies that the rate of removal of the 

contaminant from the water column per unit of time is proportional to the concentration or mass 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 36 
Aquatic Nutrient Enrichment Case Study 

that is present in a given volume of water. According to a first-order decay process, the fraction of 

contaminant removed over a given stream distance is estimated as an exponential function of a 

first-order reaction rate coefficient (expressed in reciprocal time units) and the cumulative water 

time of travel over this distance. Within SPARROW, the in-stream loss rate is assumed to vary as a 

function of stream channel length and various flow classes. 

 Landscape Variables. These variables describe properties of the landscape that relate to climatic, 

or natural- or human-related terrestrial processes affecting contaminant transport. These typically 

include properties for which there is (1) some conceptual or empirical basis for their importance in 

controlling the rates of contaminant processing and transport, and (2) broad-scale availability of 

continuous measurements of the properties for use in model estimation and prediction. Examples 

include precipitation, evapotranspiration, soil properties like organic content or permeability, 

topographic index, or slope. Particular types of land-use classes, such as wetlands or impervious 

cover, may also potentially be used to describe transport properties of the landscape. 

 Land-to-Water Delivery Factor. This factor describes the influence of landscape characteristics 

in the delivery of diffuse sources of contamination to the stream. The interaction of particular landto-water delivery factors with individual sources may also be important to consider in SPARROW 

models. 

 Monitoring Station Flux Estimation. This refers to the estimates of long-term flux used as the 

response variable in the model. Flux estimates at monitoring stations are derived from stationspecific models that relate contaminant concentrations from individual water quality samples to 

continuous records of streamflow and time. These estimates are used to calibrate the model in each 

application. 

 Non-linear Regression. The SPARROW model equation is a nonlinear function of its parameters. 

As such, the model must be estimated using nonlinear techniques. The errors of the model are 

assumed to be independent across observations and have zero mean; the variance of each 

observation may be observation-specific. A general method commonly used for these types of 

problems, one in which it is not necessary to assume the precise distribution of the residuals, is 

nonlinear weighted least squares. This is the estimation method used by SPARROW. 

 Segmented Watershed Network. This network relates to the system of joined stream reaches that 

define the watershed of interest. Previous SPARROW applications have relied on the River Reach 

File 1 (RF1) hydrography developed by U.S. EPA (1996) and the 1:100,000 scale National 

Hydrologic Dataset (USGS, 1999). These datasets may be used in their original form or modified 

as needed depending on application requirements 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 37 
Aquatic Nutrient Enrichment Case Study 

 Source. SPARROW distinguishes between source categories (e.g., point sources, atmospheric 

sources, and animal agriculture) and individual sources (i.e., the rate of supply of contaminant of a 

particular category originating in the watershed and draining to a specific stream reach). A variety 

of sources based on knowledge of the watershed and inferences from literature may be examined 

with SPARROW. 

 Stream Reach. This is the most elemental spatial unit of the infrastructure used to estimate and 

apply the basic SPARROW models. Stream reaches define the stream channel length that extends 

from one stream tributary junction to another. Each reach has an associated contributing drainage 

catchment. 

 Total Yield (load per area). This is the amount of nutrients, including upstream load, contributed 

to each stream reach. These estimates are calculated by stream reach and account for all potential 

sources cumulatively and individually (Preston and Brakebill, 1999). 

2.2.1.3 Concepts of Importance to Case Study—SPARROW Application 

Previous SPARROW applications have typically relied on atmospheric deposition 

measurements from NADP and have used wet NO3

– deposition as a surrogate for nitrogen 

deposition over the watershed of interest. Within the case studies conducted, CMAQ-modeled 

and NADP-monitored atmospheric deposition was used. Several differences in the final 

parameterization of the SPARROW model will most likely result from this variation in input 

data. 

Expected rules of model coefficient estimation based on source type are described below. 

When using direct measures of contaminant mass as a source estimate, “the source-specific 

parameter (αn) is expressed as a dimensionless coefficient that, together with standardized 

expressions of the land-to-water delivery factor, describes the proportion or fraction of the source 

input that is delivered to streams (note that source and land-to-water delivery coefficients that are 

standardized in relation to the mean values of the land-to-water delivery variables are necessary 

to compare and interpret the physical meaning of source coefficients). This fraction would be 

expected to be <1.0 but >0, reflecting the removal of contaminants in soils and ground water” 

(Schwarz et al., 2006). 

An example of a source of this type would include atmospheric deposition where the 

model input would be the mass of nitrogen deposited over the watershed. When using only wet 

NO3

– deposition as an estimate of nitrogen deposition, the model would be expected to account 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 38 
Aquatic Nutrient Enrichment Case Study 

for the additional nitrogen species (e.g., organic nitrogen, dry deposition of nitrate) to the extent 

that they are correlated with the measured inputs of NO3

– (Alexander et al., 2001). This 

accounting is revealed by estimation within the model application of a land-to-water delivery 

fraction for wet NO3

– deposition (i.e., product of the deposition coefficient and the exponential 

land-to-water delivery function) that exceeds 1.0. 

Although available estimates for the estuarine watersheds indicate that wet NO3

– 

deposition is highly correlated with dry NO3

– plus NH4+

 and organic wet deposition, and 

estimates of the ratio of (dry and wet TN) deposition to NO3

– wet deposition for the estuarine 

watersheds range from 3.2 to 4.0 with an average of 3.6 (Alexander et al., 2001), the use of 

NADP wet NO3

– measurements requires the assumption that the spatial distribution of the 

various nitrogen species across a watershed does not vary. With the inclusion of explicit nitrogen 

species in atmospheric deposition measures, this assumption will not be required, and the landto-water delivery fraction for the atmospheric deposition source term estimation is expected to be 

<1.0. This variation was explored within the case studies as was the general model fit with the 

improved atmospheric deposition inputs. 

2.2.2 ASSETS Eutrophication Index 

2.2.2.1 Background and Description 

The NEEA Program defined and developed a Pressure-State-Response framework to 

assess the potential for eutrophication termed the ASSETS EI. It is categorical, where each of 

three indices results in a score that, when combined, result in a final overall score, also known as 

the ASSETS EI score or rating, which is representative of the health of the estuary. The indices 

are as follows: 

 OHI. Physical, hydrologic, and anthropogenic factors that characterize the susceptibility of the 

estuary to the influences of nutrient inputs (also quantified as part of the index) and eutrophication. 

 OEC. An estimate of current eutrophic conditions derived from data for five symptoms linked to 

eutrophication. 

 DFO. A qualitative measure of expected changes in the system. 

The following excerpt from Whitall et al., (2007) describes the objectives in applying the 

ASSETS method: 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 39 
Aquatic Nutrient Enrichment Case Study 

The ASSETS assessment method should be applied on a periodic basis to track 

trends in nutrient-related water quality over time in order to test management 

related hypotheses and provide a basis for more successful management. The null 

hypothesis being tested in this approach is: The change in anthropogenic pressure 

as a result of management response does not result in a change of state. The 

hypothesis is tested, e.g., to verify whether decreased pressure improves State, or 

whether increased pressure deteriorates State. In many cases, a reduction in 

pressure will result in an improvement of State, but in some cases, such as 

naturally occurring harmful algal bloom (HAB) advected from offshore, it will 

not (Whitall et al., 2007). 

Influencing Factors/Overall Human Influence 

Influencing factors help to establish a link between a system’s natural sensitivity to 

eutrophication and the nutrient loading and eutrophic symptoms actually observed. This 

understanding also helps to illustrate the relationship between eutrophic conditions and use 

impairments (Bricker et al., 2007a). Influencing factors are determined by calculating two factors 

of susceptibility and nitrogen load, where “susceptibility” provides a measure of a system’s 

nutrient retention based upon flushing and dilution, and “nitrogen loads” are a ratio between the 

nitrogen input to the system from the oceans versus from the land (Figure 2.2-5).

Figure 2.2-5. Influencing factors/Overall Human Influence index description and decision 

matrix (Bricker et al., 2007a). 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 40 
Aquatic Nutrient Enrichment Case Study 

The following factors take into account both the natural characteristics of and human 

impacts to systems. 

 Susceptibility. For a coastal system, susceptibility depends on the flow of water into and out of the 

system. This flushing capability is determined by the physical properties (e.g., size, mouth) of the 

system as well as the influence of tidal waters and inflow of freshwater from tributaries. When 

water flows into and out of the system easily and quickly (i.e., there is a short residence time), 

nutrients flush out of the system rapidly, and there is not enough time for eutrophic symptoms to 

develop. Systems with short residence times have low susceptibility. The opposite also holds true. 

When water, and therefore nutrients, does not flush quickly from the estuary or coastal system, 

there is time for eutrophication effects to develop. 

 Nitrogen Load. For this assessment, the loading component is estimated as the ratio of nitrogen 

coming from the land (i.e., human-related) to that coming from the ocean and is given a rating of 

low, moderate, or high (Bricker et al., 2003; Ferreira et al., 2007). For example, a high rating 

means that >80% of the nutrient load comes from land, whereas a low rating signifies a land 

percentage of <20%. This rating also provides insight into loading management because loads to 

systems with primarily ocean-derived nitrogen are not easily controlled. Understanding the sizes of 

current and expected future loads provides further insight into the application and success of 

management measures. 

Overall Eutrophic Condition 

To assess the eutrophic conditions of a system, the NEEA relies on five symptoms. Each 

of the five symptoms, divided into primary and secondary categories, is assessed based on a 

combination of the following factors: concentration or occurrence, duration, spatial coverage, 

frequency of occurrence, and confidence in the data (Figure 2.2-6). The two primary symptoms, 

chlorophyll a and macroalgal abundance (Figure 2.2-7), were chosen as indicators of the first 

possible stage in the process of water quality degradation leading to eutrophication. The 

secondary symptoms, which in most coastal systems will develop from the primary symptoms, 

include low dissolved oxygen levels, loss of SAV, and occurrences of nuisance/toxic algal 

blooms (Figure 2.2-7). At times, the secondary symptoms may also be present or develop 

without expression of primary symptoms. Nutrient concentrations are not employed as a 

symptom indicator because concentrations may vary between low and high values based on a 

number of factors, such as estuary susceptibility, which invalidates the use of nutrient 

concentrations alone as an indicator. As stated by Bricker et al., “Through the use of a simple 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 41 
Aquatic Nutrient Enrichment Case Study 

model, the current framework was established to help understand the sequence, processes, and 

symptoms associated with nutrient enrichment. Despite its limitations, it represents an attempt to 

synthesize enormous volumes of data and derive a single value for eutrophication in each 

estuary, essentially representing a complex process in a simple way” (Bricker et al., 2007a). 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 42 
Aquatic Nutrient Enrichment Case Study 

Figure 2.2-6. Overall Eutrophic Condition index description and decision matrix 

(Bricker et al., 2007a).

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 43 
Aquatic Nutrient E

Figure 2.2-7. Detailed descriptions of primary and secondary indicators of eutrophication (Bricker et al., 

Final Risk and Exposure Assessment 

Appendix 6 - 44 
Aquatic Nutrient Enrichment Case Study 

Determined Future Outlook 

The future outlook relies on a similar combination of factors as the influencing factors 

(i.e., a rating of the system susceptibility and nutrient loading in the future). The aim of this 

index is to estimate future changes in the system through a combination of any physical, 

hydrologic, or pollutant loadings to the system itself or to its contributing watershed through 

such actions as watershed management plans, development restrictions, or policy changes 

resulting in nutrient decreases. The matrix in Figure 2.2-8 is used to determine the DFO index 

rating. 

Figure 2.2-8. Determined Future Outlook index description and decision matrix (Bricker 

et al., 2007a). 

The last step is to combine the OHI, OEC, and DFO index scores into a single overall 

ASSETS EI score. The ASSETS EI scores fall into one of six categories: High, Good, Moderate, 

Poor, Bad, or Unknown. These ratings can be summarized as follows (Bricker et al., 2007a): 

 High: Low pressure from influencing factors, low OEC, and any expected improvement or no 

future change in eutrophic condition. 

 Good: Low to moderate pressure, low to moderate-low eutrophic condition, and any expected 

future change in condition. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 45 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 46 

 Moderate: Any pressure, moderate-low to moderate-high eutrophic condition, and any expected 

future change in eutrophic condition. 

 Poor: Moderate-low to high pressure, moderate to moderate-high eutrophic condition, and any 

expected future change in condition. 

 Bad: Moderate to high pressure, moderate-high to high eutrophic condition, and any expected 

future change in eutrophic condition. 

 Unknown: Insufficient data for analysis. 

2.2.2.2 Applications and Updates 

The ASSETS EI method developed out of the NEEA was first reported in 1999. Since 

that time, it has been used in several assessments across the country and internationally and has 

undergone revision and validation (Bricker et al., 1999, 2003, 2007a; Ferreira et al., 2007; 

Whitall et al., 2007). The original NEEA ASSETS EI assessment relied on questionnaires to 

experts for each estuary considered (Bricker et al., 1999). Later assessments determined that 

reliance on monitored data and less on reports from experts provided a more valid assessment 

tool (Bricker et al., 2006, 2007a). With the NEEA Update in 2007 (Bricker et al., 2007a), an 

online database was completed in which data users and data holders could access and input data. 

Additional datasets have also been collected for smaller study areas (Bricker et al., 2006). These 

data systems provide a wealth of information from which analyses may be conducted. 

The original formulation of the ASSETS EI within the NEEA used watershed nutrient 

model estimates from SPARROW (Bricker et al., 1999). Although the updated ASSETS EI 

methodology has further apportioned nitrogen sources using the Watershed Assessment Tool for 

Evaluating Reduction Strategies for Nitrogen (WATERSN) model (Whitall et al., 2007), 

SPARROW is still appropriate for this study because atmospheric deposition inputs relative to 

other nitrogen sources can be defined. 

2.2.3 Assessments Using Linked SPARROW and ASSETS EI 

The link between the SPARROW model and the ASSETS EI occurs when the 

SPARROW output is used as the nitrogen load in the OHI index calculation of the ASSETS EI 

score. For the purposes of this study, a complete analysis from atmospheric deposition loading to 

ecological endpoint of the ASSETS EI score required an assessment of the relative changes in 

the deposition load, the resulting instream nitrogen load to the estuary, and the change in 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 47 

ASSETS EI score. An iterative assessment of the various possible ecological endpoints due to 

changing nitrogen loads has not been previously undertaken. The methods that follow have been 

designed to allow for set up of a process to link the SPARROW and ASSETS EI assessment, 

which includes uncertainty analysis. As will be detailed below, at this time the developed process 

includes an uncertainty analysis for the ASSETS EI assessment, whereas the uncertainty 

surrounding the corresponding decreased atmospheric deposition loads predicted by SPARROW 

has not yet been implemented. The process is described beginning with the individual 

components of each assessment, followed by a description of the iterative processing designed to 

sample among the assessments, producing a distribution of results. 

The first step in setting up the linked analysis was to create a series of response curves. 

The SPARROW model can be used to assess the different atmospheric deposition loads that will 

result from changes in the NOx concentrations enforced with any new policy scenarios. 

Therefore, a change in atmospheric deposition load produces a corresponding change in TN 

loading at the outlet of the watershed, in this case, to the estuary. Converting the TN load to a 

concentration value using the flow values employed in the SPARROW modeling allows for the 

creation of a relationship between decrease in atmospheric deposition load from the current 

condition scenario and instream total nitrogen concentration (TNs). A theoretical representation 

of this relationship in an ideal situation is presented in Figure 2.2-9 (dotted lines represent 

uncertainty bounds). The red “x” indicates the nitrogen concentration during the current 

condition assessment where there is no load decrease in atmospheric deposition. The vertical 

asymptote of the curve approaches the nitrogen concentration of the river if there were no 

atmospheric inputs. In theory, the watershed response to changes in any loading would be 

nonlinear because of various retention and loss processes occurring within the watershed. 

However, because SPARROW is a statistical model, the empirical representation of these 

processes does not produce a nonlinear response as shown in Figure 2.2-9. As discussed in the 

results section, the response to changes in atmospheric deposition load is predicted to be linear 

by SPARROW. This highlights one area of uncertainty due to the steady-state, statistical nature 

of SPARROW. A benefit of using SPARROW is that the various atmospheric deposition levels 

can be run through the model in a short amount of time. 

A second response curve is set up for the ASSETS EI based on the Influencing Factors/ 

OHI and OEC index scores (Section 2.2.2), which are functions of TN load. The ASSETS EI 
Aquatic Nutrient Enrichment Case Study 

assessment is essentially a pressure-state-response scenario (Figure 2.2-10) where the pressure is 

the nitrogen load (represented by TNs) and the state is the current OEC index and ASSETS EI 

scores for the system. Response would be the change in state of the estuary (represented in the 

ASSETS EI by the DFO index). Bricker et al. (2007b), noted that the shape of the response curve 

would vary based on the susceptibility of the system. Therefore, if the susceptibility is known 

and held constant, a curve can be created. 

Instream Total Nitrogen Concentration 

Percent Decrease in Oxidized Nitrogen 

(or Total Nitrogen Atmospheric Deposition Load) 

Figure 2.2-9. Example response curve of instream total nitrogen concentrations to 

atmospheric deposition loads. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 48 
Aquatic Nutrient Enrichment Case Study 

Figure 2.2-10. Example of response for case study analysis (Bricker et al., 2007b). 

With the case study analysis, response curves were created for two different estuaries 

where the susceptibility is known. As previously described, the ASSETS EI score is a 

combination of OEC, OHI, and DFO index scores. It is possible to combine all three of these 

scores with the ASSETS EI into a single response curve when the susceptibility and DFO are 

held constant. The DFO may be held constant when alternative effects levels are being evaluated 

based on a current condition scenario, such as was done in this study. The susceptibility rating is 

based on physical and hydrological conditions. Physical conditions are unlikely to change. The 

hydrologic conditions may change because of extreme conditions, such as prolonged drought or 

hurricane events, but overall the conditions should average to a steady value that can be used in 

the analysis. Figure 2.2-11 highlights this combination of scores where the susceptibility is 

“High” and the DFO is set at “Improve.” Additionally, by holding the susceptibility constant, the 

OHI index score becomes a function of the TNs. This is evident in the double x-axis. The state 

response is the OEC index score along the y-axis. Underlying these combinations of OHI and 

OEC index scores is the ASSETS EI score. 

Final Risk and Exposure Assessment September 2009 

The categorical nature of the assessment produces a mix of a continuous curve based on 

TNs and blocks where different ASSETS EI scores are valid. The shape of the curve can be 

determined by fitting a logistic relationship through a series of points (red “x”s) within the 

Appendix 6 - 49 
Aquatic Nutrient Enrichment Case Study 

pressure-state realm based on compilations of historical data. The logistic function is suggested 

because of the pressure-state-response nature of the system meaning that initial, lower pressures 

are thought to affect the system slowly, then changes become more rapid up until a point where 

the pressure is so great that the state changes little because it has almost reached a maximum 

level. A logistic response to inputs (e.g., nutrients) is a common pattern in biological systems and 

is well documented. A variety of logistic (or sigmoidal) functions are available. In this case 

study, the following four-parameter function was used: 

⎟

⎟

⎠

⎞ ⎜

⎜

⎝

⎛ − −

+

+=

D

s s CTN

exp1

B A)TN(OEC (6) 

where 

 OEC(TNs) = OEC index score as a function of TNs (unitless) 

 TNs = TNs (mg N/L) 

 A, B, C, D = Parameters to be estimated. 

Applying this logistic function as in Figure 2.2-11, parameter A affects the OEC 

intercept value, b in Figure 2.2-11; parameters A and B affect the OEC asymptote value; C

affects the “S” shape; and D shifts the “S” horizontally. The curve is fit through a nonlinear 

optimization routine using a modified version of Box’s algorithm (Box, 1965). Constraints on 

the boundary conditions for the function are described below. At this time, 500 iterations of the 

algorithm were used to define the function. A future assessment must complete a Monte Carlo 

simulation analysis to determine a justifiable estimate of the number of iterations needed to bring 

the algorithm to convergence for the problem as thus defined. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 50 
Aquatic Nutrient Enrichment Case Study 

Figure 2.2-11. ASSETS EI response curve.

Within the analysis space created by both the OHI and OEC index scores, the axes are 

limited to the scores of zero (really one) to five, but the corresponding TNs must be determined 

separately. Point “a” represents the background nitrogen concentration that would occur in the 

system with no anthropogenic inputs (assuming the system is not naturally eutrophic) or the 

system at a pristine state. In almost all cases, this value will be unknown because of the extent to 

which anthropogenic inputs have influenced the nation’s ecosystems. A lower bound and upper 

bound on this value, between which the algorithm randomly selects a different realization for 

each iteration, were specified. The lower bound was specified as the offshore TN concentration 

(used in the OHI index score) and the upper bound was specified at 1.0 milligrams per liter 

(mg/L) for the Potomac River/Potomac Estuary Case Study Area based on best professional 

judgment and at 0.1 mg/L for the Neuse River/Neuse River Estuary Case Study Area based on a 

combination of the lowest monitored nitrogen values over a number of years and best 

professional judgment. The upper bound of the TNs is the maximum nitrogen concentration at 

which the stream is nitrogen-limited; above this point, the nitrogen inputs to the system no longer 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 51 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 52 

affect the eutrophication condition. Several attempts to quantify this limit were made through 

historical data analysis, examination of nitrogen to phosphorus (N:P) ratios within the estuary, 

and consideration of the underlying eutrophication processes. For this first assessment, a lower 

and upper bound on this maximum TNs between which the algorithm randomly selects a 

different realization for each iteration were specified. The lower bound was specified as the 

maximum observed TNs, whereas the upper bound was specified as 50% greater than this value 

based on best professional judgment. 

Further measures of uncertainty could be taken into account using upper and lower 

bounds on the logistic curve. The horizontal spread at each OEC index score reflects the range of 

nitrogen concentrations that may relate to that specific OEC index score. The vertical spread 

relates to the range in OEC index scores that may occur at each nitrogen concentration. As with 

the TNs axis, the lower and upper bounds on both extremes of the OEC scale were specified 

again. At both ends of the scale (i.e., 0 and 5), the OEC maximum and OEC minimum values 

could be randomly selected at each iteration within a range of 1 OEC unit or could be set at 

constant values of 1 and 5. If randomly selected, the actual OEC minimum would be between 0 

and 1 at the “0” end of the scale and between 4 and 5 at the “5” end of the scale. This would 

account for the likelihood that an estuary may not physically be capable of reaching either a 

“pure, pristine” condition (e.g., OEC = 5) or a “completely hypereutrophic” condition (e.g., 

OEC = 0). Consideration of this allowance and of holding the scores constant is discussed in the 

uncertainty section. 

2.2.3.1 Back Calculation Method 

The creation of the two response curves allows an analyst to work backward from the 

ecological endpoint to the source of the impairment; in this case from the ASSETS EI score to 

the atmospheric deposition loading of oxidized nitrogen. To accomplish this back calculation, a 

computer program has been generated that processes through a series of user inputs, defined 

boundary conditions, and set cases for determining the ASSETS EI score to perform the iterative 

calculations described in setting the logistic curve and determining the TNs. Currently, the 

program is coded in the Visual Basic language and requires an input file defined by the user to 

contain: 

 The desired ASSETS EI ecological endpoint (i.e., score) 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 53 

 Oceanic nitrogen and salinity values 

 Instream salinity values 

 The regression equation relating the decrease in atmospheric deposition of nitrogen to TNs

 The number of realizations on which to iterate the model calculations. 

2.2.3.2 Uncertainty Bounds on TNs, OEC Min/Max Values 

Based on the degree of uncertainty, three different scenarios exist for the analysis (Figure 

2.2-12a through Figure 2.2-12c). There are two relationships, or functions, involving uncertainty 

in the proposed methodology. The first is the SPARROW-predicted TNs at the head of the 

estuary given a total nitrogen atmospheric deposition load (TNatm). (Note that SPARROW model 

predictions are actually provided in terms of instream TN loads [mass per time]; however, 

concentrations [mass per volume] can be calculated by dividing the instream TN load by the 

flow rate.) This functional relationship is denoted as TNs(TNatm) where the TNs is actually 

evaluated on changes in the loading of oxidized nitrogen, not all nitrogen species, in the TN 

atmospheric deposition load. This means that decreases are applied to the NOx load within the 

atmospheric load, and a new TNatm is calculated). (For instance if the NOx load contribution is 10 

kg N/yr and TNatm is 20 kg N/yr, then a 40% decrease would result in a TNatm equal to 16 kg 

N/yr after decreasing the 10 kg N/yr by 40%.) Because of the SPARROW regression 

uncertainties, this function is a probability distribution (i.e., given an TNatm, there are many 

alternative TNs values). This distribution function is denoted as TNs

pdf(TNatm). Under the 

standard assumptions of regression modeling, this is a normal distribution with a mean value 

represented by the SPARROW estimate of TN load. The variability around the mean value 

represents uncertainty in the SPARROW parameter estimates. However, the SPARROW 

regression model does not give rise to normally distributed residuals, and some nonparameteric 

methods have been developed by the SPARROW developers to estimate SPARROW’s 

confidence limits on predictions (Schwarz et al., 2006). This measure of uncertainty has not yet 

been incorporated into this methodology, and that is why the results for the Potomac 

River/Potomac Estuary Case Study Area and the Neuse River/Neuse River Estuary Case Study 

Area are based on uncertainty Scenario B (explained below; Figure 2.2-12b). Accordingly, the 

uncertainty presented in those results will be an underestimate of the total uncertainty. 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 54 

The second uncertain function is the semiquantitative relationship between the ASSETS 

EI score and the TN load. The ASSETS EI model yields a result based on three index “scores,” 

the OEC, the OHI, and the DFO index scores as described in Section 2.2.2. The OEC and OHI 

index scores are functions of TNs. Thus, the ASSETS EI model can be written as ASSETS EI = 

f(OEC(TNs), OHI(TNs), DFO) where “f” is the functional relationship that is the ASSETS EI 

methodology. Based on the methodology developed for creation of the response curve, the 

OEC(TNs) function also involves uncertainty, and the OEC index score resulting from any 

particular TNs is also a probability distribution, which can be assumed to be a uniform 

distribution for this first analysis. Incorporating this uncertainty, the ASSETS EI model can be 

expressed for this study as ASSETS EI = f(OECpdf(TNs), OHI(TNs), DFO). Figure 2.2-12b and 

Figure 2.2-12c present the iterative steps in which the probability distribution function of OEC 

results is created in the coded model. 

In setting up Scenario B for the evaluation of alternative effects levels, the goal was set to 

determine the change in oxidized nitrogen load required to improve the ASSETS EI score by 

one, two, and three categories from its current level set in the 2002 current condition analysis. 

Improvement in the ASSETS EI score by categorical values means moving along the logistic 

curve set to data points determined through the gathering of historical monitoring data and 

reports. Figure 2.2-13 provides a visualization of what it means graphically to improve by one 

ASSETS EI score category. In reality, the exact results of improving by one, two, or three 

categories will vary depending on the estuary and the baseline state of the estuary (i.e., where on 

the logistic curve the analysis begins). In the example shown in Figure 2.2-13, improving the 

ASSETS EI score by one category also improves the OEC index score by one category but 

allows for a decrease in the TNs, which results in the same OHI index score as the baseline. Also 

in this example, the baseline for the estuary is an ASSETS EI score of “bad,” thus it is possible 

to improve by three categories although, in doing so, the OEC and OHI/TNs would have to 

improve to an almost pristine state. If a system begins with an ASSETS EI score of “Moderate” 

or “Good,” the assessment will only be able to examine an improvement in two or one ASSETS 

EI score categories, respectively. Also noted in Figure 2.2-13 is the direction of the category 

movement. In this example, improvement of the ASSETS EI score along the determined logistic 

curve is a vertical movement down rather than a horizontal move to the left (not illustrated with 

this example); therefore, the OHI index score may remain in the same or similar state. If the 
Aquatic Nutrient Enrichment Case Study 

movement were to the left, the OHI index score would have to improve, whereas the eutrophic 

condition of the system (i.e., OEC index score) could remain the same or similar to the baseline 

conditions. 

Figure 2.2-12a. Back calculation analysis scenario A: no uncertainty.

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 55 
Aquatic Nutrient Enrichment Case Study 

Figure 2.2-12b. Back calculation analysis scenario B: uncertainty in ASSETS EI assessment. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 56 
Aquatic Nutrient Enrichment Case Study 

Figure 2.2-12c. Back calculation analysis scenario C: uncertainty in both ASSETS EI 

assessment and nitrogen loading assessment.

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 57 
Aquatic Nutrient Enrichment Case Study 

Figure 2.2-13. Example of an improvement by one ASSETS EI score category in a back 

calculation assessment.

In the second piece of the back calculation, the newly determined TNs needed to make an 

improvement in the ASSETS EI score is examined on the response curve of the TNs to TNatm. 

Figure 2.2-14 presents an example similar to those found in the case studies where the response 

is linear (note that the axes have been flipped from original representation in Figure 2.2-9 to 

reflect how the response curve is handled within the coded program developed for this study). 

While the linear nature of this curve during this preliminary assessment is a function of the use 

of SPARROW, a statistical watershed model, valuable information on the required decrease in 

TNatm, can still be determined. The scale of the axes of the response curve also provides a great 

deal of information on the system of interest. For instance, if the TNs changes that result from a 

small change in the TNatm are much greater (e.g., 5% change in atmospheric load versus 20% 

change in instream concentration), then the system is highly influenced by the atmospheric 

deposition of nitrogen. If the situation is reversed and large changes in TNatm result in only small 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 58 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 59 

changes in the TNs, then the system is not greatly influenced by the atmospheric deposition of 

nitrogen. 

Figure 2.2-14. Example for resulting change in atmospheric nitrogen loads due to 

improvement in ASSETS EI score in back calculation assessment. 

3.0 RESULTS 

3.1 CURRENT CONDITIONS 

The available air quality data for this review are based on the 2002 CMAQ model year 

and NADP data; therefore, current conditions for this case study evaluated ecosystem responses 

for the year 2002. In both case study areas, the best attempts were made to use monitoring and 

modeling data from that time period. The methods designed for the current condition ecosystem 

analysis required for this study produce annual averages for 2002. 

3.1.1 Summary of Results for the Potomac River/Potomac Estuary Case Study 

Area 

The 2002 current condition analysis of the Potomac River/Potomac Estuary Case Study 

Area relied on a previous SPARROW application calibrated for the late 1990s in the Chesapeake 

Bay watershed. The evaluation of the ASSETS EI used raw data compiled from organizations 

Percent Decrease in Atmospheric Oxidized 

Nitrogen Load 0 

Instream Total Nitrogen Concentration 

Total Nitrogen Atmospheric Deposition Load 

Specified change in instream 

total nitrogen concentration from

improvement in ASSETS EI 

Resulting change in 

atmospheric 

deposition and 

percent decrease in 

oxidized nitrogen 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 60 

reporting on various issues in the Chesapeake Bay, including the Chesapeake Bay Program 

(CBP) and the Virginia Institute of Marine Science (VIMS). 

3.1.1.1 SPARROW Assessment 

The Version 3 Chesapeake Bay SPARROW application modeled the watershed for the 

time period of the late 1990s. Stream nitrogen load estimates from 87 sites were used to calibrate 

the model. The stream reach network used in this analysis relied on a modified version of the 

RF1 used in previous Chesapeake Bay SPARROW applications, but included 68 reservoirs that 

were not previously included. This analysis examined the sources of atmospheric deposition, 

fertilizer and manure application, point sources, and land use. For details on the compilation of 

each of these GIS-based datasets, see Brakebill and Preston (2004). Watershed characteristics 

considered in the model as loss and decay variables include precipitation, temperature, slope, soil 

permeability, and hydrogeomorphic regions. 

The Version 3 calibrated model was selected to create the results for the 2002 current 

condition analysis because of its temporal proximity to the desired base year. The 5-year 

difference from 1997 to 2002 was not expected to result in a large change in the model if it were 

recalibrated to more recent data (S. Preston, personal communication, 2008). Future updates to 

this study should consider recalibrating the model to 2002 data to ensure that this assumption 

holds true. 

The SPARROW assessment for the 2002 current condition analysis used the same source 

inputs and watershed characteristics, except in the case of atmospheric deposition. The Version 3 

Chesapeake Bay SPARROW application relied on 1997 mean deposition values of wetdeposition atmospheric NO3

– using the 191-point measurements in the NADP program across the 

country. As described in Section 2.2.1.3, relying on wet NO3

– deposition as a surrogate for TN 

deposition requires an assumption of spatial homogeneity between the nitrogen species. The 

2002 current condition analysis used the CMAQ/NADP data that were prepared for the Risk and 

Exposure Assessment as atmospheric deposition inputs to the model. Although the Version 3 

Chesapeake Bay SPARROW model was calibrated against only wet NO3

– deposition loads, it 

was decided that the 2002 current condition analysis should incorporate all available forms of 

nitrogen deposition in order to fully reflect the result of changing oxidized nitrogen loads. 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 61 

The use of the 2002 atmospheric deposition loads with the 1997 Version 3 of SPARROW 

introduces a source of uncertainty to the modeled results because the source parameter for 

atmospheric deposition was calibrated to the spatial variability and magnitude of the original 

atmospheric source. This degree of uncertainty will be omitted from any future analyses when 

the time and data exist to recalibrate the SPARROW model to a full set of 2002 current condition 

data. At this time, the study’s goal, which is to reflect on the changes in magnitude of nitrogen 

loads to the estuary due to changes in the deposition of oxidized nitrogen, can still be assessed 

and described using this data compilation. The 1997 data used in the original Version 3 

Chesapeake Bay SPARROW application shows wet NO3

– yields highest in the western, 

mountainous region of the Potomac River watershed with lower values around the southern 

central portion and areas surrounding the Potomac Estuary (Brakebill and Preston, 2004). There 

is a clear trend of interpolation from the NADP data. In comparison, Figure 3.1-1a through

Figure 3.1-1c reveal highly different spatial patterns in oxidized, reduced, and TN atmospheric 

deposition yields across the Potomac River and Potomac Estuary watershed. Note that the scales 

across the three figures use the same increments and colors so that they can be directly 

compared. For the current condition 2002 analysis of the Potomac River and Potomac Estuary, 

an estimated 40,770,000 kg of TN was deposited in the 3.2-million hectares (ha) Potomac River 

watershed, for an average TN deposition of 12.9 kg N/ha/yr. 

Application of the SPARROW model provides estimates of the incremental flux derived 

within each catchment of the Potomac River watershed, as well as estimates of how much of that 

incremental flux (i.e., delivered flux) ultimately reaches the estuary (Figure 3.1-2). By looking at 

catchment-scale results, the spatial variability among flux/load contributions across the 

watershed can be shown. Differences between the incremental and delivered yields/loads reflect 

the instream losses that occur as the load from each catchment travels downstream to the target 

estuary. 

For this first application and analysis of the 2002 current condition case, SPARROW was 

used to model the loads from the Potomac River and its watershed to the upper portions of the 

Potomac Estuary. The most downstream modeled catchment in the analysis lies downstream of 

several major point sources between Washington, DC, and the mixing zone of the estuary. These 

point sources were major contributors of nutrients to the estuary, and by including them in the 

analysis a more accurate load from the Potomac River watershed is defined than if the modeling 
Aquatic Nutrient Enrichment Case Study 

stopped at the fall line of the river. Direct runoff from catchments surrounding the Potomac 

Estuary and direct deposition to the estuary were not considered in this preliminary model 

application. The majority of the nitrogen loading to the estuary was expected to derive within the 

Potomac River watershed because of its overall larger land area and applications of fertilizer and 

manure. Additionally, the major point sources to the Potomac Estuary were included in the most 

downstream watersheds at the mouth of the estuary modeled in this application. 

Figure 3.1-1a. Atmospheric deposition yields of oxidized nitrogen over the Potomac 

River and Potomac Estuary watershed.

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 62 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-1b. Atmospheric deposition yields of reduced nitrogen over the Potomac River and 

Potomac Estuary watershed. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 63 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-1c. Atmospheric deposition yields of total nitrogen over the Potomac River and 

Potomac Estuary watershed. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 64 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-2. Total nitrogen yields from all sources as predicted using the Version 3 of the 

Chesapeake Bay SPARROW application with updated 2002 atmospheric deposition inputs. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 65 
Aquatic Nutrient Enrichment Case Study 

Overall, the model produced an estimate 

of TN loading to the Potomac Estuary of 

36,660,000 kg N/yr. The atmospheric deposition 

load was estimated at 7,380,000 kg N/yr or 20% of the total loading (Figure 3.1-3). These 

modeling estimates are consistent with previous modeling estimates for the system (Preston and 

Brakebill, 1999). The TNs resulting from this loading was approximately 3.4 mg/L. 

SPARROW modeling for 2002 predicts that 

atmospheric deposition was 20% of the total 

nitrogen loading to the Potomac River’s 

estuary, producing an TNs of 3.4 mg/L. 

Relative Contributions of Nitrogen Sources to Potomac Estuary

5%

25%

4%

46%

20%

Manure

Fertilizer

Urban Area

Point Sources

Atmospheric Deposition

Total load to estaury:

36,660,000 kg N/yr

Figure 3.1-3. Source contributions to Potomac Estuary nitrogen load.

3.1.1.2 ASSETS EI Assessment 

An ASSETS EI assessment was completed for the Potomac Estuary (Figure 3.1-4) in a 

2006 NOAA project on the Gulf of Maine (Bricker et al., 2006). The data used to complete the 

scoring were from 2002. That assessment showed that the system has a high susceptibility to 

pressures and a high score for nutrient inputs, resulting in a score of High for influencing factors 

(i.e., OHI index). Individual scores for the primary and secondary indicators varied, but resulted 

in an overall score of High for the OEC (i.e., OEC index). The score of Improve Low for the 

future outlook (i.e., DFO index) is based on the expectations that future nutrient pressures will 

decrease and there will be significant population and development increases. 

The ratings for the nutrient inputs and the OEC index were recreated and verified using 

methods consistent with the 2007 NEEA Update (Table 3.1-1; Bricker et al., 2007a), which 

included separate areal-weighted consideration of the tidal fresh, mixing, and saltwater zones 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 66 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 67 

within the estuary (Bricker et al., 2007a). (Note that the Potomac Estuary is split approximately 

between tidal fresh [14.5%] and mixing [85.5%] zones.) Data for dissolved oxygen and 

chlorophyll a were downloaded from the CBP water quality database (CBP, 2008). Harmful 

algal bloom (HAB) data were taken from data compilations available from VIMS. Conflicting 

results were found for the SAV coverage where an unreasonably large increase was reported in 

the original analysis (Bricker et al., 2006). The source of the data for the updated analysis was 

the VIMS (VIMS, 2008), which was the same source cited for the original analysis. Possible 

explanations for the discrepancy are different baseline periods for comparisons between the gains 

and losses in SAV areas. The updated analysis compared only annual changes in areal growth of 

SAV (in contrast to previous NEEA studies that used a baseline year of comparison [Bricker et 

al., 1999; Bricker et al., 2007a]). Additionally, the areas of SAV measured for 2001 were listed 

as partial measures, making the estimates in small gains and losses in the tidal fresh and mixing 

areas, respectively, of the estuary uncertain. In the 2007 NEEA Update (Bricker et al., 2007a), a 

value of 0.5 was used for any uncertain index score. A value of 0.5 was, therefore, given to the 

uncertain loss estimated in the mixing zone of the Potomac Estuary for 2002. 

Index scores for the updated analysis were compiled using the scoring methods and 

matrices as shown in Figure 2.2-6 and Figure 2.2-7. Although there were uncertain/unknown 

values for macroalgae in the primary symptoms and SAV in the secondary symptoms, the high 

rankings of chlorophyll a within the primary symptoms and HAB in the secondary symptoms 

overweighed the uncertainty of the other parameters in the final scores. Even if a value of 0.5 

(denoting uncertainty) had been used for the macroalgae score, as was done with the mixing zone 

index for SAV, the overall score for that indicator would have resulted in a primary score greater 

than 0.6 and a High ranking. Combination of the primary and secondary scores (both High) 

provided an overall OEC index score of High, which agreed with the original analysis. 

The OHI index score (confirmed with the modeled nitrogen load from the 2002 

SPARROW application) and the DFO index score remain the same as in the original analysis. 

Therefore, the ASSETS EI score for the 2002 current condition scenario is Bad. 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-4. The ASSETS EI scores for the Potomac Estuary (Bricker et al., 2006). 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 68 
Aquatic Nutrient E

Table 3.1-1. Potomac Estuary Current Condition Overall Human Influence Index Score 

Year Parameter Zone Value Concentration 

Spatial 

Coverage Frequency Expression Score 

2002 

CHLA MX 13.25 MEDIUM HIGH PERSISTENT HIGH 

0.9275 

CHLA TF 22.805 HIGH LOW PERIODIC MODERATE 

Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA 

DO MX 3.6 BIO STRESS HIGH PERIODIC MODERATE 

0.4275 

DO TF 5.8 

NO 

PROBLEM HIGH PERSISTENT NO PROBLEM 

SAV MX NA 

LOSS 

(Uncertain) NA NA 

UNCERTAIN 

(0.5) 

SAV TF NA 0.43 

GAIN 

(Uncertain) NA NA 

UNCERTAIN 

(0) 

HAB ALL NA NA NA NA HIGH 1 

CHLA: Chlorophyll a

DO: Dissolved Oxygen SAV: Submerged Aquatic Vegetation HAB: Harmful/Toxic Algal Blooms MX: Mixing Zone TF: Tidal Fresh Zone ALL: All Estuary Zones 

NA: Not Applicable 

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Appendix 6 - 69 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 70 

3.1.2 Summary of Results for the Neuse River/Neuse River Estuary Case Study 

Area 

The 2002 current condition analysis of the Neuse River and Neuse River Estuary used 

recently released data from the USGS to calibrate a new SPARROW application for 2002 to the 

Neuse, Tar-Pamlico, and Cape Fear rivers’ watersheds. Developing the ASSETS EI score for the 

Neuse River Estuary proved to be a greater challenge than for the Potomac Estuary because of 

the availability of data sources that were less consolidated and more varied. 

3.1.2.1 SPARROW Assessment 

The release of digital data by the USGS for its Southeast Major River Basin SPARROW 

Assessment (area includes all of the river basins draining to the south Atlantic and the eastern 

Gulf of Mexico, as well as the Tennessee River basin [referred to collectively as the SAGT 

area]) in the summer of 2008 provided the opportunity to calibrate a new SPARROW model for 

the 2002 current condition analysis (Hoos et al., 2008). The SAGT data were compiled for 2002, 

providing the necessary data inputs and calibration TN loads for model development. Because of 

a limited number of calibration points within the Neuse River watershed itself, the SPARROW 

model was expanded to include the Tar-Pamlico and Cape Fear river watersheds, providing a 

total of 41 calibration points on which to base the SPARROW model. The river network within 

these basins was again based on the RF1, with enhancements for calibration points. Source 

variables investigated in the new model development included atmospheric deposition (modified 

from the original SAGT dataset to use the CMAQ/NADP data developed for this study), 

fertilizer application to farmland, manure from livestock production, point sources, and land 

cover (urban, forest, and nonagriculture categories). Decay and loss terms considered in the 

model included soil permeability, mean annual temperature, and slope. 

Figure 3.1-5a through Figure 3.1-5c show the atmospheric deposition inputs used within 

the modeling effort. The model was based on TN loads from deposition, but oxidized and total 

Nr yields are also presented to highlight source information within the watershed. The Neuse 

River watershed is the location of major agricultural operations focusing on swine facilities. 

These operations are evident in the high levels of reduced nitrogen found within the south-central 

catchments of the watershed (Figure 3.1-5b). For the current condition 2002 analysis of the 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 71 

Neuse River and Estuary, an estimated 18,340,000 kg of TN was deposited in the 1.3 million-ha 

Neuse River watershed, for an TN average deposition of 14.0 kg N/ha/yr. 

Development of the new SPARROW model used the USGS SAS SPARROW application 

in predictive mode, with bootstrap analyses completed for additional analysis and exploratory 

options (Schwarz et al., 2006). The model used two flow classes based on the work by McMahon 

et al. (2003) and a reservoir decay factor. In model set up, the decay factors were constrained to 

be nonnegative. Tables 3.1-2 and 3.1-3 present the calibrated model parameters and model 

evaluation statistics, respectively. The model used point sources, atmospheric deposition, 

fertilizer application, manure production, and urban land area. There was some lack of 

significance in the parameter estimations for manure production and urban land area, but these 

sources were deemed likely sources based on watershed knowledge and were left in the model. 

The land-to-water delivery factor used in the model was the soil permeability factor. As 

expected, the parameter estimate for this factor was less than one. The three decay terms (i.e., 

two flow classes and reservoir decay) all lacked significance in their parameter estimates but are 

required for the model as it was configured. Future analyses should consider alternate flow 

classes to investigate ways to improve statistical significance of these estimates. Overall, the 

model evaluation criteria reveal a strong significance in the estimated model, with high 

prediction values (i.e., R-squared values close to 1), little multicollinearity (i.e., Eigen value 

spread less than 100), and normally distributed weighted model residuals (i.e., probability plot 

correlation coefficient close to 1). 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-5a. Atmospheric deposition yields of oxidized nitrogen over the Neuse River 

and Neuse River Estuary watershed.

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 72 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-5b. Atmospheric deposition yields of reduced nitrogen over the Neuse River 

and Neuse River Estuary watershed.

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 73 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-5c. Atmospheric deposition yields of total nitrogen over the Neuse River and 

Neuse River Estuary watershed. 

Table 3.1-2. Model Parameters for 2002 Current Condition SPARROW Application for the 

Neuse River Watershed 

Source Parameters 

Parameter 

Estimate 

Standard 

Error t-statistic p-value VIF 

Point Sources 0.84 0.121 6.95 <0.001 1.4 

Atmospheric Deposition 0.082 0.045 1.80 0.081 13.3 

Fertilizer 0.13 0.038 3.38 0.002 7.1 

Manure 0.017 0.019 0.91 0.368 4.6 

Urban Area 1.6 1.7 0.96 0.345 2.5 

Decay and Loss Parameters 

Soil Permeability −0.52 0.217 −2.41 0.022 4.8 

Reach Decay Group 1 0.17 0.266 0.64 0.528 2.3 

Reach Decay Group 2 0.029 0.058 0.49 0.627 2.3 

Reservoir Decay 3.4 9.6 0.35 0.728 1.3 

Note: VIF = Variance Inflation Factor. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 74 
Aquatic Nutrient Enrichment Case Study 

Table 3.1-3. Model Evaluation Statistics for 2002 Current Condition SPARROW Application 

for the Neuse River Watershed

Evaluation Criteria Value 

Number of Observations 40 

Degrees of Freedom—model error 31 

Degrees of Freedom—coefficients 9 

Sum of the Squared Errors 1.79 

Mean Square Error 0.058 

Root Mean Square Error 0.24 

R Squared 0.99 

Adjusted R Squared 0.98 

Eigen Value Spread 84.2 

Probability Plot Correlation Coefficient 0.984 

As with the Potomac River watershed results, the Neuse SPARROW application modeled 

watershed loads to the upper edges of the estuary. Both the incremental and delivered yields are 

presented in Figure 3.1-6. The TN load estimated to enter the estuary from the Neuse River is 

4,380,000 kg N/yr, equating to a TNs of 1.11 

mg/L. The load from atmospheric deposition 

was estimated to be 1,150,000 kg N/yr, or 26% 

of the total load (Figure 3.1-7). These estimates fall in line with instream monitoring data and 

previous loadings from the Neuse River, estimated at 9.61 million pounds or 4,359,000 kg N/yr 

(Spruill et al., 2004). 

SPARROW modeling for 2002 predicts that 

atmospheric deposition was 26% of the total 

nitrogen loading to the Neuse River’s estuary, 

producing a TNs of 1.1 mg/L

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 75 
Aquatic Nutrient Enrichment Case Study 

Figure 3.1-6. Total nitrogen yields from all sources in the Neuse River watershed as 

predicted by a SPARROW modeling application for the Neuse, Tar-Pamlico, and Cape 

Fear rivers’ watersheds with 2002 data inputs. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 76 
Aquatic Nutrient Enrichment Case Study 

Relative Contributions of Nitrogen Sources to Neuse Estuary

14%

26%

48%

7%

5%

Point Sources

Atmospheric Deposition

Fertilizer

Manure

Urban Areas

Total load to estuary:

4,382,000 kg N/yr

Figure 3.1-7. Source contributions to Neuse River Estuary total nitrogen load. 

3.1.2.2 ASSETS EI Assessment 

Previous work was completed using the ASSETS EI assessment on the Neuse River 

Estuary as part of the NEEA Update (Bricker et al., 2007a). The exact source of this load 

estimate and the exact timeframe of the data used to calculate the ASSETS EI score are still 

unknown at this time, although the data should fall within the period of 2000 to 2002 (S. Bricker, 

personal communication, 2008). That analysis revealed a Highly/Moderately Influenced or High

score for influencing factors (i.e., OHI index) where the nitrogen load was ranked as Moderate to 

High and the ASSETS EI score for the estuary was a Bad overall. 

To develop an updated ASSETS EI score specific to the 2002 current conditions, raw 

data were compiled from several sources, including EPA’s Storage and Retrieval System 

(STORET), the Neuse River Estuary Modeling and Monitoring Project (MODMON), NC DWQ, 

and journal articles. While there were a variety of sources of data, information on macroalgae 

and SAV for the period of interest could not be obtained. Monitoring data for chlorophyll a, 

dissolved oxygen, and nitrogen were obtained from MODMON records. NC DWQ has only 

recently begun to measure SAV within defined areas and on defined intervals, so no data were 

available for the 2002 time period. Monitoring experts within the Neuse River/Neuse River 

Estuary Case Study Area could not identify any sources of macroalgae data (B. Peierls, personal 

communication, 2008). HAB data were gleaned from notations in journal papers (Burkholder et 

al., 2006) and in the reports beginning in 1997 tracked by NC DWQ (NC DWQ, 2008). The 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 77 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 78 

available data were combined to form a 2002 OEC index score (Table 3.1-4). Because both the 

chlorophyll a and HAB data were available and overwhelmingly pointed to a system with both 

High primary and secondary scores, a rating of High is given to the OEC index with confidence 

for 2002. 

The High susceptibility ranking combined with the TN loads estimated by the 

SPARROW assessment rank the OHI index as High as well. The DFO index score set during the 

2007 NEEA Update remains unchanged, with a ranking of Worsen High due to nutrient 

decreases from improved management practices in recent years being offset by increases in 

human populations and factors related to swine production (Burkholder et al., 2006). Combining 

the three index scores together results in an overall ASSETS EI score of Bad for the Neuse 

River/Neuse River Estuary Case Study Area for 2002.
Aquatic Nutrient E

Final Risk and Exposure Assessment 

Appendix 6 - 79 

Table 3.1-4. Current Condition Overall Eutrophic Condition Index Score for the Neuse River/Neuse River Estua

Year Parameter Zone Value Concentration 

Spatial 

Coverage Frequency Expression Score 

2002 CHLA MX 35 HIGH HIGH PERSISTENT HIGH 

0.9945 

2002 CHLA TF 9.0 MEDIUM MODERATE PERIODIC MODERATE 2002 Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA 2002 DO MX 1.6 HYPOXIA MODERATE PERIODIC MODERATE 

0.5 

2002 DO TF 2.7 BIO STRESS HIGH PERIODIC MODERATE 2002 SAV ALL NA NA NA NA UNKNOWN NA 2002 HAB ALL NA NA NA NA HIGH 1 CHLA: Chlorophyll aDO: Dissolved Oxygen SAV: Submerged Aquatic Vegetation HAB: Harmful/Toxic Algal Blooms MX: Mixing Zone TF: Tidal Fresh Zone ALL: All Estuary Zones NA: Not Applicable 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 80 

3.2 ALTERNATIVE EFFECTS LEVELS 

Alternative effects levels were assessed for both the Potomac River/Potomac Estuary and 

the Neuse River/Neuse River Estuary case study areas (separately) by applying percentage 

decreases to the oxidized nitrogen loads in the estimated atmospheric deposition. Model 

estimates then relied on the SPARROW models used (for the Potomac River/Potomac Estuary 

Case Study Area) or developed (for the Neuse River/Neuse River Estuary Case Study Area) for 

the 2002 current condition analysis to determine how the changing atmospheric inputs (i.e., TN 

load evaluated with changes in oxidized nitrogen deposition, TNatm) affect the overall TN load to 

the estuary of interest. These results were used to create the response curve relating TNs to 

TNatm, as first described in Section 2.2.3. The second response curve described in Section 2.2.3 

was defined for the alternative effects level analysis using historical data compilations of OEC 

index scores and TNs, while holding the susceptibility portion of the OHI index (at its 2002 

current condition level—in both cases a ranking of High) and the DFO index constant (at a 

ranking of No Change [3]). 

Upon creation of the two response curves, the back-calculation–coded program described 

in Section 2.2.3 (referred to as BackCalculation through the remainder of this document) was 

applied to the curves with the intent of defining the atmospheric loads that are needed to improve 

the ASSETS EI from a score of Bad (1) to Poor (2), Moderate (3), Good (4), or High (5). These 

improvements represent improvements by 1, 2, 3, and 4 categories. The BackCalculation

program was run under Uncertainty Scenario B (Figure 2.2-12b) for both case studies. 

The following sections describe the data used to create the two response curves and the 

application of the BackCalculation program for each estuary. 

3.2.1 Potomac River Watershed 

Beginning with the data and model used for the current condition analysis, the 

atmospheric deposition inputs derived from national coverage of CMAQ and NADP data were 

altered to create various alternative effects levels by decreasing the oxidized nitrogen loads by 

rates of 5%, 10%, 20%, 30%, and 40% from their original 2002 levels. A zero percent decrease 

corresponds to the 2002 current condition analysis (Table 3.2-1). The remaining inputs to the 

SPARROW model remained the same, and the model was rerun for each of these alternative 
Aquatic Nutrient Enrichment Case Study 

effects level scenarios. The TN load to the estuary calculated from the model was then converted 

to TNs using the annual average flow of the Potomac River. Plotting these concentrations against 

the new TNatm incorporating the oxidized nitrogen decreases leads to the development of the 

desired response curve and relationship (Figure 3.2-1). 

Table 3.2-1. Potomac River Watershed Alternative Effects Levels 

Percent 

Decrease in 

Oxidized 

Nitrogen 

Total Nitrogen 

Atmospheric 

Deposition Load, 

TNatm (kg/yr) 

Decrease in Oxidized 

Nitrogen (NOx) 

Atmospheric Deposition 

Load (kg/yr) 

Instream 

Total 

Nitrogen 

Load (kg/yr) 

Instream Total 

Nitrogen 

Concentration, 

TNs (mg/L) 

0 40,770,000 0 36,660,000 3.41 

5 39,450,000 1,320,000 36,420,000 3.39 

10 38,130,000 2,640,000 36,180,000 3.37 

20 35,480,000 5,290,000 35,700,000 3.32 

30 32,840,000 7,930,000 35,220,000 3.28 

40 30,200,000 10,580,000 34,740,000 3.23 

y = 1.69E-08x + 2.72

R2

 = 1

3.00

3.05

3.10

3.15

3.20

3.25

3.30

3.35

3.40

3.45

3.50

0

5,000,000

10,000,00015,000,00020,000,00025,000,00030,000,00035,000,00040,000,00045,000,000

Total Nitrogen Atmospheric Deposition Load (kg/yr)

Instream Total Nitrogen Concentration 

(mg/L)

Figure 3.2-1. Response curve relating instream total nitrogen concentration to total 

nitrogen atmospheric deposition load for the Potomac River watershed. 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 81 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 82 

Table 3.2-2 details the historical modeling data used to determine TN loads to the 

Potomac Estuary, which are then combined with annual average flow values to calculate a final 

TNs. These instream concentrations were then combined with the OEC index scores, which were 

also determined from historical data (Table 3.2-3), to create the data points needed to create the 

logistic response curve in the BackCalculation program. The years chosen for this analysis relate 

to those years in which a modeled TN load to the estuary could be estimated. OEC data were 

then gathered for those years to find the corresponding effects. 

Table 3.2-2. Historical Potomac River Total Nitrogen Loads and Concentrations 

Year 

Nitrogen Load to 

Estuary (kg/yr) Source Flow (m3/s) Source 

Concentration 

(mg/L) 

1985 32,110,000 CBM Phase 4.3 332 

USGS gage 

records 3.07 

1992 49,750,000 

CB V2 

SPARROW 340 

CB V2 

SPARROW 

Input Data 4.63 

1997 39,380,000 

CB V3 

SPARROW 340 

CB V3 

SPARROW 

Input Data 3.67 

2002 31,160,000 Model Run 196 

USGS gage 

records 5.04 

2005 23,790,000 CBM Phase 4.3 307 

USGS gage 

records 2.46 

CBM Phase 4.3: Chesapeake Bay Model Phase 4.3 reported results 

CB V2 SPARROW: Chesapeake Bay SPARROW Version 2 model results (Brakebill et al., 2001) 

CB V3 SPARROW: Chesapeake Bay SPARROW Version 3 model results (Brakebill and Preston, 2004) 

Model run: Current condition analysis results as determined using modeling efforts based on CB V3 

SPARROW 
Aquatic Nutrient E

Table 3.2-3. Additional Potomac Estuary Overall Eutrophic Condition Index Scores for Alternative Effects Leve

Year Parameter Zone Value Concentration Spatial Coverage Frequency Expression Score 

S

1985 

CHLA MX 26 HIGH MODERATE PERIODIC HIGH 

1 

CHLA TF 32 HIGH HIGH PERIODIC HIGH Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA DO MX 1.6 HYPOXIA MODERATE PERIODIC MODERATE 

0.86 

DO TF 5.6 NO PROBLEM HIGH PERSISTENT NO PROBLEM SAV MX NA GAIN NA NA NO PROBLEM 

0 

SAV TF NA GAIN NA NA NO PROBLEM HAB ALL NA NA NA NA 

LOW/ 

UNKNOWN 0.25 

1987 

CHLA MX 26 HIGH 

MODERATE/ 

HIGH PERIODIC HIGH 1 

CHLA TF 20 HIGH MODERATE PERIODIC HIGH Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA DO MX 2.2 BIO STRESS HIGH PERIODIC MODERATE 

0.86 

DO TF 6.3 NO PROBLEM HIGH PERSISTENT NO PROBLEM SAV MX NA GAIN NA NA NO PROBLEM 

0.04 

SAV TF NA LOSS NA NA VERY LOW HAB ALL NA NA NA NA 

LOW/ 

UNKNOWN 0.25 

1992 

CHLA MX 12 MEDIUM HIGH PERSISTENT HIGH 

0.93 

CHLA TF 14 MEDIUM MODERATE 

PERIODIC/ 

PERSISTENT MODERATE 

Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA DO MX 2.6 BIO STRESS HIGH PERIODIC MODERATE 

0.43 

DO TF 6.3 NO PROBLEM HIGH PERSISTENT NO PROBLEM SAV MX NA GAIN NA NA NO PROBLEM 

0.07 

SAV TF NA LOSS NA NA MODERATE 

Final Risk and Exposure Assessment 

Appendix 6 - 83 
Aquatic Nutrient E

Final Risk and Exposure Assessment 

Appendix 6 - 84 

Year Parameter Zone Value Concentration Spatial Coverage Frequency Expression Score 

S

HAB ALL NA NA NA NA 

LOW/ 

UNKNOWN 0.25 

1997 

CHLA MX 28 HIGH HIGH 

PERIODIC/ 

PERSISTENT HIGH 

1 

CHLA TF 34 HIGH HIGH 

PERIODIC/ 

PERSISTENT HIGH 

Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA DO MX 3.5 BIO STRESS HIGH PERIODIC MODERATE 

0.43 

DO TF 6.6 NO PROBLEM HIGH PERSISTENT NO PROBLEM SAV MX NA GAIN NA NA NO PROBLEM 

0.04 

SAV TF NA LOSS NA NA LOW HAB ALL NA NA NA NA 

MODERATE/ 

UNKNOWN 0.5 

2005 

CHLA MX 21 HIGH MODERATE PERIODIC HIGH 

1 

CHLA TF 15 MEDIUM HIGH PERSISTENT HIGH Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA DO MX 2.2 BIO STRESS HIGH PERIODIC MODERATE 

0.43 

DO TF 5.7 NO PROBLEM HIGH PERSISTENT NO PROBLEM SAV MX NA GAIN NA NA NO PROBLEM 

0 

SAV TF NA GAIN NA NA NO PROBLEM HAB ALL NA NA NA NA HIGH 1 

CHLA: Chlorophyll a

DO: Dissolved Oxygen SAV: Submerged Aquatic Vegetation HAB: Harmful/Toxic Algal Blooms MX: Mixing Zone TF: Tidal Fresh Zone ALL: All Estuary Zones NA: Not Applicable 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 85 

 

The 2002 Potomac Estuary TNatm (evaluated in terms of decrease in oxidized nitrogen) 

loading is estimated to be 4.08 × 107

 kg/yr (Table 3.2-1). The response curve relationship 

between atmospheric deposition and TNs (TNs [mg/L] = 2.72 + 1.69 × 10-8 ×TNatm [kg/yr]) can 

be found in Figure 3.2-1. Outside data specified for the model include the following: 

 Mean salinity in estuary = 11 (relative units) 

 Mean salinity offshore = 33 (relative units) 

 Mean offshore TN concentration = 0.028 mg/L. 

There were five sets of observed OEC and TNs data points used to fit the OEC(TNs) 

logistic model (not including ecological endpoints) from the data presented in Table 3.2-2 and 

Table 3.2-3. 

For the purpose of estimating the OEC(TNs) function at each iteration, the range on the 

OEC minimum value was specified as 0 to 0 (i.e., fixed at 0). In addition, the OEC maximum 

value was fixed at 5. Notwithstanding the capability to vary OEC minimum and OEC maximum 

from iteration to iteration, the decision was made to not do so. Varying OEC minimum and OEC 

maximum would ideally be performed to reflect natural limitations on an estuary. For instance, 

in its most pristine state, it may not fully attain OEC = 5 or, in its most degraded state, it may not 

fully attain OEC = 1. However, because of the difficulty in knowing these natural limitations, the 

option to vary these levels was not implemented, but rather left OEC minimum = 0 and OEC 

maximum = 5. The range on the TNs minimum value was specified from 0.028 mg/L (i.e., the 

offshore value) to 1.0 mg/L (i.e., a value determined by best professional judgment at this time 

due to lack of supporting data). The TNs maximum value range was specified from 4.63 mg/L 

(maximum observed above) to 1.5×4.63 = 6.95 mg/L. 

For the purpose of illustrating the overall back calculation uncertainty analysis 

methodology, each of the four ASSETS EI scores constituting state improvements (i.e., Poor-2, 

Moderate-3, Good-4, High-5) was treated as a “target” ASSETS EI score, and 5002 iterations 

were run under each target EI scenario. As previously discussed, at each iteration, the 

BackCalculation program first fits the four-parameter logistic function to the observed data, 

including the randomly sampled ecological endpoints, using a weighted least squares criterion 

2 A convergence test was performed to see how stable the various quantiles of the resulting NOx

*

i distribution were 

to 500 iterations. There was still some variability, suggesting that future analyses should use more than 500 

iterations (particularly if SPARROW model uncertainty is included). 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 86 

(Figure 3.2-2). (In this application, only the TN ecological endpoints were varied.) After the 

nonlinear, least squares optimization is performed (see Figure 2.2-12b for an example), the 

program then iteratively finds that TNs concentration, TNs

*

i, that satisfies the target ASSETS EI 

score. Once TNs

*

i is found, the SPARROW response model is then evaluated to find the TN 

atmospheric deposition load, TNatm*

i, which results in TNs

*

i. That TNatm*i is saved, and the 

process is repeated 500 times. The percentage decrease in NOx deposition load to reach TNatm*

i is 

calculated as 100 × (TNatm current load - TNatm*

i)/ TNatm current load, and it is also saved. The 

result is a distribution of 500 TNatm*

i and percentage decrease values, each of which is equally 

likely to result in the target EI (because the OEC(TNs) function is assumed to be uniformly 

distributed within its uncertain envelope). These 500 values for each result were then rankordered (i.e., ascending order) to find statistics of interest (e.g., the mean, median, 5th percentile, 

and 95th percentile). 

It should be carefully noted that the TNatm*

i value is allowed to be determined at each 

iteration without regard to what the current TNatm is. Thus, if a TNatm*

i value is greater than the 

current TNatm (4.08 × 108

 kg/yr), then the implication is that more TNatm would be required (i.e., 

added to the system) to attain the target ASSETS EI score. Obviously, in this situation, the 

estuary is currently at a better/higher ASSETS EI score than the target ASSETS EI score, and the 

target score represents a more polluted scenario. The TNatm*

i value is not prevented from 

becoming negative. As can be seen from the general form of the linear, SPARROW response 

model, TNs = a + b × TNatm, a TNs

*

i value less than parameter “a” would require more than a 

decrease in TNatm to zero. It would actually require a “negative” TNatm load. Clearly, the lower 

bound on TNatm load is zero, so the negative TNatm*

i value would represent additional nitrogen 

(beyond decreasing the TNatm [i.e., total atmospheric nitrogen] deposition load, including NOx to 

zero) in the system that must be removed to achieve the target ASSETS EI score. (The actual 

value of the negative load would be valid only if the additional nitrogen removed had the same 

characteristics [e.g., spatial distribution, speciation, sources/sinks] as the TNatm. This is unlikely; 

nonetheless, it is of interest to report the values with this caveat.) 
Aquatic Nutrient Enrichment Case Study 

0

1

2

3

4

5

0.0 1.0 2.0 3.0 4.0 5.0 6.0

TNs (mg/L)

OEC Score

Calibration Points

Fit Logistic Curve

Figure 3.2-2. Fitted Overall Eutrophic Condition curve for target ASSETS EI=2, 

median TNatm*

i (i = run 280)

The summary statistics of the 500 iterations for each target ASSETS EI scenario for the 

Potomac Estuary are presented in Table 3.2-4. 

Table 3.2-4. Summary Statistics for Target ASSETS EI Scenarios for the Potomac Estuary 

Statistic TNatm*

i (kg N/yr) % TNatm*i Decrease 

ASSETS EI = 2 (Poor) 

Mean -1.78 × 106 104 

Median -1.46 × 106 104 

5th Percentile -3.67 × 106 109 

95th Percentile 9.02 × 106 78 

ASSETS EI = 3 (Moderate) 

No feasible solutions found 

ASSETS EI=4 (Good) and ASSETS EI = 5 (High) 

All TNatm*

i = -1.61 × 108

, i.e., TNs

*

i= 0 mg/L 

Target ASSETS EI = 2 is the most interesting scenario and illustrates the power of the 

uncertainty analysis. The mean and median TNatm*

i values are negative, meaning again that not 

only must all TNatm (including all NOx) be removed, but additional nitrogen as well. However, 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 87 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 88 

there is a slim chance that ASSETS EI = 2 can be attained only from TNatm decrease, as indicated 

by the positive 95th percentile TNatm*

i value of 9.02 × 106

 kg N/yr (representing a 78% 

decrease). 

Target ASSETS EI = 3 is a unique case because all solutions were infeasible. With a 

TNs

*

i value of 0 mg/L, the other (i.e., fixed) components of the ASSETS EI scoring methodology 

(i.e., DFO index and Susceptibility Score) preclude satisfying any of the 95 combinations of 

DFO, OEC, and OHI indices that comprise the ASSETS EI=3 combinations in the ASSETS EI 

lookup table. (At TNs

*

i = 0, an ASSETS EI = 4 can be achieved, but not ASSETS EI = 3, 

according to the 95 score combinations defined by Bricker et al., (2003) for ASSETS EI scores.) 

Bricker et al. (2003) acknowledge that “not all combinations” were included in the lookup table 

within their paper, so this scenario falls into that gap. Each of the intermediate scores (i.e., OEC, 

OHI, and DFO index scores) can take on integer values of 1 to 5. Thus, there are 5x5x5=125 

different possible combinations, yet the lookup tables presented by Bricker et al. (2003) include 

only 95 combinations, so there are 30 “missing” combinations. (These are evenly distributed 

among the 5 possible DFO index scores. Each DFO index score has associated with it 19 

combinations of OEC and OHI index scores.) It is possible that these combinations were not seen 

as likely combinations in nature by the experts that defined the scoring matrix (e.g., a TNs

*

i = 0 

means there is no nitrogen coming in through the surface water and, hence, it is not feasible that 

a system would score below Good in the ASSETS EI score). Future assessments will explore the 

“missing” combinations further with the ASSETS EI experts. 

Target ASSETS EI = 4 and 5 had identical results. All 500 iterations returned a TNs

*

i = 0, 

and a corresponding TNatm*

i negative load equal to TNatm*i = (0 – 2.72)/1.69 × 10-8 = -1.61 × 10-8 

kg/yr. Clearly, target ASSETS EIs equaling 4 and 5 are very much unattainable when decreasing 

the TNatm is the only policy option. To reach the target ASSETS EI, all atmospheric nitrogen 

(i.e., TNatm) must be removed plus an additional amount (represented by the negative resultant 

load corresponding to TNs

*

i = 0) that is approximately equal to one order of magnitude greater 

than the original atmospheric deposition load. These amounts could be compared to the other 

nitrogen sources in the watershed (e.g., fertilizer and manure application or point sources) that 

were used as inputs to the SPARROW model to determine the relative nature of the required 

removal with other sources in the watershed. However, consideration must be given that this load 

is a reflection of the characteristics of the source in the SPARROW model (e.g., spatial 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 89 

distribution, magnitude of loads, sources/sinks), and a decrease required in atmospheric load is 

not equal to a decrease in another source. Relative proportions can be examined by comparing 

the source characteristics and model parameters. 

The SPARROW response curve can also be used to examine the role of atmospheric 

nitrogen deposition in achieving specified decreases in TN estuarine load. For example, the 

SPARROW modeling results predict that the 41 × 106

 kg N/yr deposited (i.e., atmospheric 

deposition input) over the Potomac River watershed in 2002 results in a loading of 7,380,000 kg 

N/yr, or 20% of the annual TN load, to the Potomac Estuary. If a 30% decrease in annual TN 

load to the estuary (i.e., a decrease of 11 × 106

 kg N/yr) were desired, a decrease of 61 × 106 kg 

N/yr in nitrogen inputs to the watershed would be required according to the SPARROW response 

curve based on atmospheric deposition. This represents a 100% decrease in the atmospheric 

deposition inputs (i.e., 41 × 106

 kg N/yr) plus an additional 20 × 106 kg N/yr removal of nitrogen 

from other sources in the Potomac River watershed (i.e., point and nonpoint sources). Note that 

this value of 20 × 106

 kg N/yr is an approximate value when applied to the other sources because 

they differ in characteristics (e.g. spatial distribution and magnitude) from atmospheric 

deposition which was used to estimate the loading. 

3.2.2 Neuse River Watershed 

The same methods for creating alternative effects levels were applied to the data from the 

Neuse River/Neuse River Estuary Case Study Area as to data from the Potomac River/Potomac 

Estuary Case Study Area. The oxidized nitrogen atmospheric deposition loads were decreased by 

rates of 5%, 10%, 20%, 30%, and 40% from their original 2002 levels. A zero percent decrease 

corresponds to the 2002 current condition analysis (Table 3.2-5). With the remaining inputs to 

the SPARROW model kept the same, the SAS-developed model was rerun for each of these 

alternative effects level scenarios. The TN load to the estuary calculated from the model was 

then converted to TNs using the annual average flow of the Neuse River. Plotting these 

concentrations against the new TNatm and incorporating the oxidized nitrogen decreases leads to 

the development of the desired response curve and relationship (Figure 3.2-3). Note that the 

instream concentration range presented in this figure is discussed at the end of this section. 
Aquatic Nutrient Enrichment Case Study 

Table 3.2-5. Neuse River/Neuse River Estuary Case Study Area Alternative Effects Levels 

Percent 

Decrease 

in 

Oxidized 

Nitrogen 

Total Nitrogen 

Atmospheric 

Deposition Load, 

TNatm (kg/yr) 

Decrease in Oxidized 

Nitrogen Atmospheric 

Deposition Load 

(kg/yr) 

Instream Total 

Nitrogen Load 

(kg/yr) 

Instream Total 

Nitrogen 

Concentration, 

TNs (mg/L) 

0 18,340,000 0 4,382,000 1.112 

5 17,920,000 410,000 4,378,000 1.111 

10 17,510,000 830,000 4,374,000 1.110 

20 16,680,000 1,660,000 4,366,000 1.108 

30 15,850,000 2,490,000 4,358,000 1.106 

40 15,020,000 3,320,000 4,351,000 1.104 

y = 2.0E-09x + 1.07

R2

 = 1

1.100

1.105

1.110

1.115

-

2,000,0004,000,0006,000,0008,000,000

10,000,00012,000,00014,000,00016,000,00018,000,00020,000,000

Total Nitrogen Atmospheric Deposition Load (kg/yr)

Instream Total Nitrogen Concentration 

(mg/L)

Figure 3.2-3. Response curve relating instream total nitrogen concentration to total 

nitrogen atmospheric deposition load for the Neuse River/Neuse River Estuary Case 

Study Area. 

Table 3.2-6 details the historical monitoring data used to determine TNs at the 

downstream end of the Neuse River where the SPARROW model was used to determine current 

condition and alternative effects levels nitrogen loads. The monitoring data were derived from 

data downloaded from EPA’s STORET Web site for monitoring location J8290000 from NC 

DWQ. These instream concentrations were then combined with the OEC index scores, which 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 90 
Aquatic Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 6 - 91 

were also determined from historical data (Table 3.2-7), to create the data points needed to 

create the logistic response curve in the BackCalculation program. Data from as many years as 

possible were gathered for both the TNs and OEC index scores. However, because of the limited 

amount of complete data from the various sources identified under the current condition analysis, 

only three corresponding years of data were found. These years are highlighted in Table 3.2-6. 

Table 3.2-6. Annual Average Instream Total Nitrogen Concentrations in the Neuse River 

Year Annual Average TNs (mg/L) 

1997 1.08 

1998 0.93 

1999 0.99 

2000 1.10 

2001 0.96 

2002 0.91 

2003 1.02 

2004 1.09 

2005 1.12 

2006 1.07 

2007 0.97 

NC DWQ Station J8290000; Results from EPA's 

STORET Summation of Total Kjeldahl Nitrogen and 

Nitrate/Nitrite 
Aquatic Nutrient E

Table 3.2-7. Additional Neuse River Estuary Overall Eutrophic Condition Index Scores for Alternative Effects L

Year Parameter Zone Value Concentration 

Spatial 

Coverage Frequency Expression Score 

P

Se

1992 

CHLA MX 28 HIGH VERY LOW PERIODIC MODERATE 0.5 

Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA DO MX 6.1 NO PROBLEM HIGH PERSISTENT NO PROBLEM 0.25 SAV ALL NA NA NA NA UNKNOWN NA HAB ALL NA NA NA NA HIGH 1 

1993 

CHLA MX 30 HIGH HIGH PERIODIC HIGH 0.99 Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN DO MX 3.4 BIO STRESS HIGH PERIODIC MODERATE 0.5 SAV ALL NA NA NA NA UNKNOWN NA HAB ALL NA NA NA NA HIGH 1 

2003 

CHLA MX 46 HIGH HIGH PERSISTENT HIGH 

1 

CHLA TF 7.2 MEDIUM HIGH PERIODIC HIGH Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA DO MX 2.0 HYPOXIA HIGH PERIODIC HIGH 

0.99 

DO TF 4.5 BIO STRESS HIGH PERIODIC MODERATE SAV ALL NA NA NA NA UNKNOWN NA HAB ALL NA NA NA NA MODERATE 0.5 

2007 

CHLA MX 52 HIGH HIGH PERSISTENT HIGH 

1 

CHLA TF 27 HIGH MODERATE PERIODIC HIGH Macroalgae ALL NA UNKNOWN UNKNOWN UNKNOWN UNKNOWN NA DO MX 2.6 BIO STRESS HIGH PERIODIC MODERATE 

0.5 

DO TF 3.1 BIO STRESS HIGH PERIODIC MODERATE 

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Aquatic Nutrient E

Final Risk and Exposure Assessment 

Appendix 6 - 93 

Year Parameter Zone Value Concentration 

Spatial 

Coverage Frequency Expression Score 

P

Se

SAV ALL NA NA NA NA UNKNOWN NA HAB ALL NA NA NA NA 

LOW/ 

MODERATE 0.5 

CHLA: Chlorophyll aDO: Dissolved Oxygen SAV: Submerged Aquatic Vegetation HAB: Harmful/Toxic Algal Blooms MX: Mixing Zone TF: Tidal Fresh Zone ALL: All Estuary Zones NA: Not Applicable 
Aquatic Nutrient Enrichment Case Study 

The current estuary TNatm (evaluated in terms of decrease in oxidized nitrogen) loading is 

estimated to be 1.83 × 107

 kg/yr (Table 3.2-5). The response curve relationship between TNatm

and TNs (TNs [mg/L] = 1.07 + 2.0 × 10-8 ×TNatm [kg/yr]) can be found in Figure 3.2-3. Outside 

data specified for the model include the following: 

 Mean salinity in estuary = 13 (relative units) 

 Mean salinity offshore = 35 (relative units) 

 Mean offshore TN concentration = 0.014 mg/L. 

There were three sets of observed OEC and TNs data points used to fit the OEC(TNs) 

logistic model (not including ecological endpoints) from the data presented in Table 3.2-6 and 

Table 3.2-7. 

For purposes of estimating the OEC(TNs) function at each iteration, the range on the 

OEC minimum value was specified as 0 to 0 (i.e., fixed at 0). In addition, the OEC maximum 

value was fixed at 5. Notwithstanding the capability to vary OEC minimum and OEC maximum 

from iteration to iteration, the decision was made to not do so. The range on the TNs minimum 

value was specified from 0.014 mg/L (i.e., the offshore value) to 0.1 mg/L (i.e., a value 

determined by best professional judgment at this time due to lack of supporting data). The TNs

maximum value range was specified from 2.57 mg/L (i.e., maximum observed value from 

STORET) to 1.5×2.57 = 3.86 mg/L. 

As for the Potomac River/Potomac Estuary Case Study Area, each of the four ASSETS 

EI scores representing state improvements (i.e., Poor-2, Moderate-3, Good-4, High-5) was 

treated as a “target” ASSETS EI score, and 500 iterations were run under each target ASSETS EI 

scenario. Figure 3.2-4 shows one of the curve fits of the logistic function to the observed OEC 

and TNs data, including the randomly sampled ecological endpoints for TNs. 

The summary statistics of the 500 iterations for each target ASSETS EI scenario are 

presented in Table 3.2-8. 

For target ASSETS EI = 2, all decreases are positive, but exceed 100%, meaning that not 

only must all TNatm be removed to meet ASSETS EI = 2, but considerably more nitrogen from 

other sources must be removed as well. Given these results, the Neuse River Estuary is clearly 

currently somewhere between these two ASSETS EI scores as was the Potomac Estuary. There 

is some evidence that it is slightly more eutrophic than the Potomac Estuary because there was at 

Final Risk and Exposure Assessment September 2009 

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Aquatic Nutrient Enrichment Case Study 

least a slim chance for the Potomac Estuary (at the 95th percentile) that a decrease in TNatm (of 

less than 100%) would achieve ASSETS EI = 2. 

0

1

2

3

4

5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

TNs (mg/L)

OEC Score

Calibration Points

Fit Logistic Curve

Figure 3.2-4. Fitted Overall Eutrophic Condition curve for target ASSETS EI=2, median 

TNatm*

i (i = run 287).

Table 3.2-8. Summary Statistics for Target ASSETS EI Scenarios for the 

Neuse River/Neuse River Estuary Case Study Area

Statistic TNatm*

i (kg N/yr) % TNatm*i Decrease 

ASSETS EI = 2 (Poor) 

Mean -1.43 × 108 880 

Median -1.43 × 108 880 

5th Percentile -1.47 × 108 901 

95th Percentile -1.01 × 108 653 

ASSETS EI = 3 (Moderate) 

No feasible solutions found 

ASSETS EI=4 (Good) and ASSETS EI = 5 (High) 

All TNatm*

i = -5.35 × 108

, i.e. TNs

*

i = 0 mg/L 

Target ASSETS EI = 3 is again a unique case because all solutions were infeasible. With 

a TNs

*

i value of 0 mg/L, the other (i.e., fixed) components of the ASSETS EI scoring 

methodology (i.e., DFO index and Susceptibility Scores) preclude satisfying any of the 95 

combinations of DFO, OEC, and OHI index scores that comprise the ASSETS EI=3 

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Appendix 6 - 95 
Aquatic Nutrient Enrichment Case Study 

combinations in the ASSETS EI lookup table. This result again depends on the experts who set 

up the ASSETS EI scoring table, defining only 95 out of 125 possible combinations. The 

likelihood that any of the 30 “missing” combinations are feasible in nature and could result in 

reaching Target ASSETS EI = 3 for this scenario will be examined in future analyses. 

Target ASSETS EI = 4 and 5 had identical results. All 500 iterations returned a TNs

*

i = 0 

mg/L, and a corresponding TNatm*

i negative load equal to TNatm*i = (0 – 1.07)/2.0 × 10-9= -5.35 × 

108

 kg/yr. Clearly, target ASSETS EI equal 4 and 5 are very unattainable when decreasing the 

TNatm (including all NOx) is the only policy option. Again, the decrease required includes all of 

the TNatm source plus an additional amount that is one order of magnitude greater than the 

original atmospheric deposition load of 108

 kg/yr). These amounts could be compared to the 

other nitrogen sources in the watershed that were used as inputs to the SPARROW model, giving 

consideration to the characteristics of each of these sources. 

As with the Potomac River and Potomac Estuary watershed analysis, the SPARROW 

response curve can be used to examine the role of nitrogen deposition in achieving desired 

decreases in load to the Neuse River Estuary. In the Neuse River watershed, modeling results 

indicate that 7 × 106

 kg N/yr was deposited in 2002. SPARROW modeling predicts that this 

deposition input results in a loading of 1.2 × 106

 kg N/yr (i.e., 20% of the annual TN load) to the 

Neuse River Estuary. Unlike the Potomac River and Potomac Estuary analysis, little change is 

seen in the TN loading to the Neuse River Estuary with large decreases in the nitrogen 

deposition. If all atmospheric nitrogen deposition inputs were eliminated (i.e., 100% decrease), 

the total annual nitrogen load to the Neuse River Estuary would only decrease by 4%. There are 

two apparent reasons for this lack of change in loadings. The first reason is a characteristic of the 

Neuse River watershed. The second reason is an inherent characteristic of the SPARROW model 

formulation. 

First, the TN loadings to the Neuse River Estuary are highly dependent on the sources 

other than atmospheric deposition within the SPARROW model. There are differences in 

characteristics among the sources within the watershed, where fertilizer, in particular, has a 

strong signature (i.e., indicating the large influence of agriculture within the watershed). This 

result demonstrates that the SPARROW response curves of TN load to other sources would be 

quite different, and the current response curve cannot be used to predict the relative magnitudes 

of loads needed to produce decreases greater than this 4%. Figure 3.2-5 illustrates the theoretical 

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Aquatic Nutrient Enrichment Case Study 

response curves that may result when the SPARROW modeled loads are plotted against the other 

TN source inputs. The green curve, or least influential source, displays the behavior of the 

atmospheric deposition for the Neuse River Estuary. The red curve, or highly influential source, 

likely corresponds to how agricultural sources within the watershed behave. These response 

curves will depend on the source magnitudes, spatial distributions, and other characteristics. Instream TN Concentration (mg/L) or 

Source Input Load (kg N/ yr) 

TN Load to Estuary (kg N/yr)

Current conditions concentration or loading 

Least influential source where a large

change in input loading produces a 

small change in load to the estuary 

Moderately influential source where a 

large change in input loading produces 

a large change in load to the estuary 

Highly influential source where a small

change in input loading produces a 

large change in load to the estuary 

Figure 3.2-5. Theoretical SPARROW response curves demonstrating relative influence 

of sources on nitrogen loads to an estuary.

Second, the decay rates used by SPARROW play a part in the differing percentage 

decreases seen. The changes in the loadings reaching the estuary in the alternate effects analyses 

also occur because of the instream and reservoir decay terms. Within SPARROW, the decay 

term is applied to the total runoff load from each catchment (i.e., the sum of the source loads) in 

the upstream to downstream cumulative load calculations. When the atmospheric deposition 

loads are decreased, the TOTAL load from a catchment is decreased, even though the other 

sources remain constant. The same rate of decay is applied no matter the magnitude of the source 

loads is because the decay rate is based on stream travel characteristics or reservoir 

characteristics. So when the total load decreases, the amount of decay decreases, leading to a 

proportionally higher delivered load. Thus, the same percentage decrease in atmospheric 

deposition and fertilizer loads would not produce the same decrease in loads to the estuary. 

Final Risk and Exposure Assessment September 2009 

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Aquatic Nutrient Enrichment Case Study 

4.0 IMPLICATIONS FOR OTHER SYSTEMS 

Selection of the analysis method for aquatic nutrient enrichment considered applications 

beyond a small number of case studies. The chosen method, consisting of a combination of 

SPARROW modeling for nitrogen loads and assessment of estuary conditions under the NOAA 

ASSETS EI, provides a highly scalable and widely applicable analysis method. Both components 

have been applied on a national scale—the national nutrient assessment using SPARROW 

(Smith and Alexander, 2000) and the NEEA using the ASSETS EI (Bricker et al., 1999, 2007a). 

Additionally, both have been used on a smaller scale. These previous analyses supply a large 

body of work—data, methods, and supporting experts—to draw from when conducting 

additional analyses or updating past applications. 

Requirements for applying this method to other systems include mandatory data inputs, 

the ability to formulate a SPARROW application on a reliable stream network, and an estuary 

that is likely to be subject to eutrophication. Data requirements and model formulations have 

been described and detailed throughout this report. 

The method is not currently designed to assess eutrophication impacts on inland waters. 

SPARROW modeling can still be applied to determine nitrogen loadings to an inland waterway, 

but the ASSETS EI assessment would not apply, and as such, the indicators and overall 

likelihood of eutrophication could not be assessed. For these inland waters, an alternate 

methodology would be necessary to examine the effects of changing nitrogen loads within the 

waterbody. A variety of methods could possibly be applied, including empirical relationships or 

dynamic modeling. It is beyond the scope of this case study to further assess these inland waters 

beyond the sensitive areas analysis in Section 1.2.1. An additional case study in this project 

examines the effects of aquatic acidification on inland waters using dynamic modeling (See 

Appendix 4). 

The scalability of the methods and approaches taken in these case studies will rely on the 

ability to group estuaries across the country into patterns of similar behavior either in terms of 

nitrogen sources or eutrophication effects. In 2003 and 2004, NOAA and the Kansas Geological 

Survey conducted a series of workshops to develop a type classification system for the 138 

estuarine systems assessed in the original NEEA (Bricker et al., 1999). Participants considered 

70 classification variables for grouping the estuarine systems. These variables included 51 

physical characteristics (e.g., estuary depth and volume, tidal range, salinity, nitrogen and 

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Aquatic Nutrient Enrichment Case Study 

phosphorus concentrations, estimates of flushing time, evaporation), 10 perturbation 

characteristics (e.g., population in watershed, estimates of nutrient loading), and nine response 

characteristics (e.g., SAV loss, presence of nuisance and toxic blooms). Ultimately, the 

workgroup selected five variables (i.e., depth, openness of estuary mouth, tidal range, mean 

annual air temperature, and the log of freshwater inflow/estuarine area) deemed to be the most 

critical physical and hydrological characteristics influencing nutrient processing and the 

expression of eutrophic symptoms in a waterbody. Based on these five variables, the 138 

estuarine systems were classified into 10 groups (Table 4-1; Figure 4-1). The two estuary 

systems included in this case study, the Potomac and Neuse River estuary systems, were in 

groups one and nine, respectively (Bricker et al., 2007a). 

Table 4-1. Typology Group Categorizations 

Group Number of Systems Overriding Characteristics 

Group 0 13 Low freshwater inflow:estuarine area ratio; low 

depth; low estuary mouth openness 

Group 1 35 Medium depth, medium openness, high annual 

air temperature 

Group 2 5 High depth, low annual air temperature 

Group 3 8 High estuary mouth openness; high depth 

Group 4 18 Low estuary mouth openness; high freshwater 

inflow:estuarine area ratio; low annual air 

temperature 

Group 5 3 High estuary mouth openness; high depth 

Group 6 2 High depth; high estuary mouth openness 

Group 7 16 High tidal range; medium estuary mouth 

openness; low annual air temperature 

Group 8 17 High freshwater inflow:estuarine area ratio; low 

depth 

Group 9 21 Low depth, medium estuary mouth openness; 

high annual air temperature 

Final Risk and Exposure Assessment September 2009 

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Aquatic Nutrient Enrichment Case Study 

Figure 4-1. Preliminary classifications of estuary typology across the nation (modified 

from Bricker et al., 2007a).

Given that the response curve of the OEC index score to TNs is expected to change 

shapes with different values of susceptibility, the typology classes thus defined in Table 4-1

provide an opportunity to assess the validity of this expectation. The first step in assessing this 

statement would be to examine the nutrient loadings in other estuaries that fall within groups 1 or 

9, the groups corresponding to the two case study areas. Once the shape and behavior of the 

response curve for the estuary grouping is confirmed, work can begin to scale the results between 

estuaries of that group. The ASSETS EI score for an estuary may also be considered within this 

analysis. For the 48 systems for which an ASSETS EI score was developed in the 2007 NEEA 

Update, only one system was rated as High (i.e., Connecticut River), whereas five were rated as 

Good (i.e., Biscayne Bay, Pensacola Bay, Blue Hill Bay, Sabine Lake, Boston Harbor). Eighteen 

systems were rated as Moderate, and 24 systems were rated as Poor or Bad. Those estuaries that 

fall within groups 1 or 9 and are rated as Poor or Bad would be the most appropriate candidates 

to start the scaling analysis. 

Scaling of results will also need to account for the response of the watershed to 

atmospheric nitrogen deposition inputs. If SPARROW is used, either through the in-development 

Web-enabled national SPARROW application or through regional or site-specific applications, 

Final Risk and Exposure Assessment September 2009 

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Aquatic Nutrient Enrichment Case Study 

the shape of the response curve will be determined by the model and its parameters. If a different 

approach is taken to develop TN loadings, then the systems will need to be grouped according to 

the shape and behavior of the response curve. Additional consideration should be given to the 

magnitude of the percentage contributions of the atmospheric deposition to the TN load to the 

watershed and the resulting TN load to the estuary. 

5.0 UNCERTAINTY 

There are several areas of uncertainty with this method of assessment for aquatic nutrient 

enrichment, which are summarized below. 

 Data inputs to SPARROW. The compilation of data needed for creation or application of 

SPARROW relies on geographic and temporal analyses. For this study, the data used were 

developed under separate studies and published by the USGS. Because the data were 

independently verified before publication by the USGS, only quality checks were performed on the 

data rather than full validation exercises. For any future analyses that require new compilations of 

data, close attention should be paid to the source and geographic and temporal precision and 

accuracy of the data because SPARROW relies on the distribution of sources across the watershed 

to create model parameters on the annual average basis. 

 Modeling uncertainty in SPARROW estimates. With any measured or modeled results, there is 

a certain amount of uncertainty that should be quantified. Because SPARROW relies on a 

nonlinear regression basis, a number of parameters can be used to assess the uncertainty within the 

model and provide confidence intervals around the estimates. The Version 3 Chesapeake Bay 

SPARROW application met evaluation criteria based on degrees of freedom, model error, and Rsquared values. The calibration of the Neuse River watershed SPARROW model using SAS 

examined the standard deviation, t-statistics, p-values, and VIFs for each estimated parameter. The 

overall model was evaluated based on minimizing model error, maximizing R-squared values, and 

ensuring that the Eigen value range was below 100 while the probability plot correlation 

coefficient was close to one. The model derived for the Neuse River/Neuse River Estuary Case 

Study Area did produce some model parameters (i.e., manure production, urban area, and decay 

terms) that did not reach desired statistical significance levels. The estimation of decay term 

parameters may be rectified by adjusting the flow classes among which the parameters are split. 

This should be examined in any future analyses. The manure production and urban area source 

terms should also be examined as to their distribution throughout the watershed and overall 

contributions to the load. 

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Aquatic Nutrient Enrichment Case Study 

 Sensitivity of SPARROW formulation due to atmospheric inputs in the Potomac 

River/Potomac Estuary Case Study Area. The parameterization of a SPARROW application for 

the Potomac River watershed is expected to change when recalibration is completed using the 

atmospheric deposition of TN based on the combination of CMAQ and NADP data created for this 

study, rather than the interpolated values of wet deposition of nitrate. As discussed in Section 

3.1.1, the spatial gradient as well as the magnitude of the atmospheric deposition of different 

nitrogen species varies across the watershed. While it is certain that the parameter estimated to 

apply to the atmospheric deposition source will change, what is uncertain at this point is the extent 

to which the other model parameters and the overall nitrogen load estimates will be affected by 

using the CMAQ/NADP estimates in the model calibrated against the wet NO3

– deposition values. 

Sensitivity of the model parameters and nitrogen load estimate can be evaluated in future studies 

where SPARROW is recalibrated against the 2002 data. 

 Calibration data for SPARROW estimates. Monitoring data will be used to calibrate the 

SPARROW model. By relying on data from federally recognized data systems, the aim is to use 

data that has undergone quality assurance/quality control (QA/QC) procedures. Additionally, 

collaboration has been completed with the researchers who have conducted the previous 

SPARROW applications in each case study area to provide a rigorous check on the data used.

 Data inputs to the ASSETS EI. Because of the numerous data requirements and sources required 

to conduct a full ASSETS EI analysis, there is a large range of uncertainty that can enter into the 

calculations. For the water quality data evaluation of dissolved oxygen and chlorophyll a, the 

numerical values of the 10th and 90th percentiles used in the evaluation were subjected to QA/QC 

procedures as processed through regulated databases with checks. The frequency of occurrence of 

these indicators and HABs events relied more on subjective judgment of temporal variations of 

concentrations across the year. Best attempts were made to apply standardized evaluation methods 

in order to minimize any uncertainties due to subjectivity or processing differences. 

 Heuristic estimates of DFO index scores. The estimation of the DFO index score in the ASSETS 

EI assessment currently relies on heuristic estimates from systems experts. Future NOAA efforts 

will seek to provide more scientifically structured estimates for this parameter, but at this time, 

reliance must be on expert judgment on whether there will be increased or decreased pressures 

because of nutrient loads, population growth, and land use change.

 Steady-state estimates/mean annual estimates. Both SPARROW and the ASSETS EI methods 

currently provide only longer-term estimates of the system conditions. There is the possibility of 

conducting the analyses on a seasonal basis, which may be appropriate because the trends in 

eutrophication indicators are likely to vary seasonally. Producing annual averages actually 

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Aquatic Nutrient Enrichment Case Study 

introduces some leverage to the uncertainty in the input data as previously discussed. Because the 

ultimate values used to base the analysis on are averages, there is less reliance on the certainty of 

individual measures. 

 Use of a screening method. The methods used in this study are only of the screening level. The 

screening level was more appropriate for a scalable, widely applicable set of case studies than for a 

highly detailed modeling effort. Undoubtedly, details, such as the degree to which the soilgroundwater system affects atmospherically deposited nitrogen, will be less quantified than 

detailed processes using this method. However, for an initial approach to determining the aquatic 

nutrient enrichment effects on a system, the screening method provides a response curve that can 

be used in the evaluation of ecosystem services. Additionally, many of the complex concepts 

linking the indicators of eutrophication to the effects of eutrophication are not highly developed or 

understood at this time (Howarth and Marino, 2006). While some targeted studies may produce the 

type of linked results from indicators to ecological endpoints that are the goal of this study, these 

results cannot be readily expanded to multiple areas in multiple climate zones without great levels 

of effort. As the base of literature and results expands, the concepts applied in this methodology 

can be expanded to more deterministic, temporarily varying analyses. 

 Use of a partially empirical framework. Because SPARROW is, at its core, an empirical 

relationship, any model obtained using SPARROW is a function of the data used in the 

calibration. Therefore, the predictions remain valid as long as there is no great change in the 

conditions (in this case, the nitrogen loadings within each subbasin) underlying the model. This 

aspect of the model introduces uncertainty into the alternative effects results because they are 

calculated using a model calibrated under current conditions. 

Uncertainties in Back Calculation Methods 

 Missing ASSETS EI scores per combinations of index scores. The combinations of OHI, OEC, 

and DFO index scores provided by Bricker et al. (2003) leave out 30 of the possible 125 

combinations that represent overall ASSETS EI scores. In both case study analyses, these missing 

scores have led to a conclusion of infeasible scenarios because an overall ASSETS EI score could 

not be determined from the resulting instream nitrogen load found during the back calculation 

method. The methods used to determine the 95 combinations will be investigated, and the missing 

30 scores pursued for future case study analyses. 

 Better rationale for TNs minimum and maximum uncertainty range. The uncertainty about 

how to best quantify the TNs ecological endpoint uncertainty is the biggest limitation of the current 

analyses. This is particularly true for the TNs high end (i.e., the maximum TNs that would be 

Final Risk and Exposure Assessment September 2009 

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Aquatic Nutrient Enrichment Case Study 

expected to result in an OEC index score of 1). The assigned uncertainty ranges were based on best 

professional judgment, but more research is needed. It is expected for the results of the back 

calculation methodology to be very sensitive to these ecological endpoint ranges, especially on the 

maximum TNs end. Because of this limitation, the results presented herein for the Potomac and 

Neuse River estuaries should be interpreted as illustrative of the methodology, not strictly valid. 

 Methodology to incorporate uncertainty in the SPARROW model. Estimates of TNs at the 

head of the estuary, predicted by SPARROW and driven by the TNatm (i.e., TN deposition 

evaluated on decreases in NOx) over the watershed and other nitrogen sources, are uncertain. That 

uncertainty was not considered in these two case studies; therefore, the probability distributions of 

TNatm*

i presented are artificially “tight” (i.e., the true distributions would exhibit more variability). 

There is a need to explore the SPARROW literature more thoroughly to determine how to 

incorporate the non-parametric confidence limits that have been developed for the SPARROW 

model. Once such limits are incorporated, it is very unlikely that one would be able to explicitly 

solve the SPARROW model, including these confidence limit terms explicitly for a TNatm*

i,j as a 

function of the TNs

*

i value and the “jth” probability of confidence limit term. Some sort of implicit, 

iterative method would be needed. An application of the Newton’s method algorithm has already 

been developed for these purposes and tested using an artificial confidence limit term. It seems to 

work remarkably well (i.e., convergence within a few iterations to very tight convergence criteria), 

and the researchers are optimistic that expanding the overall methodology to include SPARROW 

uncertainty is very tractable. 

 More convergence testing to determine appropriate numbers of samples. As briefly 

mentioned, some modest convergence testing was completed to determine how many samples of 

the OEC(TNs) function need to be used for the statistics of interest for the resulting TNatm*

i

distributions to be reasonably stable. The answer is something more than 500, which will 

undoubtedly increase when SPARROW uncertainty is incorporated. More convergence testing is 

needed. 

 Crossing of a categorical ranking system with a continuous nitrogen concentration scale.

Several assumptions and considerations had to be made in order to create and evaluate the logistic 

response curve because the OEC index score is a categorical ranking of 1 through 5, whereas TNs

is a continuous variable. The functions evaluated in BackCalculation treat the OEC index score as 

a continuous function. Until higher level models are developed to relate the nitrogen 

concentrations in the system to eutrophication effects, these assumptions are necessary. Future 

applications with additional data should be used to test and validate these assumptions and results. 

Final Risk and Exposure Assessment September 2009 

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Aquatic Nutrient Enrichment Case Study 

6.0 CONCLUSIONS 

 A screening-level method has been determined to be an appropriate approach to assessing the 

effects of atmospheric deposition of oxidized nitrogen on eutrophication/nutrient enrichment 

because there is a lack of a generalized link development between these characteristics in the 

literature. 

 Both the Potomac and Neuse River estuaries have an ASSETS EI score of Bad for 2002, meaning 

that both systems are highly eutrophic and are not expected to improve greatly in the near future. 

Atmospheric deposition over the watersheds account for approximately 24% and 26% of the 

instream loads to the Potomac and Neuse River estuaries, respectively. 

 The BackCalculation program designed and set up for this study succeeded in assessing the links 

between TNs responding to changes in TNatm and the OEC index and ASSETS EI scores. Results 

of this assessment for the Potomac Estuary reveal that it is possible to improve the ASSETS EI 

score by only one category when the only change is in a decrease of the oxidized nitrogen 

component of the atmospheric deposition (Table 3.2-4). This result showed that there was a 5% 

chance (i.e., 95th Percentile of results) that decreasing the TNatm by 78% would result in the one 

category improvement in the ASSETS EI score. Within the Neuse River Estuary, this analysis 

revealed that it would not be possible to improve the ASSETS EI score by decreasing the oxidized 

nitrogen in the atmospheric deposition loading to the estuary alone (Table 3.2-8; all percentage 

decreases greater than 100). Additional source decreases would be necessary to produce OHI and 

OEC index scores good enough to improve the ASSETS EI score. 

 Scaling of this methodology was a priority in development. Demonstration of the back calculation 

methods was the first step to expanding the results to estuaries across the nation. Alternative 

evaluation methods of eutrophication will be needed to assess nutrient enrichment in inland waters. 

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September 2009 

Appendix 7 

Terrestrial Nutrient Enrichment Case Study 

Final 

EPA Contract Number EP-D-06-003 

Work Assignment 3-62 

Project Number 0209897.003.062 

Prepared for 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27709 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709-2194 

Terrestrial Nutrient Enrichment Case Study 

TABLE OF CONTENTS 

Acronyms and Abbreviations ........................................................................................................ iv

Introduction ..................................................................................................................................... 1

1.0 Background ............................................................................................................................... 2

1.1 Indicators, Ecological Endpoints, and Ecosystem Services ............................................. 3

1.1.1 What Is Known .................................................................................................... 3

1.1.2 What Is Not Known ............................................................................................. 6

1.1.3 Benchmarks Selected for This Case Study .......................................................... 7

1.1.4 Ecosystem Services .............................................................................................. 8

1.2 Case Study Site Selection ................................................................................................ 9

1.2.1 National Overview of Sensitive Areas ................................................................. 9

1.2.1.1 Presence of Acidophytic Lichens .......................................................... 9

1.2.1.2 Anthropogenic Land Cover ................................................................... 9

1.2.1.3 Nitrogen-Sensitive Species Identified in Literature ............................ 10

1.2.1.4 Excluded Datasets ............................................................................... 10

1.2.1.5 Overlay Results ................................................................................... 10

1.2.2 Use of ISA Information and Rationale for Site Selection .................................. 12

1.3 Ecosystem Overview ..................................................................................................... 14

1.3.1 Coastal Sage Scrub ............................................................................................ 14

1.3.2 Mixed Conifer Forest ......................................................................................... 18

1.4 Historical Trends ............................................................................................................ 23

1.4.1 Coastal Sage Scrub ............................................................................................ 23

1.4.2 Mixed Conifer Forest ......................................................................................... 24

2.0 Approach and Methodology ................................................................................................... 25

2.1 Published Research ........................................................................................................ 26

2.2 GIS Methodology........................................................................................................... 26

2.2.1 Overview ............................................................................................................ 26

2.2.2 Available Data Inputs ........................................................................................ 26

2.2.2.1 Nitrogen Deposition ............................................................................ 26

2.2.2.2 Range of Coastal Sage Scrub .............................................................. 27

2.2.2.3 Fire Threat ........................................................................................... 29

2.2.2.4 Changes in Coastal Sage Scrub Communities .................................... 29

2.2.2.5 Distribution of Invasive Species ......................................................... 29

2.2.2.6 Threatened and Endangered Species Habitat ...................................... 29

2.2.2.7 Range of Mixed Conifer Forest .......................................................... 30

2.2.2.8 Distribution of Acid-Sensitive Lichens .............................................. 30

3.0 Results ..................................................................................................................................... 30

3.1 Literature Review Findings............................................................................................ 31

3.1.1 Coastal Sage Scrub ............................................................................................ 31

3.1.1.1 Atmospheric Nitrogen Deposition ...................................................... 34

3.1.1.2 Nonnative Grasses .............................................................................. 36

3.1.1.3 Mycorrhizae ........................................................................................ 36

3.1.1.4 Soil Nitrogen ....................................................................................... 37

Final Risk and Exposure Assessment September 2009 

Appendix 7 - i 
Terrestrial Nutrient Enrichment Case Study 

3.1.2 Fire ..................................................................................................................... 38

3.1.3 Coastal Sage Scrub Model ................................................................................. 39

3.1.4 Mixed Conifer Forest Ecosystems ..................................................................... 40

3.1.4.1 Nitrogen and Ozone Effects ................................................................ 40

3.1.4.2 Nitrogen Effects on Lichens ............................................................... 44

3.1.4.3 Nitrogen Saturation ............................................................................. 46

3.2 Results Summary ........................................................................................................... 48

4.0 Implications for Other Systems .............................................................................................. 50

5.0 Uncertainty .............................................................................................................................. 55

5.1 Coastal Sage Scrub ........................................................................................................ 55

5.2 Mixed Conifer Forest ..................................................................................................... 56

6.0 Conclusions ............................................................................................................................. 56

7.0 References ............................................................................................................................... 57

LIST OF FIGURES 

Figure 1.1-1. Observed effects from ambient and experimental atmospheric nitrogen 

deposition loads in relation to 2002 CMAQ/NADP deposition data. 

Citations for effect results can be found in the ISA, Table 4-4 (U.S. EPA, 

2008). ...................................................................................................................... 5

Figure 1.2-1. Areas of highest potential nutrient enrichment sensitivity. (Acidophytic 

lichens, tree species, and the extent of the Mojave Desert are from data 

obtained from the USFS. The extents of coastal sage scrub and California 

mixed conifer are from the California Fire and Resource Assessment 

Program. Grasslands are from the National Land Cover Dataset [USGS]). ......... 11

Figure 1.3-1. Range of coastal sage scrub ecosystems. ................................................................ 15

Figure 1.3-2. Presence of three threatened and endangered species in California’s coastal 

sage scrub ecosystem. ........................................................................................... 16

Figure 1.3-3. Range of California’s mixed conifer forests. .......................................................... 19

Figure 1.3-4. Presence of two threatened and endangered species and mixed conifer 

forests. ................................................................................................................... 19

Figure 1.4-1. Change in coastal sage scrub extent from 1977 to 2002. ........................................ 24

Figure 3.1-1. Coastal sage scrub range and total nitrogen deposition using CMAQ 2002 

modeling results and NADP monitoring data. ...................................................... 35

Figure 3.1-2. Current fire threats to coastal sage scrub ecosystems. ............................................ 39

Figure 3.1-3. Model of coastal sage scrub ecosystem in relation to fire and atmospheric 

nitrogen deposition................................................................................................ 40

Figure 3.1-4. Mixed conifer forest range and total atmospheric nitrogen deposition using 

CMAQ 2002 modeling results and NADP monitoring data. ................................ 42

Figure 3.1-5. Conceptual model for increased susceptibility to wildfire in mixed conifer 

forests (Grulke et al., 2008). ................................................................................. 44

Figure 3.1-6. Importance of lichens as an indicator of ecosystem health (Jovan, 2008). ............. 44

Final Risk and Exposure Assessment September 2009 

Appendix 7 - ii 
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Final Risk and Exposure Assessment September 2009 

Appendix 7 - iii 

Figure 3.1-7. Presence of acidophyte lichens and total nitrogen deposition in the 

California mountain ranges using CMAQ 2002 modeling results and 

NADP monitoring data. ........................................................................................ 46

Figure 3.2-1. Illustration of the range of terrestrial ecosystem effects observed relative to 

atmospheric nitrogen deposition. .......................................................................... 49

Figure 4.1-1. 2002 CMAQ-modeled and NADP monitoring data for deposition of total 

nitrogen in the western United States. .................................................................. 51

Figure 4.1-2. Benchmarks of atmospheric nitrogen deposition for several ecosystem 

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

lakes ...................................................................................................................... 52

Figure 4.1-3. Habitats that may experience ecological benchmarks similar to coastal sage 

scrub and mixed conifer forest. ............................................................................. 53

LIST OF TABLES 

Table 1.2-1. Potential Assessment Areas for Terrestrial Nutrient Enrichment Identified in 

the ISA (U.S. EPA, 2008) ..................................................................................... 13

Table 1.3-1. Selected Flora and Fauna Associated with the Coastal Sage Scrub Habitat ............ 17

Table 1.3-2. Selected Flora and Fauna Associated with the Mixed Conifer Forest Habitat ......... 20

Table 1.3-3. List of Lichen Species Present in the Sierra Nevada Range and San 

Bernardino Mountains (Jovan, 2008; Sigal and Nash, 1983) ............................... 22

Table 3.1-1. Summary of Selected Experimental Variables across Multiple Coastal Sage 

Scrub Study Areasa

 ............................................................................................... 32

Table 3.1-2. Coastal Sage Scrub Ecosystem Area and Nitrogen Deposition ............................... 36

Table 3.1-3. Research Evidence of Ecosystem Responses to Nitrogen Relevant to Coastal 

Sage Scrub ............................................................................................................ 37

Table 3.1-4. Mixed Conifer Forest Ecosystem Area and Nitrogen Deposition ............................ 48

Table 4.1-1. Areas of Coastal Sage Scrub and Mixed Conifer Forest That Exceed 

Benchmark Nitrogen Deposition Levels. .............................................................. 53
Terrestrial Nutrient Enrichment Case Study 

ACRONYMS AND ABBREVIATIONS 

AM arbuscular mycorrhizae 

C:N carbon to nitrogen ratio 

cm centimeter 

CMAQ Community Multiscale Air Quality model 

CO2 carbon dioxide 

CSS coastal sage scrub 

FIA Forest Inventory and Analysis National Program 

FRAP Fire and Resource Assessment Program 

FWS U.S. Fish and Wildlife Service 

GAP Gap Analysis Project 

GIS geographic information systems 

HNO3 nitric acid 

ISA Integrated Science Assessment 

kg kilogram 

km kilometer 

m meter 

MCF mixed conifer forest 

MEA Millennium Ecosystem Assessment 

mm millimeter 

NADP National Atmospheric Deposition Program 

NH3 ammonia gas 

NH4

+ ammonium 

NHx reduced nitrogen 

NO nitric oxide 

NO3

- nitrate 

NOx nitrogen oxides 

O3 ozone 

PNV Potential Natural Vegetation 

SMB Simple Mass Balance 

TM Thematic Mapper 

μg/g micrograms per gram 

USFS U.S. Forest Service 

USGS U.S. Geological Survey 

VTM Vegetation Type Map 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - iv 
Terrestrial Nutrient Enrichment Case Study 

INTRODUCTION 

For the last half century, landscapes in the United States have been exposed to 

atmospherically deposited nitrogen from anthropogenic activities. Some of the highest deposition 

has occurred in Southern California, where researchers have documented measurable ecological 

changes related to atmospheric deposition. This case study investigated the coastal sage scrub 

(CSS) and the mixed conifer forest (MCF) ecosystems. Research was conducted on these 

complex ecosystems to understand the relationships among the effects of nitrogen loads, fire 

frequency and intensity, and invasive plants. The breadth of spatial and temporal data needed for 

quantitative modeling of ecological response in the CSS and MCF ecosystems is not currently 

available. However, biologically meaningful ecological endpoints were identified and compared 

to ecological endpoints identified in the other case studies presented in the Risk and Exposure 

Assessment (Chapters 4 and 5), as well as similar ecological endpoints from ecosystems in 

different parts of the United States. The results in this case study report are based on geospatial 

analysis and published empirical research. 

Evidence from the two ecosystems discussed in this case study report supports the 

finding that nitrogen alters the CSS and MCF ecosystems. For this analysis, the loss of the native 

shrubs in the CSS and the increase in nonnative annual grasses were investigated. In MCF on the 

slopes of the San Bernardino and Sierra Nevada Range, lichen communities associated with the 

forest stands and nitrogen saturation were investigated to identify the effects of nitrogen 

loadings. Changes in nitrogen loading may also affect the ecological services provided by the 

CSS and MCF ecosystems, including regulation (e.g., water, habitat), cultural and aesthetic value 

(e.g., recreation, natural landscape, sense of place), and provisioning (e.g., timber) (MEA, 2005). 

In addition, these locations have the following characteristics that make them good candidates 

for case studies: 

 There is public interest 

 Data were available to begin investigation (especially geographic information systems 

[GIS] datasets) 

 Effects observed have implications for other ecosystems and ecosystem services 

 Ecological endpoints related to nitrogen deposition can be identified 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 1 
Terrestrial Nutrient Enrichment Case Study 

 Observed effects, such as mycorrhizal responses, increase in nonnative annual grasses, 

decrease in certain lichen species, and nitrate (NO3

-

) leaching are considered by 

researchers to be linked to atmospheric nitrogen deposition. 

Section 3.3 of the Integrated Science Assessment (ISA) for Oxides of Nitrogen and 

Sulfur–Ecological Criteria (Final Report) (ISA) (U.S. EPA, 2008) describes the ecosystems and 

species of concern, identifies trends in the ecosystems and the effects of these trends, and 

discusses research efforts that investigated the variables and driving forces that may affect the 

communities. The Community Multiscale Air Quality (CMAQ) 2002 modeling results and 2002 

National Atmospheric Deposition Program (NADP) data were used to gain an understanding of 

how atmospheric deposition of nitrogen is spatially distributed. GIS data on the spatial extent of 

the habitat and changes in that extent, the location of fire threat (an important variable in both 

CSS and MCF ecosystems), and the location of species of concern were used to compare these 

patterns to the CMAQ/NADP data. In sum, spatial information and observed, experimental 

effects were used to help identify the trends in these ecosystems and describe the past and current 

spatial extent of the ecosystems. 

The following ecological endpoints were identified for CSS: 

 Loss of CSS native shrubs 

 Mycorrhizal (a symbiotic association of fungi and plant roots) responses 

 Nonnative annual grass biomass. 

The following ecological endpoints were identified for MCF: 

 Lichen community species 

 NO3

-

 leaching. 

1.0 BACKGROUND 

Current analysis of the effects of terrestrial nutrient enrichment from atmospheric 

nitrogen deposition in both CSS and MCF seeks to improve scientific understanding of the 

interactions among nitrogen deposition, fire events, and community dynamics. The available 

scientific information is sufficient to identify ecological endpoints that are affected by nitrogen 

deposition. These ecological endpoints can be compared to the ecological endpoints identified 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 2 
Terrestrial Nutrient Enrichment Case Study 

from modeling conducted for other case studies in the Risk and Exposure Assessment (Chapters 4 

and 5). These ecological endpoints can also be compared to similar ecological endpoints from 

different ecosystems. 

1.1 INDICATORS, ECOLOGICAL ENDPOINTS, AND ECOSYSTEM 

SERVICES 

1.1.1 What Is Known 

Determining an acceptable ambient air concentration of nitrogen oxides (NOx) for this 

case study required knowledge of ecosystem sensitivity to subsequent atmospheric deposition. 

Terrestrial nutrient enrichment research has measured ecosystems’ exposure to deposition of 

various atmospheric nitrogen species, including nitrogen oxides, reduced nitrogen, and total 

nitrogen. The ISA (U.S. EPA, 2008, Section 3.3) documents current understanding of the effects 

of nitrogen nutrient enrichment on terrestrial ecosystems. The report concludes that there is 

sufficient information to suggest a causal relationship between atmospheric nitrogen deposition 

and biogeochemical cycling and fluxes of nitrogen in terrestrial systems. The ISA further 

concludes that there is a causal relation between atmospheric nitrogen deposition and changes in 

species richness, species composition, and biodiversity in terrestrial systems. These conclusions 

are based on an extensive literature review that is summarized in Table 4-4 of the ISA. The 

research involves both observational and experimental (e.g., nitrogen addition) projects. Alpine 

ecosystems, grasslands (e.g., arid and semiarid ecosystems), forests, and deserts were included. 

This extensive documentation was used to assist in selecting the case study areas to identify and 

compare ecological endpoints from different habitats. 

CSS is subject to several pressures, such as land conversion, grazing, fire, and pollution, 

all of which have been observed to induce declines in other ecosystems (Allen et al., 1998). 

Research suggests that both fire and increased nitrogen can enhance the growth of nonnative 

grasses in established CSS ecosystems. It is hypothesized that many stands are no longer limited 

by nitrogen and have instead become nitrogen-saturated due to atmospheric nitrogen deposition 

(Allen et al., 1998; Westman, 1981a). Nitrogen availability may favor the germination and 

growth of nonnative grasses, which can create a dense network of shallow roots that slow the 

diffusion of water through soil, decrease the percolation depth of precipitation, and decrease the 

water storage capability of the soil and underlying bedrock (Wood et al., 2006). Establishment of 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 3 
Terrestrial Nutrient Enrichment Case Study 

CSS species may be decreased because of decreased water and nitrogen availability at depths 

where more woody CSS tap roots are found (Keeler-Wolf, 1995; Wood et al., 2006). 

The ISA (U.S. EPA, 2008, Section 3.3) notes that there are areas of CSS of Southern 

California where dry nitrogen deposition approaches 30 kilograms (kg) N/ha/yr (Bytnerowicz 

and Fenn, 1996). Seedlings of native shrubs and nonwoody plants in these areas of high nitrogen 

deposition are unable to compete with dense stands of exotic grasses, and thus are gradually 

replaced by grasses, particularly following disturbances, such as fire (Eliason and Allen, 1997; 

Yoshida and Allen 2001; Cione et al., 2002). CSS has been declining in land area and in shrub 

density for the past 60 years, and in many places is being replaced by nonnative annual grasses 

(Allen et al., 1998; Padgett and Allen, 1999). Nitrogen deposition has been suggested as a 

possible cause or factor in this ecosystem alteration (U.S. EPA, 2008, Section 3.3). 

The ISA (U.S. EPA, 2008, Section 3.3) discusses the extensive land areas in the western 

United States that receive low levels of atmospheric nitrogen deposition and which are 

interspaced with areas of relatively higher atmospheric deposition downwind of large 

metropolitan centers and agricultural areas. Fenn et al. (2008) determined empirical critical loads 

for atmospheric nitrogen deposition in MCF, based on changes in leached NO3

-

 in receiving 

waters and decreased fine-root biomass in Ponderosa pine (Pinus ponderosa), and based on 

changes in epiphytic lichen communities. An atmospheric nitrogen deposition of 17 kg N/ha/yr 

was found to be associated with NO3

-

leaching and an approximately 25% reduction in fine root 

biomass. The study further noted that lichens are good early indicators of atmospheric nitrogen 

deposition effects on other MCF species because lichens rely entirely on atmospheric nitrogen 

and cannot regulate uptake. From the lichen data, Fenn et al. (2008) predicted that a critical load 

of 3.1 kg N/ha/yr would be protective for all components of the forest ecosystem. 

Figure 1.1-1 displays a map of observed effects from ambient and experimental 

atmospheric nitrogen deposition loads in relation to 2002 CMAQ-modeled deposition results. 

The map depicts the areas where empirical effects of terrestrial nutrient enrichment have been 

observed and the area’s proximity to elevated levels of nitrogen deposition. The ISA (U.S. EPA, 

2008, Section 3.3) also identifies areas of the western United States where atmospheric nitrogen 

deposition effects have been reported. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 4 
Terrestrial Nutrient Enrichment Case Study 

1. Nitrogen enrichment or eutrophication of lakes (Loch Vale, CO: 0.5 to1.5 kg N/ha/yr; Niwot Ridge, CO: 4.71 kg N/ha/yr) 

2. Alpine lakes increase shift in diatom species (Rocky Mountains, CO: 2 kg N/ha/yr) 

3. Alpine meadows’ elevated NO3

-

 levels in runoff (Colorado Front Range: 20, 40, 60 kg N/ha/yr) 

4. Alpine meadows’ shift toward hairgrass (Niwot Ridge, CO: 25 kg N/ha/yr) 

5. Nitrogen enrichment or nitrogen saturation (e.g., soil and foliar nitrogen concentration) (eastern slope of Rocky Mountains: 1.2; 

3.6 kg/ha/yr; Fraser Forest, CO: 3.2 to 5.5 kg N/ha/yr) 

6. Increased nitrogen mineralization rates and nitrification (Loch Vale, CO (spruce): 1.7 kg N/ha/yr) 

7. Alpine tundra with increased plant foliage and decreased species richness (Niwot Ridge, CO: 50 kg N/ha/yr) 

8. Nitrogen saturation, high NO3

-

 in streamwater, soil, leaves; high nitric oxide (NO) emissions (Los Angeles, CA air basin: 

saturation at 24 to 25 kg N/ha/yr (dry) and at 0.8 to 45 kg N/ha/yr (wet); northeastern U.S.: 3.3 to 12.7 kg N/ha/yr) 

9. Nitrogen saturation, high NO3

-

in streamwater (San Bernardino Mountains, CA (coniferous): 2.9 and 18.8 kg N/ha/yr) 

10. NO3

-

 leaching (New England: Adirondack lakes: 8 to10 kg N/ha/yr) 

11. Nitrogen saturation, high dissolved inorganic nitrogen (San Bernardino Mountains, San Gabriel Mountains, CA, chaparral, 

hardwood, coniferous): 11 to 40 kg N/ha/yr) 

12. Increased tree mortality and beetle activity (San Bernardino Mountains, CA (Ponderosa): 8 and 82 kg N/ha/yr) 

13. Enhanced growth of black cherry and yellow poplar; possible decline in red maple vigor; increased foliar nitrogen (Fernow 

Forest, WV: 35.5 kg N/ha/yr) 

14. Impacts on lichen communities (California MCF: 3.1 kg N/ha/yr; Columbia R. Gorge, OR/WA: 11.5 to 25.4 kg N/ha/yr) 

15. Evidence that threatened and endangered species impacted San Francisco Bay, CA (checkerspot butterfly and serpentinitic 

grass invasion: 10 to15 kg N/ha/yr; Jasper Ridge, CA: 70 kg N/ha/yr) 

16. Decreased diversity of mycorrhizal communities (Southern California: ~10 kg N/ha/yr; Northern Michigan (maple/sugar maple): 5 

to 9 kg N/ha/yr) 

17. Decreased abundance of CSS (Southern California: 3.3 kg N/ha/yr) 

18. Loss of grasslands (Cedar Creek, MN: 5.3 [1.3 to 9.8] kg N/ha/yr) 

19. Decrease in abundance of desert creosote bush, increase in nonnative grasses (Mojave Desert and Chihuahuan Desert, CA: 

1.7 kg N/ha/yr and up) 

20. Decrease in pitcher plant (Hawley Bog, MA; Molly Bog, VA: 10 to 14 kg N/ha/yr) 

Figure 1.1-1. Observed effects from ambient and experimental atmospheric 

nitrogen deposition loads in relation to 2002 CMAQ/NADP deposition data. 

Citations for effect results can be found in the ISA, Table 4-4 (U.S. EPA, 2008). 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 5 
Terrestrial Nutrient Enrichment Case Study 

1.1.2 What Is Not Known 

The ISA (U.S. EPA, 2008, Section 3.3) indicates that information is limited about the 

spatial extent and distribution of terrestrial ecosystems most sensitive to nutrient enrichment 

from atmospheric nitrogen deposition: “Effects are most likely to occur where areas of relatively 

high atmospheric nitrogen deposition intersect with nitrogen-limited plant communities. The 

factors that govern the sensitivity of terrestrial ecosystems to nutrient enrichment from 

atmospheric nitrogen deposition include the degree of nitrogen limitation, rates and form of 

atmospheric nitrogen deposition, elevation, species composition, length of growing season, and 

soil nitrogen retention capacity.” Examples of sensitive ecosystems include the following: 

 Alpine tundra (low rates of primary production, short growing season, low temperature, 

wide moisture variation, low nutrient supply). 

 Western U.S. ecosystems, such as the alpine ecosystems of the Colorado Front Range, 

chaparral watersheds of the Sierra Nevada Range, lichen communities in the San 

Bernardino Mountains and the Pacific Northwest, and CSS communities in Southern 

California. 

 Eastern U.S. ecosystems where sensitivities are typically assessed in terms of the degree 

of NO3

-

 leaching from soils into ground and surface waters. These ecosystems are 

expected to include hardwood forests, semiarid lands, and grassland ecosystems, but 

effects on individual plant species have not been studied well. 

Major indicators for nutrient enrichment to terrestrial systems from atmospheric 

deposition of total reactive nitrogen, such as those described above, require measurements based 

on available monitoring stations for wet deposition (NADP/National Trends Network) and 

limited networks for dry deposition (Clean Air Status and Trends Network [CASTNet]).). 

However, data have been limited, particularly at the spatial scale required for a more accurate 

analysis. Wet deposition monitoring stations can provide more information on an extensive range 

of nitrogen species than can dry deposition monitoring stations. In the Mediterranean systems of 

Southern California where rainfall is concentrated during some months of the year, dry 

deposition is particularly important. Individual studies measuring atmospheric nitrogen 

deposition to terrestrial ecosystems that involve throughfall estimates for forested ecosystems 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 6 
Terrestrial Nutrient Enrichment Case Study 

can provide better approximations for total atmospheric nitrogen deposition levels; however, 

such estimates and related bioassessment data are not available for the entire country. 

Finally, in the area of what is still unknown, the exact relationship between atmospheric 

nitrogen loadings, fire frequency and intensity, and nonnative plants, particularly in the CSS 

ecosystem, have not been quantified. Various conceptual models linking these factors have been 

developed, but an understanding of cause and effect, seasonal influences, and benchmarks 

remains undeveloped. These potential confounders are discussed at greater length in Section 3. 

1.1.3 Benchmarks Selected for This Case Study 

The data limitations on atmospheric nitrogen deposition (described above), along with 

current data to describe the full extent and distribution of nitrogen-sensitive U.S. ecosystems, 

presented a barrier to designing a case study that used quantitative monitoring and modeling 

tools. Instead, this case study used published research results to identify meaningful ecological 

endpoints associated with different levels of atmospheric nitrogen deposition. 

The ecological endpoints that were identified for the CSS and the MCF are included in 

the suite of ecological endpoints identified in the ISA (U.S. EPA, 2008, Section 3.3). There are 

sufficient data to confidently relate the ecological effect to a loading of atmospheric nitrogen. 

For the CSS community, the following ecological benchmarks were identified: 

 3.3 kg N/ha/yr—the amount of nitrogen uptake by a vigorous stand of CSS; above this 

level, nitrogen may no longer be limiting 

 10 kg N/ha/yr—mycorrhizal community changes 

For the MCF community, the following ecological benchmarks were identified: 

 3.1 kg N/ha/yr—shift from sensitive to tolerant lichen species 

 5.2 kg N/ha/yr—dominance of the tolerant lichen species 

 10.2 kg N/ha/yr—loss of sensitive lichen species 

 17kg N/ha/yr—leaching of NO3

-

 into streams. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 7 
Terrestrial Nutrient Enrichment Case Study 

1.1.4 Ecosystem Services 

Ecosystem services are generally defined as the benefits individuals and organizations 

obtain from ecosystems. In the 2005 Millennium Ecosystem Assessment (MEA), ecosystem 

services are classified into four main categories: 

 Provisioning—includes products obtained from ecosystems 

 Regulating—includes benefits obtained from the regulation of ecosystem processes 

 Cultural—includes the nonmaterial benefits people obtain from ecosystems through 

spiritual enrichment, cognitive development, reflection, recreation, and aesthetic 

experiences 

 Supporting—includes those services necessary for the production of all other ecosystem 

services (MEA, 2005). 

Atmospheric nitrogen deposition affects CSS and MCF ecological processes that, in turn, 

are related to ecosystem services. These processes include the following: 

For CSS: 

 Decline in CSS habitat, shrub abundance, species of concern—cultural and 

regulating 

 Increased abundance of nonnatives—cultural and regulating 

 Increase in wildfires—cultural and regulating. 

For MCF: 

 Change in habitat suitability and increased tree mortality—cultural and regulating 

 Decline in MCF aesthetics—cultural 

 Increase in fire intensity, change in forest’s nutrient cycling, other nutrients 

becoming limiting—regulating 

 Decline in surface water quality—regulating. 

Terrestrial nutrient enrichment for CSS potentially affects ecosystem services, such as 

biodiversity; threatened and endangered species and rare species (both national and state); 

landscape view; water quality; and fire hazard mitigation. Linking ecological endpoint to 

services involves the measurement of changes in biodiversity and abundance and distribution of 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 8 
Terrestrial Nutrient Enrichment Case Study 

threatened and endangered species, comparison of past and present photography, and 

measurement of the distribution of soil moisture with depth and possible NO3

-

 leaching. The 

relationship between fire frequency, CSS ecosystem, and property values will be investigated in 

the ecosystem services analysis. 

The case study approach for MCF focused on ecosystem services, such as visual and 

recreational aesthetics provided by the CSS community and water quality. Linking ecological 

endpoints to services includes measurement of the density of stands, shifts in tree dominance, 

shifts in lichen communities, foliar nitrogen increases, and increased NO3

–

 concentrations in 

streams due to leaching. 

1.2 CASE STUDY SITE SELECTION 

1.2.1 National Overview of Sensitive Areas 

The selection of case study areas specific to terrestrial nutrient enrichment began with 

national GIS mapping. GIS datasets of physical, chemical, and biological properties that were 

indicative of potential terrestrial nutrient enrichment were considered in order to identify 

sensitive areas in the United States. The publicly available geospatial datasets outlined in the 

following paragraphs have been identified as important contributors to terrestrial nutrient 

enrichment and met the selection criteria. 

1.2.1.1 Presence of Acidophytic Lichens 

Acidophytic lichens are known to be sensitive to increased levels of nitrogen loading. 

Other species are dependent upon lichens for both food and habitat. For this exercise, the list of 

acidophytic species from Fenn et al. (2008) was used. Data on these species were available for 

the years 2001 to 2006. Geospatial data were obtained from the U.S. Forest Service (USFS) 

Forest Inventory and Analysis National Program (FIA) (USFS, 2008a). Locations where 

acidophytic lichen were identified were defined as being sensitive. 

1.2.1.2 Anthropogenic Land Cover 

Urban and agricultural land covers were mapped to so that they could be used to exclude 

areas that are not sensitive to terrestrial nutrient enrichment, such as agricultural areas and 

urbanized areas. This information was obtained from the U.S. Geological Survey (USGS) 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 9 
Terrestrial Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 10 

National Atlas of the United States (USGS, 2006) and covered the continental United States at a 

spatial resolution of 1-km grid cells. 

1.2.1.3 Nitrogen-Sensitive Species Identified in Literature 

Although there is no known nationwide species that has shown range loss because of 

additional nitrogen, it was possible to assemble a “patchwork quilt” of species and forest types 

from across the United States that are identified as sensitive in the published literature. A range 

was extracted from national datasets for each species or forest type where the range existed. The 

cumulative extent of all ranges allowed for the definition of sensitive areas in the United States. 

1.2.1.4 Excluded Datasets 

The publicly available spatial datasets outlined below were considered for inclusion in 

the national sensitivity assessment, but were not used. 

 Soil Nitrogen Content. This pre-1980 dataset was requested but not received at the time 

of this report’s production. The quality of data is uncertain. 

 Presence of Mountains. The physiographic provinces of the United States were 

considered to provide leeward sides of mountains that tend to receive a greater amount of 

atmospheric nitrogen deposition. Continental U.S. data identified were from USGS and 

dated 1946. The spatial resolution was a scale of 1:7,000,000. If used, the benchmark 

value would have been for mountain ranges only. However, this dataset was not used 

because terrain is already taken into account by the CMAQ modeling. 

1.2.1.5 Overlay Results 

The extraction of the areas of greatest nutrient enrichment sensitivity was constrained by 

the relative lack of available national datasets. Therefore, the review involved two steps within 

the GIS. First, the ranges of sensitive species identified in the literature were combined with a 

layer of acidophytic lichen distribution. Second, areas of human use (i.e., urban and agricultural 

land covers) were removed. The resulting map illustrates the area of highest potential sensitivity 

(see Figure 1.2-1.) 
Terrestrial Nutrient E

Figure 1.2-1. Areas of highest potential nutrient enrichment sensitivity. (Acidophytic lichens, tree specie

extent of the Mojave Desert are from data obtained from the USFS. The extents of coastal sage scrub and mixed conifer are from the California Fire and Resource Assessment Program. Grasslands are from the NCover Dataset [USGS]).

Final Risk and Exposure Assessment 

Appendix 7 - 11 
 Terrestrial Nutrient Enrichment Case Study 

1.2.2 Use of ISA Information and Rationale for Site Selection 

Potential case study areas identified in the ISA (U.S. EPA, 2008) were considered in site 

selection along with information gathered in the national GIS analysis. Table 1.2-1 contains the 

relevant nutrient enrichment areas identified in the ISA. 

After considering this information, California’s CSS and MCF ecosystems were selected 

for this case study analysis. The following selection factors supplement those listed in the 

Introduction: 

 Availability of atmospheric ambient and deposition data (monitored or modeled) 

 Availability of digitized datasets of biotic communities; fire-prone areas; and sensitive, 

rare species 

 Scientific results of research on nitrogen effects for the case study area 

 Representation of western U.S. ecosystems potentially impacted by atmospheric nitrogen 

deposition 

 Scalability and generalization opportunities for risk analysis results from the case studies. 

California’s CSS has been the subject of intensive research in the past 10 years, which 

has provided the data needed for a first phase of GIS analysis of the role of atmospheric nitrogen 

deposition in terrestrial ecosystems. California’s MCF have an even longer record of study that 

includes investigations into the effects of atmospheric pollution, changes to forest structure, 

changes to the lichen communities, and measurements of nitrogen saturation. Another ecosystem 

that was considered but not selected for this case study was the alpine ecosystem in the Rocky 

Mountains. As noted in the ISA (U.S. EPA, 2008, Section 3.3), results from a number of studies 

indicate that nitrates may be leaching from alpine catchments, and there appear to be changes in 

plant communities related to the deposition of atmospheric nitrogen. The amount of data from 

these alpine ecosystems is more limited than that from the CSS and MCF. However, the 

ecological benchmarks suggested for alpine ecosystems were comparable to the benchmarks 

from CSS and MCF ecosystems. 

In summary, CSS and MCF were selected as case study areas for the following reasons: 

 The two ecosystems have significant geographic coverage and are located where urban 

areas interface with wilderness areas. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 12 
 Terrestrial Nutrient Enrichment Case Study 

 Both sites are located in areas of sharp atmospheric nitrogen deposition gradients, 

ranging from low background levels to some of the highest deposition levels recorded in 

the United States. 

 The two ecosystems have been researched for extended periods to understand the 

interactive effects of deposition, climate change, fire, and other stressors. 

 The results of these research investigations for CSS and MCF result are well documented 

in the peer-reviewed literature. 

Table 1.2-1. Potential Assessment Areas for Terrestrial Nutrient Enrichment Identified in the 

ISA (U.S. EPA, 2008) 

Area Indicator 

Detailed 

Indicator References in U.S. EPA, 2008 Source 

Alpine and 

subalpine 

communities 

of the eastern 

slope of the 

Rocky 

Mountains, 

CO 

Terrestrial 

nutrient 

enrichment 

Biomass 

production; 

NO3

-

leaching; 

species 

richness 

Baron et al., 1994; Baron et al., 2000; 

Baron, 2006; Bowman, 2000; Bowman 

and Steltzer, 1998; Bowman et al., 1993; 

Bowman et al., 1995; Bowman et al., 

2006; Burns, 2004; Fenn et al., 2003a; 

Fisk et al., 1998; Korb and Ranker, 2001; 

Rueth et al., 2003; Seastedt and Vaccaro, 

2001; Sherrod and Seastedt, 2001; 

Steltzer and Bowman, 1998; Suding et 

al., 2006; Williams and Tonnessen, 2000; 

Williams et al.,1996a; Wolfe et al., 2001 

ISA, Section 

3.3, 4.3, 

Annex A, 

Annex C, and 

Annex D 

Fernow 

Experimental 

Forest near 

Parsons, WV 

Terrestrial 

nutrient 

enrichment 

Forest 

growth 

Adams et al., 1997, 2000; DeWalle et al., 

2006; Edwards and Helvey, 1991; 

Gilliam et al., 2006; Peterjohn, 1996 

ISA, Section 

3.3, 4.3, 

Annex A, 

Annex B 

Bear Brook, 

ME 

Terrestrial 

acidification 

Sugar 

maple; red 

spruce 

Elvir et al., 2003 ISA, Section 

3.3, 4.3, 

Annex A, 

Annex B, 

Annex C 

Harvard 

Forest, MA 

Terrestrial 

nutrient 

enrichment 

Forest 

growth— 

species 

Magill et al., 2004; Magill, 2004 ISA, Sections 

2.8, 2.10, 3.2, 

3.3, 4.3, 

Annex B, 

Annex C 

Southern 

California 

Terrestrial 

nutrient 

enrichment 

Forest 

growth—

species; 

coastal sage 

scrub 

Bytnerowicz and Fenn, 1996, 2003a; 

Takemoto et al., 2001 

ISA, Section 

3.2, 3.3, 3.4, 

4.3, Annex B, 

Annex C 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 13 
 Terrestrial Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 14 

Area Indicator 

Detailed 

Indicator References in U.S. EPA, 2008 Source 

Jasper Ridge 

Biological 

Preserve, CA 

Terrestrial 

nutrient 

enrichment 

Grasslands Zavaleta et al., 2003 ISA, Sections 

3.3, 4.3 

Loch Vale, 

CO 

Terrestrial 

nutrient 

enrichment 

Old-spruce 

growth 

Rueth et al., 2003 ISA, Section 

3.3, 4.3, 

Annex B 

Rocky 

Mountain 

National Park, 

CO 

Terrestrial 

nutrient 

enrichment 

Tundra 

composition 

switch 

Interlandi and Kilham, 1998 ISA, Section 

3.3, 4.3, 

Annex C 

Source: U.S. EPA, 2008. 

1.3 ECOSYSTEM OVERVIEW 

1.3.1 Coastal Sage Scrub 

CSS consists of more than 50 aromatic shrub and subshrub species, which range from 

approximately 0.5 meters (m) to 2 m in height (Burger et al., 2003; Westman, 1981a). The range 

of CSS extends from north of San Francisco down to Baja California in the lower elevation 

coastal range of California (see Figure 1.3-1); however, the species composition may vary with 

location (Westman, 1981b). According to the California Natural Diversity Database, there are 22 

floristic alliances of CSS (e.g., Riversidian Sage Scrub, Venturan Sage Scrub, and Diegan Sage 

Scrub). These alliances consist of similar species that help determine the significance, rarity, and 

growth patterns of California vegetation types. 

CSS grows in a warm Mediterranean climate and is characterized by approximately 300 

millimeters (mm) of annual rainfall falling from December through March and little or no 

rainfall from April through November (Egerton-Warburton and Allen, 2000; Westman, 1981b). 

Underlying substrate types of CSS vary greatly across the CSS stands, although many CSS 

floristic alliances are found on unconsolidated sand, sandstone, conglomerate, and shale 

(Westman, 1981b). 

CSS is also known as “soft chaparral” because of its semideciduousness, drought-tolerant 

nature, and less-rigid leaves, respective to chaparral species (Westman, 1981b). CSS is 

considered a fire-adapted community; meaning that although vegetation layers may be destroyed 

in fires, CSS soil seed banks can withstand fire, and in some species, require fire to open the seed 

cases. However, many CSS species can flourish and propagate in the absence of any fire (Keeler-
 Terrestrial Nutrient Enrichment Case Study 

Wolf, 1995). CSS has been observed to maintain a permanent cover without fire or other 

disturbance regimes (e.g., land conversion, grazing) for at least a century (Westman, 1981a). 

Figure 1.3-1. Range of coastal sage scrub ecosystems.

The resprouting and competition of species post-fire is generally dependent upon fire 

intensity, fire frequency, and seasonal timing (Keeler-Wolf, 1995). CSS species are generally 

poor colonizers after a fire (Minnich and Dezzani, 1998). Annual forbs and any grass seedlings 

present in the post-fire soils are usually dominant in the first few growth cycles. Significant 

shrub growth is most likely to occur in later cycles, further disturbance not withstanding (KeelerWolf, 1995). 

The CSS ecosystem also supports the growth of more than 550 herbaceous annual and 

perennial species between and beneath the shrub canopy. Of these herbs, nearly half are 

endangered, sensitive, or of special status (Burger et al., 2003). Additionally, several avian, 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 15 
 Terrestrial Nutrient Enrichment Case Study 

arthropod, reptilian, amphibian, and mammalian species depend on CSS habitat for foraging, 

breeding, and/or residence. These include several threatened and endangered species, such as the 

coastal California gnatcatcher (Polioptila californica californica), the Stephens’ kangaroo rat 

(Dipodomys stephensi), and the Quino checkerspot butterfly (Euphydryas editha quino). Figure 

1.3-2 presents the range of these three species. Table 1.3-1 presents a selected list of flora and 

fauna species that are associated with CSS habitat. 

Figure 1.3-2. Presence of three threatened and endangered species in 

California’s coastal sage scrub ecosystem.

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 16 
Terrestrial Nutrient E

Table 1.3-1. Selected Flora and Fauna Associated with the Coastal Sage Scrub Habitat

Scientific Name Common Name Life Form Federal Listing* 

Buteo swainsoni Swainson’s Hawk Bird Not listed Polioptila californica californica Coastal California Gnatcatcher Bird Threatened Dipodomys merriami parvus San Bernardino Kangaroo Rat Mammal Endangered Dipodomys stephensi Stephens’ Kangaroo Rat Mammal Endangered Bufo microscaphus californicus Arroyo Toad Amphibian Endangered Euphydryas editha quino Quino Checkerspot Butterfly Insect Endangered Rhaphiomidas terminatus abdominalis 

Delhi Sands Flower-Loving Fly Insect Endangered 

Allium munzii Munz’s Onion Perennial Forb Endangered Rosa minutifolia Small-Leaved Rose Shrub Not listed Deinandra conjugens Otay Tarplant Annual Forb Threatened Cordylanthus orcuttianus Orcutt’s Bird’s Beak Annual Forb Not listed Ambrosia pumila San Diego Ambrosia Perennial Forb Proposed Endangered Acanthomintha ilicifolia San Diego Thorn-Mint Annual Forb Threatened Campylorhynchus brunneicapillus couesi 

Coastal Cactus Wren Bird Not listed 

Athene cunicularia Burrowing Owl Bird Not listed Cnemidophorus hyperythrus Orange-Throated Whiptail Reptile Not listed Phrynosoma coronatum blainvillei San Diego Horned Lizard Reptile Not listed Masticophis lateralis euryxanthus Alameda Whipsnake Reptile Threatened * Status listed for threatened and endangered species only. Others may be species of concern, on federal watch lists, or sta

Final Risk and Exposure Assessment 

Appendix 7 - 17 
Terrestrial Nutrient Enrichment Case Study 

The principal source of nitrogen to the CSS ecosystem is atmospheric nitrogen (e.g., 

NOx, reduced nitrogen (NHx)). These nitrogen species are transported and deposited onto the 

historically nitrogen-limited CSS soil in the form of nitrates and nitric acid. In the soil, these 

nitrogen species are potentially available for plant uptake and nutrient cycles. The effects of 

increased availability of nitrogen species in the CSS ecosystem were the focus of this case study. 

1.3.2 Mixed Conifer Forest 

MCF stand approximately 30 to 50 m tall and consist of conifer species that dominate 

mid-range elevations (1300 to 2800 m) of the California San Bernardino and Sierra Nevada 

mountain ranges. The San Bernardino Mountains lie east of the Los Angeles air basin, and the 

Sierra Nevada Range span the majority of the state longitudinally. Figure 1.3-3 illustrates the 

range of MCF in California. MCF have historically adapted to withstand fire at low, medium, 

and even high intensities. As in the range of CSS, the climate is Mediterranean, where 80% of 

rainfall occurs from October through March (Takemoto et al., 2001). 

Dominant tree species shift along a precipitation gradient. Ponderosa pine (Pinus 

ponderosa), white fir (Abies concolor), sugar pine (P. lambertiana), and incense cedar 

(Calocedrus decurrens) are the predominant species on moist windward slopes, whereas Jeffrey 

pine (P. jeffreyi) and white fir are commonly found on leeward slopes and at higher elevations in 

the mixed conifer elevation range. Important deciduous components of the MCF are canyon live 

oak (Quercus chrysolepis), black oak (Quercus kelloggi), and quaking aspen (Popus 

tremuloides). These stands support a number of shrubs, subshrubs, and annual and perennial 

forbs, as well as mountain meadows (Minnich, 2007). Federal-listed species, Rana sierrae and

Rana muscosa (both called the mountain yellow-legged frog), and a number of state-listed 

species, such as the Peninsular bighorn sheep (Ovis canadensis nelsoni), utilize MCF habitat. 

The range of two of these selected species is illustrated in Figure 1.3-4. Table 1.3-2 shows 

selected flora and fauna associated with MCF habitat. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 18 
Terrestrial Nutrient E

Figure 1.3-3. Range of California’s mixed conifer forests. Figure 1.3-4. Presence of two threate

endangered species and mixed conifer 

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Appendix 7 - 19 
Terrestrial Nutrient E

Table 1.3-2. Selected Flora and Fauna Associated with the Mixed Conifer Forest Habitat 

Scientific Name Common Name Life Form Federal Listing* 

Abies concolor White Fir Tree Not listed Pinus ponderosa Ponderosa Pine Tree Not listed Pinus lambertiana Sugar Pine Tree Not listed Calocedrus decurrens Incense Cedar Tree Not listed Rana sierrae Sierra Madre Yellow-Legged Frog Amphibian Endangered Spea hammondii Western Spadefoot Amphibian Not listed Rana muscosa Sierra Madre Yellow-Legged Frog Amphibian Endangered Glaucomys sabrinus Northern Flying Squirrel Mammal Not listed Glaucomys sabrinus californicus San Bernardino Flying Squirrel Mammal Not listed Ovis canadensis nelsoni Peninsular Bighorn Sheep Mammal Endangered Odocoileus hemionus Black-Tailed Deer Mammal Not listed Charina umbratica Southern Rubber Boa Reptile Not listed Packera bernardina San Bernardino Ragwort Perennial Forb Not listed Sidalcea pedata Bird-Foot Checkerbloom Perennial Forb Endangered Perideridia parishii ssp. parishii Parish’s Yampah Perennial Forb Not listed Taraxacum californicum California Dandelion Perennial Forb Endangered Gilia leptantha ssp. leptantha San Bernardino Gilia Shrub Not listed Piranga rubra Summer Tanager Bird Not listed Haliaeetus leucocephalus Bald Eagle Bird Delisted Strix occidentalis occidentalis California Spotted Owl Bird Not listed Strix nebulosa Great Gray Owl Bird Not listed * Status listed for Threatened and Endangered species only. Others may be species of concern, on federal watch lists, or s

Final Risk and Exposure Assessment 

Appendix 7 - 20 
 Terrestrial Nutrient Enrichment Case Study 

Additionally, several lichen species are associated with the MCF habitats. Lichens are 

formed by a symbiotic relationship between fungus and algae or cyanobacterium. In the MCF 

ecosystem, lichens are generally epiphytic, living on conifers and obtaining nutrients from the 

atmosphere. Epiphytic lichens serve as food, habitat, and nesting material for various species in 

the pine stands (Fenn et al., 2008). The presence of individual species is determined by the 

amount of nitrogen present and the pH of the vegetation on which it grows; however, general 

categories for lichens have been developed according to species’ sensitivity to nitrogen. These 

categories include nitrophytes, neutrophytes, and acidophytes (Jovan, 2008). Nitrophytes are 

generally associated with ammonia and high pH environments. Neutrophytes tolerate increased 

pH and ammonia, but exhibit slower growth patterns than nitrophytes when exposed to these 

conditions. Acidophytes are sensitive to nitrogen species and deteriorate or die after relatively 

small increments of exposure to nitrogen species (Fenn et al., 2008). Table 1.3-3 presents a list 

of lichen species, classified by nitrogen sensitivity, that have been observed in the San 

Bernardino Mountains and Sierra Nevada Range. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 21 
Terrestrial Nutrient E

Table 1.3-3. List of Lichen Species Present in the Sierra Nevada Range and San Bernardino Mountains (Jovan, 2008; Sigal and Nash, 1983) 

Nitrophytes Potential Acidophytes Potential Neutrophytes 

Candelaria concolor Bryoria fremontii Melanelia elegantula Ahtiana sphFlavopunctelia flaventiorb Cetraria canadensis Melanelia exasperatula Alectoria saPhaeophyscia orbicularis Cetraria chlorophylla Melanelia glabra Collema furPhyscia adscendens Cetraria merrillii Melanelia subargentifera EsslingeriaPhyscia aipolia Cetraria orbata Melanelia subelegantula Leptogium Physcia dimidiate Cetraria pallidula Melanelia subolivacea Letharia coPhyscia stellaris Cetraria platyphylla Parmelia hygrophilab Letharia vuPhyscia tenella Evernia prunastri Parmelia sulcata NodobryoriPhysconia enteroxantha Hypogymnia enteromorpha Ramalina subleptocarphab NodobryoriPhysconia perisidiosa Hypogymnia imshaugii — Parmelina qXanthomendoza fallax Hypogymnia occidentalis — Parmelina eXanthomendoza fulva Parmeliopsis ambigua — Physcia bizXanthomendoza hasseana Platismatia glauca — Physconia aXanthomendoza oregano Usnea filipendula — Physconia iXanthoria candelaria — — Xanthoria polycarpa — — 

Final Risk and Exposure Assessment 

Appendix 7 - 22 
Terrestrial Nutrient Enrichment Case Study 

1.4 HISTORICAL TRENDS 

1.4.1 Coastal Sage Scrub 

The CSS ecosystem is a unique system that has experienced a significant decline in 

coverage since vegetation types in Southern California were inventoried in 1929. Subsequently, 

this community was designated for special status in California (CA DFG, 1993). This decline is 

due to urban encroachment and sprawl, increased fire frequencies, and pollution (Minnich and 

Dezzani, 1998). CSS is decreasing at a higher rate than habitat destruction alone would indicate 

(Allen et al., 1998; Fenn et al., 2003; Minnich and Dezzani, 1998). 

Nonnative grasses were introduced to California by explorer expeditions and Franciscan 

missionaries arriving in the region prior to documentation of indigenous vegetation. However, 

accounts of herbaceous vegetation in the coastal range exist from the late 1700s and throughout 

the 1800s (Minnich and Dezzani, 1998). CSS was first scientifically inventoried during the 

California Forest and Range Experiment Station Vegetation Type Map (VTM) Survey, 

beginning in 1929. Recently, 54 of the VTM sites in Southern California that were dominated by 

CSS cover in the 1930s were resampled (Talluto and Suding, 2008). Since the 1930s, CSS 

declined 49% and was mainly replaced by nonnative annual grasses (Talluto and Suding, 2008). 

Figure 1.4-1 illustrates the decline in CSS from 1977 to 2002. 

Based on changes in CSS cover from VTM data since the early 1930s, it is estimated that 

approximately 18% of the extent of Riverside County CSS had been completely converted to 

nonnative grasses, and an additional 42% of the cover had nonnative grasses intermixed with 

CSS. Therefore, only 40% of the original extent of CSS in Riverside County remained intact and 

contiguous. The 2005 resampling of part of Riverside and Orange counties indicated that 15% of 

the remaining CSS had not been invaded by annual grasses (Talluto and Suding, 2008). Across 

the entire CSS range, Westman (1981a) estimated that only 10% to 15% of the historical CSS 

extent remained in the late 1970s. This estimate is based upon the fraction of potential CSS land 

cover (in the absence of pressures) in which CSS vegetation was actually observed at the time of 

the study. The potential CSS land cover estimates may also be supported by the broad range in 

which specimens of the Quino checkerspot butterfly have historically been observed and 

collected (Mattoni et al., 1997). Therefore, the remaining extent of CSS is most likely 10% to 

82% of the historical CSS coverage, depending on the development pressures and the spread of 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 23 
Terrestrial Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 24 

nonnative grasses in each stand. Additionally, these nonnative grasses are less diverse and are 

not likely to support the majority of the sensitive, threatened, and endangered species that 

currently rely on CSS (Allen et al., 2005). 

Figure 1.4-1. Change in coastal sage scrub extent from 1977 to 2002.

1.4.2 Mixed Conifer Forest 

The major trends observed in MCF are “densification” and increased litter accumulation. 

Densification occurs when aboveground biomass is stimulated, resulting in increased numbers of 

needles, decreased average tree age, decreased overall trunk size, and increased branches (Grulke 

et al 2008; Minnich et al., 1995; Takemoto et al., 2001). In a retrospective comparison of MCF 

stands in the San Bernardino Mountains from 1932 to 1992, Minnich et al., (1995) noted 

significant shifts in age distribution, stand density, and branch density. Tree density increased 

approximately 77% according to the VTM surveys, and there were 3 to 10 times the number of 

trees in the younger age brackets when compared to conifer populations 60 years earlier. 
Terrestrial Nutrient Enrichment Case Study 

Additionally, a 79% increase in the average number of tree branches was reported in the San 

Bernardino conifer forests. Studies have indicated that increasing stand densities are also 

occurring within the Sierra Nevada Range (Minnich et al., 1995). 

Increased litter on the forest floor has also been observed across the conifer ecosystems, 

particularly in the MCF stands in the San Bernardino Mountains. These MCF stands have been 

observed to shed needles approximately six times faster than more remote northern Sierra 

Nevada Range conifer stands (Takemoto et al., 2001). Additionally, litterfall depths up to 15 

centimeters (cm) have been noted in MCF stands near Camp Paivika in the eastern San 

Bernardino Mountains (Grulke et al., 2008). 

Across the San Bernardino Mountains, a tree community composition shift was also 

noted. In MCF stands where Ponderosa pine has been historically dominant, trees in the youngest 

age bracket are now predominantly white fir and incense cedar. Additional research is needed to 

determine if a shift in community composition is also occurring in the Sierra Nevada Range 

MCF (Minnich et al., 1995). Although research on understory communities revealed no clear 

trends with atmospheric nitrogen deposition and ozone (O3), it was noted that native diversity 

had declined in those areas receiving the highest loads of atmospheric nitrogen (Allen et al., 

2007). 

Lichen communities associated with the MCF habitat have also been dramatically altered 

(Fenn et al., 2003, 2008; Sigal and Nash, 1983). Of the16 lichen species reported to be associated 

with the San Bernardino Mountains MCF in the early part of the 20th century, only 8 species 

were found 60 years later. Additionally, deterioration was observed on some of the lichens, 

particularly in the areas with the highest levels of air pollution (Sigal and Nash, 1983). Lichens 

are significant members of the MCF community. They serve as forage for wildlife, and changes 

in the lichen community are considered by some to be a warning signal for deteriorating 

conditions in the rest of the forest. 

2.0 APPROACH AND METHODOLOGY 

Using the approach and methodology described below, a number of significant ecological 

endpoints have been identified. These results come from empirical results and from spatial 

databases. Dose/response relationships beyond benchmark values were investigated, but these 

have not yet been well quantified. Nitrogen deposition data was available at a 12-km resolution, 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 25 
Terrestrial Nutrient Enrichment Case Study 

and many of the ecosystems, especially CSS, are fragmented into smaller areas. The analysis is, 

therefore, somewhat limited by the discrepancy between resolution of the nitrogen deposition 

data and the distribution of habitats, as well as by the specific areas where ecological processes 

were measured. Further, some models have been tested, but with limited results. For example, 

the steady-state simple mass-balance model (UNECE, 2004) still has many unresolved 

uncertainties. Uncertainty exists in establishing the linkage between soil and biological impacts 

and the ability to account for forest management and wildfires (Fenn et al., 2008). The DayCent 

biogeochemical model is not a watershed-scale model and may not represent NO3

-

 leaching 

accurately. Although, application of DayCent yielded results more comparable to empirically 

based findings than the steady-state model (Fenn et al., 2008). 

For the above reasons, empirical data, in tandem with GIS analysis, was deemed more 

suitable to develop potential correlations between atmospheric nitrogen deposition and 

ecological endpoints. 

2.1 PUBLISHED RESEARCH 

The ISA (U.S. EPA 2008, Sections 3.3, 4.3) was used as the basis for identifying the 

published scientific literature on CSS and MCF ecosystems. 

2.2 GIS METHODOLOGY 

2.2.1 Overview 

For both the CSS and MCF ecosystems, spatially distributed data are available. Some of 

the variables that are known to influence terrestrial nutrient enrichment and have been cited in 

the literature are available as either state-level or national-level datasets. It is important that 

spatial data are temporally and spatially compatible and have well-documented metadata. It is 

also desirable that they possess the ability to be scaled-up for a national characterization. 

2.2.2 Available Data Inputs 

2.2.2.1 Nitrogen Deposition 

Wet nitrogen deposition in the forms of NO3

–

 and ammonium (NH4

+

) are available 

nationally from the NADP. This national network of 321 sampling stations is the best wet 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 26 
Terrestrial Nutrient Enrichment Case Study 

monitored data available. Scientists at the NADP have created continuous surfaces of deposition 

values by using interpolation algorithms to estimate values between measurements at known 

locations. Dry nitrogen deposition can be estimated using the output from the CMAQ 2002 

modeling system. This model produced estimates of many nitrogen species aggregated to 12-

kilometer (km) squares. Although these data are fairly coarse spatially, they are the best that are 

currently available. 

2.2.2.2 Range of Coastal Sage Scrub 

Several publicly available spatial vegetation datasets were examined for this analysis. 

They range in dates from 1945 to 2002 and are compiled from a combination of field data and 

remotely-sensed imagery. 

The Wieslander VTM (USFS, 2008b) collection is a dataset published in 1945 that 

brought together data recorded on photos, species inventories, plots maps, and vegetation maps. 

This dataset was obtained from the California Spatial Information Library at the University of 

California, Davis. It divided the entire state of California into polygons that were attributed with 

23 different vegetation types (i.e., communities) such as “coastal sagebrush” or “chaparral.” 

Individual species were not recorded. 

Another vegetation layer named CALVEG (California Vegetation) was created in 1977 

from LANDSAT imagery that was used to create 1:1,000,000 scale maps. This dataset is 

available from the California Department of Forestry and Fire Protection's Fire and Resource 

Assessment Program (FRAP) Web site (California Department of Forestry and Fire Protection, 

2007a). This dataset contains land cover/land use polygons for California digitized from the 

1:1,000,000 scale maps. The minimum mapping unit is approximately 400 acres, and the data 

contains vegetation attributes for series-level species groups only. 

A land cover change dataset is also available from the FRAP Web site that uses Thematic 

Mapper (TM) data from 1993 and 1997 to determine areas of change (California Department of 

Forestry and Fire Protection, 2007b). This dataset also contains information on the cause of the 

change. The spatial resolution of this dataset is 30-meter pixels. These data do not contain 

species-level data; these contain only community-level data. 

California Gap Analysis Project (GAP) data is available from the University of 

California, Santa Barbara Biogeography Lab (Davis et al., 1998). It contains vegetation attributes 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 27 
Terrestrial Nutrient Enrichment Case Study 

for landscape scale map units, including canopy dominant species, canopy density, presence of 

regional endemic species, and inclusion of wetland habitats. These data were published in 1998 

and used a variety of sources including TM data, aerial photography, Wieslander VTM data, and 

field maps. 

The most recent land cover data for the state of California is also available from the 

FRAP website. It was published in 2002 and was created by compiling the best available land 

cover data into a single data layer. This agency classified California’s vegetation into 59 

different categories, including CSS, at a spatial resolution of 100 m. Decision rules were 

developed that controlled which layers were given priority in areas of overlap. Cross-walks were 

used to compile the various sources into the common California Wildlife Habitat Relationships 

system classification. No species specific data are available. 

One of the central analytical tasks for this case study was to quantify the amount of CSS 

and MCF extent loss and to see if loss corresponded spatially to areas of high nitrogen 

deposition, fire threat, or both. The land cover change layer created by the California Department 

of Forestry and Fire Protection was used for this case study analysis. While the temporal 

difference for this layer depicting land cover change was fairly small (i.e., 5 years), the two 

datasets used to create the change layer were fully compatible, and the results were verified by 

field confirmation. 

The feasibility of using the 1945 VTM, the 1977 CALVEG, the 1998 GAP, and the 2002 

FRAP land cover data to determine changes in the extent of CSS and MCF ecosystems was 

evaluated. In each case, the data sources, spatial resolution, and classification schemes were 

different enough to prevent any meaningful measurement of change in these communities. 

In addition to publicly available datasets, research datasets were obtained and plotted. 

Field data were obtained directly from Talluto and Suding (2008). While these data provided 

very detailed measurements of species distribution and percentage ground cover for their study 

areas, they were not sufficiently spatially dispersed across the CSS range, nor were they 

compatible with the very spatially coarse (i.e., 12-km grid size) CMAQ-modeled nitrogen 

deposition data. 

Additionally, The Kuchler Potential Natural Vegetation (PNV) Groups (Kuchler, 1988) 

data layer that was created to show “climax” vegetation was not used because the intent of this 

case study was to quantify known changes to the extent of CSS. The PNV data illustrates where 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 28 
Terrestrial Nutrient Enrichment Case Study 

the vegetation might potentially be found without disturbance or climate change. PNV is an 

expression of environmental factors, such as topography, soils, and climate across an area. 

2.2.2.3 Fire Threat 

The California Department of Forestry and Fire Protection’s FRAP also compiles data 

about fire threat. These data consider fire rotation (i.e., how frequently fire occurs) and potential 

fire behavior, which take into account topography and potential vegetative fuels. Fire threat is 

classified into four unique categories that range from moderate to extreme. 

2.2.2.4 Changes in Coastal Sage Scrub Communities 

Although spatial datasets mapping CSS communities exist for 1945, 1977, 1998, and 

2002, none are compatible enough to calculate meaningful change (e.g., the methods used to 

ascertain CSS extent and define ecosystems were not consistent across datasets). Therefore, a 

spatial dataset published by the California Department of Forestry and Fire Protection was 

chosen. This dataset documented change to CSS and other ecosystems between 1993 and 1997. 

2.2.2.5 Distribution of Invasive Species 

Two data sources for invasive species were found for California. The first is the PLANTS 

program, which is part of the U.S. Department of Agriculture (USDA) (USDA, 2009; 

http://plants.usda.gov/index.html). This resource posts maps that indicate whether a species is 

present or not in a given county, but not the distribution of that species within the county. The 

second is the California Invasive Plant Council (2008), (http://www.calipc.org/ip/mapping/statewide_maps/index.php), which lists the relative abundance by county of a 

select number of species. 

2.2.2.6 Threatened and Endangered Species Habitat 

The U.S. Fish and Wildlife Service (FWS) publishes critical habitat range information for 

threatened and endangered species by state, county, and species through the Critical Habitat 

Portal (http://crithab.fws.gov/) (U.S. FWS, 2008). For example, the Critical Habitat Portal 

locates 16 species for Riverside County, 5 of which are associated with CSS habitat. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 29 
Terrestrial Nutrient Enrichment Case Study 

2.2.2.7 Range of Mixed Conifer Forest 

The most recent (2002) land cover dataset from the California Department of Forestry 

and Fire Protection’s FRAP Web site was used to extract the range of MCF. Research data were 

also obtained from a series of sample plot locations documented in Fenn et al. (2008). The 

locations of the field sites were listed as latitude and longitude coordinates, which were 

converted into a GIS layer with atmospheric nitrogen deposition as an attribute. 

2.2.2.8 Distribution of Acid-Sensitive Lichens 

 The USFS FIA datasets were the source of lichen distributions. 

3.0 RESULTS 

Effects of elevated atmospheric nitrogen deposition on the CSS and MCF ecosystems are 

the result of increased long-term chronic, rather than short-term pulsed, nitrogen deposition. It is 

difficult to quantify effects in both ecosystems because of confounding stressors, such as fire and 

O3. The literature available on long-term research and application of robust models on these 

ecosystems is extremely limited. 

The CSS analysis relies upon peer-reviewed literature and spatial analyses to derive 

major conclusions regarding the effects of nitrogen. Spatial analyses were used to determine the 

changes in the extent of CSS ecosystems and their associated habitat, as well as to investigate the 

effects of nitrogen and fire, another driving component in the alteration of the CSS ecosystem. 

The reviewed literature includes greenhouse experiments, field observations, and field 

manipulation experiments that document the observed and measured effects of nitrogen. 

The MCF analysis also contains a summary of the peer-review literature; however, this 

case study focused on the empirical loading benchmarks derived from an analysis by Fenn et al. 

(2008), which employed observational data and the Simple Mass Balance (SMB) model and the 

DayCent simulation model to estimate critical loads. However, there are identified limitations to 

both models (e.g., SMB does not account for the effects of prescribed burns or wildfires on 

nutrient uptake, and DayCent is not a watershed-scale model, and thus, does not accurately 

represent NO3

-

 concentrations in surface and groundwater). Fenn et al. (2008) conclude that the 

empirical approach is the most reliable source of information. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 30 
Terrestrial Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 31 

3.1 LITERATURE REVIEW FINDINGS 

3.1.1 Coastal Sage Scrub 

CSS is subject to several pressures, such as land conversion, grazing, fire, and pollution, 

all of which have been observed to induce declines in other ecosystems (Allen et al., 1998). At 

one extreme, development pressure (i.e., the conversion of CSS to residential and commercial 

land uses) will simply eliminate acres of CSS. Other pressures will come into play in modifying 

the remaining habitat. Research suggests that both fire and increased atmospheric nitrogen 

deposition can enhance the growth of nonnative grasses in established CSS ecosystems. 

Additionally, CSS declines have been observed when fire frequency is held constant and/or 

nitrogen is held constant, suggesting that both fire and nitrogen play a role in CSS decline when 

direct destructive factors are not an imminent threat. Table 3.1-1 contains a summary of selected 

experimental variables across multiple CSS study areas. 
Terrestrial Nutrient E

Table 3.1-1. Summary of Selected Experimental Variables across Multiple Coastal Sage Scrub Study Areasa

Study Locations 

Soil 

Nitrogen 

Atmospheric 

Nitrogen 

Vegetation 

Change 

Mycorrhizae 

Change 

Fire 

Cycle 

Riverside-Perris Plainb x x x Allen et al.Santa Margarita Ecological Reserve 

x Burger et a

Santa Monica Mountains x x Carrington Orange Countyb x DiffendorfeRancho Jamul Ecological Reserve 

x 

Voorhis Ecological Reserve x Drus, 2004 Riverside-Perris Plainb x x Egerton-W

2000 

Sedgwick Ranch Natural Reserve 

x x Fierer and G

Southern California fuel breaksb

x x Merriam et 

Critical reviewb x x Keeley, 200Southern California burn sitesb

x x Keeley et a

Riverside-Perris Plainb x x Minnich anGreenhouse experiment x Padgett et aRiverside-Perris Plainb x x Padgett and University of California–Riverside Agricultural Research Station 

x Padgett et a

Riverside-Perris Plainb x x Siguenza et 

Final Risk and Exposure Assessment 

Appendix 7 - 32 
Terrestrial Nutrient E

Final Risk and Exposure Assessment 

Appendix 7 - 33 

Study Locations 

Soil 

Nitrogen 

Atmospheric 

Nitrogen 

Vegetation 

Change 

Mycorrhizae 

Change 

Fire 

Cycle 

Riverside-Perris Plainb x x Sirulnik et Lake Skinner x Sirulnik et Riverside-Perris Plainb x Vourlitis et 67 sites across CSS rangeb x Westman, Riverside-Perris Plainb x Wood et alLake Skinner Western Riverside County MultiSpecies Reserve 

x x Yoshida an

Greenhouse experiment x x a Empty cells indicate not studies identified.

b Multiple data sites within the study location. 
Terrestrial Nutrient Enrichment Case Study 

3.1.1.1 Atmospheric Nitrogen Deposition 

Increased atmospheric nitrogen deposition has been observed to alter vegetation types 

when nitrogen is a limiting nutrient to growth. This has been observed in alpine plant 

communities in the Colorado Front Range, as well as in lichen communities in the western Sierra 

Nevada region (Fenn et al., 2003, 2008); however, in the case of CSS, it is hypothesized that 

many stands are no longer limited by nitrogen and have instead become nitrogen-saturated due to 

atmospheric nitrogen deposition (Allen et al., 1998; Westman, 1981a). This is supported by the 

positive correlation between atmospheric nitrogen and soil nitrogen, increased long-term 

mortality of CSS shrubs, and increased nitrogen-cycling rates in soil and litter and soil fertility 

(Allen et al., 1998; Padgett et al., 1999; Sirulnik et al., 2007a; Vourlitis et al., 2007). Figure 

3.1-1 illustrates the levels of atmospheric nitrogen deposition on CSS ecosystems using 2002 

CMAQ/NADP data. 

Wood et al. (2006) investigated the amount of nitrogen used by healthy and degraded 

CSS ecosystems. In healthy stands, the authors estimated that 3.3 kg N/ha/yr was used for CSS 

plant growth (Wood et al., 2006). It is assumed that 3.3 kg N/ha/yr is near the point where 

nitrogen is no longer limiting in CSS. Therefore, this amount can be considered an ecological 

benchmark for CSS. Figure 3.1-1 displays the spatial extent of CSS where nitrogen deposition is 

above the ecological benchmark of 3.3 kg N/ha/yr. As shown in Figure 3.1-1 and Table 3.1-2, 

almost all of CSS receive >3.3 kg N/ha/yr of nitrogen through atmospheric deposition. Note that 

CSS is observed in areas receiving >3.3 kg N/ha/yr. This distribution may result from time lags 

(i.e., years may be required for the CSS ecosystem to completely disappear), or it may indicate 

that 3.3 kg nitrogen, although ecologically meaningful, may not be the benchmark value. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 34 
Terrestrial Nutrient Enrichment Case Study 

Figure 3.1-1. Coastal sage scrub range and total nitrogen deposition using CMAQ 

2002 modeling results and NADP monitoring data.

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 35 
Terrestrial Nutrient Enrichment Case Study 

Table 3.1-2. Coastal Sage Scrub Ecosystem Area and Nitrogen Deposition

N Deposition 

(kg/ha/yr) Area (hectares) 

Percent of 

CSS Area, % 

≥3.3 654,048 93.51 

≥10 138,019 19.73 

3.1.1.2 Nonnative Grasses 

The ecological effects of increased nitrogen are most easily explained by considering the 

seasonal stages of a semiarid Mediterranean ecosystem. In the rainy, winter season, deposited 

surface nitrogen is transported deeper into the soil and is rapidly mineralized by microbes, thus 

making it available for plants. Faster nitrogen availability may favor the germination and growth 

of nitrophylous colonizers, more specifically nonnative grasses (e.g., Bromus madritensis, Avena 

fatua, and Hirschfeldia incana). This earlier flourishing of grasses can create a dense network of 

shallow roots, which slows the diffusion of water through soil, decreases the percolation depth of 

precipitation, and decreases the amount of water for soil and groundwater recharge (Wood et al., 

2006). Growth of CSS species, such as Artemisia californica, Eriogonum fasciculatum, and 

Encelia farinose, may be decreased because of decreased water and nitrogen availability at the 

deeper soil layers where more woody CSS tap roots are found (Keeler-Wolf, 1995; Wood et al., 

2006). Furthermore, an increased percentage of shrub species is established during wet years, 

suggesting that percolation of nutrient-carrying water may be limited in years with average or 

below average precipitation (Keeley et al., 2005). 

3.1.1.3 Mycorrhizae 

Elevated nitrogen may also play a role in altering the nutrient uptake of CSS plants by 

decreasing the species richness and abundance of mutualistic fungal communities, such as 

arbuscular mycorrhizae (AM) (Egerton-Warburton and Allen, 2000; Siguenza et al., 2006). 

Although both CSS and nonnative grass species have AM and other mycorrhizal associations, 

which increase the surface area and capacity for nutrient uptake, CSS is predominantly colonized 

by a coarse AM species, and nonnative grasses are more likely mutualistic with finer AM 

species. In the presence of elevated nitrogen, coarse AM colonizations were depressed in number 

and volume. Egerton-Warburton and Allen (2000) documented shifts in AM species as well as 

declines in spore abundance and colonization at approximately 10 kg N/ha/yr. In areas with the 

highest levels of soil nitrogen tested (e.g., 57 micrograms per gram (μg/g) average annual soil 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 36 
Terrestrial Nutrient Enrichment Case Study 

nitrogen present in Jurupa Hills, Riverside County), a shift in the timing of AM growth was also 

observed. Therefore, it is suggested that these diminished mutualistic associations may 

contribute to a decline in the overall health of CSS via a loss in nutrient uptake capacity and may 

represent an ecological endpoint for the CSS ecosystem. Figure 3.1-1 displays the levels of 

atmospheric nitrogen deposition on CSS ecosystems above the ecological benchmark of 10 kg 

N/ha/yr using 2002 CMAQ/NADP data. The 12-km resolution CMAQ/NADP data indicate that 

CSS within the Los Angeles and San Diego airsheds are likely to experience the noted effects at 

the10 kg N/ha/yr ecological benchmark. 

3.1.1.4 Soil Nitrogen 

In a greenhouse fertilization experiment, soil nitrogen levels of 50 μg/g ammonium NO3

-

had a 100% mortality rate after 9 months of continuous growth. The plants began to senesce at 

approximately 6 months, whereas all lower-exposure individuals were still healthy and remained 

healthy for more than 1 year (Allen et al., 1998). In the field, seasonal changes do not allow for 

12 months of uninterrupted growth; therefore, the increased mortality shown in this study may be 

realized over much longer periods of time in situ. Additionally, studies have suggested that soil 

nitrogen may now be increasing because of soil fertility in conjunction with atmospheric 

deposition, so that the soil itself becomes an intrinsic source (Padgett et al., 1999). In 

combination with decreased establishment and the capacity for nutrient uptake, these responses 

to elevated nitrogen levels may represent a detrimental and long-term pressure on CSS at varying 

levels of nitrogen additions. Table 3.1-3 summarizes the various ecosystem responses to 

nitrogen levels that affect CSS communities. 

Table 3.1-3. Research Evidence of Ecosystem Responses to Nitrogen Relevant to 

Coastal Sage Scrub 

Environmental Impact Location Reference 

Enhanced growth of 

nonnative species 

Southern California Minnich and Dezanni, 1998; Allen et al., 

1998; Weiss, 2006; Westman, 1981a,b 

Nutrient enrichment of 

soil and plants 

Riverside-Perris Plain, 

San Diego County 

Sirulnik et al., 2007a; Allen et al., 1998; 

Padgett et al., 1999; Vourlitis et al., 2007 

Decreased growth regulation 

of shrubs 

Greenhouse experiment Padgett and Allen, 1999 

Decreased diversity of 

mycorrhizal communities 

Riverside-Perris Plain Egerton-Warburton and Allen, 2000; 

Siguenza et al., 2006 

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Terrestrial Nutrient Enrichment Case Study 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 38 

Environmental Impact Location Reference 

Increased runoff and nutrient 

loss 

Santa Barbara Fierer and Gabet, 2002 

Altered fire cycle Riverside-Perris Plain Wood et al., 2006 

Increased dependent species 

vulnerability 

All CSS; San Diego 

County 

Weiss, 2006; Weaver, 1998 

Increased erosion California shrublands Keeler-Wolf, 1995 

3.1.2 Fire 

Fire is also an inextricable and significant component in CSS losses. Although CSS 

species are fire resilient, nonnative grass seeds are quick to establish in burned lands, reducing 

the water and nutrient amounts available to CSS species for reestablishment (Keeler-Wolf, 

1995). Additionally, when nonnative annual grasses have established dominance, these species 

alter and increase the fire frequency by senescing earlier in the annual season and increasing the 

dry, ignitable fuel availability (Keeley et al., 2005). With increased fire frequencies and faster 

nonnative colonizations, CSS seed banks are eventually eradicated from the soil, and the 

probability of re-establishment decreases significantly (Keeley et al., 2005). Figure 3.1-2

represents the fire threats to CSS ecosystems. 
Terrestrial Nutrient Enrichment Case Study 

Figure 3.1-2. Current fire threats to coastal sage scrub ecosystems.

3.1.3 Coastal Sage Scrub Model 

It appears that both atmospheric nitrogen deposition and fire are critical factors involved 

in the decline of CSS. Figure 3.1-3 presents a model of CSS ecosystem response to nitrogen and 

fire. Note that the model indicates that both nitrogen and fire play critical roles, and that there 

may be positive feedback loops and possible synergies between fire and nitrogen loadings. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 39 
Terrestrial Nutrient Enrichment Case Study 

Figure 3.1-3. Model of coastal sage scrub ecosystem in relation to fire and 

atmospheric nitrogen deposition. 

3.1.4 Mixed Conifer Forest Ecosystems 

The MCF ecosystem has been a subject of study for many years. There are a number of 

important stressors on the community, including atmospheric fire, bark beetles, O3, particulates, 

and nitrogen. Although fire suppression in the 20th century is probably the most significant 

change that has led to alterations in morphology and perhaps to shifts in forest composition 

(Minnich et al., 1995), stress from elevated levels of ambient atmospheric nitrogen 

concentrations is the subject of increasing research. 

3.1.4.1 Nitrogen and Ozone Effects 

Measurements documenting increases in atmospheric nitrogen deposition have been 

recorded with some regularity since the 1980s (Bytnerowicz and Fenn, 1996); however, the Los 

Angeles area has seen elevated ambient atmospheric nitrogen concentrations for the last 50 years 

(Bytnerowicz and Fenn, 1996). Also, some data have been published for the primary nitrogen 

species of dry atmospheric nitrogen deposition in the San Bernardino Mountains (i.e., nitric acid 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 40 
Terrestrial Nutrient Enrichment Case Study 

[HNO3] and ammonia gas [NH3]) from passive samplers (Bytnerowicz et al., 2007). The 

pressures exerted on MCF ecosystems in California form a gradient across the Sierra Nevada 

Range and San Bernardino Mountains. Nitrogen throughfall levels in the northern Sierra Nevada 

Range are as low as 1.4 kg N/ha/yr, whereas forests in the western San Bernardino Mountains 

experience measured throughfall nitrogen levels up to 33 to 71 kg N/ha/yr. (Note that the high 

levels of nitrogen seen in some measured throughfall values are not reflected in the CMAQ 

modeled results. This may be an artifact of using a 12-km grid.) The primary source of nitrogen 

in the western San Bernardino Mountains stems from fossil fuels combustion, such as vehicle 

exhaust. Other sources, such as agricultural processes, also play a prominent role in the western 

portions of the San Bernardino Mountains and Sierra Nevada Range (Grulke et al., 2008). 

Figure 3.1-4 illustrates the current total atmospheric nitrogen deposition on MCF in California. 

At the individual tree level, elevated atmospheric nitrogen can shift the ratio of 

aboveground to belowground biomass. Elevated pollution levels may result in increased uptake 

of nutrients via the canopy, decreased nitrogen intake requirements on root structures, and 

increased demand for carbon dioxide (CO2) uptake and photosynthetic structures to maintain the 

carbon balances. Therefore, the increased nutrient availability stimulates aboveground growth 

and increases foliar production, while reducing the demand for belowground nutrient uptake 

(Fenn et al., 2000). Carbon allocation gradually shifts from root to shoot, and fine-root biomass 

is decreased (Fenn and Bytnerowicz, 1997; U.S. EPA, 2008, Section 3.3). Grulke et al. (1998) 

observed a 6- to 14-fold increase in fine-root mass in areas of low atmospheric nitrogen 

deposition compared to areas of high deposition. Medium roots also declined at high levels 

(Fenn et al., 2008). 

At the stand level, elevated atmospheric nitrogen has been associated with increased 

stand density, although other factors, such as fire suppression, also contribute to increased 

density and can increase mortality rates (U.S. EPA, 2008, Section 3.3). As older trees die, they 

are replaced with younger, smaller trees. Smaller trees allow more sunlight through the canopy 

and, combined with an increased availability of nitrogen, may allow for more trees to be 

established. Increased stand densities with younger-age classes are observed in the San 

Bernardino Mountains, where air pollution levels are among the highest found in the California 

MCF ranges studied (Minnich et al., 1995; Fenn et al., 2008). These shifts in stand density and 

age distribution result in vegetation structure shifts which, in turn, may impact population and 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 41 
Terrestrial Nutrient Enrichment Case Study 

community dynamics of understory plants and animals, including threatened and endangered 

species. 

Figure 3.1-4. Mixed conifer forest range and total atmospheric nitrogen 

deposition using CMAQ 2002 modeling results and NADP monitoring data.

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 42 
Terrestrial Nutrient Enrichment Case Study 

It should be noted that the effects of O3 and atmospheric nitrogen are difficult to separate. 

The atmospheric transformation of NOx can yield moderate concentrations of O3 as a byproduct 

(Grulke et al., 2008). Therefore, since elevated nitrogen levels are generally correlated with O3

concentrations, researchers often report changes in tree growth and vigor as being the result of 

both (i.e., Grulke and Balduman, 1999). 

High concentrations of O3 and atmospheric nitrogen can generate increased needle and 

branch turnover. In areas subjected to low pollution, conifers may retain needles across 4 or 5 

years; however, in areas of high pollution, such as Camp Paivika in the San Bernardino 

Mountains, needle retention is generally less than 1 year (Grulke and Balduman, 1999; Grulke et 

al, 2008). Needle turnover significantly increases litterfall. Litter biomass has been observed to 

increase in areas with elevated atmospheric nitrogen deposition up to 15 times more than in areas 

with low deposition, and the litter is seen to have higher concentrations of nitrogen (Fenn et al., 

2000; Grulke et al., 2008). Elevated nitrogen levels in litter may facilitate faster rates of 

microbial decomposition initially, but over the long term, high nitrogen levels slow litter 

decomposition, and litter accumulates on the forest floor (Grulke et al., 2008; U.S. EPA, 2008). 

The increased litter depth may then affect subcanopy growth and stand regeneration over long 

periods of time. 

At the highest levels of nitrogen deposition, native understory species were seen to 

decline (Allen et al., 2007). In addition to this decline in native understory diversity, changes in 

decreased fine-root mass, increased needle turnover, and the associated chemostructural 

alterations, MCF that are exposed to elevated pollutant levels have an increasing susceptibility to 

drought and beetle attack (Grulke et al., 1998, 2001; Takemoto et al., 2001). These stressors 

often result in the death of trees, producing an increased risk of wildfires. This complex model is 

displayed in Figure 3.1-5 as a graphic developed by Grulke et al. (2008). 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 43 
Terrestrial Nutrient Enrichment Case Study 

Figure 3.1-5. Conceptual model for increased susceptibility to wildfire in mixed 

conifer forests (Grulke et al., 2008). 

3.1.4.2 Nitrogen Effects on Lichens 

Lichens emerged as an indicator of nutrient enrichment from the research on the effects 

of acid rain. Lichen species can be sensitive to air pollution; in particular, atmospheric nitrogen 

deposition. Since the 1980s, information about lichen communities has been gathered, and 

lichens have been used as indicators to detect changes in forest communities. Jovan (2008) 

depicts how lichens might be considered as sentinels in the MCF community (Figure 3.1-6). 

Figure 3.1-6. Importance of lichens as an indicator of ecosystem health (Jovan, 2008).

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 44 
Terrestrial Nutrient Enrichment Case Study 

As atmospheric nitrogen deposition increases, the relative abundance of acidophytic 

lichens decreases, and the concentration of nitrogen in one of those species, Letharia vulpine, 

increases (Fenn et al., 2008). Fenn et al. (2008) were able to quantify the change in the lichen 

community, noting that for every 1 kg N/ha/yr increase, the abundance of acidophytic lichens 

declined by 5.6%. Figure 3.1-7 illustrates the presence of acidophyte lichens and the total 

atmospheric nitrogen deposition in the California ranges. 

In addition to abundance changes, species richness, cover, and health are affected in areas 

of high O3 and nitrogen concentrations. Fifty percent fewer lichen species were observed after 60 

years of elevated air pollution in San Bernardino Mountains MCF, with the areas of highest 

pollution levels exhibiting low species richness, decreased abundance and cover, and 

morphological deterioration of existing lichens (Sigal and Nash, 1983). 

Ecological endpoints relating to shifts in the abundance of acidophilic lichens were 

identified by Fenn et al. (2008). They found that at 3.1 kg N/ha/yr, the community of lichens 

begins to change from acidophilus to tolerant species; at 5.2 kg N/ha/yr, the typical dominance 

by acidophilus species no longer occurs; and at 10.2 kg N/ha/yr, acidophilic lichens are totally 

lost from the community. Additional studies in the Colorado Front Range of the Rocky Mountain 

National Park support these findings and are summarized in Chapter 5 of the Risk and Exposure 

Assessment. These three values are one set of ecologically meaningful benchmarks for the MCF. 

As shown in Figure 3.1-7, much of the MCF receives nitrogen deposition levels above the 3.1 

kg N/ha/yr ecological benchmark according to the 2002 CMAQ/NADP data, with the exception 

of the easternmost Sierra Nevada Range. MCF in the southern portion of the Sierra Nevada 

forests and nearly all MCF communities in the San Bernardino forests receive nitrogen 

deposition levels above the 5.2 kg N/ha/yr ecological benchmark. Figure 3.1-7 also displays the 

potential areas where acidophilic lichens are extirpated due to nitrogen deposition levels >10.2 

kg N kg/ha/yr. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 45 
Terrestrial Nutrient Enrichment Case Study 

Figure 3.1-7. Presence of acidophyte lichens and total nitrogen deposition in the 

California mountain ranges using CMAQ 2002 modeling results and NADP 

monitoring data. 

3.1.4.3 Nitrogen Saturation 

The established signs of nitrogen saturation have been shown within the MCF ecosystem. 

These symptoms include the following: 

 Increased carbon and nitrogen cycling. The foliar turnover rates and changes in 

microbial decomposition both suggest that carbon and nitrogen cycles have been altered 

as a result of elevated nitrogen. Additionally, nitrogen fluxes in San Bernardino 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 46 
Terrestrial Nutrient Enrichment Case Study 

Mountains soils are elevated when compared to MCF in the northern Sierra Nevada 

Range (Bytnerowicz and Fenn, 1996). 

 Decreased nitrogen uptake efficiency of plants. Changes in root:shoot ratio 

demonstrate structural alterations in response to increasing available nitrogen. 

 Increased loss of forest nitrates to streamwater (i.e., NO3

-

 leachate). Elevated NO3

- 

leachate levels are estimated to have begun in the late 1950s and have been observed 

from the western MCF in the San Bernardino Mountains since 1979 (Fenn et al., 2008). 

These losses are a result of high soil nitrogen driven by the combined litter, needle 

turnover, and throughfall nitrogen exerted in these areas (Bytnerowicz and Fenn, 1996). 

Changes in root biomass and stream leachate, in addition to lichen species compositional 

shifts, have been used to develop benchmarks for nitrogen benchmarks in the MCF ecosystem. 

These critical loading benchmarks, or empirical loads, are designed to estimate the levels at 

which atmospheric nitrogen concentrations and subsequent deposition begin to affect selected 

components of the ecosystem, such as forest growth, health, and composition. Some benchmarks 

aim to estimate individual changes to an ecosystem, whereas others assess the levels at which the 

entire ecosystem will not be altered because of atmospheric nitrogen deposition. The possibility 

of using the MCF as a model for benchmarking is discussed below. 

Fenn et al. (2008) established a critical loading benchmark of 17 kg throughfall N/ha/yr 

in the San Bernardino Mountains and Sierra Nevada Range MCF ecosystems. This benchmark 

represents the level of atmospheric nitrogen deposition at which elevated concentrations of 

streamwater NO3

-

 leachate or potential nitrogen saturation may occur. At this deposition level, a 

26% reduction in fine-root biomass is anticipated (Fenn et al., 2008). Root:shoot ratios are, 

therefore, altered, and changes in nitrogen uptake efficiencies, litterfall biomass, and microbial 

decomposition are anticipated to be present at this atmospheric nitrogen deposition level. This 

benchmark is based on 30 to 60 years of exposure to elevated atmospheric concentrations. At 

longer exposure levels, the benchmark is lower because of decreased nitrogen efficiencies of the 

ecosystem. This benchmark is exceeded in areas of the western San Bernardino Mountains, such 

as Camp Paivika. 

NO3

-

 leaching is a symptom that an ecosystem is saturated by nitrogen. NO3

-

 leaching is 

also known to cause acidification in adjacent surface waters. The ecological benchmark of 17 kg 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 47 
Terrestrial Nutrient Enrichment Case Study 

N/ha/yr is the last benchmark identified in this study. At this level of atmospheric nitrogen 

deposition, NO3

-

 is observed in streams in the MCF (Fenn et al., 2008), denoting a change in 

ecosystem function. 

Table 3.1-4 displays the area in hectares of MCF experiencing different nitrogen 

deposition levels. 

Table 3.1-4. Mixed Conifer Forest Ecosystem Area and Nitrogen Deposition

N Deposition 

(kg/ha/yr) 

Area 

(hectares) 

Percent of MCF 

Area, % 

≥3.1 1,099,133 38.62 

≥5.2 130,538 4.59 

≥10.2 11,963 0.42 

≥17 0 0.00 

3.2 RESULTS SUMMARY 

A range of ecological benchmarks were developed in the results. All benchmarks are tied 

to a level of atmospheric nitrogen deposition, but include a number of different ecological 

processes and ecological endpoints. All of the benchmarks are ecologically significant in that 

changes are seen that are related to community structure and function. The benchmarks span a 

range from 3.1 to 17 kg N/ha/yr and include: 

 3.1 kg N/ha/yr—shift in lichen communities are first observed in MCF 

 3.3 kg N/ha/yr—nitrogen no longer limiting in CSS 

 5.2 kg N/ha/yr—dominance of tolerant lichen species in MCF 

 10 kg N/ha/yr—AM community shift in CSS 

 10.2 kg N/ha/yr—loss of acidophilic lichen species from MCF 

 17 kg N/ha/yr—NO3

-

 leaching in MCF. 

This range of ecological benchmarks may be used to develop a “green line/red line” 

schematic, similar to the forest screening model discussed in Lovett and Tear (2007) that 

illustrates the levels at which ecosystem effects may occur or are known to occur. In Figure 

3.2-1, the green area/line denotes that point at which there do not appear to be any effects and the 

red line the point at which known negative effects occur. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 48 
Terrestrial Nutrient Enrichment Case Study 

Figure 3.2-1. Illustration of the range of terrestrial ecosystem effects observed 

relative to atmospheric nitrogen deposition. 

For the benchmarks identified, effects may occur at the level of atmospheric nitrogen 

deposition associated with the “green line” illustrated in Figure 3.2-1, so the “green line” may be 

somewhat lower. Whereas, the higher level of atmospheric nitrogen deposition (i.e., both at 10.2 

and 17 kg N/ha/yr) better resembles a “red line,” where a known negative effect occurs. 

The range of ecological benchmarks in CSS and MCF are not dissimilar from ecological 

endpoints and benchmarks identified in other ecosystems with related characteristics, such as 

arid systems, other forested systems, or grasslands. Egerton-Warburton et al. (2001) report that at 

10 kg N/ha/yr, nitrogen changes in mycorrhizal communities/grass biomass are observed in 

chaparral habitats. Nitrates are found to leach into streams from nitrogen-saturated forest soils at 

deposition levels between 9 and 13 kg N/ha/yr (Aber et al., 2003). Results from several studies 

suggest ecosystem changes that are related to nitrogen deposition. The capacity of alpine 

catchments to sequester nitrogen is exceeded at input levels <10 kg N/ha/yr (Baron et al., 1994). 

Changes in the Carex plant community were observed to occur at deposition levels near 10 kg 

N/ha/yr (Bowman et al., 2006). Clark and Tilman (2008) predict that at 5.3 kg N/ha/yr, there is a 

loss of species diversity in grasslands. In the Pacific Northwest and in Central California, a 

number of investigators have observed declines in sensitive lichen species as air pollution 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 49 
Terrestrial Nutrient Enrichment Case Study 

increases (Jovan and McCune, 2005; Geiser and Neitlich, 2007). In Europe, acidophyte decline 

has been identified in regions with 8 to 10 kg N/ha/yr (Bobbink, 1998; Bobbink et al., 1998). 

4.0 IMPLICATIONS FOR OTHER SYSTEMS 

This Terrestrial Nutrient Enrichment Case Study examined the effects of atmospheric 

nitrogen on two ecosystem types in California: CSS and MCF. Figure 4.1-1 presents the 

coverage of 2002 CMAQ/NADP data for total nitrogen deposition in the western United States, 

including California. Ecological effects have been documented across the United States where 

elevated nitrogen deposition has been observed. Benchmarks documented in the literature for the 

negative effects on ecosystems are summarized in Figure 4.1-2 and are discussed in this case 

study report. Looking across the United States, Figure 4.1-3 illustrates the occurrence of these 

ecosystems which are sensitive to nitrogen and/or have similar characteristics to the ecosystems 

explored in this case study. These ecosystems may also experience levels of atmospheric 

nitrogen deposition that exceed the benchmark levels identified in Figure 4.1-2. Table 4.1-1 lists 

the area of CSS and MCF that exceed benchmark nitrogen levels. 

In the western United States, other arid and forested ecosystems exposed to deposition at 

levels discussed in this case study may experience altered effects. As noted in Section 3, research 

on grasslands and chaparral habitats is underway. These arid systems may respond to 

benchmarks similar to those observed for CSS, as was shown by Clark and Tilman (2008) for 

bluestem grasslands in Minnesota. NO3

-

 leaching in forests with elevated deposition (similar to 

the range found in this study) may result in nutrient enrichment in streams which can affect 

aquatic ecosystems (Aber et al., 2003). Research is also being conducted on lichen species in the 

Pacific Northwest and in Central California that are exposed to elevated levels of atmospheric 

nitrogen deposition (Jovan, 2008). Extensive research on the eastern Front Range of the Rocky 

Mountain National Park has been conducted in alpine and subalpine terrestrial and aquatic 

systems at elevations above 3,300 m, where communities are typically adapted to low nutrient 

availability but are now being exposed to >10 kg N/ha/yr in some study areas (Baron et al. 2000; 

Baron, 2006). (Chapters 5 and 6 of the Risk and Exposure Assessment also provide discussion on 

this topic.) 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 50 
Terrestrial Nutrient Enrichment Case Study 

Figure 4.1-1. 2002 CMAQ-modeled and NADP monitoring data for deposition of 

total nitrogen in the western United States.

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 51 
Terrestrial Nutrient Enrichment Case Study 

Figure 4.1-2. Benchmarks of atmospheric nitrogen deposition for several 

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

Mountain lakes. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 52 
Terrestrial Nutrient Enrichment Case Study 

Note: PNW = Pacific Northwest; NE = Northeast. 

Figure 4.1-3. Habitats that may experience ecological benchmarks similar to 

coastal sage scrub and mixed conifer forest. 

Table 4.1-1. Areas of Coastal Sage Scrub and Mixed Conifer Forest That Exceed Benchmark 

Nitrogen Deposition Levels.

CSS:

N Deposition (kg/ha/yr) Area (hectares) Percentage of CSS Area, % 

≥3.3 654,048 93.51 

≥10 138,019 19.73 

MCF

N Deposition (kg/ha/yr) Area (hectares) Percentage of MCF Area, % 

≥3.1 1099,133 38.62 

≥5.2 130,538 4.59 

≥10.2 11,963 0.42 

≥17 0 0.00 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 53 
Terrestrial Nutrient Enrichment Case Study 

Other systems with the following characteristics may also be found to be sensitive: 

 Ecosystems with nitrogen-sensitive epiphytes, such as lichens or mycorrhizae. Such 

systems may demonstrate shifts in community structure through changes in nutrient 

availability or modified provisioning services. 

 Ecosystems that may have been exposed to long periods of elevated atmospheric 

nitrogen deposition. The established signs of nitrogen saturation are increased leaching 

of NO3

-

 into streamwater, decreased nitrogen uptake efficiency of plants, and increased 

carbon and nitrogen cycling. At prolonged elevated nitrogen levels, ecosystems are 

generally less likely to use, retain, or recycle nitrogen species efficiently at both the 

species and community levels. 

 Critical habitats. Ecosystems that are necessary for endemic species or special 

ecosystem services should be monitored for possible changes due to nitrogen. 

 Locations where there are seasonal releases of nitrogen. In both the California CSS 

and MCF ecosystems discussed in this case study report, a large portion of nitrogen is 

dry-deposited and remains on the foliage and soil surface until the beginning of the 

winter rainy season when nitrogen will be flushed into the soil. 

Current analysis of the effects of terrestrial nutrient enrichment from atmospheric 

nitrogen deposition in both CSS and MCF seeks to improve scientific understanding of the 

interactions among nitrogen deposition, fire events, and community dynamics. The available 

scientific information is sufficient to identify ecological thresholds that are affected by nitrogen 

deposition, and ecological thresholds have been identified for CSS and MCF. This case study 

report has examined the sensitivity and effects of nutrient enrichment on terrestrial ecosystems, 

and although a diverse array of U.S. ecosystems exist, exposure levels and thresholds for effects 

appear to be generally comparable to levels identified in other sensitive U.S. ecosystems (e.g., 

thresholds range from 3.1 to 30.5 kg N/ha/yr), including thresholds identified from modeling 

conducted for other case studies in the Risk and Exposure Assessment (Chapters 4 and 5). 

Knowledge and understanding of such relevant exposure levels can help inform decision makers. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 54 
Terrestrial Nutrient Enrichment Case Study 

5.0 UNCERTAINTY 

5.1 COASTAL SAGE SCRUB

There are several areas of uncertainty associated with this case study of CSS. 

 Although current research indicates that both atmospheric nitrogen deposition and fire 

have contributed to the decline of CSS, the interaction between the variables and the 

extent of their contributions requires further research. CSS declines have been observed 

in the absence of fire when elevated nitrogen levels are present, and declines have also 

been observed in the absence of elevated nitrogen, but due to fire. Therefore, there is still 

a need for quantifiable and predictive results to indicate the pressure of each variable, as 

well as the pressure of the combined variables (if synergism is present). Additional 

studies are also required to test the proposed nitrogen-fire feedback loop and the 

associated biogeochemical elements (e.g., changes in water availability and mycorrhizal 

associations) that contribute to CSS decline. 

 Many studies allude to a degradation of CSS by assessing species richness and 

abundance, but it is not clear that indicators of CSS ecosystem health have been 

adequately explored. Assessing the health of CSS ecosystems may help to identify a 

response curve to the factors associated with CSS decline. 

 Ongoing experiments are beginning to show changes in CSS in response to elevated 

nitrogen over relatively long periods of time (Allen, personal communication, 2008). The 

incremental process may be occurring slower than previous field research experiments 

have lasted, making the reasons for the decline appear variable or imperceptible over the 

duration of a typical study. 

 At this point, CSS is fragmented into many relatively small parcels. The CMAQ/NADP 

2002 data is being modeled at 4-km resolution. The availability of these 4-km resolution 

data will provide a better sense of the relationship between the current distribution of 

CSS and atmospheric nitrogen loads and fire threat. 

 Very little research exists regarding the effects of O3 on CSS. Although there is some 

support that O3 is negatively correlated with CSS, the role has yet to be quantified or 

consistently studied (Westman, 1981a). 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 55 
Terrestrial Nutrient Enrichment Case Study 

 The last area of uncertainty is the relationship between current CSS distribution and the 

changing climate. 

5.2 MIXED CONIFER FOREST 

The currently known areas of uncertainty for MCF are as follows: 

 The long-term consequences of increased nitrogen on conifers are unclear. Although the 

results indicate an increased susceptibility to wildfire and disease, the long-term health of 

the stands and risk of cascading effects into the ecosystem require further investigation. 

 The effects of O3 for both MCF and lichens confound the effects of nitrogen. 

 The intermingling of fire and nitrogen cycling require additional research. 

 Research suggests that critical loading benchmarks can decrease over time if the nitrogen 

benchmark is exceeded for long periods of time because of decreasing nitrogen 

efficiencies within nitrogen-saturated ecosystems (Fenn et al., 2008). This may indicate 

that a sliding-scale approach will be required when evaluating ecosystems of varying 

nitrogen responses. 

 There remains considerable uncertainty in the potential response of soil carbon to 

increases in total reactive nitrogen additions. 

6.0 CONCLUSIONS 

Evidence from the two ecosystems discussed in this case study report supports the 

finding that nitrogen alters CSS and MCF. For this analysis, the loss of the native shrubs in CSS 

and the increase in nonnative annual grasses were investigated. In MCF on the slopes of the San 

Bernardino Mountains and Sierra Nevada Range, lichen communities associated with the forest 

stands and nitrogen saturation were investigated to identify the effects of nitrogen loadings. 

California’s CSS and MCF have important recreational value, protect water resources, and 

provide habitats for many other species. In the CSS ecosystem, there is compelling evidence that 

elevated atmospheric nitrogen deposition is a driving force in the degradation of CSS. A CSS 

model was developed to help identify and parse the pressures and changes occurring within the 

ecosystem. In the MCF ecosystem, lichen communities and nitrogen saturation can provide a 

means to monitor and quantify the effects of nitrogen loadings. 

Final Risk and Exposure Assessment September 2009 

Appendix 7 - 56 
Terrestrial Nutrient Enrichment Case Study 

Ecological benchmarks for a suite of indicators were identified in both ecosystems: 

 3.1 kg N/ha/yr—shift from sensitive to tolerant lichen species in MCF 

 3.3 kg N/ha/yr—the amount of nitrogen uptake by a vigorous stand of CSS; above this 

level, nitrogen may no longer be limiting 

 5.2 kg N/ha/yr—dominance of tolerant lichen species in MCF 

 10 kg N/ha/yr—mycorrhizal community changes in CSS 

 10.2 kg N/ha/yr—loss of sensitive lichen species from MCF 

 17 kg N/ha/yr—NO3

-

 leaching in MCF 

Because these benchmarks are comparable to levels identified in other sensitive U.S. 

ecosystems and are also comparable to modeled values found in the other case studies, this set of 

ecological benchmarks supports the need for continued monitoring, research, and protection of 

sensitive ecosystems and informs the decision-making process. 

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June 5, 2009 

Appendix 8 

Analysis of Ecosystem Services Impacts for the 

NOx/SOx Secondary NAAQS Review

Final

Prepared for 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Climate, International and Multi-Media Group (CIMG) 

(MD-C504-04) 

Research Triangle Park, NC 27711 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709-2194 

Maura Flight 

Industrial Economics, Inc. 

EPA Contract Number EP-D-06-003 

RTI Project Number 0209897.003.066 

Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

TABLE OF CONTENTS 

1.0 Introduction ............................................................................................................................... 5

1.1 Ecosystem Service Categories ......................................................................................... 7

1.1.1 Descriptions and Examples of MEA Ecosystem Services ................................... 9

1.2 References ...................................................................................................................... 11

2.0 Aquatic Acidification .............................................................................................................. 12

2.1 Overview of Affected Ecosystem Services .................................................................... 12

2.1.1 Provisioning Services......................................................................................... 12

2.1.2 Cultural Services ................................................................................................ 13

2.1.3 Regulating Services ........................................................................................... 13

2.2 Changes in Ecosystem Services Associated with Alternative Levels of 

Ecological Indicators ..................................................................................................... 14

2.2.1 Improvements in Recreational Fishing Services due to Increased Acid 

Neutralizing Capacity Levels in Adirondack and Other New York Lakes ....... 18

2.2.2 Improvements in Total Ecosystem Services due to Increased Acid 

Neutralizing Capacity Levels in Adirondack Lakes .......................................... 29

2.3 References ...................................................................................................................... 35

3.0 Terrestrial Acidification .......................................................................................................... 37

3.1 Overview of Affected Ecosystem Services .................................................................... 37

3.1.1 Provisioning Services......................................................................................... 37

3.1.2 Cultural Services ................................................................................................ 39

3.1.3 Regulating Services ........................................................................................... 43

3.2 Changes in Ecosystem Services Associated with Alternative Levels of 

Ecological Indicators ..................................................................................................... 44

3.2.1 Increased Provisioning Services from Sugar Maple Timber Harvests due 

to Elimination of Critical Load Exceedances .................................................... 44

3.3 References ...................................................................................................................... 57

4.0 Aquatic Enrichment ................................................................................................................ 60

4.1 Overview of Affected Ecosystem Services .................................................................... 62

4.1.1 Provisioning Services......................................................................................... 62

4.1.2 Cultural Services ................................................................................................ 66

4.1.3 Regulating Services ........................................................................................... 66

4.2 Changes in Ecosystem Services Associated with Alternative Levels of 

Ecological Indicators ..................................................................................................... 67

4.2.1 The Chesapeake Bay Estuary ............................................................................. 69

4.2.2 Neuse River Estuary .......................................................................................... 93

4.3 References ...................................................................................................................... 98

5.0 Terrestrial Enrichment .......................................................................................................... 104

5.1 Overview of Affected Ecosystem Services .................................................................. 104

5.1.1 Cultural ............................................................................................................ 105

5.1.2 Regulating ........................................................................................................ 116

5.2 Value of Coastal Sage Scrub and Mixed Conifer Forest Ecosystem Services ............ 120

Final Risk and Exposure Assessment September 2009 

Appendix 8 - i 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

5.3 References .................................................................................................................... 121

6.0 Conclusion ............................................................................................................................ 123

6.1 Benefits from Enhanced Provisioning Services ........................................................... 123

6.2 Benefits from Enhanced Cultural Services .................................................................. 125

6.3 Benefits from Enhanced Regulating Services.............................................................. 126

Attachment A: Annual Recreational Fishing Benefit Estimates for Reductions in 

New York Lake Acidification Levels, 2002–2100 ........................................ A-1 

LIST OF FIGURES 

Figure 1.1-1. Conceptual Framework for Linking Changes in Ambient NOx and SOx

Levels to Changes in Ecosystem Services and Public Welfare .............................. 6

Figure 1.1-2. MEA Categorization of Ecosystem Services and their Links to Human 

Well-Being (Source: MEA, 2005b). ....................................................................... 9

Figure 2.2-1. Summary of Acid Neutralizing Capacity Values Relevant for Lake and 

Fish Health (Source: Industrial Economics, Inc., 2008). ...................................... 17

Figure 3.1-1. Combined Nitrogen and Sulfur Deposition (from 2002 CMAQ Dry 

Deposition and NADP Wet Deposition Estimates) and the Range of Sugar 

Maple in the United States .................................................................................... 38

Figure 3.1-2. Combined Nitrogen and Sulfur Deposition (from 2002 CMAQ Dry 

Deposition and NADP Wet Deposition Estimates) and the Range of Red 

Spruce in the United States ................................................................................... 39

Figure 3.1-3. Annual Value of Sugar Maple and Red Spruce Harvests and Maple Syrup 

Production, 2006 ................................................................................................... 40

Figure 3.2-1. Estimated Time Path of Welfare Gains in the Forestry and Agricultural 

Sector due to Increased Sugar Maple and Red Spruce Growth (2000–

2065) ..................................................................................................................... 55

Figure 4-1. Conceptual Model of Eutrophication Impacts in Estuaries (Source: Adapted 

from Bricker et al. [2007] and Bricker, Ferreira, and Simas [2003]). .................. 61

Figure 4.2-1. Chesapeake Bay Coastal Block Groups ................................................................. 86

Figure 5.1-1. Coastal Sage Scrub Areas and Population ........................................................... 106

Figure 5.1-2. Mixed Conifer Forest Areas and Population ........................................................ 107

Figure 5.1-3. Boundaries of the NCCP Region and Subregions for Coastal Sage Scrub 

(Source: California Department of Fish and Game, n.d.). .................................. 108

Figure 5.1-4. Mixed Conifer Forest Areas and National and State Park Boundaries ................ 110

Figure 5.1-5. Coastal Sage Scrub Areas and Housing Values ................................................... 113

Figure 5.1-6. Presence of Three Threatened and Endangered Species in California’s 

Coastal Sage Scrub Ecosystem ........................................................................... 114

Figure 5.1-7. Presence of Two Threatened and Endangered Species in California’s 

Mixed Conifer Forest .......................................................................................... 115

Figure 5.1-8. Coastal Sage Scrub Areas and Fire Threat ........................................................... 118

Figure 5.1-9. Mixed Conifer Forest Areas and Fire Threat ....................................................... 119

Figure 5.1-10. Mixed Conifer Forest Areas and Major Lakes and Rivers ................................. 120

Final Risk and Exposure Assessment September 2009 

Appendix 8 - ii 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

LIST OF TABLES 

Table 2.2-1. Participation in Freshwater Recreational Fishing in Northeastern States in 

2006....................................................................................................................... 14

Table 2.2-2. Acid Neutralizing Capacity Levels (in μeq/L) at 44 MAGIC-Modeled Lakes 

in the Adirondacks ................................................................................................ 15

Table 2.2-3. Random Effects Model Results ............................................................................... 20

Table 2.2-4. Count of Impacted Lakes ......................................................................................... 23

Table 2.2-5. Per Capita Willingness to Pay (2007 $) ................................................................... 24

Table 2.2-6. Present Value and Annualized Benefits, Adirondack Region ................................. 26

Table 2.2-7. Present Value and Annualized Benefits, New York State ....................................... 27

Table 2.2-8. Comparison of Resources for the Future Contingent Valuation Scenarios 

and EPA Zero-Out Scenario ................................................................................. 30

Table 2.2-9. Aggregate Benefit Estimates for the Zero-Out Scenario ......................................... 33

Table 3.1-1. Participation in Hunting and Wildlife Viewing in Northeastern States in 

2006....................................................................................................................... 42

Table 3.2-1. Summary of Plot-Level Data on Sugar Maple Growth and Exceedances (for 

Plots above the Glaciation Line) ........................................................................... 46

Table 3.2-2. Summary of Plot Level Data on Sugar Maple Growth and Exceedances (for 

Plots above the Glaciation Line) ........................................................................... 47

Table 3.2-3. Linear Exposure–Response Model for Exceedances (above a Critical Load 

Calculated with Bc/Al = 10) and Sugar Maple Tree Growth: OLS 

Regression Results (for Plots above the Glaciation Line) .................................... 48

Table 3.2-4. Linear Exposure–Response Model for Exceedances (above a Critical Load 

Calculated with Bc/Al = 10) and Red Spruce Tree Growth: OLS 

Regression Results (for Plots above the Glaciation Line) .................................... 49

Table 3.2-5. Estimated Increments in Sugar Maple and Red Spruce Timber Volume 

(Resulting from Elimination of Critical Load Exceedances), by 

FASOMGHG Region............................................................................................ 52

Table 3.2-6. Proportions of Hardwood in Sugar Maple Production and Proportions of 

Softwood in Red Spruce Production, by FASOM Region ................................... 53

Table 3.2-7. Proportion of Timberland under Private and Public Ownership by FIA 

Regiona: 2002 ........................................................................................................ 54

Table 4.1-1. Annual Values of East Coast Commercial Landings (in millions) .......................... 63

Table 4.1-2. Value of Commercial Landings for Selected Species in 2007 (Chesapeake 

Bay Region) .......................................................................................................... 64

Table 4.2-1. Participation in Selected Marine Recreation Activities in East Coast States 

in 1999–2000 ........................................................................................................ 68

Table 4.2-2. Regression Analysis of the Chesapeake Water Quality Index on Water 

Quality Parameters ................................................................................................ 71

Table 4.2-3. Average Catch Rate per Fishing Trip in the Chesapeake Bay, by State and 

Targeted Fish Species ........................................................................................... 73

Table 4.2-4. Aggregate Number of Fishing Trips to the Chesapeake Bay, by State and 

Targeted Fish Species ........................................................................................... 75

Table 4.2-5. Input Estimates for the Chesapeake Bay Boating Benefit Transfer Model ............. 78

Table 4.2-6. Input Estimates for the Chesapeake Bay Beach-Use Benefit Transfer Model ........ 82

Final Risk and Exposure Assessment September 2009 

Appendix 8 - iii 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - iv 

Table 4.2-7. Summary of Housing Unit Numbers and Average Prices in Chesapeake 

Coastal Block Groups in 2007 .............................................................................. 87

Table 5.1-1. Recreational Activities in California in 2006 by Residents and Nonresidents ...... 111

Table 6-1. Summary of Aggregate Benefit Estimates for Selected Ecosystem Services 

and Areas (Zero Out of Nitrogen and Sulfur Deposition)a

 ................................. 124
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 5 

1.0 INTRODUCTION 

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

secondary National Ambient Air Quality Standards (NAAQS) for nitrogen oxides (NOx) and 

sulfur oxides (SOx). As part of the review, EPA is interested in linking changes in NOx and SOx

ambient air concentrations to the changes in ecosystem services and ultimately to changes in 

public welfare. This process of linking changes in ambient NOx and SOx levels to public welfare 

through the effects on ecosystem services is illustrated in Figure 1.1-1. Reducing NOx and SOx

concentrations will reduce the stresses on aquatic and terrestrial ecosystems by reducing 

atmospheric deposition of nitrogen and sulfur compounds. As shown in the figure, EPA has 

identified four main categories of adverse ecosystem effects—aquatic acidification, terrestrial 

acidification, aquatic nutrient enrichment, and terrestrial nutrient enrichment.1 For each of these 

categories, EPA has identified key ecological indicators, which provide quantitative measures of 

adverse impacts on the affected ecosystems. 

The purpose of this report is to identify, characterize, and, to the extent possible, quantify 

the ecosystem services that are primarily impacted by nitrogen and sulfur deposition (see Section 

1.1 for the definition and categorization framework used to define ecosystem services) and the 

changes in ecosystem services that are expected to result from changes in the ecological 

indicators. By linking indicators of ecological function to ecosystem service provision through 

risk and economic assessments, the objective is to inform decisions regarding the adequacy of 

current NAAQS and the ecosystem protection afforded by potential revisions to the current 

primary standards for NOx and SOx. 

This report includes four main sections (after this one), each dedicated to one of the main 

ecosystem effect categories defined above and in Figure 1.1-1. Section 2 focuses on aquatic 

acidification and provides an overview of the main ecosystem services affected by acidification 

of freshwater. The section then applies the results of the Aquatic Acidification Case Study of 

Adirondack lakes to quantify specifically the value of improved recreational fishing and other 

cultural services caused by reductions in lake acidification in this part of the country. 

 

1 Although other effects exist, the magnitude and/or scientific evidence of these effects is much more limited. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Ecosystem Effects/

Symptoms

Ambient Air Quality

Stressor

Affected Ecosystems

Ecological Indicators

Affected Ecosystem 

Services

NOx/SOx Levels Under 

Current Conditions

NOx/SOx Levels Under 

Alternative Conditions

Atmospheric N and S Deposition

Aquatic Terrestrial

Provisioning

Cultural

Regulating

Changes in 

Ecosystem 

Services

Public Welfare 

Gains

Acidification

Nutrient Enrichment

•

•

•

Acidification

Nutrient Enrichment

•

•

•

Lake ANC Levels

Eutrophication Indicators

•

•

•

Forest Soil Chemistry

Lichen Community Changes

•

•

•

Figure 1.1-1. Conceptual Framework for Linking Changes in Ambient NOx and 

SOx Levels to Changes in Ecosystem Services and Public Welfare 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 6 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Section 3 focuses on terrestrial acidification, providing an overview of the main 

ecosystem services affected by acidification of forest soils. It then applies the results of the 

Terrestrial Acidification Case Study and additional analyses of impacts on sugar maple trees to 

quantify the value of improved provisioning services associated with expected enhancements to 

forest productivity. 

Section 4 focuses on aquatic nutrient enrichment. It describes and characterizes the 

ecosystem services that are primarily affected by the eutrophication processes in estuaries that 

result from excess nitrogen loadings. It then applies the results of the Aquatic Nutrient 

Enrichment Case Study of the Potomac River/Potomac Estuary Case Study Area and the Neuse 

River/Neuse River Estuary Case Study Area to quantify improvements in provisioning and 

cultural services associated with reduced nitrogen loadings and improvements in eutrophic 

conditions in the Chesapeake Bay and Neuse estuaries. 

Section 5 focuses on terrestrial nutrient enrichment. It provides an overview of the 

ecosystem services that are primarily affected by excess nitrogen loadings in two main terrestrial 

ecosystems—California coastal sage scrub (CSS) and mixed conifer forest (MCF) habitats. It 

also applies the findings from the Terrestrial Nutrient Enrichment Case Study of these affected 

ecosystems; however, like the case study, because of data limitations and current knowledge 

gaps, Section 5 does not quantify expected changes due to reductions in nitrogen and sulfur 

deposition. 

1.1 ECOSYSTEM SERVICE CATEGORIES 

Ecosystem services are generally defined as the benefits individuals and organizations 

obtain from ecosystems. This report uses the classification framework for ecosystem services 

developed by the Millennium Ecosystem Assessment (MEA) (2005a, 2005b). In the MEA, 

ecosystem services are defined to include both natural and human-modified ecosystems. Services 

are further defined to encompass both tangible and intangible benefits that individuals and 

organizations derive from ecosystems. In the MEA, ecosystem services are classified into four 

main categories: 

 Provisioning: includes products obtained from ecosystems. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 7 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 8 

 Cultural: includes the nonmaterial benefits people obtain from ecosystems through 

spiritual enrichment, cognitive development, reflection, recreation, and aesthetic 

experiences. 

 Regulating: includes benefits obtained from the regulation of ecosystem processes. 

 Supporting: includes those services necessary for the production of all other ecosystem 

services. 

Figure 1.1-2, taken from the MEA, displays the impact of the ecosystem services on 

human well-being. The first three categories directly affect human well-being and economic 

measures of welfare change. Supporting services do not have a direct effect on human well-being 

but are vital to the functioning of the ecosystem.2 While other authors have proposed 

categorizing ecosystem services using different systems, the MEA framework was chosen 

because it is a well-developed and widely accepted system. 3 The valuation of ecosystem 

services benefits, however, is based on a careful linking of the MEA framework with 

neoclassical economics. 

 

2 One of the criticisms of the MEA framework from the perspective of economic analysis is that even some of the 

regulating services, such as climate regulation, are more like ecosystem functions/processes or “supporting 

services” that are only indirectly related to welfare (Boyd and Banzhaf [2007], Wallace [2007]). 

3 Alternatives to the MEA ecosystem service classifications include, for example, Daily et al. (1997), National 

Research Council (2005), and Wallace (2007). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Figure 1.1-2. MEA Categorization of Ecosystem Services and their Links to 

Human Well-Being (Source: MEA, 2005b). 

1.1.1 Descriptions and Examples of MEA Ecosystem Services 

For each service category, the MEA identifies a variety of subcategories. The list below 

(adapted from MEA [2005b]) highlights the services that are most relevant to this report; 

however, the MEA framework contains more services than those listed. Note that supporting 

services, which do not link directly to welfare, are not included, and that there is some overlap 

between the categories. 

1.1.1.1 Provisioning Services 

 Food and fiber: This includes the vast range of food products derived from plants, animals, 

and microbes, as well as materials such as wood, jute, hemp, silk, and many other products 

derived from ecosystems. 

 Fuel: Wood, manure, and other biological materials serve as sources of energy. 

 Genetic resources: This includes the genes and genetic information used for animal and 

plant breeding and biotechnology. 

 Biochemicals, natural medicines, and pharmaceuticals: Many medicines, biocides, food 

additives such as alginates, and biological materials are derived from ecosystems. 

 Fresh water: Fresh water is another example of linkages between categories—in this case, 

between provisioning and regulating services. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 9 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

1.1.1.2 Regulating Services 

 Air quality maintenance: Ecosystems both contribute chemicals to and extract chemicals 

from the atmosphere, influencing many aspects of air quality. 

 Climate regulation: Ecosystems influence climate both locally and globally. For example, 

on a local scale, changes in land cover can affect both temperature and precipitation. On a 

global scale, ecosystems play an important role in climate regulation by either sequestering 

or emitting greenhouse gases. 

 Water regulation: The timing and magnitude of runoff, flooding, and aquifer recharge can 

be strongly influenced by changes in land cover, including, in particular, alterations that 

change the water storage potential of the system, such as the conversion of wetlands or the 

replacement of forests with croplands or croplands with urban areas. 

 Erosion control: Vegetative cover plays an important role in soil retention and the 

prevention of landslides. 

 Water purification and waste treatment: Ecosystems can be a source of impurities in 

freshwater but also can help filter out and decompose organic wastes introduced into 

inland waters and coastal and marine ecosystems. 

 Biological control: Ecosystem changes affect the prevalence of crop and livestock pests 

and diseases. 

 Biological control—food chain: Ecosystem changes affect the availability of vegetation 

and, in turn, animals that comprise and sustain delicate food chains within an ecosystem. 

 Storm protection: The presence of coastal ecosystems such as mangroves and coral reefs 

can dramatically reduce the damage caused by hurricanes or large waves. 

1.1.1.3 Cultural Services 

 Spiritual and religious values: Many religions attach spiritual and religious values to 

ecosystems or their components. 

 Educational values: Ecosystems and their components and processes provide the basis for 

both formal and informal education in many societies. 

 Inspiration: Ecosystems provide a rich source of inspiration for art, folklore, national 

symbols, architecture, and advertising. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 10 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

 Aesthetic values: Many people find beauty or aesthetic value in various aspects of 

ecosystems, as reflected in the support for parks, “scenic drives,” and the selection of 

housing locations. 

 Recreation and ecotourism: People often choose where to spend their leisure time based, in 

part, on the characteristics of the natural or cultivated landscapes in a particular area. 

Environmental economists also have identified a category of services associated with 

ecological benefits termed “nonuse values” (also referred to as “existence values” or “passive 

use values”). As the name implies, nonuse values capture those values people have for the 

environment or natural resources separate from the direct or indirect use value the resources 

provide. The value some individuals hold for wilderness areas that they will never visit is one 

type of nonuse value. This report includes nonuse values as a subcategory of cultural services. 

1.2 REFERENCES 

Boyd, J., and S. Banzhaf. 2007. “What are Ecosystem Services? The Need for Standardized 

Environmental Accounting Units.” Ecological Economics 63:616-626. 

Daily, G.C., S. Alexander, P.R. Ehrlich, L. Goulder, J. Lubchenco, P.A. Matson, H.A. Mooney, 

S. Postel, S.H. Schneider, D. Tilman, and G.M. Woodwell. 1997. “Ecosystem Services: 

Benefits Supplied to Human Societies by Natural Ecosystems.” Issues in Ecology 2:1-16. 

Millennium Ecosystem Assessment (MEA). 2005a. Ecosystems and Human Well-being: Current 

State and Trends, Volume 1. R. Hassan, R. Scholes, and N. Ash, eds. Washington, DC: 

Island Press. Available at http://www.millenniumassessment.org/documents/ 

document.766.aspx.pdf. 

Millennium Ecosystem Assessment (MEA). 2005b. Ecosystems and Human Well-being: 

Synthesis. Washington, DC: World Resources Institute. 

National Research Council. 2005. Valuing Ecosystem Services: Toward Better Environmental 

Decision-Making. Washington, DC: National Academies Press. 

Wallace, K.J. 2007. “Classification of Ecosystem Services: Problems and Solutions.” Ecological 

Conservation 139:235-246. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 11 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

2.0 AQUATIC ACIDIFICATION 

High levels of nitrogen and sulfur deposition, particularly in areas with soils containing 

relatively low levels of alkaline chemical bases such as calcium or magnesium ions, often lead to 

acidification of surface waters such as lakes and streams. These processes contribute to low pH 

levels and other chemical changes that can be toxic to fish and other aquatic life. Evidence of 

both chronic and episodic acidification of surface waters is particularly evident in the Eastern 

and northeastern United States, where levels of nitrogen and sulfur deposition have also been 

relatively high in recent decades. These surface waters support a wide variety of ecosystem 

services, many of which can be affected adversely by acidification. 

2.1 OVERVIEW OF AFFECTED ECOSYSTEM SERVICES 

Because acidification primarily affects the diversity and abundance of aquatic biota, it 

also primarily affects the ecosystem services that are derived from the fish and other aquatic life 

found in these surface waters. 

2.1.1 Provisioning Services 

Food and freshwater are generally the most important provisioning services provided by 

inland surface waters (Millennium Ecosystem Assessment [MEA], 2005). Whereas acidification 

is unlikely to have serious adverse effects on, for example, water supplies for municipal, 

industrial, or agricultural uses, it can limit the productivity of surface waters as a source of food 

(i.e., fish). In the northeastern United States, the surface waters affected by acidification are not a 

major source of commercially raised or caught fish; however, they are a source of food for some 

recreational and subsistence fishers and for other consumers. Although data and models are 

available for examining the effects on recreational fishing (see Section 2.1.2), relatively little 

data are available for measuring the effects on subsistence and other consumers. For example, 

although there is evidence that certain population subgroups in the northeastern United States, 

such as the Hmong and Chippewa ethnic groups, have particularly high rates of self-caught fish 

consumption (Hutchison and Kraft, 1994; Peterson et al., 1994), it is not known if and how their 

consumption patterns are affected by the reductions in available fish populations caused by 

surface water acidification. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 12 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

2.1.2 Cultural Services 

Inland surface waters support several cultural services, such as aesthetic and educational 

services; however, the type of service that is likely to be most widely and significantly affected 

by aquatic acidification is recreational fishing, since it depends directly on the health and 

abundance of aquatic wildlife. Other recreational activities such as hunting and birdwatching are 

also likely to be affected, to the extent that fish eating birds and other wildlife are harmed by the 

absence of fish in acidic surface waters. 

Recreational fishing in lakes and streams is among the most popular outdoor recreational 

activities in the northeastern United States. Data from the 2006 National Survey of Fishing, 

Hunting, and Wildlife Associated Recreation (FHWAR), as summarized in Table 2.2-1, indicate 

that more than 9% of adults in this part of the country participate annually in freshwater 

(excluding Great Lakes) fishing. The total number of freshwater fishing days occurring in those 

states (by both residents and nonresidents) in 2006 was 140.8 million days. Roughly two-thirds 

of these fishing days were at ponds, lakes, or reservoirs in these states, and the remaining onethird were at rivers or streams. Based on studies conducted in the northeastern United States, 

Kaval and Loomis (2003) estimated an average consumer surplus value per day of $35.91 for 

recreational fishing (in 2007 dollars). Therefore, the implied total annual value of freshwater 

fishing in the northeastern United States was $5.06 billion in 2006. 

2.1.3 Regulating Services 

In general, inland surface waters such as lakes, rivers, and streams provide a number of 

regulating services, such as hydrological regime regulation and climate regulation. There is little 

evidence that acidification of freshwaters in the northeastern United States has significantly 

degraded these specific services; however, freshwater ecosystems also provide biological control 

services by providing environments that sustain delicate aquatic food chains. The toxic effects of 

acidification on fish and other aquatic life impair these services by disrupting the trophic 

structure of surface waters (Driscoll et al., 2001). Although it is difficult to quantify these 

services and how they are affected by acidification, it is worth noting that some of these services 

may be captured through measures of provisioning and cultural services. For example, these 

biological control services may serve as “intermediate” inputs that support the production of 

“final” recreational fishing and other cultural services. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 13 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

2.2 CHANGES IN ECOSYSTEM SERVICES ASSOCIATED WITH 

ALTERNATIVE LEVELS OF ECOLOGICAL INDICATORS 

This section estimates values for changes in ecosystem services associated with 

reductions in lake acidification in Adirondack State Park and in other lakes in the state of New 

York. Using the results of the Adirondack Case Study Area of aquatic acidification effects, the 

value of reducing nitrogen and sulfur deposition in the affected areas to background levels (i.e., a 

“zeroing out” of anthropogenic sources of nitrogen and sulfur) was estimated. Although this 

scenario is not realistic from a policy perspective, it allows the examination of the upper bound 

of ecosystem service gains that would result from reducing the number of acidified (i.e., low acid 

neutralizing capacity [ANC]) lakes to the lowest possible level. 

Table 2.2-1. Participation in Freshwater Recreational Fishing in Northeastern States in 2006 

State 

Participation 

Rates by State 

Residentsa

Activity Days by Residents and Nonresidents (in 

thousands) 

Ponds, Lakes, or 

Reservoirs 

Rivers or 

Streams Total 

Connecticut 7.3% 2,856 2,409 5,265 

Delaware 6.4% 764 770 1,534 

Illinois 9.5% 10,318 5,088 15,406 

Indiana 13.8% 6,843 1,819 8,662 

Maine 19.9% 3,734 1,521 5,255 

Maryland 6.6% 2,882 2,379 5,261 

Massachusetts 6.0% 4,494 978 5,472 

Michigan 12.3% 15,175 4,426 19,601 

New Hampshire 10.0% 2,144 627 2,771 

New Jersey 4.0% 2,377 1,116 3,493 

New York 4.6% 8,548 5,086 13,634 

Ohio 11.6% 9,781 3,710 13,491 

Pennsylvania 8.3% 7,507 6,998 14,505 

Rhode Island 5.3% 504 104 608 

Vermont 13.2% 1,264 453 1,717 

West Virginia 20.9% 3,069 3,617 6,686 

Wisconsin 21.4% 13,026 4,439 17,465 

Total 9.3% 95,286 45,540 140,826 

a Ages 16 or older. 

Source: U.S. Department of the Interior (DOI), Fish and Wildlife Service, and U.S. Department of Commerce, U.S. 

Census Bureau, 2007. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 14 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

The case study analysis focused on 44 lakes in the Adirondacks. It estimated ANC levels 

at each of these lakes under the alternative scenarios shown in Table 2.2-2. Using the MAGIC 

model, it predicted median ANC levels for the years 2005, 2020, 2050, and 2100 under 

“business-as-usual” conditions (i.e., accounting for expected emission controls associated with 

Title IV regulations but no additional measures to reduce nitrogen and sulfur deposition). In 

contrast, the model run for the year 1860 represents ANC levels for “background” conditions by 

simulating the effect of zeroing out anthropogenic sources of nitrogen and S. 

Table 2.2-2. Acid Neutralizing Capacity Levels (in μeq/L) at 44 MAGIC-Modeled Lakes in the 

Adirondacks 

 Observed MAGIC Model Simulations 

Year: 2002 2005 2020 a 2050a 2100a 1860b

Lake Name (“Background”) 

Clear Pond (61) 218.1 233.0 243.2 246.7 247.6 290.3 

Long Pond (65) 66.1 73.5 78.3 80.4 81.2 106.4 

Hope Pond 62.6 72.9 78.4 81.1 82.8 126.5 

Second Pond 71.3 75.8 77.0 75.3 72.5 121.5 

Squaw Lake 19.5 25.6 27.1 24.9 21.3 73.8 

Indian Lake −8.0 1.4 6.2 6.2 5.1 52.2 

Big Alderbed 57.4 67.5 72.6 74.5 75.6 124.1 

Long Lake −32.1 −20.8 −15.4 −16.0 −17.6 34.4 

Gull Pond 160.9 166.8 170.7 173.0 174.6 208.8 

Little Lilly Pond 47.0 54.4 57.9 58.5 58.6 95.5 

Upper Sister Lake 31.0 37.4 39.9 39.4 38.0 80.3 

Dry Channel Pond 23.8 31.7 34.3 33.2 31.6 78.6 

Bennett Lake 33.0 37.5 39.2 37.8 35.0 69.7 

Effley Falls Pond 50.8 59.8 64.2 64.2 63.7 132.4 

Parmeter Pond 75.6 85.7 91.7 94.3 95.4 134.8 

North Lake −1.6 6.9 10.9 10.0 8.1 66.0 

Razorback Pond 33.2 39.6 42.4 40.5 37.4 94.3 

Snake Pond 3.6 12.3 15.5 14.5 13.4 78.5 

South Lake −7.7 0.1 3.7 2.3 −0.2 56.6 

Boottree Pond 53.2 59.0 63.2 65.3 66.1 84.5 

Horseshoe Pond 51.4 63.0 70.0 73.4 74.8 117.6 

Rock Pond 86.7 95.1 98.8 99.5 99.5 151.5 

Antediluvian Pond 66.2 70.1 72.0 71.4 69.9 95.3 

Seven Sisters Pond −14.1 −9.1 −6.9 −7.2 −8.1 21.9 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 15 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 16 

 Observed MAGIC Model Simulations 

Year: 2002 2005 2020 a 2050a 2100a 1860b

Lake Name (“Background”) 

Canada Lake 53.8 69.4 77.6 80.1 81.4 151.2 

Bickford Pond 19.9 33.6 41.3 45.2 46.9 101.3 

Wolf Pond −5.7 4.8 9.8 11.2 11.9 58.3 

Blue Mountain Lake 118.0 126.7 129.3 127.8 125.2 184.3 

Carry Falls Reservoir 121.2 133.2 140.8 144.1 145.8 205.8 

Rocky Lake 43.7 58.6 66.3 68.3 68.8 113.7 

Bog Pond 88.2 107.0 117.2 120.5 121.6 178.1 

Clear Pond (82) 85.9 97.1 104.1 107.4 108.2 145.6 

Seventh Lake 198.4 217.3 223.4 227.1 229.1 317.6 

Trout Pond 39.9 53.4 61.9 65.7 67.6 127.2 

Hitchins Pond 150.2 162.7 170.0 172.6 173.8 214.7 

Piseco Lake 98.2 114.7 123.7 127.2 128.6 186.2 

Mccuen Pond 37.6 46.0 50.2 51.7 52.4 90.0 

Arbutus Pond 88.0 101.6 108.6 111.3 113.1 187.1 

Witchhopple Lake 27.2 35.7 39.4 38.9 37.6 91.7 

Willys Lake −48.4 −38.8 −33.5 −33.3 −33.4 47.5 

Lower Beech Ridge 

Pond 

−19.6 −10.8 −6.9 −7.4 −8.8 41.5 

Dismal Pond −23.6 −12.0 −7.6 −7.3 −7.6 40.4 

Payne Lake 53.5 56.2 58.1 59.0 59.4 75.1 

Whitney Lake 22.3 30.7 33.7 32.9 31.5 84.3 

a

 Based on predicted future scenarios for nitrogen+sulfur deposition, accounting for Title IV emissions controls. 

b Represents background levels and levels that would eventually result from a “zero-out” of anthropogenic sources 

of nitrogen+sulfur deposition. 

In the following subsections, ecosystem service gains associated with going from the 

business-as-usual reference conditions to the zero-out condition are estimated. It was assumed 

that the zero out of nitrogen and sulfur deposition would occur in 2010 and that it would take 10 

years for the full effect of these reductions on lake ANC levels to occur. In other words, by the 

year 2020, lake ANC levels would increase and fully return to their estimated 1860 background 

levels, as shown in Table 2.2-2. 

In Section 2.2.1, a model that focuses specifically on recreational fishing services is 

applied, and the value of current and future enhancements to these services from Adirondack and 

other New York lakes is estimated. In Section 2.2.2, a model that takes a broader perspective on 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

ecosystem services is applied, and the value of improving all of the ecosystem services that are 

affected by acidification of Adirondack lakes is estimated. 

In both cases, the analysis focuses on ANC and evaluates the sensitivity of different ANC 

thresholds for aquatic functioning. In general, moderate shifts in ANC levels may result in 

changes in species composition, where acid-sensitive species are replaced by less sensitive 

species. At more extreme acidification levels, however, species richness, defined as the total 

number of species occupying a system, may be affected. Research has shown that the number of 

fish species present is positively correlated with ANC (Driscoll et al., 2003). In the Adirondacks, 

recent research indicates that aquatic biota begin to exhibit effects at an ANC of 50 

microequivalents per liter (μeq/L) (Chen and Driscoll, 2004). Uncertainty exists regarding 

threshold levels of ANC: the levels at which predictable effects occur. Several ANC thresholds 

have been observed, however, at which lakes and fish are affected, as summarized in Figure 2.2-

1. To account for the uncertainty in the threshold level of acidification above which Adirondack 

lakes may support recreational fishing, this analysis considers three threshold levels: 20 μeq/L, 

50 μeq/L, and 100 μeq/L. 

Lakes are chronically acidic and fishless

Level below which all biota are affected

Lakes are sensitive to episodic acidification

Level below which aquatic biota are affected

Species richness 

increases between 50 

and 100 μeq/L

Lakes are not sensitive to 

acidification at values approaching 

200 μeq/L

ANC THRESHOLD VALUES (UEQ/L)

0 20 40 50 100 200

Figure 2.2-1. Summary of Acid Neutralizing Capacity Values Relevant for Lake 

and Fish Health (Source: Industrial Economics, Inc., 2008). 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 17 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

2.2.1 Improvements in Recreational Fishing Services due to Increased Acid 

Neutralizing Capacity Levels in Adirondack and Other New York Lakes 

To estimate the value of improved services, this analysis relied on commonly accepted 

economic models to relate the predicted changes in lake acidity to a change in recreational 

fishing behavior throughout the study area. First, a random effects model was used to extrapolate 

lake ANC levels from the ecological model forecast for a subset of lakes to a broader suite of 

regional lakes. This random effect model does this by relating acidification levels to lake 

characteristics and geographic location. That is, the forecast ANC levels of the lakes modeled in 

MAGIC for each year in the study period are tied to explanatory variables so that the forecast 

changes in ANC can be extrapolated to other potentially affected lakes in the region. This model 

was first applied to forecast ANC levels at lakes in the Adirondack region and then repeated to 

forecast ANC levels for lakes in New York State (with the exception of New York City). The 

result of this effort is a full time-series dataset of ANC levels for Adirondack and New York 

State lakes. 

The second economic model applied describes changes in behavior of recreational fishers 

in response to changes in lake acidification levels. This step of the process relies on the 

assumption that below the specified ANC threshold (of 20 μeq/L, 50 μeq/L, or 100 μeq/L) lakes 

are no longer fishable. The specific type of model applied here is a “discrete choice model.” 

Generally, a discrete choice model predicts a binary decision (which may be thought of as “yes” 

or “no”) regarding whether to fish at a given site made by an individual as a function of a 

number of independent variables (Greene, 2003). The independent variable is the catch rate at 

the water body (itself a function of lake acidity). Additional independent variables may include 

travel time required to reach the site and the concentration of fisherman at the site, among others. 

A specific form of discrete choice model called a “random utility model,” or RUM is 

applied. In the study of economics, utility is defined as a measure of the happiness or satisfaction 

gained from a good or service. In keeping with the tenets of neoclassical economics, this utility is 

sought to be maximized subject to a constraint (often represented by income or time). Put more 

simply, the model assumes that the fisherman will seek the most happiness at the lowest cost. 

Section 2 describes the application of these models and the results of this analysis. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 18 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 19 

2.2.1.1 Analytic Method 

The following steps were followed to connect the modeled changes in lake ANC levels to 

the benefits of improved recreational fishing services. 

Step 1: Development of the Random Effects Model 

To develop this model, it was first necessary to compare the subset of lakes considered in 

the ecological model (see Table 2.2-2) with the subset of lakes included in the database of lake 

characteristics contained within the RUM. Nine of the 44 lakes were not usable for the analysis 

because they did not appear in the database of lake characteristics within the RUM.4 As a result, 

the analysis relied on data for a subset of 35 Adirondack lakes. 

Because forecasted ANC levels were provided for the 35 lakes only, the next step of the 

analysis was extrapolating these forecasts to the broader suite of lakes within the Adirondack 

region. To this end, a random effects model was developed to determine the statistical 

relationship between the lakes’ ANC levels and their characteristics. Significant uncertainty 

exists regarding the relationships between lake characteristics and ANC levels. Ecologists at the 

Environmental Protection Agency (EPA) are researching the characteristics that best explain a 

lake’s sensitivity to acidification. 

The random effects model used in this analysis to forecasted lake ANC levels was also 

limited by the lake characteristic data that are currently available; in this case, elevation, surface 

area, shoreline, and county location were considered as potential explanatory variables in 

forecasting ANC levels. The relationship between these characteristics and the forecast ANC 

levels for the 35 lakes informed the extrapolation of the results from the MAGIC model to the 

broader population of lakes, first in the Adirondack region and then in New York State. These 

variables that describe the lake characteristic and geographic location are the explanatory 

variables in the model. The random effects model helps identify the influences of these 

explanatory variables, net of other factors that are unknown and cannot be controlled.5

 

4Those lakes excluded include the following: Bickford Pond, Bog Pond, Hope Pond, Little Lilly Pond, Lower Beech 

Ridge, Razorback Pond, Seven Sisters Pond, Snake Pond, and Witchhopple Lake. 

5 Several important conditions must be satisfied for the random effects model to be appropriate. In this case, these 

conditions are met. For both models, the Breusch-Pagan test for random effects rejects the null hypothesis of no 

random effects in the data. The Hausman specification test (against a fixed effects alternative) rejects the null 

hypothesis of systematic differences between random and fixed effects models for the Clean Air Interstate Rule 

(CAIR) and t variables, which indicates that omitted variables are not biasing the coefficients for those variables. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 20 

The model cannot perfectly predict ANC levels in lakes; there are not enough available 

data and there is no existing knowledge about the best determinants of ANC levels. Given that 

there is some uncertainty and limited information available to explain ANC levels, a method 

must be used that can remove the net effects of the unknown data and identify the effects of the 

available information. The random effects model generates estimates of the net effects of the 

explanatory variables. 

Furthermore, random effects models are appropriate for situations where the study 

sample is a random sample of a larger universe and one wishes to make inferences about the 

larger universe of data (Kennedy, 2003).6 In this case, the group of lakes analyzed is sampled 

from the total number of lakes for which ANC levels are forecast (the lakes to which the ANC 

levels are forecast is the universe). 

The modeled ANC levels for the 35 aforementioned lakes, along with the lake 

characteristic information, served as inputs for a random effects regression analysis to isolate the 

impact of each variable on ANC. Table 2.2-3 details the results of the random effects model. 

Table 2.2-3. Random Effects Model Results 

Variable Coefficient Std. Error 

constant −106.171 75.050 

elevation −0.047 0.128 

area 0.125 0.074 

ln(shoreline) −36.005 18.802 

T 0.108 0.013 

Hamilton 9.430 27.760 

Essex 55.149 46.894 

Fulton −16.793 80.273 

Franklin 49.538 39.176 

Herkimer −38.655 40.142 

Lewis −19.160 45.899 

Warren 24.924 66.423 

 

6 This criterion assumes that there are no omitted variable effects present; the previous footnote explains that there is 

no evidence of this. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Variables describing elevation, total area, and shoreline length were included to capture 

physical differences between lakes. While the coefficients are not statistically significant, the 

variables do lend some explanatory power to the model. The variable identified as “T” is an 

annual time trend included to capture changes through time manifested in the greater system and 

not a specific lake. The final seven variables listed in the table are binary variables indicating the 

counties in which the lakes occur. The omitted variable is for St. Lawrence County. These 

variables are intended as a proxy for a host of location-specific factors, including subsurface 

geology and degree of forest cover because data were not available for these variables. 

Step 2: Extrapolation to All Lakes in Adirondack Region/New York State 

The Montgomery-Needelman RUM includes lake characteristic data for a total of 2,586 

lakes in New York State. As described previously, the MAGIC model predicts ANC levels for 

35 lakes within the Adirondack region that could be included in the random effects model. These 

35 lakes are located in Hamilton, Essex, Fulton, Franklin, Herkimer, Lewis, Warren, and St. 

Lawrence counties. Their explanatory value for lakes outside of this eight-county region is 

uncertain. Therefore, this study performed a “tiered” extrapolation, where the random effects 

model results were first extrapolated only to lakes in the Adirondack region represented by the 

modeled lakes; this exercise was then repeated for the full suite of New York State lakes. 

For the first tier (for the Adirondack region), the analysis was limited by two dimensions: 

(1) only including lakes within the eight counties containing the 35 modeled lakes and (2) 

limiting the analysis to lakes within the size range of the modeled lakes. Because none of the 35 

modeled lakes occur in Clinton, Saratoga, and Oneida counties (all within the Adirondack 

region), this analysis did not apply the model to forecast lake acidification in these three 

counties. This assumption may lead to an understatement of the total benefits associated with 

decreased lake acidification in the Adirondack region, but it avoids some uncertainty associated 

with extrapolating ANC outside of the scope of the modeled region. 

The second tier of the analysis (for all of New York State except New York City) was 

also limited to consider only lakes within the size range of the modeled lakes. This portion of the 

analysis required consideration of lakes outside of the eight-county geographic scope, however. 

Therefore, an average of the eight-county binary variable coefficients for all lakes outside of the 

eight counties was used. Further, as with the first tier of the analysis, all lakes with an area 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 21 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 22 

greater than the largest lake in the ecological subset of 35 were “hardwired” to be unimpaired, 

because changes in their ANC levels are unlikely to be represented by the subset of modeled 

lakes.7 A total of 62 lake sites were determined to be too large to be represented by the sample 

MAGIC data and were, therefore, hardwired. 

Step 3: Application of ANC Thresholds 

This analysis employs three ANC threshold assumptions—20 μeq/L, 50 μeq/L, and 100 

μeq/L—to indicate whether a lake is fishable. A lake was deemed to be affected if it was above 

the threshold (fishable) in the “zero-out” scenario and below the threshold (impaired) in the 

baseline scenario. As previously described and shown in Table 2.2-2, zero-out conditions are 

defined by lake ANC levels in the year 1860 as estimated by MAGIC. MAGIC provided these 

data for the subset of 35 lakes within the Adirondack region. To determine zero-out conditions 

for the broader suite of lakes in the Adirondacks and in New York State, a simple ordinary least 

squares (OLS) regression was run to determine whether lake size is a reasonable indicator of the 

difference between the observed ANC level in 2002 and the pristine condition in 1860 for the 35 

lakes. This analysis determined that no statistically significant relationship existed. Therefore, an 

average difference in ANC level between the 2002 observed level and the 1860 pristine 

condition for the 35 lakes was calculated; the average difference is 64.6 μeq/L. This average 

difference was then added to the 2002 ANC levels for each lake (forecast by extrapolation using 

the random effects model), and the resulting value was considered to be the “pristine” ANC 

value in 2020, 2050, and 2100. 

Table 2.2-4 reports the number of “impacted” lakes in each year, where impact means 

that the lake is predicted to be below the ANC threshold under business-as-usual conditions and 

above the threshold under zero-out conditions. This definition of impacted lakes is needed 

because the RUM framework only estimates benefits accruing from lakes that switch from 

nonfishable to fishable status. The lake counts for 2005 are zero because in this year no change 

 

7 Hardwired lakes (in order of decreasing size) include Lake Ontario, Lake Erie, Great Sacandaga Lake, Oneida 

Lake, Seneca Lake, Lake Champlain, Cayuga Lake, Lake George, Canandaigua Lake, Ashokan Reservoir, 

Cranberry Lake, Owasco Lake, Chautauqua Lake, Tupper Lake, Stillwater Reservoir, Keuka Lake, Pepacton 

Reservoir, Allegheny Reservoir, Raquette Lake, Cannonsville Reservoir, Indian Lake, Skaneateles Lake, Black 

Lake, Long Lake, Otsego Lake, Saratoga Lake, Mount Morris Reservoir, Salmon River Reservoir, Great Sodus 

Bay, Conesus Lake, Whitney Point Reservoir, and Onondaga Lake. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

occurs in ANC level relative to the baseline (i.e., the reduction in emissions beginning the return 

to pristine conditions was not assumed to have occurred in those years). The zero-out scenario 

was assumed to be implemented in 2010, with lakes reaching their pristine conditions by 2020. It 

should be noted that the nature of this model allows for lakes to switch between impaired and 

unimpaired between years. As a result, the lake counts reported in Table 2.2-4 are not cumulative 

counts and, in fact, may reflect different subsets of lakes. 

Step 4: Application of the Random Utility Model 

The Montgomery-Needelman model applied in this analysis is a repeated discrete choice 

RUM that describes lake fishing behavior of New York residents (Montgomery and Needelman, 

1997). In particular, the model characterizes decisions regarding (1) the number of lake fishing 

trips to take each season and (2) the specific lake sites to visit on each fishing trip. The model 

can be used to develop estimates of economic losses or gains associated with changes in the set 

of lakes available to anglers. 

Table 2.2-4. Count of Impacted Lakes 

ANC Threshold 

(in μeq/L) Year 

Lake Count 

Adirondack Region New York State 

20 2005 0 0 

20 2020 107 110 

20 2050 95 97 

20 2100 74 75 

50 2005 0 0 

50 2020 244 365 

50 2050 222 316 

50 2100 200 254 

100 2005 0 0 

100 2020 430 1,500 

100 2050 404 1,399 

100 2100 354 1,228 

Note: There are 1,076 lakes in the “Adirondack Region” and 2,586 lakes in New York State (less New York City). 

The data used to estimate the RUM were obtained from a 1989 repeat-contact telephone 

survey of New York residents conducted as part of the National Acid Precipitation and 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 23 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 24 

Assessment Program (NAPAP).8 This survey provided information on the destinations of 

anglers’ fishing trips (day trips only) taken during the 1989 fishing season. The survey data were 

supplemented with lake characteristics data obtained from New York State Department of 

Environmental Conservation’s (NYSDEC’s) Characteristics of New York State Lakes: Gazetteer 

of Lakes and Ponds and Reservoirs, New York State’s Fishing Guide, and New York’s 305(b) 

report for 1990. Travel distances between anglers’ homes and lake fishing sites were calculated 

using Hyways/Byways. The model and data used in the present analysis are described in greater 

detail in a 1997 journal article by Montgomery and Needelman.9 

The list of affected lakes generated in the previous step serves as the primary input to the 

RUM. The model estimates the economic welfare value of enhancements to recreational fishing 

services derived from shifting specific lakes from nonfishable to fishable status.10 The economic 

benefits estimates represent New York State residents’ average willingness to pay (WTP) to 

improve recreational fishing services by reducing lake acidification levels. Table 2.2-5 reports 

the estimated per capita values generated by the RUM. These values have been adjusted from 

1989 dollars to 2007 dollars using the Consumer Price Index-All Urban Consumers (CPI-U). 

Note that the zero-out scenario is assumed to begin at the end of 2010; therefore, the benefits do 

not begin to accrue until the following year, and they are zero in 2005 and 2010. 

Table 2.2-5. Per Capita Willingness to Pay (2007 $) 

ANC Threshold 

(in μeq/L) Year 

Per Capita Benefits of a Return to Pristine Conditions 

by 2020 

Adirondack Region New York State 

20 2005 $0.00 $0.00 

20 2010 $0.00 $0.00 

20 2020 $0.41 $0.47 

20 2050 $0.34 $0.38 

20 2100 $0.28 $0.32 

50 2005 $0.00 $0.00 

 

8 New York City counties were excluded from the sampling frame. 

9 The published version of the model has had several minor updates, all of which have been discussed with Mark 

Montgomery. 

10 Since the RUM uses travel distances and travel costs to infer economic values, the benefit estimates are sensitive 

to the spatial locations and distributions of the impacted lakes (i.e., the benefit estimates do not depend only on 

the number or percentage of lakes impacted). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 25 

ANC Threshold 

(in μeq/L) Year 

Per Capita Benefits of a Return to Pristine Conditions 

by 2020 

Adirondack Region New York State 

50 2010 $0.00 $0.00 

50 2020 $0.74 $2.55 

50 2050 $0.73 $2.26 

50 2100 $0.70 $1.47 

100 2005 $0.00 $0.00 

100 2010 $0.00 $0.00 

100 2020 $0.79 $11.05 

100 2050 $0.77 $10.61 

100 2100 $0.68 $9.40 

Step 5: Interpolation of RUM Output 

The RUM provided per capita loss estimates (reported in 1989 nominal dollars) for 2010, 

2020, 2050, and 2100.11 Rather than running this model separately for each year, estimates for 

the intervening years (between the four point estimates provided by the RUM) were generated 

via simple linear interpolations. 

Step 6: Estimation of Aggregate Benefits through Application of Per Capita Results 

to Affected Population 

To estimate aggregate benefits for New York residents, the per capita benefit estimates 

must be multiplied by the corresponding population of residents. To match the characteristics of 

the population surveyed in developing the RUM, this analysis required estimating the population 

of New York State that will be over 18 years old and reside outside of New York City for each 

year from 2011 through 2100. The U.S. Census Bureau provides estimated population figures for 

2002 through 2008 and projected population through 2030 at the state level. Absent projection 

information, the population was held constant from 2030 through the period of the analysis 

(through 2100). The ratio of the New York State population residing outside New York City 

(that is, the five counties of Bronx County, Kings County, New York County, Queens County, 

 

11 As mentioned previously, the CPI-U, provided by the U.S. Bureau of Labor Statistics, was used to inflate these 

estimates to 2007 dollars. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 26 

and Richmond County) was calculated for 2006 and assumed to remain constant throughout the 

analysis. The U.S. Census also estimates and projects the 18+ population at the state level 

through 2030. The 18+ population was held constant from 2030 through the end of the analysis 

in 2100. The ratio of adults (18+) to the entire population was calculated for New York State, 

and that ratio was applied to the population residing outside New York City. 

2.2.1.2 Results 

Table 2.2-6 summarizes the estimated present value and annualized benefits for each 

acidification threshold assumption applying discount rates of 3% and 7%. The estimated present 

value of benefits in 2010 range from $60.1 million to $298.7 million depending on the threshold 

and discount rate assumptions applied. In comparison, a previous study of the recreational 

fishing benefits in the Adirondacks associated with the Clean Air Act Amendments (CAAA) 

estimated benefits ranging from $13.7 million to $100.6 million (EPA, Office of Air and 

Radiation, 1999).12 Annualizing these benefits over the period 2010 to 2100 results in annual 

benefit estimates ranging from $3.9 million to $9.3 million per year. Six tables containing 

detailed results for each scenario (threshold assumption and geographic scope) by year are 

included in Attachment A. 

Table 2.2-7 describes total present value and annualized benefits associated with reduced 

lake acidification in all of New York State. Estimated present value benefits in 2010 range from 

$68.3 million to $4.16 billion, depending on the threshold and discount rate assumptions applied. 

Annualizing these benefits over the period 2010 to 2100 results in annual benefit estimates 

ranging from $4.5 million to $130 million per year. 

Table 2.2-6. Present Value and Annualized Benefits, Adirondack Region 

ANC 

Threshold 

(in μeq/L) 

Present Value Benefitsa

(in million of 2007 dollars) 

Annualized Benefitsb

(in million of 2007 dollars) 

3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate 

20 $142.59 $60.05 $4.46 $3.94 

50 $285.15 $114.18 $8.91 $7.49 

 

12 For comparison to the results in our analysis, presented in 2007 dollars, the estimated benefits from the Clean Air 

Act report were inflated from 1999 to 2007 dollars using the GDP deflator 

(http://www.bea.gov/bea/dn/nipaweb/SelectTable.asp?Selected=Y). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 27 

ANC 

Threshold 

(in μeq/L) 

Present Value Benefitsa

(in million of 2007 dollars) 

Annualized Benefitsb

(in million of 2007 dollars) 

3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate 

100 $298.67 $120.61 $9.33 $7.91 

a

 Annual benefits for 2010 to 2100 discounted to 2010. 

b Present value benefits annualized over 2009 to 2100. 

Table 2.2-7. Present Value and Annualized Benefits, New York State 

ANC 

Threshold 

(in μeq/L) 

Present Value Benefitsa

(in million of 2007 dollars) 

Annualized Benefitsb

(in million of 2007 dollars) 

3% Discount Rate 7% Discount Rate 3% Discount Rate 7% Discount Rate 

20 $161.76 $68.34 $5.05 $4.48 

50 $897.20 $378.00 $28.04 $24.78 

100 $4,159.64 $1,685.80 $129.98 $110.52 

a

 Annual benefits for 2010 to 2100 discounted to 2010. 

b

 Present value benefits annualized over 2010 to 2100. 

2.2.1.3 Assumptions and Caveats 

The following assumptions and caveats are particularly important for interpreting the 

results and the application of the ecological model for lake acidification: 

 This analysis assumed that the level of impairment is binary as applied to a specific lake: 

that is, the ANC threshold indicates whether a lake is fishable. 

 The available literature suggests that ANC levels between 20 and 100 cover the range 

where ecological affects are realized. Three points within this range (20, 50, and 100) were 

tested as point estimates at which the fishability of lakes is affected. 

 This analysis assumed that the 35 modeled lakes are a representative subset of lakes in the 

Adirondacks (for the first tier of the analysis) and in New York State (for the second tier of 

the analysis). 

 This analysis used the ANC levels of the 35 modeled lakes in the year 1860 as a proxy for 

“pristine” acidification levels. 

 In the first tier, the analysis is not used to forecast acidification effects in Clinton, 

Saratoga, and Oneida counties, which are generally considered to be part of the 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Adirondack region because they are not represented by the subset of lakes subject to the 

ecological model. This restriction contributes to an underestimation of total benefits. 

 Pristine ANC levels for the full population of New York State lakes are estimated by first 

finding the average difference between 2002 observed ANC levels and the 1860 ANC 

values for the 35 lakes modeled by MAGIC and then adding this average difference to the 

2002 ANC values for all lakes (as estimated by extrapolating using the random effects 

model). The ANC levels assumed to represent pristine lake conditions are therefore subject 

to significant uncertainty. 

The following assumptions and caveats are particularly important for interpreting the 

application of the RUM model for estimating recreational fishing benefits to New York 

residents: 

 The RUM only considers the behavior of New York State residents. It may be reasonable 

to assume that residents of neighboring jurisdictions (the Canadian provinces of Ontario 

and Quebec, along with the State of Vermont) may also take day trips to these lakes and 

respond in a rational manner comparable to New York State residents. This restriction 

contributes to an underestimation of benefits. 

 The output of the RUM is on a per capita basis. The results are presented in terms of 

impacts to the entire population. This requires an extrapolation of the population through 

2100. Absent specific projection information beyond 2030, the population was held 

constant beyond this year. 

 This analysis assumed that the demand for fishing, in other words, an individual's 

propensity to fish, has remained constant from the time of the survey underlying the RUM 

to the present. That is, this analysis does not account for any potential change in interest in 

both recreational fishing and park use since the survey was conducted in 1989. In the case 

that general demand for recreational fishing has decreased, this analysis may overstate 

benefits. This restriction contributes to an overestimation of benefits. 

 This analysis did not take into account income adjustments through time. The RUM holds 

income to be constant and a lack of detailed demand elasticity functions precludes the 

incorporation of an adjustment. Other EPA analyses have shown that increases in real 

income over time lead to increases in WTP for a wide range of health effects and some 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 28 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

welfare effects, such as recreational visibility. This restriction contributes to an 

underestimation of benefits. 

2.2.2 Improvements in Total Ecosystem Services due to Increased Acid 

Neutralizing Capacity Levels in Adirondack Lakes 

To develop estimates of the overarching ecological benefits associated with reducing lake 

acidification levels in Adirondacks National Park, researchers at Resources for the Future (RFF) 

conducted a detailed contingent valuation (CV) survey (Banzhaf et al., 2006). Unlike other 

valuation studies described in this report, the RFF study did not identify the specific categories of 

ecosystem services that would be enhanced by improving aquatic conditions. Rather, the survey 

described and elicited values for specific improvements in acidification-related water quality and 

ecological conditions in Adirondack lakes. For this reason, and because the survey was 

administered to a random sample of New York households, in this section the benefit estimates 

from the RFF study are interpreted as measures that incorporate values for all ecosystem services 

adversely affected by lake acidification. 

In this section, the RFF study results were adapted and transferred to estimate the 

ecological benefits of the zero-out scenario for Adirondack lakes. The fundamental benefit 

transfer model can be summarized as follows: 

AggBlAdr = Adr NY Δ%** ILNWTP , (2.1) 

where 

AggBAdr = aggregate annual benefits (in 2007 dollars) to New York households in 2010 

due to lake ecosystem improvements resulting from the zero-out scenario, 

WTPAdr = average annual household WTP (in 2007 dollars) per unit of long-term 

change in the percentage of Adirondack lakes impaired by acidification, 

NNY = projected total number of households in New York in 2010, and 

∆%IL = long-term change in the percentage of Adirondack lakes impaired by 

acidification as a result of the zero-out scenario. 

To develop estimates of WTPAdr, the estimates from the RFF study were used with results 

reported in Banzhaf et al. (2006). The CV survey for the study was distributed to a random 

sample of nearly 6,000 New York residents in 2003 to 2004 through the Internet and mail. As 

part of the design and development of the survey instrument, experts were interviewed on the 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 29 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

ecological damages, and a summary of the science was used as the foundation for the description 

of the park’s existing condition and the hypothetical changes to be valued. The scientific review 

indicated that there was significant uncertainty regarding the future status of lakes in the Park in 

the absence of specific programs to improve lake acidification conditions. To bracket the range 

of uncertainty in the science as well as to test the sensitivity of respondents’ WTP to the scale of 

ecological improvements, two versions of the survey instrument were developed and randomly 

administered to separate subsamples. 

Table 2.2-8 summarizes key features of the two survey versions. In both survey versions, 

respondents were provided with information on the current (circa 2004) condition of the 3,000 

lakes in the Park. Both versions describe half (1,500) of them as “lakes of concern” (i.e., 

unhealthy lakes where “fish and other aquatic life have been reduced or eliminated because of air 

pollution in the past”), and both versions propose policies that would improve the lakes over a 

period of 10 years (using lime to neutralize the excess acidity). 

Table 2.2-8. Comparison of Resources for the Future Contingent Valuation Scenarios and EPA 

Zero-Out Scenario 

Percentage of Adirondack Lakes that Are “Unhealthy” 

Future 

Current 

(A) 

No Programa

(B) 

With Programb

(C) 

Reduction 

(B) – (C) 

RFF “Base” Scenario 

Year = 2004 Year = 2014 

50% 50% 30% 20% 

RFF “Scope” Scenario 

Year = 2004 Year = 2014 

50% 55% 10% 45% 

EPA “Zero-Out” Scenario 

ANC Threshold Year = 2010 Year = 2020 

20 μeq/L 22% 22% 0% 22% 

50 μeq/L 43% 42% 11% 31% 

 100 μeq/L 79% 77% 51% 26% a

 Business-as-usual conditions. 

b Lake liming program for the RFF survey scenarios and a zero-out policy for the EPA scenario. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 30 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

The “base” version of the survey asserts that, in the absence of any direct policy 

intervention, the condition of the 1,500 unhealthy lakes and 1,500 healthy lakes is expected to 

remain unchanged over the next 10 years. However, if a liming program is undertaken, it would 

improve 20% (600) of the lakes in the Park relative to their expected 2014 condition without the 

program. 

In contrast, the “scope” version describes a gradually worsening status quo without the 

liming program, in which 5% (150) of the healthy lakes are expected to gradually become 

unhealthy. In other words, without the program, 55% (1,650) of the lakes would be unhealthy in 

2014. With the liming program, however, only 10% of the lakes would be unhealthy in 2014, so 

the program improves 45% (1,350) of the lakes relative to their expected 2014 condition without 

the program. 

Although scientific evidence indicates that a liming policy would not significantly 

improve the condition of birds and forests, pretesting of the survey indicated that respondents 

nonetheless tended to assume that these other benefits would occur. Therefore, to make the 

scenarios more acceptable to respondents, other nonlake effects were added to the two survey 

versions. In the base case, the red spruce (covering 3% of the forests’ area) and two aquatic bird 

species (common loon and hooded merganser) are said to be affected. In this version, the health 

of birds and forests is described as unchanged in the absence of intervention, and minor 

improvements are said to result from the program. In the scope version, a broader range of 

damages is associated with acid rain—two additional species of trees (sugar maple and white 

ash, all together covering 10% of forest area) and two additional birds (wood thrush and tree 

swallow) are said to be affected. The scope version describes a gradually worsening status quo 

along with large improvements due to the program. 

Each respondent was presented with one of these (base or scope) policy scenarios and 

then asked how they would vote in a referendum on the program, if it were financed by an 

increase in state taxes for 10 years. To estimate the distribution of WTP, the annual tax amounts 

were randomly varied across respondents. 

Based on a detailed analysis of the survey data, Banzhaf et al. (2006) defined a range of 

best WTP estimates, which were converted from 10-year annual payments to permanent annual 

payments using discount rates of 3% and 5%. For the base version, the best estimates ranged 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 31 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

from $48 to $107 per year per household (in 2004 dollars), and for the scope version they ranged 

from $54 to $154. 

To specify values for WTPAdr, these estimates were converted to 2007 dollars using the 

CPI and each of them was divided by the corresponding change in the percentage of lakes that 

are unhealthy (20% for the base version and 45% for the scope version). For the base version, the 

WTPAdr estimates range from $2.63 to $5.87 per percentage decrease in unhealthy lakes, and for 

the scope version they range from $1.32 to $3.76. 

To estimate NNY, the Census population projection for New York for 2010 was used, 

which is 19.26 million people, and this amount was divided by the ratio of population size to the 

number of households in New York (2.69) in the year 2000 (assuming that this ratio stays 

constant from 2000 to 2010). 

Finally, to estimate ∆%IL the MAGIC model results reported in Table 2.2-2 were used, 

and it was assumed that the distribution of ANC levels for these 44 lakes is representative of all 

3,000 lakes in the Adirondacks Park. For each of the three ANC thresholds, column (A) of Table 

2.2-8 reports the estimated percentage of “unhealthy” (below the ANC threshold) lakes in 2010. 

In columns (B) and (C), it also reports the percentage of unhealthy lakes in 2020 for the 

reference and zero-out conditions, respectively. In 2020, the reduction in the percentage of lakes 

that are unhealthy in the zero-out condition compared to the reference condition is 22% for the 

20 μeq/L threshold. For the 50 μeq/L, and 100 μeq/L thresholds, it is 31% and 26%, respectively. 

These 3% reduction values were used as the main estimates of ∆%IL.

To estimate aggregate benefits for the zero-out scenario using the RFF survey results, it is 

important to use the results from the survey version that most closely match this scenario. Table 

2.2-8 provides direct comparisons of the percentage of lakes that are defined as unhealthy under 

the different conditions and scenarios. Although both RFF survey versions use 2004 as the 

“current” year instead of 2010, they both use a 10-year horizon, which corresponds to the zeroout scenario. Although no direct matches exist, the closest correspondence is between the zeroout scenario assuming a 50 μeq/L threshold and the RFF scope survey. Under current and future 

conditions with no additional policy interventions, the RFF scope scenario assumes a small 

increase in unhealthy lakes from 50% to 55%, whereas the 50 μeq/L threshold is expected to 

result in a small decrease from 43% to 42%. With the program, the RFF scope survey describes a 

45% decrease in unhealthy lakes, whereas the zero-out scenario projects a 31% decrease. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 32 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Moreover, although the RFF survey does not specify ANC thresholds, the survey’s description of 

unhealthy lakes is arguably closest to what the science defines for a 50 μeq/L threshold (as 

summarized in Figure 2.2-1). 

2.2.2.1 Results: Aggregate Benefits from Reduced Acidification in Adirondack Lakes 

Table 2.2-9 reports the aggregate benefit estimates for the zero-out scenario using the 50 

μeq/L threshold. As described above, the projected long-term decrease in the percentage of 

unhealthy lakes (∆%IL) for this scenario is 31%. Using the range of WTPAdr values from the RFF 

scope survey and the projected number of New York households in 2010 and applying 

Table 2.2-9. Aggregate Benefit Estimates for the Zero-Out Scenario 

ANC 

 Threshold 

Reduction in 

Percentage of 

Unhealthy 

Lakes 

Range of Average 

Household WTP per 

Percentage 

Reduction 

Number of NY 

Households 

(in millions) 

Range of Aggregate 

Benefits 

(in millions of 2007 

dollars) 

∆%IL WTPAdr NNY AggBAdr

20 μeq/L 22% $2.63 $5.87 7.162 $410.6 $916.4 

50 μeq/L 31% $1.32 $3.76 7.162 $291.2 $829.4 

100 μeq/L 26% $2.63 $5.87 7.162 $491.6 $1,097.2 

Equation (2.1), the aggregate annual benefits of the zero-out scenario are estimated to 

range from $291 million to $829 million. 

Table 2.2-9 also reports aggregate benefit estimates for the zero-out scenarios using the 

20 μeq/L and 100 μeq/L thresholds for ANC. Neither of these scenarios corresponds well with 

the baseline descriptions of either the base or scope version of the RFF survey. The baseline 

percentage of unhealthy lakes using the 20 μeq/L threshold (22%) is much lower than in either 

the survey version. In contrast, the percentage using the 100 μeq/L threshold (77%) is much 

higher. Nevertheless, the future reductions in the percentage of unhealthy lakes (22% and 26%) 

are closest to the reductions described in the base version of the RFF survey. Therefore, the 

aggregate benefits of the zero-out scenario with these thresholds are evaluated using the range of 

WTPAdr values from the RFF base survey. With the 20 μeq/L threshold, the aggregate benefits are 

estimated to range from $411 million to $916 million per year. With the 100 μeq/L threshold, the 

aggregate benefits are estimated to range from $492 million to $1.1 billion per year. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 33 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

2.2.2.2 Limitations and Uncertainties 

The benefit transfer model summarized in Equation (2.1) estimates the aggregate benefits 

to New York households in 2010 due to lake ecosystem improvements resulting from the zeroout scenario. To do this, estimates from two different studies were linked and combined. The 

measures of improvements in lake ecosystems were obtained from the MAGIC model (as 

described in Table 2.2-2), and the value estimates were obtained from the RFF survey study. 

Uncertainties are associated with the estimates drawn from each study, and additional 

uncertainties arise when these estimates were combined in the analysis. Some of these main 

uncertainties and limitations are described below. 

First, uncertainties are associated with extrapolating results from the 44 MAGIC-modeled 

lakes to all (roughly 3,000) Adirondack lakes. The 44 modeled lakes are drawn from a larger, 

randomly drawn sample of lakes; however, the representativeness of these 44 lakes for the 

Adirondacks as a whole is uncertain. 

Second, the time frame required for the zero-out scenario to match 1860 conditions is 

uncertain. It was assumed that it takes 10 years for lakes to fully adjust to the reductions in 

nitrogen and sulfur deposition and that conditions equivalent to “background” 1860 conditions 

are achieved in 2020. The present value and annualized benefits would be lower if a longer time 

frame were assumed. 

Third, there is also some uncertainty related to the exact types of ecosystem services that 

are included in these RFF study values, particularly regarding provisioning and regulating 

services, which survey respondents may have been less likely to consider when formulating 

responses to the CV questions. Importantly though, the values estimated by the RFF study are 

likely to include (1) recreational fishing services, which means they cannot be added to the RUM 

results, and (2) other cultural services, in particular recreational and nonuse services. 

Fourth, the inclusion of other ecosystem changes (trees, birds, etc.) in the RFF CV survey 

scenarios implies that respondents’ stated values will overstate WTP for just changes in lake 

acidification. This feature, therefore, contributes to potential overestimation of benefits. 

Fifth, the lack of direct correspondence between the RFF CV scenarios and the zero-out 

scenario requires assumptions for making the benefit transfer. In particular, baseline and future 

levels (percentage of unhealthy lakes) are very different from those in the RFF survey if one uses 

a 20 or 100 ANC threshold. Although the percentage changes are reasonably close to the RFF 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 34 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

20% and 45% decline scenarios, they are not exact and may not be applicable when applied to a 

different baseline (something that was not specifically tested in the CV survey). Rescaling the 

WTP estimates for different percentage changes in unhealthy lakes also requires the somewhat 

strong assumption that there is a constant WTP per percentage decline in unhealthy lakes. 

Finally, the reported results only apply to Adirondack lakes and to New York residents. 

The Adirondack region is more sensitive to acidity in contrast to many other areas of New York 

State, which have calcium-rich limestone deposits that neutralize the acid. The bedrock soil and 

shallow soil deposits have a lower buffering capacity. These geological factors together with 

high and acidic precipitation levels contribute to the vulnerability of this region to acidification. 

The uniqueness of the Park makes simple extrapolations of ecological conditions and human 

values to other lakes very uncertain. Similarly, residents of other states are likely to value 

improved ecosystem services from Adirondack lakes, but the magnitude of these values is 

difficult to assess and, therefore, not included in the reported benefit estimates. 

2.3 REFERENCES 

Banzhaf, S., D. Burtraw, D. Evans, and A. Krupnick. 2006. “Valuation of Natural Resource 

Improvements in the Adirondacks.” Land Economics 82:445-464. 

Chen, L., and C.T. Driscoll. 2004. “Modeling the Response of Soil and Surface Waters in the 

Adirondack and Catskill Regions of New York to Changes in Atmospheric Deposition 

and Historical Land Disturbance.” Atmospheric Environment 38:4099-4109. 

Driscoll, C.T. et al. 2003. “Effects of Acidic Deposition on Forest and Aquatic Ecosystems in 

New York State.” Environmental Pollution 123:327-336.Driscoll, C.T., G.B. Lawrence, 

A.J. Bulger, T.J. Butler, C.S. Cronan, C. Eagar, K.F. Lamber, G.E. Likens, J.L. Stoddard, 

and K.C. Weathers. 2001. Acidic Deposition in the Northeastern United States: Sources 

and Inputs, Ecosystem Effects and Management Strategies. BioScience 51:180-198. 

Greene, W.H. 2003. Econometric Analysis, 5th Ed. New Jersey: Prentice Hall. 

Hutchison, R., and C.E. Kraft. 1994. “Hmong Fishing Activity and Fish Consumption.” Journal 

of Great Lakes Research 20(2):471-487. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 35 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 36 

Industrial Economics, Inc. June 2008. “The Economic Impact of the Clean Air Interstate Rule on 

Recreational Fishing in the Adirondack Region of New York State.” Prepared for the 

Clean Air Markets Division, Office of Air and Radiation, U.S. EPA. 

Kaval, P., and J. Loomis. 2003. Updated Outdoor Recreation Use Values With Emphasis On 

National Park Recreation. Final Report October 2003, under Cooperative Agreement CA 

1200-99-009, Project number IMDE-02-0070. 

Kennedy, P. 2003. A Guide to Econometrics, pp. 312-313. Cambridge, MA: MIT Press. 

Millennium Ecosystem Assessment (MEA). 2005. Ecosystems and Human Well-being: 

Wetlands and Water. Synthesis. A Report of the Millennium Ecosystem Assessment. 

Washington, DC: World Resources Institute. 

Montgomery, M. and M. Needelman. 1997. “The Welfare Effects of Toxic Contamination in 

Freshwater Fish.” Land Economics 73(2):211-223. 

Peterson, D.E., M.S. Kanarek, M.A. Kuykendall, J.M. Diedrich, H.A. Anderson, P.L. 

Remington, and T.B. Sheffy. 1994. “Fish Consumption Patterns and Blood Mercury 

Levels in Wisconsin Chippewa Indians.” Archives of Environmental Health 49(1):53-58. 

U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce, 

U.S. Census Bureau. 2007. 2006 National Survey of Fishing, Hunting, and WildlifeAssociated Recreation. 

U.S. Environmental Protection Agency (EPA), Office of Air and Radiation. November 1999. 

The Benefits and Costs of the Clean Air Act 1990 to 2010: EPA Report to Congress. 

EPA-410-R-99-001. Washington, DC: U.S. Environmental Protection Agency. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

3.0 TERRESTRIAL ACIDIFICATION 

Terrestrial acidification is the result of natural processes and anthropogenic sources of 

acidic deposition. Elevated levels of atmospheric deposition of nitrogen and sulfur can alter the 

chemical composition of soils by accelerating rates of base cation (e.g., calcium and magnesium) 

leaching, which depletes available plant nutrients, and by mobilizing and leaching aluminum, 

which can be toxic to tree roots. Consequently, among the most visible and significant effects of 

acid deposition are damages to forest health and resulting reductions in tree growth. 

Evidence of adverse effects due to terrestrial acidification is particularly strong for two 

common tree species in the northeastern United States where levels of nitrogen and sulfur 

deposition have historically been relatively high—sugar maples and red spruce. Therefore, the 

discussion of ecosystem service effects focuses on these two species; however, more widespread 

impacts that include other tree species are also possible. 

3.1 OVERVIEW OF AFFECTED ECOSYSTEM SERVICES 

The existing ecosystem services that are primarily affected by the terrestrial acidification 

resulting from nitrogen and sulfur deposition are described and, to the extent possible, quantified 

using the classification system outlined in Section 1. 

3.1.1 Provisioning Services 

Forests in the northeastern United States provide several important and valuable 

provisioning services, which are reflected in measures of production and sales of tree products. 

Sugar maples (also referred to as hard maples) are a particularly important commercial 

hardwood tree species in the United States. As shown in Figure 3.1-1, the main range of the 

sugar maple covers most of the United States east of the Mississippi River and north of Alabama 

and Georgia. This range is also the area with the highest levels of nitrogen and sulfur deposition 

in the country, according to monitored estimates from the National Atmospheric Deposition 

Network (NADP) and modeled estimates from the Community Multiscale Air Quality (CMAQ) 

modeling system. 

The two main types of products derived from sugar maples are wood products and maple 

syrup. The wood from sugar maple trees is particularly hard, and its primary uses include 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 37 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

construction, furniture, and flooring (Luzadis and Gossett, 1996). According to data from the 

U.S. Forest Service’s National Forest Inventory and Analysis (FIA) databases 

(http://199.128.173.26/fido/mastf/index.html), in 2006, the total removal of sugar maple saw 

timber from timberland in the United States was almost 2.12 million cubic meters. Assuming an 

average price of $169.5 per cubic meter, the total value of these removals in 2006 was 

approximately $358 million. 

Figure 3.1-1. Combined Nitrogen and Sulfur Deposition (from 2002 CMAQ Dry 

Deposition and NADP Wet Deposition Estimates) and the Range of Sugar Maple 

in the United States 

During winter and early spring (depending, in part, on location and diurnal temperature 

differences), sugar maple trees also generate sap that is used to produce maple syrup. From 2005 

to 2007, annual production of maple syrup in the United States varied between 1.2 million and 

5.3 million liters, which accounted for roughly 19% of worldwide production. The total annual 

value of U.S. production in these years varied between $157 million and $168 million (National 

Agricultural Statistics Service [NASS], 2008). 

Red spruce is a common commercial softwood species. As shown in Figure 3.1-2, its 

range in the United States is much more limited than the sugar maple’s range, but it also 

primarily grows in areas with relatively high levels of nitrogen and sulfur deposition. Red spruce 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 38 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 39 

is now mainly found in northern New England, New York, and in a few high-elevation ar

the Appalachian Mountain range. Wood from red spruce is used in a variety of products 

including lumber, pulpwood, poles, plywood, and musical instruments. According to FIA data, in

2006, the total removal of red spruce saw timber from timberland in the United States was 0.77

million cubic meters. Assuming an average price of $42.

eas of 

37 per cubic meter, the total value of 

these remo

ates) and the Range of Red Spruce 

 red 

ncentrated in 

Maine, whereas m ont and New York. 

e 

vals in 2006 was approximately $33 million. 

Figure 3.1-2. Combined Nitrogen and Sulfur Deposition (from 2002 CMAQ Dry

Deposition and NADP Wet Deposition Estim

in the United States 

Figure 3.1-3 shows and compares the value of annual production of sugar maple and

spruce wood products and of maple syrup in 2006. Across states in the northeastern United 

States, sugar maple timber harvests consistently generated the highest total sales value of the 

three products. Although total sales of red spruce saw timber and maple syrup were of roughly 

the same magnitude in the United States as a whole, the red spruce harvest was co

aple syrup production was largest in Verm

3.1.2 Cultural Services 

Forests in the northeastern United States are also an important source of cultural 

ecosystem services—in particular recreational and aesthetic services. Forestlands support a wid
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 40 

variety of outdoor recreational activities, including fishing, hiking, camping, off-road driving, 

hunting, and wildlife viewing. Regional statistics on recreational activities that are specifically 

forest based are not available; however, more general data on outdoor recreation provide some

insights into the overall level of recreational services provided by forests. For example, most 

recent data from the National Survey on Recreation and the Environment (NSRE) indicate

from 2004 to 2007, 31% of the U.S. adult (16 or older) population visited a wilderness or 

primitive area during the previous year, and 32% engaged in day hiking (Cordell et al., n.d.). 

From 1999 to 2004, 16% of adults in the northeastern United States

 that, 

 of off-road driving recreation in the northeastern United States was 

lue of Sugar Maple and Red Spruce Harvests and Maple 

Syrup Production, 2006 

 

1 participated in off-road

vehicle recreation, for an average of 27 days per year (Cordell et al., 2005). Using the metaanalysis results reported by Kaval and Loomis (2003), which found that the average consumer 

surplus value per day of off-road driving in the United States was $25.25 (in 2007 dollars), the 

implied total annual value

more than $9.25 billion. 

Figure 3.1-3. Annual Va

1 This area includes Connecticut, Delaware, District of Columbia, Illinois, Indiana, Maine, Maryland,

Massachusetts, Michigan, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, 

West Virginia, and Wisconsin. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

State-level data on other outdoor recreational activities associated with forests are also 

available from the 2006 FHWAR (U.S. Department of the Interior [DOI], 2007). As summarized 

in Table 3.1-1, 5.5% of adults in the northeastern United States participated in hunting, and the 

total number of hunting days occurring in those states was 83.8 million. Data from the survey 

also indicated that 10% of adults in northeastern states participated in wildlife viewing away 

from home. The total number of away-from-home wildlife viewing days occurring in those states 

was 122.2 million in 2006. For these recreational activities in the northeastern United States, 

Kaval and Loomis (2003) estimated average consumer surplus values per day of $52.36 for 

hunting and $34.46 for wildlife viewing (in 2007 dollars). The implied total annual value of 

hunting and wildlife viewing in the northeastern United States was, therefore, $4.38 billion and 

$4.21 billion, respectively, in 2006. 

As previously mentioned, it is difficult to estimate the portion of these recreational 

services that are specifically attributable to forests and to the health of specific tree species. 

However, one recreational activity that is directly dependent on forest conditions is fall color 

viewing. Sugar maple trees, in particular, are known for their bright colors and are, therefore, an 

essential aesthetic component of most fall color landscapes. Thus, declines in sugar maple stocks 

due to terrestrial acidification are expected to have detrimental effects on these landscapes. 

Statistics on fall color viewing are much less available than for the other recreational and tourism 

activities; however, a few studies have documented the extent and significance of this activity. 

For example, based on a 1996 to 1998 telephone survey of residents in the Great Lakes area, 

Spencer and Holecek (2007) found that roughly 30% of residents reported at least one trip in the 

previous year involving fall color viewing. In a separate study conducted in Vermont, Brown 

(2002) reported that more than 22% of households visiting Vermont in 2001 made the trip 

primarily for the purpose of viewing fall colors. Unfortunately, data on the total number or value 

of these trips are not available, although the high rates of participation suggest that numbers 

might be similar to the wildlife viewing estimates reported above. 

Although these statistics provide useful indicators of the total recreational and aesthetic 

services derived from forests in the northeastern United States, they do not provide estimates of 

how these services are affected by terrestrial and forest acidification. Very few empirical studies 

have directly addressed this issue; however, two studies have estimated values for protecting 

high-elevation spruce forests in the Southern Appalachians. Kramer, Holmes, and Haefele (2003) 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 41 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

conducted a CV study estimating households’ WTP for programs to protect remaining highelevation spruce forests from damages associated with air pollution and insect infestation 

(Haefele, Kramer, and Holmes, 1991; Holmes and Kramer, 1995). The study collected data from 

486 households using a mail survey of residents living within 500 miles of Asheville, North 

Carolina. The survey presented respondents with photographs representing three stages of forest 

decline and explained that, without forest protection programs high-elevation spruce forests 

would all decline to worst conditions (with severe tree mortality). The survey then presented two 

Table 3.1-1. Participation in Hunting and Wildlife Viewing in Northeastern States in 2006 

Participation Rates by State 

Residentsa

Activity Days by Residents and 

Nonresidents (in thousands) 

State Hunting Wildlife Viewingb Hunting Wildlife Viewingb

Connecticut 1.2% 11.0% 509 4,184 

Delaware 3.1% 7.0% 654 855 

Illinois 2.8% 8.0% 4,688 5,686 

Indiana 5.3% 13.0% 4,808 24,013 

Maine 13.6% 20.0% 2,283 4,778 

Maryland 3.5% 7.0% 2,262 4,782 

Massachusetts 1.3% 11.0% 1,149 8,461 

Michigan 9.2% 11.0% 11,905 10,043 

New Hampshire 5.0% 12.0% 1,057 3,165 

New Jersey 1.3% 8.0% 1,457 7,965 

New York 3.3% 8.0% 10,289 13,521 

Ohio 5.4% 13.0% 10,633 7,816 

Pennsylvania 9.5% 11.0% 16,863 11,972 

Rhode Island 1.2% 11.0% 155 2,948 

Vermont 11.3% 16.0% 1,111 2,459 

West Virginia 13.6% 9.0% 3,939 4,005 

Wisconsin 15.0% 10.0% 10,059 5,547 

Total 5.5% 10.0% 83,821 122,200 

a Ages 16 or older. 

b Wildlife viewing away from home. 

Source: U.S. Department of the Interior (DOI), Fish and Wildlife Service, and U.S. Department of 

Commerce, U.S. Census Bureau, 2007. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 42 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

potential forest protection programs—one would prevent further decline in forests along roads 

and trail corridors (one-third of the at-risk ecosystem) and the other would prevent decline in all 

at-risk forests. Both programs would be funded by tax payments going to a conservation fund. 

Median household WTP was estimated to be roughly $29 (in 2007 dollars) for the first program 

and $44 for the more extensive program. 

Jenkins, Sullivan, and Amacher (2002) conducted a very similar study in 1995 using a 

mail survey of households in seven Southern Appalachian states. In this study, respondents were 

presented with one potential program, which would maintain forest conditions at initial (status 

quo) levels. It was explained that, without the program, forest conditions would decline to worst 

conditions (with 75% dead trees). In contrast to the previously described study, in this survey the 

initial level of forest condition was varied across respondent. In one version of the survey, the 

initial condition was described and shown as 5% dead trees, while the other version described 

and showed 30% dead trees. Household WTP was elicited from 232 respondents using a 

dichotomous choice and tax payment format. The overall mean annual WTP for the forest 

protection programs was $208 (in 2007 dollars), which is considerably larger than the WTP 

estimates reported by Kramer, Holmes, and Haefele (2003). One possible reason for this 

difference is that respondents to the Jenkins, Sullivan, and Amacher (2002) survey, on average, 

lived much closer to the affected ecosystem. Multiplying the average WTP estimate from this 

study by the total number of households in the seven-state Appalachian region results in an 

aggregate annual value of $3.4 billion for avoiding a significant decline in the health of highelevation spruce forests in the Southern Appalachian region. 

3.1.3 Regulating Services 

Forests in the northeastern United States also support and provide a wide variety of 

valuable regulating services, including soil stabilization and erosion control, water regulation, 

and climate regulation (Krieger, 2001). As terrestrial acidification contributes to root damages, 

reduced biomass growth, and tree mortality, all of these services are likely to be affected; 

however, the magnitude of these impacts is very uncertain. Forest vegetation plays an important 

role in maintaining soils in order to reduce erosion, runoff, and sedimentation that can adversely 

impact surface waters. In addition to protecting the quality of water in this way, forests also help 

store and regulate the quantity and flows of water in watersheds. Finally, forests help regulate 

Final Risk and Exposure Assessment September 2009 

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Final Risk and Exposure Assessment September 2009 

Appendix 8 - 44 

climate locally by trapping moisture and globally by sequestering carbon. The total value of 

these ecosystem services is very difficult to quantify in a meaningful way, as is the reduction in 

the value of these services associated with nitrogen and sulfur deposition. 

3.2 CHANGES IN ECOSYSTEM SERVICES ASSOCIATED WITH 

ALTERNATIVE LEVELS OF ECOLOGICAL INDICATORS 

This section estimates values for changes in ecosystem services associated with 

reductions in damages to commercial forests resulting from terrestrial acidification. With high 

levels of acidifying nitrogen and sulfur deposition, trees may experience an increased 

susceptibility to drought and pest damage, aluminum toxicity in roots, a reduced tolerance to 

cold, and a greater propensity to frost injury (DeHayes et al., 1999; Driscoll et al., 2001; Fenn et 

al., 2006). As a result, total stand volume and growth may be reduced. The tree growth response 

and value of reducing nitrogen+sulfur deposition loads across the range of sugar maples and red 

spruces (as shown in Figures 3.1-1 and 3.1-2, respectively) was estimated using a critical load 

assessment methodology (described in the case study analysis) of terrestrial acidification. More 

specifically, the beneficial effects of eliminating all exceedances of critical load for sugar maples 

and red spruces in this range were estimated. 

3.2.1 Increased Provisioning Services from Sugar Maple Timber Harvests due to 

Elimination of Critical Load Exceedances 

 A three-stage approach was used to estimate the value of increased provisioning services 

from sugar maple and red spruce timber harvests. In the first stage, exposure–response models2

for sugar maple and red spruce trees were estimated, which measure the empirical relationship 

between exceedances of critical loads and growth in volume of live trees. In the second stage, 

these exposure–response models were applied to estimate the average increase in sugar maple 

and red spruce growth rates (in three regions) that would result from eliminating critical load 

exceedances in the range of these tree species. In the third stage, these increased growth rates 

were incorporated into an existing forest market model for North America and the value (i.e., 

 

2 See the case study report for alternative models of exposure–response. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 45 

increase in consumer and producer surplus) of expected future increases in sugar maple and red 

spruce timber harvests and sales was estimated. Each of these stages is described below in detail. 

3.2.1.1 Stage 1: Estimation of the Exposure–Response Model

The analysis of the relationship between critical load exceedances and sugar maple and 

red spruce trees’ growth was conducted using data from the USFS FIA database for 16 states in 

the sugar maple range and 5 states in the red spruce range. Each data point in the analysis 

corresponds with a permanent sampling plot location on classified forestland (timberland for 

New York) covering 0.07 ha. Estimation of critical loads for each plot was based on a Simple 

Mass Balance (SMB) modeling approach (described in the case study). FIA plots were only 

included in the analysis if they (1) were nonunique3 permanent sampling plots; (2) provided 

necessary soil, parent material, atmospheric deposition, and run-off data to apply the SMB model 

for critical load estimation; (3) were located to the north of the glaciation line (this line 

represents the southernmost extension of the most recent glacial advancement)4; and (4) had 

positive exceedances in deposition above the most protective critical load (Bc/Al = 10.0).5 With 

these restrictions, 2,205 sugar maple plots and 187 red spruce plots were included in the analysis. 

Tables 3.2-1 and 3.2-2 summarize the plot-level FIA sugar maple and red spruce data 

used to model the exposure–response relationship. For each plot, exceedances of critical loads 

were calculated by subtracting the results of the SMB analysis (estimated critical loads 

estimates) from corresponding 2002 CMAQ nitrogen+sulfur deposition estimates. Overall, 74% 

of sugar maple plots above the glaciation line exceeded the critical loads, ranging from 21% in 

Maine to over 95% in Connecticut, Massachusetts, New Jersey, New York, Pennsylvania, Ohio, 

and Vermont. Thirty-one percent of red spruce above the glaciation line exceeded the critical 

loads, ranging from 16% in Maine to 100% in Massachusetts and Vermont. For sugar maple 

 

3 Nonunique permanent sampling plot locations are those that have maximum critical load attribute values (soils, 

runoff, and atmospheric deposition) that are not distinct and are repeated within a 250-acre area of the plot 

location. This “confidentiality” filter is a requirement of the USFS to prevent the disclosure of data that can be 

directly linked to a location on private land. To comply with the necessary “confidentiality,” full coverages of the 

data required for the critical load calculations were given to the USFS, and the USFS matched and provided the 

data for each nonunique permanent sampling plot. 

4 This is because the base cation weathering term, one of the key components of critical load, may not have been 

accurately estimated for plots from south of the glaciation line (see case study for details) that have older, more 

weathered soils. Thus, using such plots in the analysis may potentially increase error in the data used. 

5 For analysis using lower levels of protection, see the case study report. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 46 

plots with positive exceedances, the average exceedance ranged from less than 100 eq/ha/yr in 

Missouri and Iowa to over 450 eq/ha/yr in Connecticut, Massachusetts, New Jersey, and Ohio. 

For red spruce plots with positive exceedances, the average exceedance ranged from less than 

150 eq/ha/yr in Maine to over 600 eq/ha/yr in Massachusetts. 

Net annual individual tree volume growth and tree volumes for all live sugar maple and 

red spruce trees6 (greater than 12.7 cm diameter at 1.3 m) were acquired from the USFS FIA 

database for each plot. The volume growth calculations were based on the most recent 

measurement period, and the time interval between measurements for the plots (to determine 

annual growth rates) ranged from 1 to 11 years. These calculations included the influences of 

growth and volume reductions or losses due to natural damage (pest, wind, frost) or natural 

mortality. Average volume growth ranged from 0.1 in Massachusetts to 0.62 in Indiana for sugar 

maple and from 0.14 in Massachusetts to 0.26 in Maine for red spruce. Volumes and volume 

growth measures for the sugar maple and red spruce trees in each plot were averaged to produce 

single values of each measure for each species. 

Table 3.2-1. Summary of Plot-Level Data on Sugar Maple Growth and Exceedances (for Plots 

above the Glaciation Line) 

State 

Total 

Number 

of Plots 

Number of 

Plots North 

of 

Glaciation 

Line 

Number of 

Plots with 

Positive 

CL 

Exceedance 

Values 

Number of 

Plots with 

Positive CL 

Exceedance 

Values North 

of Glaciation 

Line 

Average 

CL 

Exceedance 

(eq/ha/yr) 

Average 

Tree 

Volume 

Growth

(m3/yr) 

Average 

Tree 

Volume 

(m3) 

Alabama 12 0 3 

Arkansas 8 0 1 

Connecticut 33 33 33 33 487.76 0.009 0.279 

Illinois 25 20 17 12 117.17 0.007 0.227 

Indiana 266 234 235 204 390.90 0.018 0.397 

Iowa 8 8 2 2 48.07 0.005 0.123 

Kentucky 14 0 12 

Maine 242 242 51 51 130.42 0.011 0.323 

Maryland 4 0 4 

Massachusetts 27 27 27 27 473.31 0.003 0.366 

 

6 All trees with reported volumes of “0” were excluded from the analyses. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 47 

State 

Total 

Number 

of Plots 

Number of 

Plots North 

of 

Glaciation 

Line 

Number of 

Plots with 

Positive 

CL 

Exceedance 

Values 

Number of 

Plots with 

Positive CL 

Exceedance 

Values North 

of Glaciation 

Line 

Average 

CL 

Exceedance 

(eq/ha/yr) 

Average 

Tree 

Volume 

Growth

(m3/yr) 

Average 

Tree 

Volume 

(m3) 

Michigan 596 596 418 418 242.17 0.011 0.307 

Minnesota 257 257 79 79 156.06 0.010 0.256 

Missouri 122 31 58 18 84.02 0.012 0.246 

New Hampshire 72 72 60 60 378.40 0.009 0.304 

New Jersey 6 6 6 6 601.39 0.013 0.357 

New York 280 280 264 264 437.94 0.010 0.344 

North Carolina 13 0 9 

Ohio 55 27 54 26 452.60 0.013 0.545 

Pennsylvania 270 133 263 126 387.35 0.011 0.366 

Tennessee 264 0 132 

Vermont 162 162 160 160 301.67 0.008 0.411 

Virginia 104 0 63 

West Virginia 337 0 318 

Wisconsin 870 870 719 719 185.16 0.009 0.304 

TOTAL Observations 

(used in calculations) 4,047 2,998 2,988 2,205 2,205 2,205 2,205 

CL = critical load 

Table 3.2-2. Summary of Plot Level Data on Sugar Maple Growth and Exceedances (for Plots 

above the Glaciation Line) 

State 

Total 

Number 

of Plots 

Number 

of Plots 

North of 

Glaciation Line 

Number of 

Plots with 

Positive 

CL 

Exceedance 

Values 

Number of 

Plots with 

Positive CL 

Exceedance 

Values North 

of Glaciation 

Line 

Average 

CL 

Exceedance 

(eq/ha/yr) 

Average 

Tree 

Volume 

Growth

(m3/yr) 

Average 

Tree 

Volume 

(m3) 

Maine 483 483 78 78 133.1048 0.007 0.245

Massachusetts 3 3 3 3 628.5439 0.004 0.203

New Hampshire 42 42 32 32 368.9527 0.006 0.245

New York 18 18 14 14 282.5433 0.004 0.221

Tennessee 1 0 1 0 

Vermont 60 60 60 60 292.4329 0.007 0.328

West Virginia 6 0 6 0 

TOTAL Observations 

(used in calculations) 613 606 194 187 187 187 187
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

The results of a multivariate OLS regression, using average tree growth (measured in 

cubic meters per year) as the dependent variable, are reported in Table 3.2-3 and 3.2-4. The 

explanatory variables include the critical load exceedance (measured in eq/ha/year) for each plot, 

linear and squared terms of average tree volumes (measured in cubic meters), and a categorical 

(dummy) variable for each state (with Connecticut as the reference category for sugar maple and 

Vermont for red spruce). The purpose of the state variables is to control for other unobserved 

sources of variation in tree growth, which are related to a plot’s general geographic location. 

Examples of potential unobserved factors include differences in data collection methods and 

measurements across reporting state, climatic factors, and geological characteristics. An F test 

applied to the state categorical variables indicated that their coefficients are jointly significant at 

the 5% level for sugar maple. In general, the growth of a tree rises with age but at a decreasing 

rate. Because data on the age were unavailable, average tree volume was instead included as a 

proxy variable in the regression to control for this relationship. 

The coefficient of the critical load exceedance was negative for both species and was 

statistically significant at the 5% level (p-value of 0.035) for red spruce and at the 10% (p-value 

of 0.101) for sugar maple, thus supporting the theory that when critical loads are exceeded by 

atmospheric nitrogen and sulfur deposition, tree health and growth can be impaired. 

Table 3.2-3. Linear Exposure–Response Model for Exceedances (above a Critical Load 

Calculated with Bc/Al = 10) and Sugar Maple Tree Growth: OLS Regression Results (for Plots 

above the Glaciation Line) 

Dependent 

Variable: 

Average Tree 

Growth (m3/yr) 

Explanatory Variables Coefficient t-statistic p-value 

Intercept 0.004875 1.48 0.1385 

Critical load exceedance −3.344E-06 −1.64 0.1008 

Average tree volume 0.021150 10.12 <.0001 

Square of average tree volume 8.944E-04 1.1 0.27 

Illinois −0.001884 −0.31 0.755 

Indiana 0.005452 1.63 0.1029 

Iowa −0.002052 −0.16 0.8743 

Maine −0.000895 −0.22 0.8245 

Massachusetts −0.008403 −1.82 0.0685 

Michigan 0.000222 0.07 0.9456 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 48 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 49 

Dependent 

Variable: 

Average Tree 

Growth (m3/yr) 

Explanatory Variables Coefficient t-statistic p-value 

Minnesota 0.000210 0.06 0.9553 

Missouri 0.001850 0.35 0.7255 

New Hampshire −0.001647 −0.43 0.6696 

New Jersey 0.001956 0.25 0.8042 

New York −0.000817 −0.25 0.8035 

Ohio −0.002104 −0.45 0.6522 

Pennsylvania −0.000803 −0.23 0.8177 

Vermont −0.005168 −1.51 0.131 

Wisconsin −0.002195 −0.68 0.4958 

Number of Observations 2,205 

Adjusted R2 0.1722 

3.2.1.2 Stage 2: Estimation of Average Increments in Tree Volume 

In this stage of the analysis, the effect of eliminating all critical load exceedances in the 

range of sugar maples and red spruce was simulated and the resulting average (at a region level) 

percentage increase in tree volume was estimated. 

Table 3.2-4. Linear Exposure–Response Model for Exceedances (above a Critical Load 

Calculated with Bc/Al = 10) and Red Spruce Tree Growth: OLS Regression Results (for Plots 

above the Glaciation Line) 

Dependent Variable: 

Average Tree 

Growth (m3/yr) 

Explanatory Variables Coefficient t-statistic p-value 

Intercept 0.006034 4.96 <.0001 

Critical load exceedance −5.162E-06 −2.12 0.0354 

Tree volume 0.005590 1.26 0.2093 

Square of tree volume 5.100E-03 1.23 0.2218 

Maine 0.000285 0.32 0.7489 

Massachusetts −0.000132 −0.05 0.9629 

New Hampshire 0.000435 0.42 0.6736 

New York −0.001805 −1.32 0.1897 

Number of Observations 187 

Adjusted R2 0.1963 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Based on the results of the regression equation reported in Table 3.2-3 and 3.2-4, for each 

plot i with a positive critical loads exceedance, the following equation was first used to estimate 

what tree volume growth would be under conditions with no critical loads exceedances: 

 ) (* (3.1) 01 0

ii −+= CLEbgg i

where 

0 CLEi = critical load exceedance at plot i under observed conditions 

0 gi = annual tree volume growth on plot i under observed conditions (in m3

/yr) 

1

gi = annual tree volume growth on plot i under conditions with no exceedance of 

critical load (CLEi = 0) (in m3

/yr) 

B = regression coefficient (slope) for critical load exceedance (from Table 3.2-3 and 

3.2-4, equals −3.344E-06 for sugar maple and −5.162E-06 for red spruce) 

Since this study calculated the effects of eliminating positive exceedances with the aim of 

estimating reductions in damages to sugar maple and red spruce forests resulting from terrestrial 

acidification, it was assumed that there is no change in growth for plots without positive critical 

load exceedances. In practice, however, some reduced growth may be possible due to lower 

nitrogen availability in plots that are below the critical load. Thus, the calculations should be 

interpreted as an upper bound to the value of reducing nitrogen+sulfur deposition loads. 

To apply these results in the market model used in the next stage of the analysis, these 

volume growth estimates were then converted into an average percentage increment in volume. 

In other words, in each period t, tree volume on plot i is expected to be greater (by a factor fi) 

under conditions with no critical loads exceedance, compared to conditions with currently 

observed critical loads exceedances. In formal terms: 

 (3.2) 0

1,

0

1,

0 1

1,

0

1,

1 0 0 ))/(1())/(1)(1()1( it iti i ++=+= VVgfVfV titii −− += VVg titii −−

where 

0 Vit = average tree volume on plot i under observed conditions in period t (in m3

) 

1 Vit = average tree volume on plot i under conditions with no exceedance of critical load 

in period t (in m3

) 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 50 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 51 

Solving Equation (3.2) for fi results in 

1

)/(1(

)/(1(

0

1

0

0

1

1

− +

+ =

−

−

iti

iti

i Vg

Vg f . (3.3) 

Using the plot-level estimates of g

1

, g

0

, and V0, fi for each plot in the data set was 

estimated, and these estimates were then averaged across each region. 

3.2.1.3 Stage 3: Estimation of Increased Market-Based Benefits from Sugar Maple and Red 

Spruce Timber Harvests 

The next critical step in establishing the link between changes in nitrogen and sulfur 

deposition and the changes in forest provisioning services is modeling the effect of the average 

increase in tree growth (obtained in Stage 2) on public welfare. This section describes the 

approach to obtaining valuation estimates for this incremental increase in the volume of 

commercial sugar maple and red spruce stands.7 To implement this approach, the increase in the 

percentage volume of timber was applied to all age categories, and FASOMGHG (Forest and 

Agricultural Sector Optimization Model—Green House Gas version) was used to calculate the 

resulting market-based welfare effects in the forest and agricultural sectors of the United States. 

Data obtained from the FIA were used as inputs into FASOMGHG, which enabled the 

adaptation of the model for this application. The different components of these input data are 

described below. 

FASOMGHG is a price-endogenous, dynamic, nonlinear programming model of the 

forest and agricultural sectors in the United States (Adams et al., 2005). The model simulates the 

allocation of land over time to competing activities in these two sectors and the resultant 

consequences for the commodity markets supplied by these lands. It was developed to evaluate 

the welfare and market impacts of public policies that cause changes in land use and activities 

both between and within the two sectors. The results from this model yield a dynamic simulation 

of prices, production, management, consumption, greenhouse gas (GHG) effects, and other 

environmental and economic indicators within these two sectors. For this application, 

 

7 Holmes (1992) describes a similar approach to estimate welfare effects for a decline in southern pine forest 

productivity in the United States. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 52 

FASOMGHG’s key outputs include economic welfare measures, such as changes in producer 

and consumer surplus.8

 

The following discussion summarizes the other main features of FASOMGHG and 

describes how they were used and adapted for this application: 

 Temporal Frame: The time frame of this model is typically 70 to 100 years, and the 

model is solved on a 5-year time-step basis. The base year for this model is 2002. 

 Geographical Regions: FASOMGHG models forest and agricultural activity across the 

conterminous United States, which is broken into 11 market regions. Forestry production 

occurs in nine of these regions. The selection of FASOMGHG regions for this model 

application was determined by comparing maps showing the regions where sugar maples 

grow with a list of FASOM regions. Table 3.2-5 lists the states in each of the FASOM 

regions used in this application. It also shows the average increase in tree growth (obtained 

from Stage 2) for each of these regions. 

Table 3.2-5. Estimated Increments in Sugar Maple and Red Spruce Timber Volume (Resulting 

from Elimination of Critical Load Exceedances), by FASOMGHG Region 

Key Region States 

Average 

Percentage 

Increment in Sugar 

Maple Tree 

Volume 

(i.e., average fi) 

Average Percentage 

Increment in Red 

Spruce Tree Volume 

(i.e., average fi) 

NE Northeast Connecticut, Delaware, Maine, 

Maryland, Massachusetts, New 

Hampshire, New Jersey, New 

York, Pennsylvania, Rhode 

Island, Vermont, West Virginia 

0.59% 0015% 

LS Lake 

States 

Michigan, Minnesota, Wisconsin 0.28% 

CB Corn Belt Illinois, Indiana, Iowa, Missouri, 

Ohio 

0. 57% 

Source: Adams et al., 2005. 

 Types of Forests: Two types of forests are considered when evaluating policy effects in 

FASOMGHG—softwood and hardwood. To adapt these categories for the application, 

 

8 For a detailed documentation of FASOMGHG, please see Adams et al. (2005). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 53 

sugar maples and red spruce trees needed to be expressed as a proportion of hardwoods 

and softwoods, respectively. This was done for each of the regions modeled in this 

application. These relevant data were obtained from FIA (Table 3.2-6) and are a 

component of the input into FASOMGHG. 

Table 3.2-6. Proportions of Hardwood in Sugar Maple Production and Proportions of Softwood 

in Red Spruce Production, by FASOM Region 

FASOM Regions Proportion of Hardwood/Softwood 

Sugar Maple NE 13% 

LS 

CB 

11% 

Red Spruce NE 14.5%9

Source: U.S. Department of Agriculture, Forest Service, 2002. 

 Forestland: The FASOMGHG model does not track land under forest cover that produces 

less than 0.57 m3

/yr (called unproductive forestland) or on timberland that is reserved for 

other uses, because these are not a part of the U.S. timber base. Endogenous land use 

modeling is only done for privately held land, not publicly owned or managed timberlands. 

The model assumes that the amount of public land in forests does not adjust to market 

conditions but is set by the government. Thus, the average percentage increase in volume 

is applied to only forests growing on private land. The proportions of the timberland under 

private and public ownership are shown in Table 3.2-7 (obtained from FIA data). 

 Welfare Measure: Mathematically, FASOMGHG solves an objective function to 

maximize net market surplus. This is represented by the area under the product demand 

function (an aggregate measure of consumer welfare) less the area under the factor supply 

curves (an aggregate measure of producer costs). The value of the resultant objective 

function is consumers’ and producers’ surplus. The welfare effects of a productivity 

improvement are obtained from FASOMGHG as the difference in annual net market 

surplus between a base case (without the policy in place) and a control case (with the 

policy in place). 

 

9 The RPA Assessment tables report the proportion of the spruce and balsam fir category as 29%. We assume that 

half of this is due to red spruce. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

To apply FASOMGHG for this analysis, the main input required for the model is the 

annual percentage increase in total hardwood and total softwood volume by region. To address 

this requirement, the estimate of the average percentage increment in sugar maple tree volume 

(average fi, shown in Table 3.2-5) was multiplied by the proportion of hardwoods in sugar maple 

production (shown in Table 3.2-6) for each FASOM region, which ranges from 11% to 13%. 

Similarly, the estimate of the average percentage increment in red spruce tree volume (average 

fi, shown in Table 3.2-5) was multiplied by the proportion of softwoods in red spruce production 

(shown in Table 3.2-6) for the NE region. 

Table 3.2-7. Proportion of Timberland under Private and Public Ownership by FIA Regiona: 2002 

FIA Region Private Timberland Public Timberland 

Northeast 87% 13% 

NorthCentral 28% 72% 

a

 The states in the Northeast FIA region correspond exactly to states in NE in FASOM. 

The states in the NorthCentral FIA region correspond exactly to states in LS and CB in FASOM. 

Source: U.S. Department of Agriculture, Forest Service, 2002, Table 10. 

3.2.1.4 Results: Aggregate Benefit Estimates 

Figure 3.2-1 summarizes the FASOMGHG model results. These results are reported as 

the present discounted values of future welfare changes in the forestry sector (in 5-year 

increments from 2000 to 2065) due to increased tree growth as well as the future welfare changes 

in the agricultural sector. Summing over this 65-year period, the value of gains to the forestry 

sector is $17.1 million (in 2007 dollars, using a 4% discount rate). The agricultural sector has a 

welfare loss of $1 million. This loss is possibly due to a shift in land use from agriculture to 

forestry. The total present value of these welfare changes due to both the sectors is $16.09 

million (in 2007 dollars, using a 4% discount rate). On an annualized basis (at 4%), this is 

equivalent to $684,000 per year. Figure 3.2-1 presents the time path of these welfare estimates 

for the forestry and agricultural sector as well as the total welfare estimates. The cyclical pattern 

of the estimates is most likely driven by the fact that if more harvesting is done in any period, 

this leads to less stock to harvest from in the next period. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 54 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Welfare Changes in Forestry and Agriculture 

-2000

-1000

0

1000

2000

3000

4000

2000 2010 2020 2030 2040 2050 2060 2070

Year

Estimates of Changes in Welfare (in 

thousands 2007 dollars)

Forestry

Agriculture

Total Welfare

Figure 3.2-1. Estimated Time Path of Welfare Gains in the Forestry and Agricultural 

Sector due to Increased Sugar Maple and Red Spruce Growth (2000–2065) 

Limitations and Uncertainties 

This analysis links two separate models to estimate values of reductions in damages to 

sugar maple forests due to terrestrial acidification. The first is an exposure–response model 

relating maximum acid deposition load exceedances with tree growth. Simulating the effect of 

eliminating all exceedances, an average percentage increase in tree volume was obtained. a 

market model of the forest and agricultural sectors (FASOMGHG) was then used to calculate the 

welfare effects of this increased volume. 

In doing this, certain limitations and uncertainties are associated with each component of 

the analysis as well as with the linkages between them. These are described below. 

Linking changes in deposition levels to changes in tree growth: 

 In interpreting the results of this model, readers should keep certain data limitations in 

mind. First, plot-level data were completely unavailable for some of the states and partially 

unavailable for others for both the species. Second, only plots that were above the 

glaciation line were used in this analysis. It is not known whether the plots in other states 

and also those below the glaciation line that are part of the FASOMGHG regions have 

characteristics that are correlated with the critical load exceedances, which might lead to 

biased estimates of the exposure–response relationship. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 55 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

 The estimated reduction in the forest damages, as explained in Section 3.2.1.2, should be 

interpreted as an upper bound on the benefits of reducing nitrogen+sulfur deposition, since 

it only includes the gains from reducing critical load exceedances. Nitrogen deposition 

below the critical load may actually promote tree growth through fertilization effects; 

therefore, reducing deposition may have potential counteracting effects on tree growth. 

The current analysis does not estimate or include these counteracting effects. 

 Although this analysis of tree growth response is done for sugar maple and red spruce, 

gains are also expected for other commercial species. Thus, we are underestimating the 

total benefits of reducing nitrogen+sulfur deposition. 

 Because of data limitations, the exposure–response analysis does not control for other 

factors that may affect tree growth, such as elevation, slope, density (to account for 

sunlight and competition among trees for nutrients), age (though tree volume was used as a 

crude proxy for this variable), different management practices, and climate. Also, 

differences in measurement and reporting across plots and states may result in 

discrepancies in the data. Although this study attempted to capture the differences in 

measurement and in climate by using state dummies, this is not a perfect control, since, for 

example, there is substantial variation in climate within a state. Inadequate controls for 

these other factors could potentially lead to omitted variable bias. Other uncertainties and 

limitations associated with the estimation of the exposure–response relationships are 

discussed in the case study. 

Linking changes in tree growth to economic welfare changes: 

 In applying the estimates from the exposure–response model to the FASOM model, it was 

assumed that the plots used to calculate the percentage increase in tree volume are 

representative of the FASOMGHG regions. However, because data on all plots in the 

region are unavailable, there is some uncertainty regarding the representativeness of plots 

used. 

 The tree volume growth estimates used in the exposure–response model were calculated 

based on live trees on forestland, not timberland, which is what FASOMGHG uses. This 

may potentially give rise to some uncertainty in applying the results to FASOMGHG 

because the estimates of the slopes (b) may be different for timberland than for forestland. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 56 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

 The exposure–response model uses data from both private and public lands, while in 

FASOMGHG the growth is applied to private lands only. This is an additional source of 

uncertainty because different management practices could potentially affect the 

relationship between exposure and growth differently. 

 The age structure (and consequently volume of trees) may not be the same. So the stands 

to which the change in growth rates are applied in FASOMGHG may be different from the 

ones used in the exposure–response model, and this study may be assuming a change in 

growth rate that is not realistic for these stands. 

 A general limitation when using FASOMGHG is that it is a very aggregated region-level 

model; thus, effects pertaining to areas particularly vulnerable to acidification cannot be 

identified. Also, to make future timber market projections, FASOMGHG requires several 

assumptions regarding future product demands, production capacity, and timber inventory. 

 It must also be emphasized that the economic welfare changes reported in this section are 

only those associated with markets for sugar maple and red spruce timber. They do not 

include potential gains associated with other provisioning services, such as sugar maple 

syrup production or production of other hardwood or softwood species affected by 

terrestrial acidification. They also do not include gains outside the United States (in 

particular, Canada) or in other sectors of the U.S. economy. 

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Appendix 8 - 57 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

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Appendix 8 - 58 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 59 

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Spencer, D.M., and D.F. Holecek. 2007. “Basic Characteristics of the Fall Tourism Market.” 

Tourism Management 28:491-504. 

U.S. Department of Agriculture, Forest Service. Forest Inventory and Analysis National 

Program, RPA Assessment Tables. 2002. Available at 

http://Ncrs2.Fs.Fed.Us/4801/Fiadb/Rpa_Tabler/Draft_RPA_2002_Forest_Resource_Tabl

es.pdf. 

U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce, 

U.S. Census Bureau. 2007. 2006 National Survey of Fishing, Hunting, and WildlifeAssociated Recreation. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 60 

4.0 AQUATIC ENRICHMENT 

One of the main adverse ecological effects resulting from nitrogen deposition, 

particularly in the Mid-Atlantic region of the United States, is the effect associated with nutrient 

enrichment in estuarine waters. A recent assessment of 141 estuaries nationwide by the National 

Oceanic and Atmospheric Administration (NOAA) concluded that 19 estuaries (13%) suffered 

from moderately high or high levels of eutrophication due to excessive inputs of both nitrogen 

and phosphorus, and a majority of these estuaries are located in the coastal area from North 

Carolina to Massachusetts (NOAA, 2007). By several measures, the aquatic ecosystem of the 

Chesapeake Bay estuary is particularly suffering from the effects of excessive nitrogen loads, 

and roughly one-third of these loads are associated with atmospheric deposition of nitrogen in 

the watershed (Sweeney, 2007).1 For other estuaries in the Mid-Atlantic region, the contribution 

of atmospheric distribution to total nitrogen loads is estimated to range between 10% and 58% 

(Valigura et al., 2001). 

Eutrophication in estuaries is associated with a range of adverse ecological effects. Using 

the conceptual framework developed by NOAA, Figure 4-1 illustrates the main links between 

nutrient loadings and ecological symptoms in estuaries. The framework emphasizes four main 

types of eutrophication effects—low dissolved oxygen (DO), harmful algal blooms (HABs), loss 

of submerged aquatic vegetation (SAV), and low water clarity. 

Low DO (i.e., hypoxia) has become a chronic problem in several estuaries, particularly 

during summer months. Five of the 22 estuaries evaluated by NOAA in the Mid-Atlantic region 

suffer from serious DO problems. The mainstem of the Chesapeake Bay has been a particular 

area of concern. For example, between 2005 and 2007, only about 12% of the Bay met DO 

standards during the summer months (Chesapeake Bay Program, n.d.). Low DO disrupts aquatic 

habitats, causing stress to fish and shellfish, which, in the short term, can lead to episodic fish 

kills and, in the long term, can damage overall growth in fish and shellfish populations. Low DO 

also degrades the aesthetic qualities of surface water. 

 

1 Phosphorus loads, primarily from agricultural runoff and wastewater dischargers in the Chesapeake Bay 

watershed, are the other main source of nutrients contributing to eutrophication in the Bay. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

HABs were also rated by NOAA as a major problem in five Mid-Atlantic estuaries, 

including the mainstem of the Chesapeake Bay and the Potomac River estuary. In addition to 

often being toxic to fish and shellfish and leading to fish kills and aesthetic impairments of 

estuaries, HABs can, in some instances, also be harmful to human health. 

Nutrient Inputs to Estuary

Primary Symptoms of 

Eutrophication

Secondary Symptoms

of Eutrophication

Impacts on 

Ecosystem Endpoints

Nitrogen Loadings from Direct

and Indirect Deposition

Nitrogen and Phosphorous 

Loadings from Other Sources

Chlorophyll a

(Phytoplankton)

Blooms

Macroalgal 

Blooms

Low Dissolved 

Oxygen (DO) 

(Hypoxia/ 

Anoxia)

Loss of 

Submerged 

Aquatic 

Vegetation 

(SAV)

Nuisance/ 

Toxic/Harmful 

Algal Blooms 

(HABs)

Low Water 

Clarity and 

Light 

Availability

Increases in 

Fish Kills

Declines in 

Shoreline 

Quality

Declines in 

Aesthetic 

(Visual, Odor) 

Quality 

Declines in 

Fish/Shellfish 

Abundance

Provisioning Services

• production of fish

• production of shellfish

Cultural Services

• recreational fishing, 

boating, swimming, 

etc. 

• aesthetic enjoyment

• nonuse services

Regulating services

• erosion control

• storm protection

Affected Ecosystem Services

Figure 4-1. Conceptual Model of Eutrophication Impacts in Estuaries (Source: 

Adapted from Bricker et al. [2007] and Bricker, Ferreira, and Simas [2003]). 

SAV provides critical habitat for many aquatic species in estuaries and, in some 

instances, can also protect shorelines by reducing wave strength; therefore, declines in SAV due 

to nutrient enrichment are an important source of concern. Although less prevalent than low DO 

and HABs as a problematic symptom of eutrophication, it is nonetheless rated by NOAA as a 

serious problem in the mainstem of the Chesapeake Bay and the New Jersey Inland Bays. 

Low water clarity is the result of accumulations of both algae and sediments in estuarine 

waters. In addition to contributing to declines in SAV, high levels of turbidity also degrade the 

aesthetic qualities of the estuarine environment. Although NOAA’s assessment of estuaries did 

not focus on turbidity separately as an indicator of eutrophication, it is nonetheless a common 

problem in the Mid-Atlantic region. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 61 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

4.1 OVERVIEW OF AFFECTED ECOSYSTEM SERVICES 

Figure 4-1 also extends the NOAA framework to include links to the main types of 

ecosystem services that are affected by the primary and secondary symptoms of eutrophication. 

The following sections provide a discussion and overview of the primarily affected provisioning, 

cultural, and regulating services. 

4.1.1 Provisioning Services 

Estuaries in the eastern United States are an important source of food production, in 

particular fish and shellfish production. The estuaries are capable of supporting large stocks of 

resident commercial species, and they serve as the breeding grounds and interim habitat for 

several migratory species. 

To provide an indication of the magnitude of provisioning services associated with 

coastal fisheries, Table 4.1-1 reports the annual value of commercial landings in recent years for 

15 East Coast states. From 2005 to 2007, the average value of total catch was $1.5 billion per 

year. It is not known, however, what percentage of this value is directly attributable to or 

dependent upon the estuaries in these states. Table 4.1-2 focuses specifically on commercial 

landings in Maryland and Virginia in 2007, and it reports values for the main commercial species 

in these states. Although these values also include fish caught outside of the Chesapeake Bay, the 

values for two key species—blue crab and striped bass—are predominantly from the estuary 

itself. These data indicate that blue crab landings in 2007 totaled nearly $44 million in the Bay. 

The value of striped bass and menhaden totaled about $9 million and $25 million, respectively. 

To most accurately assess how eutrophication in East Coast estuaries is related to the 

long-term provisioning services from their fishery resources requires bioeconomic models (i.e., 

models that combine biological models of fish population dynamics with economic models 

describing fish harvesting and consumption decisions). In most cases, these models address the 

dynamic feedback effects between fish stocks and harvesting behavior, and they characterize 

conditions for a “steady-state” equilibrium, where stocks and harvest levels are stabilized and 

sustainable over time. 

Section 4.2 describes one bioeconomic model linking blue crab harvests to nutrient loads 

in the Neuse River estuary, and it applies the model to estimate how reductions in nitrogen loads 

to the estuary would affect the societal value of future blue crab harvests. In practice, however, 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 62 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

very few other studies have developed empirical bioeconomic models to estimate how changes 

in environmental quality affect fish harvests and the value of these services (Knowler, 2002). 

One exception is Kahn and Kemp (1985), which estimated a bioeconomic model of commercial 

and recreational striped bass fishing using annual data from 1965 to 1979, measuring the effects 

of SAV levels on fish stocks, harvests, and social welfare. They estimated, for example, that a 

50% reduction in SAV from levels existing in the late 1970s (similar to current levels 

[Chesapeake Bay Program, 2008]) would decrease the net social benefits from striped bass by 

roughly $16 million (in 2007 dollars). 

In a separate analysis, Anderson (1989) developed an empirical dynamic simulation 

model of the effects of SAV changes on commercial blue crab harvests in the Virginia portion of 

the Chesapeake Bay. Applying the empirical model results, long-run (15-year) dynamic 

equilibria were estimated under baseline conditions (assuming SAV area constant at 1987 levels) 

and under conditions with “full restoration” of SAV (i.e., 284% increase). In equilibrium, the 

increase in annual producer surplus and consumer surplus with full restoration of SAV was 

estimated to be $3.5 million and $4.4 million (in 2007 dollars), respectively. 

Table 4.1-1. Annual Values of East Coast Commercial Landings (in millions) 

State 2004 2005 2006 2007 

Connecticut $33.40 $37.57 $36.89 $42.08 

Delaware $5.42 $6.11 $5.69 $7.58 

Florida, East Coast $39.98 $35.49 $42.00 $42.74 

Georgia $14.37 $13.46 $11.53 $10.08 

Maine $367.09 $391.90 $361.85 $319.52 

Maryland $49.29 $63.67 $53.58 $52.27 

Massachusetts $326.00 $427.07 $437.05 $457.18 

New Hampshire $17.21 $22.12 $18.84 $19.09 

New Jersey $145.86 $159.01 $136.05 $152.46 

New York $46.89 $56.45 $57.73 $58.94 

North Carolina $79.70 $64.89 $70.12 $82.31 

Pennsylvania $0.07 $0.04 $0.10 $0.13 

Rhode Island $76.25 $91.58 $98.58 $76.79 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 63 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 64 

State 2004 2005 2006 2007 

South Carolina $18.54 $17.57 $17.03 $15.57 

Virginia $160.51 $155.26 $109.07 $130.56 

Total $1,380.60 $1,542.20 $1,456.11 $1,467.31 

Source: National Oceanic and Atmospheric Administration (NOAA), 2007. 

Table 4.1-2. Value of Commercial Landings for Selected Species in 2007 (Chesapeake Bay 

Region) 

State Species Value 

Maryland 

Blue crab $30,433,777 

Striped bass $5,306,728 

Clams or bivalves $5,007,952 

Sea scallop $2,808,984 

Oyster, Eastern $2,524,045 

Other $6,190,474 

Total $52,271,960 

Virginia 

Sea scallop $62,891,848 

Menhaden $25,350,740 

Blue crab $13,222,135 

Croaker, Atlantic $4,615,924 

Striped bass $3,834,906 

Clam, Northern Quahog $3,691,319 

Summer flounder $3,186,229 

Other $16,954,893 

 Total $130,561,765 

Source: National Oceanic and Atmospheric Administration (NOAA), 2007. 

One study examining the short-term effects of DO levels on crab harvests is Mistiaen, 

Strand, and Lipton (2003). Focusing on three Chesapeake Bay tributaries—the Patuxent, 

Chester, and Choptank rivers—they estimated a “stress-availability” model measuring the effects 

of DO levels on the availability of blue crabs for commercial harvest, given the stock levels and 

number of fishing vessels. The model results indicated that, below a threshold of 5 mg/L, 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 65 

reductions in DO cause a statistically significant reduction in commercial harvest and revenues. 

For the Patuxent River alone, a simulated reduction of DO from 5.6 to 4.0 mg/L was estimated to 

reduce crab harvests by 49% and reduce total annual earnings in the fishery by $275,000 (in 

2007 dollars). However, this is an upper-bound estimate because it does not account for changes 

in fishing effort that would likely occur, and if the measured changes are due to migration of crab 

populations to other areas rather than to crab mortality, then the broader net effects on crab 

harvests may also be considerably smaller.2 

In addition to affecting provisioning services through commercial fish harvests, 

eutrophication in estuaries may also affect these services through its effects on the demand for 

seafood. For example, a well-publicized toxic pfiesteria bloom in the Maryland Eastern Shore in 

1997, which involved thousands of dead and lesioned fish, led to an estimated $56 million (in 

2007 dollars) in lost seafood sales for 360 seafood firms in Maryland in the months following the 

outbreak (Lipton, 1999). Additional evidence regarding potential losses in provisioning services 

due to eutrophication-related fish kills is provided by Whitehead, Haab, and Parsons (2003) and 

Parsons et al. (2006). The survey used in both studies was conducted with more than 5,000 

respondents in states bordering the Chesapeake Bay area and in North Carolina. The survey 

asked respondents to consider how their consumption patterns would change in response to news 

about a large fish kill caused by a toxic pfiesteria bloom. To address the fact that not all fish kills 

are the same, the size and type of the described fish kill―either “major,” involving more than 

300,000 dead fish and 75% with pfiesteria lesions, or “minor,” involving 10,000 dead fish and 

50% with lesions―were randomized across respondents. Based on respondents’ stated 

behaviors, the studies estimated reductions in consumer surplus per seafood meal ranging from 

$2 to $5.3 The survey also found that 42% of residents in the four-state area (Maryland, Virginia, 

Delaware, and North Carolina) were seafood consumers and that the average number of seafood 

meals per month among these consumers was between four and five. As a result, they estimated 

 

2 The estimated relationship between harvest and DO is discontinuous at 5 mg/L. The size of the measured effect on 

harvests is relatively small below 5 mg/L and zero above the 5 mg/L threshold; therefore, any sizable benefits 

would require DO to cross the 5 mg/L threshold. Moreover, the 5 mg/L threshold was an assumption of the model 

rather than a tested hypothesis, which raises additional questions about the accuracy of benefit estimates for 

changes across the threshold. 

3 Surprisingly, these estimates were not sensitive to whether the fish kill was described as major or minor or to the 

different types of information included in the survey. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

aggregate consumer surplus losses of $43 million to $84 million (in 2007 dollars) in the month 

after a fish kill. 

4.1.2 Cultural Services 

Estuaries in the eastern United States also provide an important and substantial variety of 

cultural ecosystem services, including water-based recreational and aesthetic services. One of the 

difficulties with quantifying recreational services from estuaries is that much of the national and 

regional statistics are jointly collected and reported for estuarine and other coastal areas. 

Nevertheless, even these combined statistics provide several useful indicators of recreational 

service flows. For example, data from the FHWAR indicate that, in 2006, 4.8% of the 16 or older 

population in coastal states from North Carolina to Massachusetts participated in saltwater 

fishing. The total number of days of saltwater fishing in these states was 26.1 million in 2006. 

Based on estimates from Kaval and Loomis (2003), the average consumer surplus value for a 

fishing day was $35.91 (in 2007 dollars) in the Northeast and $87.23 in the Southeast. Therefore, 

the total recreational consumer surplus value from these saltwater fishing days was 

approximately $1.28 billion (in 2007 dollars). 

Recreational participation estimates for several other coastal recreational activities are 

also available for 1999 to 2000 from the NSRE. These estimates are summarized in Table 4.2-1

based on data reported in Leeworthy and Wiley (2001). Almost 6 million individuals aged 16 or 

older participated in motorboating in coastal states from North Carolina to Massachusetts, for a 

total of nearly 63 million days annually during 1999–2000. Using a national daily value estimate 

of $32.69 (in 2007 dollars) for motorboating from Kaval and Loomis (2003), the aggregate value 

of these coastal motorboating outings was $2.08 billion per year. Almost 7 million people 

participated in birdwatching, for a total of almost 175 million days per year, and more than 3 

million participated in visits to nonbeach coastal waterside areas, for a total of more than 35 

million days per year. In contrast, fewer than 1 million individuals per year participated in 

canoeing, kayaking, or waterfowl hunting. 

4.1.3 Regulating Services 

Estuaries and marshes have the potential to support a wide range of regulating services, 

including climate, biological, and water regulation; pollution detoxification; erosion prevention; 

and protection against natural hazards (Millennium Ecosystem Assessment [MEA], 2005). It is 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 66 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 67 

more difficult, however, to identify the specific regulating services that are significantly 

impacted by changes in nutrient loadings. One potentially affected service is provided by SAV, 

which can help reduce wave energy levels and thus protect shorelines against excessive erosion. 

Declines in SAV may, therefore, also increase the risks of episodic flooding and associated 

damages to near-shore properties or public infrastructure. In the extreme, these declines may 

even contribute to shoreline retreat, such that land and structures are lost to the advancing 

waterline. 

4.2 CHANGES IN ECOSYSTEM SERVICES ASSOCIATED WITH 

ALTERNATIVE LEVELS OF ECOLOGICAL INDICATORS 

This section estimates values for changes in several ecosystem services associated with 

reduced nutrient enrichment effects in the Chesapeake Bay and Neuse River estuaries. Using the 

results of the Potomac River and Neuse River Case Studies, the value of removing all 

atmospheric sources of nitrogen loadings to these estuaries was estimated. Although such a large 

change represents an upper bound on possible loading reductions through controls on 

atmospheric sources, it corresponds with the findings of the case studies, which indicate that 

reductions of this magnitude are the minimum required to improve the eutrophication index (EI) 

score (based on NOAA’s ASSETS framework) from current “bad” conditions (EI = 1) in these 

two estuaries to somewhat better “poor” conditions (EI = 2). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Seco

Table 4.2-1. Participation in Selected Marine Recreation Activities in East Coast States in 1999–2000 

Visiting 

Watersides besides 

Beaches Motorboating Canoeing Kayaking Bird Watch

State Na Daysb Na Daysb Na Na Na D

Connecticut 0.18 2.41 0.39 6.76 0.05 0.10 0.45 1Delaware 0.08 —c 0.38 4.56 0.04 0.02 0.43 1

Maryland 0.47 5.89 0.97 8.13 0.16 0.03 0.82 1

Massachusetts 0.47 2.93 0.61 6.05 0.07 0.17 1.02 2

New Jersey 0.45 4.58 0.89 12.45 0.07 0.10 0.80 1

New York 0.56 3.74 0.90 9.48 0.07 0.06 0.88 2

North Carolina 0.44 4.16 0.55 7.25 0.04 0.12 1.04 2

Rhode Island 0.27 3.31 0.38 4.37 0.15 0.11 0.56 1

Virginia 0.48 8.27 0.60 4.54 0.15 0.06 0.86 1Total 3.41 35.29 5.67 63.59 0.79 0.76 6.84 17

a Number of resident and nonresident participants annually (in millions). b Number of days by residents and nonresidents annually (in millions). c Insufficient data for estimate. Source: Leeworthy and Wiley, 2001 Final Risk and Exposure Assessment 

Appendix 8 - 68 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 69 

4.2.1 The Chesapeake Bay Estuary 

For the Chesapeake Bay analysis, the results of the Potomac River/Potomac Estuary Case 

Study were applied. Other than the mainstem of the Bay (6,074 km2

), the Potomac estuary is the 

largest subestuary within the Chesapeake Bay estuary system (1,260 km2

), and other than the 

Susquehanna River, which flows directly into the mainstem, it contributes the largest portion of 

freshwater (19%) to the Bay. Eutrophic conditions within the Potomac estuary are also reflective 

of more widespread conditions in the Bay. For example, when assessing estuarine conditions 

across the country in 2004, NOAA (2007) evaluated nine subestuaries of the Bay, including the 

mainstem and the Potomac. Five subestuaries in the Bay, including the mainstem and the 

Potomac, rated “high” with respect to overall eutrophic conditions (the worst level on a 5-point 

scale from low to high). The remaining four subestuaries were all rated as “moderate high” (the 

second worst level). Therefore, for this analysis, it was assumed that the results of the Potomac 

River estuary case study are representative of the Chesapeake Bay as a whole. 

According to the Aquatic Nutrient Enrichment Case Study, atmospheric deposition is 

estimated to contribute 24% (7.38 million kg nitrogen/year) of total nitrogen loadings to the 

Potomac estuary. This percentage falls within the range of the 23% to 33% that has been 

estimated for the Chesapeake Bay as a whole (Valigura et al., 2001). The case study also 

estimates that a reduction in nitrogen loadings roughly equivalent to the contribution from 

atmospheric deposition in the Potomac River watershed would be required to improve the 

Potomac estuary from “bad” to “poor” on the 5-point ASSETS EI.4 

For the Chesapeake Bay analysis, the change in selected ecosystem services associated 

with a 24% reduction in loadings to the Chesapeake Bay as a whole was estimated, and it was 

assumed that this reduction would also improve the Bay’s overall EI score from “bad” to “poor.” 

The selection of ecosystem services for this analysis, which includes recreational, aesthetic, and 

nonuse services (i.e., specific cultural services), was based on availability of existing models, 

data, and empirical results. 

For each of the ecosystem service categories addressed in this section, the geographic 

extent of aggregate benefits to residents and recreators in Maryland, Virginia, and Washington, 

 

4 The mean and median estimate of required loading reductions is 104% of the annual atmospheric deposition 

component (almost 25% of all N loadings). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

DC (DC) was limited. Because these areas are directly adjacent to the Bay, this approach is 

expected to include a large majority of the beneficiaries; however, this approach also will 

unavoidably contribute to some underestimation of aggregate benefits. Other specific limitations 

and uncertainties in the proposed methods are described in each of the subsections below. 

4.2.1.1 Recreational Fishing Services 

This section describes and applies a three-part “benefit transfer” framework for 

estimating the recreational fishing benefits of improved eutrophic conditions in the Chesapeake 

Bay. The first component translates changes in the 5-point EI into equivalent changes in average 

DO levels in the Bay. This step is required to link eutrophic conditions to existing recreational 

catch rate models. 

The second component predicts the effect of changes in average DO levels on 

recreational fishing catch rates. These catch rates can be interpreted as indicators of the 

recreational fishing services provided by the Bay. Two catch rate models are described: one 

based on a study of striped bass fishing in the Bay and the other based on a study of summer 

flounder fishing in the Maryland coastal bays. 

The third component estimates the benefits of catch rate improvements using willingness 

to pay (WTP) estimates derived from a meta-analysis study by Johnston et al. (2005) and annual 

fishing trip estimates to the Bay using data from the Marine Recreation Fishing Statistics Survey 

(MRFSS). 

4.2.1.1.1 Converting Changes in EI to Changes in DO 

As described above, low DO is one of several ecosystem symptoms associated with 

estuarine eutrophication; therefore, DO levels are one of several factors included in the ASSETS 

framework to derive the composite 5-point EI. 

To derive changes in DO that are equivalent to a 1-unit change on the EI, data for a 

comparable water quality index were used. In collaboration with the University of Maryland’s 

Center for Environmental Science, NOAA has also developed a 100-point Chesapeake Water 

Quality Index (CWQI) based on three main eutrophication symptom indicators—chlorophyll a, 

water clarity, and DO (Chesapeake Eco-Check, 2007). Using annual data for the three water 

quality parameters, a water quality index score was generated for 141 monitoring stations across 

the Bay in 2007. Table 4.2-2 reports the results of regressing the CWQI score for each station 

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Appendix 8 - 70 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 71 

against four corresponding water quality measures—(1) average surface DO, (2) average bottom 

DO, (3) average secchi depth, and (4) average chlorophyll a. Both DO measures have positive 

and statistically significant effects (with a p-value less than 0.05) on the index score, although 

the estimated effect of bottom DO is somewhat larger. A 1-unit change in both bottom and 

surface DO is predicted to change the CWQI by a combined effect of 8.3 points. If it was 

assumed that the 100-point CWQI and the 5-point EI are directly proportional, then a 1-unit 

change in both bottom and surface DO is predicted to change the EI by 0.415 (= 8.3/20) points. 

Alternatively, a 1-point increase in the EI (e.g., from “bad” to “good”) would be predicted to 

result from a 2.41-unit increase in both surface and bottom DO.5 Therefore, going forward, the 

effects on recreational fishing resulting from an increase in bottom and surface DO of 2.41 mg/L, 

which is assumed to be equivalent to a 1-unit increase in the EI, were estimated. 

Table 4.2-2. Regression Analysis of the Chesapeake Water Quality Index on Water Quality 

Parameters 

 Water Quality 

Parameter 

Regression Results Summary Statistics 

Coefficient P-value Mean Std. Dev. Min Max 

CWQI (dependent 

variable) 

— — 48.85 22.30 9 100 

Explanatory variables 

Chlorophyll a (ug/L) −0.556 0.000 15.99 14.09 2.0 82.0 

Secchi depth (m) 0.163 0.443 2.32 7.44 0.1 50.4 

Bottom DO (mg/L) 5.069 0.000 5.01 2.00 0.1 9.3 

Surface DO (mg/L) 3.235 0.028 6.91 0.97 4.5 9.1 

Constant 6.296 0.556 — — — — 

Observations 141 

R-squared 0.357 

 

5 Based on the results in Table 4.2-2, other combinations of bottom DO and surface DO changes can also produce a 

1-point increase in the EI; however, for simplicity, equivalent changes in the two DO measures were considered. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 72 

4.2.1.1.2 Estimating Changes in Recreation Services (Catch Rates) 

In two related papers, Lipton and Hicks (1999, 2003) reported the results of a travel cost 

study of recreational striped bass fishing in the Chesapeake Bay. One of the main focuses of the 

study was measuring the effect of DO levels on striped bass catch rates. The fishing data for this 

study were drawn from the National Marine Fisheries Service’s (NMFS’s) 1994 MRFSS, which 

included 407 intercept sites in the Bay and 1,806 striped bass angler respondents. The DO water 

quality data were from biweekly summer sampling at 207 locations in the Bay. 

The striped bass catch model assumes that the number of fish caught per trip (in 

logarithmic form) at a site is a linear function of several factors, including the hours spent by the 

angler at the site on the trip, the angler’s experience and skill in saltwater fishing, and water 

quality conditions at the site. Water quality is characterized in the model by surface temperature 

(ST), bottom temperature (BT), surface DO (SDO), and bottom DO (BDO). According to the 

functional form of the estimated model, the change in the expected striped bass catch rate per 

trip due to a water quality change can be expressed as 

01 00 −=Δ = B Δ + )ln)(exp( − QQWQfQQQ , (4.1) 

where Qi is the expected number of striped bass caught per trip under conditions i, such 

that i = 0 represents reference conditions and i = 1 represents conditions after the water quality 

change. The function fB(∆WQ) represents the combined effect of changes in temperature and DO 

on expected catch rates. Using the parameter estimates from the empirical catch rate model, this 

function for striped bass can be expressed as 

(2589.0 (2253.0) (0167.0) )

)(3225.0)(2548.0.lnln)(

2

0

2

01 01 1

01 01 01

SDO SDO BDO BDO BDO BDO

B QQWQf STST BTBT

+ +− −− −

−=−=Δ − + − (4.2) 

To quantify baseline catch rates (Q0), recent MRFSS data for the Bay, which are 

summarized in Table 4.2-3, were used. The table reports average catch rates for striped bass and 

other key recreational species for 2001 to 2005. Over the 5-year period, striped bass catch rates 

averaged 1.65 fish per trip in Maryland and 0.59 fish per trip in Virginia.6

 

6 For comparison, Lipton and Hicks (1999) reported that average catch rates in 1994 were 0.71 in Maryland and 

0.66 in Virginia. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 73 

With these baseline catch rate estimates, Equations (4.1) and (4.2) can be used to predict 

the change in average catch rate ( ) associated with specific changes in surface and bottom 

temperature and DO levels. For example, if average surface and bottom DO levels in the Bay 

both increase by 2.41 units (with no change in temperature), the striped bass catch rate is 

predicted to increase by 1.57 in Maryland and by 0.56 in Virginia (a 94.9% increase). 

ΔQ

Table 4.2-3. Average Catch Rate per Fishing Trip in the Chesapeake Bay, by State and Targeted 

Fish Species 

Fishing Trip 2001 2002 2003 2004 2005 Average 2001–2005 

Maryland residents 

Striped bass 1.20 1.58 1.99 1.81 1.70 1.65 

Summer flounder 0.09 0.08 0.09 0.34 0.04 0.12 

Other species 0.29 0.32 0.32 0.27 0.45 0.33 

All species 0.34 0.38 0.40 0.35 0.51 0.39 

Virginia residents 

Striped bass 0.42 0.44 0.52 0.82 0.68 0.59 

Summer flounder 0.96 0.80 0.91 0.93 0.69 0.86 

Other species 0.34 0.40 0.26 0.27 0.34 0.32 

All species 0.37 0.42 0.29 0.32 0.36 0.35 

Source: National Oceanic and Atmospheric Administration (NOAA), 2009. 

It is more difficult to develop catch rate predictions for other recreational species, 

because of the apparent lack of any other empirical studies that have estimated the relationship 

between water quality conditions and recreational catch rates in the Bay.7 One alternative is to 

assume that the striped bass model described above is applicable to other species; however, the 

resulting catch rate change estimates would inevitably have higher levels of uncertainty 

associated with them. 

A second approach is to use catch rate models developed in areas outside the Bay; 

however, only one such study was found.8 Massey, Newbold, and Gentner (2006) used data from 

 

7 Bricker et al. (2006) described similar models for the Potomac and Patuxent River estuaries and other East Coast 

estuaries; however, they did not provide parameter estimates for these models. 

8 Kaoru, Smith, and Liu (1995) also estimated the effects of estuarine water quality on recreational fishing in North 

Carolina; however, rather than using ambient water quality measures, he used estimates of nutrient and 

biochemical oxygen demand loadings as proxies for water quality conditions. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

the Maryland coastal bays to estimate a catch rate model for recreational summer flounder 

fishing. They found significant effects from DO, temperature (T), and water clarity (secchi depth 

[SD]) on recreational catch. Using the parameter estimates from this model, the following 

function summarizes the measured effects of water quality on summer flounder catch rates: 

(117.0)( )(392.1)(126.0) F WQf =Δ 1 − DODO 0 + − TT 01 + − SDSD 01 . (4.3) 

Applying this function to Equation (4.1) in place of fB(∆WQ), a 2.41-unit increase in DO

(with no change in T or SD) is predicted to increase summer flounder catch by an additional 0.04 

fish per trip in Maryland and 0.28 fish per trip in Virginia (a 32.6% increase). Transferring this 

model from the Maryland coastal bays to the Chesapeake Bay also contributes to the uncertainty 

in catch rate predictions for summer flounder, although arguably less so than transferring models 

from other species (i.e., striped bass) within the Bay. 

4.2.1.1.3 Valuing Changes in Catch Rates 

The second component of the proposed benefit transfer model for recreational fishing can 

be summarized as follows: 

AggBfish = Σj (WTPfish × Tj ) × ∆Qj, (4.4) 

where 

AggBfish = aggregate annual benefits (in 2007 dollars) to Chesapeake Bay anglers for 

specified increases in species-specific average catch rates per trip (∆Qj, 

where j is the species indicator), 

∆Qj = predicted change in average catch rate per trip for species j in the 

Chesapeake Bay (as described in Section 4.2.1.1.2), 

WTPfish = average WTP per additional fish caught per trip, and 

Tj = total number of annual fishing trips (in 2007) targeting species j in the 

Chesapeake Bay. 

A large number of revealed- and stated-preference studies have estimated welfare 

changes associated with changes in recreational fishing catch rates in the United States. Most of 

these results have been synthesized in a meta-analysis study by Johnston et al. (2006), which 

estimated meta-regression models controlling for differences across studies in type of water 

resource, context, angler attributes, and in-study methods. Using these summary models, they 

Final Risk and Exposure Assessment September 2009 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

predicted average WTP per fish per trip for different species categories. For both Atlantic small 

game (including striped bass) and Atlantic flatfish (including summer flounder), they predicted 

WTP ranging from $3 to $11 in 2003 dollars. This meta-analysis study included one WTP 

estimate from a Chesapeake Bay striped bass study (Bockstael, McConnell, and Strand, 1989), 

which falls slightly below this range ($2.23), but it did not include a more recent striped bass 

estimate from the Lipton and Hicks (1999) study, which falls within the upper end of the range 

($10.91). Johnston et al.’s (2006) study also did not include the estimate for summer flounder in 

Maryland coastal bays from Massey, Newbold, and Gentner (2006), which falls within the lower 

end of the range ($4.22 in 2002 dollars). 

Based on these WTP results from the literature, a value range of $2.50 to $12.50 for 

WTPfish, with a midpoint of $7.50, was selected. 

To quantify annual trips by species (Tj), recent MRFSS data for the Bay, which are 

summarized in Table 4.2-4, were again used. The table reports total annual trips for striped bass 

and other key recreational species from 2001 to 2005. To approximate trips in 2007, the average 

number of trips from 2001 to 2005 by species were used. The same methodology was used to 

approximate baseline catch rates for 2007. 

Table 4.2-4. Aggregate Number of Fishing Trips to the Chesapeake Bay, by State and Targeted 

Fish Species 

Fishing Trip 2001 2002 2003 2004 2005 

Average 

2001–2005 

Maryland 

residents 

Striped bass 2,594,971 2,014,818 2,579,771 2,176,824 2,351,145 2,343,506 

Summer 

flounder 

2,106,810 1,268,048 1,598,484 1,486,154 1,734,101 1,638,719 

Other species 33,457,937 31,349,971 48,352,248 39,740,106 34,503,965 37,480,845 

All species 38,159,717 34,632,837 52,530,503 43,403,083 38,589,211 41,463,070 

Virginia residents 

Striped bass 2,043,025 1,911,180 2,369,576 2,525,057 2,549,248 2,279,617 

Summer 

flounder 

2,285,628 1,982,130 2,300,633 2,556,902 2,549,248 2,334,908 

Other species 49,915,214 47,535,158 67,839,883 65,345,054 58,036,434 57,734,349 

All species 54,243,868 51,428,468 72,510,092 70,427,013 63,134,930 62,348,874 

Final Risk and Exposure Assessment September 2009 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Source: National Oceanic and Atmospheric Administration (NOAA), 2009. 

4.2.1.1.4 Results: Aggregate Recreational Fishing Benefits 

Combining the three model components, the aggregate recreational fishing benefits from 

a 1-point EI increase in Chesapeake Bay from “bad” to “poor” can now be estimated. Assuming 

that this change is equivalent to a 2.41 mg/L increase in both surface and bottom DO, the result 

is an annual benefit of $37.2 million to striped bass anglers in Maryland and Virginia and an 

annual benefit of $5.4 million to summer flounder anglers. 

Recognizing the uncertainties associated with transferring these models to other species, 

the same benefit transfer framework can also be applied to other recreational fishing trips. 

Striped bass and summer flounder fishing only accounted for 7.4% of the total number of trips 

and 15.5% of the total catch from 2001 to 2005. If the striped bass catch rate model 

(Equation [4.2]) is applied to all other types of fish species, then a 2.41 mg/L increase in surface 

and bottom DO would result in an estimated aggregate benefit of $217 million for recreational 

anglers targeting these other species in the Bay. 

4.2.1.1.5 Limitations and Uncertainties 

Although the objective of the previously described approach is to make the best use of 

existing research to quantify the relationship between changes in eutrophic conditions and 

recreational fishing benefits in the Bay, the following limitations and uncertainties must also be 

noted. 

First, the conversion of changes in EI to changes in DO requires several strong 

assumptions. One key assumption is that the EI and CWQI are directly proportionate to one 

another. The reasonableness of this assumption rests on the fact that the two indexes use similar 

symptom indicators (DO, SD, and chlorophyll a) and both have been designed and used by 

NOAA as summary metrics of eutrophic conditions in estuaries. Another key assumption is that 

the regression model in Table 4.2-2 can be used to generate equivalent changes in DO. Because 

several water quality parameters besides DO are also measures of eutrophic symptoms, there is 

no guarantee that a 1-unit change in EI is uniquely associated with a 2.41 mg/L change in surface 

and bottom DO (i.e., that the other factors are held constant). 

Second, the catch rate models summarized in Equations (4.2) and (4.3) are most likely to 

understate the effects of long-term changes (i.e., over several years) in water quality across the 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

entire Bay. Both models are based on analyses that use spatial and short-term (during a single 

year’s fishing season) temporal variation to measure the relationship between catch rates and 

water quality conditions. Therefore, these measured relationships cannot be expected to capture 

the dynamic effects of long-term changes in DO on the overall growth and abundance of the 

striped bass and summer flounder populations in the Bay. 

Third, as previously noted, empirical catch rate models are only available for striped bass 

and summer flounder, and the model for the latter species is based on data from outside the Bay. 

Although it is not difficult to apply these models to estimate catch rate changes for other species 

within the Bay, the resulting estimates are subject to significant uncertainty, because there is 

little evidence about how well these models transfer to other species. 

Fourth, the valuation model summarized in Equation (4.4) uses a number of simplifying 

assumptions. In particular, the value per fish caught is assumed to be constant, but within a large 

range—$2.50 to $12.50—which can significantly affect the aggregate benefit estimates. In 

addition, the total number of fishing trips is assumed to be unaffected by changes in catch rates. 

This restriction is expected to understate the true aggregate benefits of increased catch rates, 

because higher catch rates would most likely increase the number of fishing trips. 

4.2.1.2 Boating 

To estimate benefits to Chesapeake Bay boaters, a benefit transfer approach that uses 

value estimates developed by Lipton (2004) is described. That study used a CV method and 

survey data from 755 Maryland boaters in 2000 to estimate the individual and aggregate benefits 

of a 1-unit improvement in respondents’ water quality rating (on a 1 to 5 scale from “poor” to 

“excellent”) for the Bay. The benefit transfer model based on this study can be summarized as 

follows: 

AggBboat = Σi Σj(WTPboat,i × Ni,j × bj) × ∆WQ5, (4.5) 

where 

∆WQ5 = change in Chesapeake Bay water quality, expressed on a 5-point rating scale 

(from “poor” to “excellent”); 

AggBboat = aggregate annual benefits (in 2007 dollars) to Maryland, Virginia, and DC 

boat owners who use the Chesapeake Bay as their principal boating area for 

a specified ∆WQ5 increase in water quality; 

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WTPboat,i = average annual WTP (in 2007 dollars) per boater for a 1-unit increase in 

water quality on the WQ5 scale (i = sailboat, trailered powerboat, or in-water 

powerboat); 

Ni,j = total number of boats by type i and location j (j = Maryland, Virginia, or 

DC) of boat ownership in 2007; and 

bj = the ratio of (1) registered boat owners whose principal boating area is the 

Chesapeake Bay to (2) the total number of registered boats (by location j). 

Lipton (2004) reported estimates of average WTP by boat owners in three different 

categories for a 1-unit increase in water quality (∆WQ5 = 1) in the Chesapeake Bay. Sailboat 

owners had the highest average WTP of $93.26 (in 2000 dollars). Trailered and in-water 

powerboat owners had an average WTP of $30.25 and $77.98, respectively. 

Converting these Lipton (2004) estimates to 2007 dollars with the consumer price index 

(CPI) results in WTPboat estimates of $112.29, $36.42, and $93.89 for sailboat, trailered 

powerboat, and in-water powerboat owners, respectively (Table 4.2-5). 

Table 4.2-5. Input Estimates for the Chesapeake Bay Boating Benefit Transfer Model

Number of Registered Boats Adjustment Factor 

Boat Type NMD NVA NDC bMD bVA bDC WTPboat

Sailboat 8,200 9,200 100 60.76% 56.92% 60.76% $112.29 

Trailered powerboat 93,300 104,600 1,100 60.77% 56.93% 60.77% $36.42 

In-water powerboat 23,600 26,400 300 60.77% 56.93% 60.77% $93.89 

Total 125,100 140,300 1,500 

NMD was estimated for the three boater categories using data on Maryland boat ownership 

from Lipton (2008) and Lipton (2006). The former data source quantifies sailboat and powerboat 

ownership for 2007, but it does not break out powerboats according to whether they were 

trailered or in-water boats. To develop separate estimates for these two subcategories, the 

proportions reported for 2005 (Lipton, 2006), which indicated that 79.8% of powerboats in 

Maryland were trailered, were applied. To estimate NVA and NDC, the total number of registered 

boats in Virginia and DC in 2006 was obtained from the National Marine Manufacturers 

Association (2008), and this number was augmented by the observed growth rate in Maryland 

boat ownership from 2006 to 2007. To separate these total numbers into the three categories of 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

boat ownership, the same proportions estimated for Maryland registered boats in each category 

were applied. 

The value bMD represents a two-part adjustment to the total number of registered boats in 

Maryland, as estimated by Lipton (2004). The first converts the total number of registered boats 

to the total number of boat owners, because some boat owners own more than one boat. The 

second adjusts for the fact that, for some Maryland boaters, the Chesapeake Bay is not their 

principal boating area. Every 100 registered boats correspond to an estimated 60.8 boat owners 

whose principal boating area is the Chesapeake Bay. The same adjustment factor for registered 

boaters in DC was applied to estimate bDC. 

To estimate bVA, the expected 6.3% of registered boats in Virginia Beach (Murray and 

Lucy, 1981), which is the main Virginia coastal area outside the Bay, was first excluded; then the 

same adjustment factor developed for Maryland and DC was applied. Thus, in Virginia, for 

every 100 registered boats, there are 56.9 boat owners whose principal boating area is the 

Chesapeake Bay. 

4.2.1.2.1 Results: Aggregate Recreational Boating Benefits 

To apply the previously described framework, it was first assumed that there is a direct 

one-to-one correspondence between the 5-point EI and the 5-point subjective WQ5 index. Based 

on this assumption, a 1-unit increase in Chesapeake Bay water quality (∆WQ5 = 1 and ∆EI = 1) 

was estimated to yield an annual aggregate benefit of $8.2 million for Maryland, Virginia, and 

DC boat owners whose principal boating area is the Chesapeake Bay. 

4.2.1.2.2 Limitations and Uncertainties 

A potential limitation of the proposed benefit transfer model for boating services is the 

uncertainty associated with directly translating the WQ5 index into the EI index. Although both 

metrics are summary 5-point measures of Chesapeake Bay water quality, the first is a subjective 

index based on boaters’ perceptions and experience. These perceptions may be based on 

observations unrelated to eutrophic conditions (e.g., trash in the water or advisories based on 

pathogen levels) or boaters may implicitly assign more or less importance to eutrophic 

conditions than is assigned by the EI. 

The other main source of uncertainty is with the number of affected boaters. As in the 

recreational fishing model, the affected number of recreators is assumed to be unaffected by the 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

change in water quality. This assumption is likely to lead to an underestimate of the aggregate 

benefit to boaters of a water quality improvement. 

One alternative approach is to use value estimates from Bockstael, McConnell, and 

Strand (1989), who also estimated changes in consumer surplus for trailered boat owners in 

Maryland resulting from a 20% decrease in the product of total nitrogen and phosphorus (TNP) 

levels in the Bay. By rescaling and updating their estimates to 2007 dollars, the implied average 

WTP per Maryland trailered boat owner per 1% decrease in TNP is $5.38. Applying this value to 

the estimated total number of trailered powerboat owners in Maryland, Virginia, and DC (see 

Table 4.2-1), implies that the aggregate benefits to these boaters per 1% decrease in TNP in the 

Bay would be $120,000. Assuming that a 24% decrease in nitrogen loadings would result in a 

24% reduction in TNP levels in the Bay, the resulting estimate of annual aggregate benefits is 

$2.9 million. The main advantage of this approach compared with the model summarized in 

Equation (4.5) is that it is based on an objective measure of water quality. The fact that it is 

based on values estimated through a revealed-preference travel cost model of actual boating 

behavior, compared with a stated-preference CV approach, may be seen as an advantage. 

However, this approach also has several drawbacks: (1) it is based on considerably older data 

(from 1984), (2) it only includes direct estimates for trailered boaters, and (3) it includes a 

potentially narrower measure of value than the Lipton (2004) study because it uses revealedrather than stated-preference data. This approach also requires the assumption that decreases in 

nitrogen loads to the Bay are proportional to decreases in TNP levels in the Bay. 

4.2.1.3 Beach Use 

To estimate benefits to Chesapeake Bay beach users, the benefit transfer approaches 

developed by Morgan and Owens (2001) and Krupnick (1988) were adapted and updated. Both 

of these studies estimated the aggregate benefits to Maryland, Virginia, and DC households of 

percentage reductions in levels in the Bay. The fundamental benefit transfer model can be 

summarized as follows: 

beach beach beach WQTNP AggB WTP (*( tbNbN %*)*) = + 2211 Δ , (4.6) 

where 

∆%WQTNP = percentage change in Chesapeake Bay water quality, expressed in terms 

of the average TNP levels, each measured in parts per million (ppm); 

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Final Risk and Exposure Assessment September 2009 

Appendix 8 - 81 

AggBbeach = aggregate annual benefits (in 2007 dollars) to Maryland, Virginia, and 

DC households for a specified ∆%WQTNP increase in water quality in the 

Bay; 

WTPbeach = average annual household WTP (in 2007 dollars) per trip for a 1% 

reduction in TNP levels in the Bay; 

N1 = total number of households in the 1980 Baltimore and DC standard 

metropolitan statistical areas (SMSA) in 2007; 

N2 = total number of Maryland and Virginia households outside the SMSA in 

2007; 

b1 = portion of SMSA households with at least one Chesapeake Bay beach trip 

in the year; 

b2 = portion of non-SMSA households in Maryland and Virginia with at least 

one Chesapeake Bay beach trip in the year; and 

tbeach = average number of Chesapeake Bay beach trips per year for beach-going 

Maryland, Virginia, and DC households. 

Table 4.2-6 summarizes value estimates for these model components. Values for 

WTPbeach were derived using estimates from Bockstael, McConnell, and Strand (1988, 1989). 

Using data from 408 summer beach users in 1984 at nine Maryland western shore beaches and 

average county-level summer TNP values, they estimated a varying parameter travel cost model. 

Based on the model results, they reported aggregate annual consumer surplus gains of $34.66 

million (in 1987 dollars) for beachgoers residing in the SMSA associated with a 20% decrease in 

TNP in the Bay. The study also reported that (1) 401,000 SMSA households per year (in the 

early 1980s) visited Chesapeake Bay beaches and (2) the average number of trips per year for 

these beach-going households was 4.35,9 which implies that there were an estimated 1,745,000 

trips to the Bay by SMSA households in 1984. Dividing the aggregate benefit estimate by this 

number of trips implies an average per-trip benefit of $19.86 (in 1987 dollars), for a 20% 

reduction in TNP. 

 

9 This number is actually inferred from a description of values Bockstael, McConnell, and Strand (1989) derived 

from an alternate model. The value per household user ($4.70) was divided by the value per trip ($1.08) to get 

trips per household (4.35). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Table 4.2-6. Input Estimates for the Chesapeake Bay Beach-Use Benefit Transfer Model 

Beach Use 

Number of 

Households 

(N) 

Percentage of Bay 

Beachgoers 

(b) 

Average Beach 

Trips per Year 

(t) WTPbeach

SMSA 2,744,217 21.00% 4.35 $1.81 

Non-SMSA 2,540,214 3.08% 4.35 $1.81 

Total 5,284,431 12.38% 4.35 $1.81 

To estimate WTPbeach, the $19.86 estimate was divided by 20 (i.e., it was assumed that 

each percentage reduction in TNP has the same value), and the estimate was converted to 2007 

dollars using the CPI to adjust for inflation. The resulting estimate for WTPbeach is $1.81. 

N1 and N2 were estimated using the Census estimates of population by county in 2007, 

multiplied by the ratio of households to population by county in the 2000 U.S. Census. From this 

calculation, it was estimated that a total of 5.28 million households are in Maryland, Virginia, 

and DC, and 2.74 million of these are within the SMSA. 

For b1, the Bockstael, McConnell, and Strand (1989) estimate that 21% of households in 

the SMSA take at least one beach trip to the Chesapeake Bay a year was applied. To derive b2, 

this estimate was combined with data from the 2006 Virginia Outdoors Survey (Virginia 

Department of Conservation and Recreation, 2007), which reports that 8% of all the households 

in Virginia take at least one beach trip to the Chesapeake Bay (or other tidal bays) per year. 

Taken together, these estimates imply that approximately 3% of non-SMSA Virginia households 

take at least one beach trip per year to the Bay. Applying this estimate to Maryland non-SMSA 

households as well, it was assumed that b2 equals 3%. 

To estimate tbeach, the Bockstael, McConnell, and Strand (1989) estimate of 4.35 trips per 

year was applied, recognizing that it is most likely an overestimate for non-SMSA beach-going 

households. 

4.2.1.3.1 Results: Aggregate Beach Use Benefits 

To apply this benefit estimation framework, it was assumed that changes in nitrogen 

loads to the Bay are directly proportional to changes in average TNP concentrations in the Bay 

(i.e., a 24% reduction in loadings results in a 24% decline in TNP). It was estimated that the 

aggregate annual benefits to Maryland, Virginia, and DC Chesapeake Bay beachgoers 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

(AggBbeach) per 1% decrease in TNP is $5.16 million (in 2007 dollars); therefore, the benefit of a 

24% decrease is $124 million. 

4.2.1.3.2 Limitations and Uncertainties 

One of the main limitations of the beach-use valuation model described above is that it is 

based on value estimates that are from 1984 and, therefore, may be outdated. Beach conditions 

and recreator preferences in the Bay may have changed significantly since then. In addition, 

several uncertainties are associated with the estimated number of beach trips by Maryland, 

Virginia, and DC households in 2007. These estimates are based on limited and, in some cases, 

relatively old data regarding the percentage of households in each state that use the Bay’s 

beaches and the average number of annual beach trips for those who do. A second limitation is 

that it, again, requires the assumption that decreases in nitrogen loads to the Bay are proportional 

to decreases in TNP levels in the Bay. 

4.2.1.4 Aesthetic Services 

To estimate the benefits of improved aesthetic services due to improvements in 

Chesapeake Bay water quality, a benefit transfer model that is based on estimates of near-shore 

residents’ values for small water-quality changes was developed and applied. The transfer 

function has the following form: 

AggBhome = Σk MWTPk × ∆DINk × Nk, (4.7) 

where 

∆DINk = reduction in dissolved inorganic nitrogen (DIN) levels in the portion of the 

Chesapeake Bay closest to coastal Census block group k; 

AggBhome = aggregate annual benefits (in 2007 dollars) to homeowners in all 

Chesapeake Bay coastal block groups for specified ∆DINk changes in water 

quality; 

Nk = estimated number of specified owner-occupied homes in block group k in 

2007; and 

MWTPk = estimated annual marginal WTP (in 2007 dollars) for a 1-unit reduction in 

water quality, ∆DINk = 1, in block group k. 

To parameterize this function, results from a hedonic housing price study by Poor, 

Pessagno, and Paul (2007) were used. Using data on 1,377 residential home sales from 1993 to 

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Final Risk and Exposure Assessment September 2009 

Appendix 8 - 84 

2003 in St. Mary’s River watershed in Maryland, this study regressed the natural log of real 

home prices (in 2003 dollars) against structural, neighborhood, and environmental water quality 

characteristics. It specifically estimated the effect of differences in DIN (mg/L), as measured by 

the annual average in the year of sale at the closest water monitoring station, on log home 

prices.10 The study found a statistically significant effect with a model coefficient estimate of 

−0.0878. 

To convert this semielasticity coefficient, which measures the marginal effect of DIN on 

the log of home price, to MWTPk, which represents the annualized average dollar value of a 

1-unit reduction in DIN for homes in block group k, the following conversion equation was used: 

MWTP ,(**0878.0 TiAP ) k = k , (4.8) 

where 

Pk = average price of specified owner-occupied homes in block group k and 

A = annualization factor, which is a function of the assumed interest rate (r) and 

average lifetime of homes in years (T).11 For r = 0.05 and T = 50, A = 0.0522. 

To implement the model, Chesapeake Bay coastal block groups were defined as those 

block groups with a Chesapeake Bay coastline, as delineated by the Census block group 

boundary files (Environmental Systems Research Institute, Inc. [ESRI], 2002), as well as those 

block groups whose geographic centroids are located within 1 mile of the coast. This second 

condition was added to ensure that a majority of the included properties are located within 

roughly 2 miles of the coast. As shown in Figure 4.2-1, 1,066 block groups met these criteria. 

Within these block groups, the study focused on Census “specified owner-occupied 

housing units,” which include only single-family houses on less than 10 acres without a business 

or medical office on the property. These properties match best with the types of properties 

analyzed in the hedonic study described above, and the decennial Census provides both count 

and property value estimates for these homes. Thirty-six of the identified 1,066 block groups had 

no specified owner-occupied homes and were excluded from the analysis. 

 

10 A separate model reported in Poor, Pessagno, and Paul (2007) used total suspended solids (mg/L) instead of DIN 

as the water quality measure. It was also found to have a statistically significant effect on home prices. 

11 ⎟

⎠

⎞ ⎜

⎝

⎛

∑ += =

−− T

t

t TrA r 1)1( )1(/1),(
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 85 

To estimate Nk, the number of specified owner-occupied homes in each block group in 

2000 was augmented by the growth rate in housing units in the block group’s county from 2000 

to 2007 (U.S. Census Bureau, 2008b). 

To estimate Pk, the average price of specified owner-occupied homes in 2000 in each 

block group was adjusted to 2007 using the CPI-Shelter values for Washington-Baltimore, DCMD-VA-WV.12 Table 4.2-7 summarizes the estimated values for Nk and Pk. 

4.2.1.4.1 Results: Aggregate Aesthetic Benefits to Near-Shore Residents 

To approximate the aggregate annual benefits from a 24% reduction in nitrogen loadings 

to the Bay, the benefit transfer model summarized in Equations (4.7) and (4.8) was applied, 

assuming uniform 24% reductions in DIN across all Chesapeake Bay waters. Based on 2005 

monitoring data for the Potomac River estuary, the 25th and 75th percentile values of average 

DIN levels were 0.46 mg/L and 1.22 mg/L, respectively. Using this range of initial DIN values, a 

reduction of 24% translates to DIN decreases of between 0.11 mg/L and 0.29 mg/L. It was 

estimated that these changes would result in aggregate annual benefits of between $38.7 million 

and $102.2 million to residents of specified owner-occupied homes in the Chesapeake coastal 

block groups. 

 

12 In the decennial Census, values of specified owner-occupied homes were grouped into ranges of values (e.g., 

from $250,000 to $300,000). With the exception of the highest range, which is $1,000,000 and greater (no upper 

bound), the midpoint of each range was used to calculate the mean value for each block group. For the highest 

range, a central value of $1,250,000 was selected. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Figure 4.2-1. Chesapeake Bay Coastal Block Groups 

Final Risk and Exposure Assessment September 2009 

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Table 4.2-7. Summary of Housing Unit Numbers and Average Prices in Chesapeake Coastal Block Groups in 20

Number 

of Coastal 

Block 

Groups 

Number of Specified SingleUnit Dwellings per Block 

Group (Nk) 

Average Value of Spe

per Block Grou

State, County Mean 

Std. 

Dev. Min Max Mean Std. Dev. 

Maryland 

Anne Arundel County 163 470 258 13 1,604 $298,348 $128,985 $Baltimore County 101 339 160 16 859 $154,975 $54,654 $

Calvert County 25 583 380 190 1,957 $257,582 $68,476 $1

Caroline County 2 346 37 310 383 $145,589 $3,499 $1

Cecil County 18 408 206 195 1,088 $206,114 $41,341 $1

Charles County 6 466 208 302 909 $263,744 $46,708 $1

Dorchester County 20 263 125 46 584 $167,317 $61,534 $

Harford County 23 397 296 20 926 $187,685 $39,578 $1

Kent County 13 294 105 55 418 $225,531 $48,381 $1

Prince George’s County 1 175 0 175 175 $296,739 $0 $2

Queen Anne’s County 18 584 249 158 1,104 $277,352 $73,004 $1

St. Mary’s County 29 458 195 92 910 $252,720 $41,564 $1

Somerset County 14 258 118 81 528 $130,391 $30,635 $Talbot County 20 431 181 192 943 $341,180 $162,586 $1

Wicomico County 7 304 129 163 576 $145,763 $29,367 $1

Worcester County 1 148 0 148 148 $98,596 $0 $

Baltimore city 116 168 95 10 436 $106,483 $52,800 $

Virginia 

Accomack County 9 247 93 128 388 $127,159 $56,731 $

Charles City County 4 270 46 219 340 $163,344 $43,411 $1

Essex County 5 255 69 135 348 $175,598 $52,691 $1

Gloucester County 16 438 248 174 1,063 $194,628 $40,091 $1

Isle of Wight County 10 515 204 292 1,076 $210,389 $42,265 $1

James City County 7 817 551 85 1,734 $306,926 $173,652 $

Final Risk and Exposure Assessment 

Appendix 8 - 87 
Analysis of Ecosystem Services Impacts for the NOx/SOx Seco

Final Risk and Exposure Assessment 

Appendix 8 - 88 

Number 

of Coastal 

Block 

Groups 

Number of Specified SingleUnit Dwellings per Block 

Group (Nk) 

Average Value of Spe

per Block Grou

State, County Mean 

Std. 

Dev. Min Max Mean Std. Dev. 

King and Queen County 2 200 7 193 206 $121,113 $3,296 $1King George County 3 227 175 3 431 $193,030 $31,483 $1

King William County 1 398 0 398 398 $128,864 $0 $1

Lancaster County 11 325 78 145 447 $255,256 $51,809 $1

Mathews County 4 700 335 279 1,131 $203,309 $28,957 $1

Middlesex County 9 301 88 138 415 $205,784 $36,392 $1

New Kent County 1 497 0 497 497 $188,507 $0 $1

Northampton County 9 267 48 178 365 $151,509 $34,176 $1

Northumberland County 9 363 124 219 670 $233,922 $35,330 $1

Prince George County 4 362 220 88 591 $197,887 $46,549 $1

Richmond County 3 307 33 276 353 $172,231 $19,788 $1

Surry County 2 317 67 250 384 $172,904 $12,214 $1

Westmoreland County 13 311 83 181 458 $161,391 $32,705 $

York County 14 549 257 97 1,114 $239,291 $54,067 $1

Chesapeake city 29 347 200 38 747 $140,654 $51,189 $

Hampton city 53 301 149 17 810 $134,117 $43,962 $

Newport News city 40 281 287 5 1,374 $122,332 $59,341 $

Norfolk city 109 168 118 7 528 $168,857 $98,567 $Poquoson city 10 348 150 156 733 $226,740 $58,274 $1Portsmouth city 53 296 242 14 1,031 $115,948 $40,479 $

Suffolk city 5 1,426 644 545 2,198 $214,610 $35,399 $1

Virginia Beach city 18 391 210 14 829 $218,105 $81,187 $1
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

4.2.1.4.2 Limitations and Uncertainties 

Many of the limitations and uncertainties surrounding this benefit transfer model are 

associated with the limitations and uncertainties inherent in the hedonic “implicit price” estimate, 

MWTPk. From a strictly conceptual standpoint, the hedonic implicit price provides a correct 

measure of the welfare gains to residents of relatively small and localized improvements in the 

amenity, in this case changes in DIN water quality. However, caution is required when using this 

implicit price to estimate the benefits of either a large water-quality change or a change that 

affects many housing consumers. The accuracy of the benefit transfer model summarized by 

Equation (4.7) will tend to decline as the value of ∆DINk increases and as Nk increases. This is 

because changes that are larger and that affect more consumers are also more likely to cause 

shifts in the housing market, resulting in potentially large transaction (e.g., moving) costs and 

changes in the market price equilibrium. Nevertheless, Bartik (1988) has shown that, under many 

common conditions, models such as Equation (4.7) can be interpreted as providing an upperbound estimate of aggregate benefits. 

From an empirical standpoint, there are other potential limitations and uncertainties. First, 

there are potential errors in the hedonic parameter estimate. For example, DIN may be correlated 

with other influential housing or neighborhood characteristics that are not included in the 

hedonic model, in which case the parameter estimate is likely to overstate the implicit price of 

DIN. Second, for this benefit transfer model, it was assumed that the Census block groups along 

the Chesapeake Bay coast represent the areas in which the hedonic estimates can most 

reasonably be applied; however, this spatial extrapolation has inherent limitations. In particular, 

the implicit price estimates are expected to be less accurate as a measure of WTP in areas that are 

farther from the hedonic study area (e.g., St. Mary’s River watershed), particularly areas that are 

more urban and densely populated. By excluding homes in other noncoastal Census block groups 

that are also near the Bay, the benefit transfer model is also likely to exclude some beneficiaries 

of improved aesthetic services and, therefore, underestimate aggregate benefits. Third, the 

implicit price was measured using data on individual homes and water quality measures within at 

most a few miles from these homes; however, the model summarized in Equation (4.7) uses 

properties aggregated at the Census block group level and (most likely) more spatially averaged 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 89 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 90 

water quality. These differences are likely to reduce the accuracy of applying Equation (4.7) to 

estimate benefits. 

It is also important to recognize the expected overlap in ecosystem services captured by 

the hedonic implicit price estimates and the WTP estimates summarized in Section 4.2. In 

principle, the hedonic price estimate includes residents’ values for all of the use-related services 

they receive that depend on water quality. Therefore, in addition to capturing the aesthetic 

services received by living near the Bay, the hedonic implicit price should include values for 

recreational services received by near-shore residents. Unfortunately, the hedonic estimates do 

not provide separate value estimates for these different use-related services. Decomposing the 

value estimates into separate use-related categories requires additional assumptions, data, or 

analysis. 

Finally, to specify reductions in DIN levels across the Bay resulting from a 24% 

reduction in nitrogen loadings, strong assumptions were made that DIN levels decline by the 

same percentage. In addition, to address variation in initial DIN levels across the Bay, this 

percentage reduction was applied to the range (25th to 75th percentile) of recently observed 

values in the Potomac River estuary. 

4.2.1.5 Nonuse Services 

Some of the ecosystem services provided by the Chesapeake Bay may be independent of 

individuals’ recreational or other specific uses of the estuary. Measuring values for these nonuse 

services is more difficult and involves more uncertainty than for recreational and aesthetic 

services. Nevertheless, several stated-preference studies have estimated water quality values 

using sample populations that include nonusers. Evidence from these studies indicates that, 

compared with users of water resources, nonusers have significantly lower but still positive WTP 

for water quality improvements. Based on this evidence, the following simple benefit transfer 

equation was specified for estimating nonuse benefits: 

(* ) AggBNU = NU WTPN NU ΔWQ10 , (4.9) 

where 

∆WQ10 = change in Chesapeake Bay water quality, expressed on a 10-point 

rating scale; 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 91 

AggBNU = aggregate annual benefits (in 2007 dollars) to nonusers of the 

Chesapeake Bay in Maryland, Virginia, and DC for a specified 

∆WQ10 increase in water quality; 

WTPNU(∆WQ10) = average annual WTP (in 2007 dollars) per nonuser, as a function of 

the ∆WQ10 increase in water quality; and 

NNU = total number of nonusers in Maryland, Virginia, and DC in 2007. 

To estimate the WTPNU function, results from two meta-analytic studies summarizing 

evidence from the water quality valuation literature were used. The first, Johnston et al. (2005), 

included 81 WTP estimates from 34 stated-preference studies. Although these studies addressed 

a wide variety of water quality changes, for the meta-analysis, they were all converted to a 10-

point index (where 0 and 10 represent the worst and best possible water quality, respectively) 

based on the “Resources for the Future (RFF) water quality ladder” (Vaughan, 1986). The metaanalysis regressed average WTP estimates on water quality measures (baseline and change), 

characteristics of the water resource and study population, and several study method descriptors. 

The resulting WTP function can be simplified and summarized as follows:13

02* , (4.10) )02/*005.0(

)*129.0())ln(*6827.0(45.2

exp

10 10 CP

CPINC

WQ WQ WTP base

NU ⎥

⎦

⎤ ⎢

⎣

⎡

+

+ −Δ =

where 

WQ10base = baseline Chesapeake Bay water quality, expressed on the 10-point rating 

scale; 

INC = average annual household income of Maryland, Virginia, and DC nonusers 

in 2007; and 

CP02 = price adjustment factor for 2002 to 2007. 

The second study, Van Houtven, Powers, and Pattanayak (2007), conducted a similar 

meta-analysis using a somewhat different sample of studies (18 studies, including 11 for 

freshwater resources) and WTP estimates (131). A 10-point index based on the RFF ladder was 

 

13 The function is a simplified version of the translog unweighted parameter estimate model (Model 2) in Johnston 

et al. (2005). This model includes several explanatory variables and coefficients, which are summarized in the 

constant term (2.45). To derive this constant, values were assigned to the other explanatory variables as follows: 

year is 2007 (year_index = 37), study method is a dichotomous choice through a personal interview 

(discrete_ch = 1 and interview = 1) to a nonuser-only population (nonusers = 1) with a high response rate 

(hi_response = 1), protest and outlier bids are excluded (protest_bids = 1 and outlier_bids = 1), and the species 

benefiting from the water quality change are unspecified (lnWQnon = lnwq_change). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 92 

also used to convert water quality changes to a common scale. The resulting WTP function from 

this study can also be simplified and summarized as follows:14

00* , (4.11) ))00/ln(*8969.0(

)*0801.0())ln(*823.0(197.1

exp

10 10 CP

CPINC

WQ WQ WTP base

NU ⎥

⎦

⎤ ⎢

⎣

⎡

+

+− +Δ =

where 

CP00 = price adjustment factor for 2000 to 2007. 

Using these functions, WTPNU can be estimated for selected values of ∆WQ10, WQ10base, 

and INC. To estimate WTPNU for a 1-unit change in the 5-point EI scale, it was assumed that the 

EI scale is directly proportional to the 10-point WQ10 scale. In other words, it was assumed that 

EI = 1 is equivalent to WQ10base = 2, and a 1-unit increase in EI is equivalent to ∆WQ10 = 2. For 

INC, U.S. average household income in 2007 of $67,610 was used (U.S. Census Bureau, 2008a). 

Based on these inputs, WTPNU for a 2-unit change in water quality is estimated to be $16.33 

using the Johnston et al. (2005) function and $27.75 using the Van Houtven, Powers, and 

Pattanayak (2007) function. 

Estimates of the percentage of Maryland, Virginia, and DC residents who are nonusers of 

the Chesapeake Bay are not readily available; however, they can be roughly approximated from 

recreational participation statistics for the area. For example, data from the 2006 Virginia 

Outdoors Survey suggest that (1) 92% of households in Virginia did not take any beach trips to 

the Chesapeake Bay, (2) 84% did not engage in saltwater fishing, and (3) 92% did not engage in 

powerboating. Assuming that these proportions represent independent probabilities of nonuse, 

then the combined probability (proportion) of nonuse for these primary activities is roughly 70%. 

Applying this percentage to the Maryland, Virginia, and DC population in 2007, which was 

13,918,727, suggests that the number of nonusers (NNU) is approximately 9,743,109. 

 

14 This function is a simplified version of the parsimonious log-linear model in Table 5 in Van Houtven, Powers, 

and Pattanayak (2007). This model also includes several explanatory variables and coefficients, which are 

summarized in the constant term (−1.197). To derive this constant in a way that is consistent with the previous 

function, values were assigned to the other explanatory variables as follows: year is 2007 (studyyr73 = 34), study 

method is a personal interview (inperson = 1) to a nonuser-only population (pctuser = 0) with a high response rate 

(responserate = 100), publication outlet is peer reviewed (dpubjrlbk = 1), and the water quality change is not 

expressed in terms of recreational uses (lnwq10chru = 0). 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

4.2.1.5.1 Results: Aggregate Nonuse Benefits 

Applying these estimates to the benefit transfer models summarized in Equations (4.9) 

through (4.11), the aggregate annual nonuse benefits of a 1-unit improvement in the EI scale 

(∆WQ10 = 2) are estimated to range from $159.1 million to $270.4 million. 

4.2.1.5.2 Limitations and Uncertainties 

As with the recreational boating services model described in Section 4.2.1.2, one of the 

main practical limitations of applying these meta-analysis models is the water quality index used. 

Translating changes in the EI scale to the WQ10 metric requires strong assumptions. Another 

inherent limitation of using the meta-analytic models as benefit transfer functions is their lack of 

sensitivity to the spatial scale of water quality changes. 

In addition to the limitations that primarily contribute uncertainty in the WTPNU

estimates, there is also significant uncertainty associated with the measurement of NNU. First, 

defining criteria for distinguishing users and nonusers of the Bay is somewhat inherently 

subjective. Second, statistics on overall rates of visitation and use of the Bay by Maryland, 

Virginia, and DC households are not readily available. 

A final caveat for this approach to estimating nonuse values for water quality 

improvements in the Bay is that, by design, it only includes nonuse values for nonusers. 

However, it is not unreasonable to suspect that users also benefit to some extent from nonuse 

services from the Bay. Whereas these types of nonuse values are likely to be captured in, for 

example, the Lipton (2004) WTP values for boaters used in Equation (4.5), they are not included 

in the benefit estimates in Equations (4.4), (4.6), and (4.7) for recreational anglers, beach users, 

and residents, respectively. 

4.2.2 Neuse River Estuary 

To analyze changes in ecosystem services for the Neuse River, the results of the Neuse 

River/Neuse Estuary Case Study were applied. This case study concluded that atmospheric 

deposition contributes 26% (1.15 million kg nitrogen/year) of total nitrogen loadings to the 

estuary. In contrast to the Potomac River/Potomac Estuary Case Study, it estimates that a much 

larger reduction in nitrogen loadings than this 26% would be required to improve the Neuse 

River estuary from “bad” to “poor.” Therefore, for this analysis, the change in selected 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

ecosystem services associated with a 26% reduction (100% of atmospheric deposition) in 

nitrogen loadings to the Neuse estuary was estimated. 

4.2.2.1 Provisioning Services from the Blue Crab Fishery 

As discussed in Section 4.1.1, there are few examples of empirical bioeconomic models 

that link changes in nutrient-related water quality to changes in productivity of commercial 

fisheries; however, one exception is a study by Smith (2007). This study, which is applied to the 

Neuse River estuary, estimated the dynamic effects of a 30% reduction in nitrogen loads to the 

estuary on blue crab stocks, commercial catch levels, and the producer and consumer surplus 

derived from this fishery. 

Smith (2007) applied a two-patch predator-prey model that incorporated both direct and 

indirect effects of hypoxia (i.e., low DO) on blue crab communities. Direct effects include the 

movement of blue crab to water habitats with higher DO content. Indirect effects include the 

dying off of blue crab prey. The model compares producer and consumer surplus changes under 

the existing open-access institutional structure to a 30% reduction of nitrogen loadings in the 

same structure. The model was parameterized using results and estimates derived from several 

other studies. To address uncertainty, the values of three key parameters—economic speed of 

adjustment under open-access conditions, biological spatial connectivity, and price elasticity of 

demand—were each allowed to take on three different values. For a 30% reduction in nitrogen 

loadings to the estuary, the present value (100-year time horizon and 4.5% discount rate) of 

producer benefits ranged from $0.7 million to $5.9 million (in 2002 dollars), and the present 

value of consumer surplus ranged from $3.15 million to $425.20 million. The combined present 

value of producer and consumer surplus changes was estimated to range from $3.8 to $31.0 

million. 

To estimate the annual aggregate benefits from the blue crab fishery due to a 26% 

reduction in nitrogen loads, (1) the results reported in Smith (2007) were rescaled by the 

percentage difference between 30% and 26%, (2) the benefit estimates (using the 100-year 

horizon and 4.5% discount rate) were annualized, and (3) the estimates were converted to 2007 

dollars using the CPI. 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

4.2.2.1.1 Results: Aggregate Benefits from the Blue Crab Fishery 

Applying this modeling framework, the aggregate annual benefits to Neuse River crab 

fishers and consumers from a 26% reduction in nitrogen loadings is estimated to range from 

$0.12 million to $1.01 million. 

4.2.2.1.2 Limitations and Uncertainties 

The large range of the benefit estimates reported above reflects uncertainty in three key 

model parameters—economic speed of adjustment under open-access conditions, biological 

spatial connectivity, and price elasticity of demand. However, the model includes at least 16 

other parameters whose values are drawn from other studies; thus, the overall uncertainty in 

these benefit estimates is most likely understated by this range. In addition, by simply rescaling 

the results reported in Smith (2007) to address a 26% rather than a 30% reduction in nitrogen 

loads, it was assumed that benefits are directly proportional to the percentage reduction in 

nitrogen loads. This assumption adds additional (albeit, most likely small) uncertainty to the 

reported benefit estimates. 

4.2.2.2 Recreational Fishing Services 

To estimate the benefits from improvements in recreational fishing services due to 

reductions in nitrogen loadings to the Neuse, a benefit transfer model originally developed to 

assess the nutrient-reduction benefits of EPA’s effluent guidelines for Consolidated Animal 

Feeding Operations (CAFOs) (EPA, 2002) was applied. For that analysis, EPA conducted a case 

study evaluating the potential economic benefits of a reduction in nutrient loadings via changes 

in recreational fishing opportunities in North Carolina’s Albemarle and Pamlico Sounds (APS) 

estuary (Van Houtven and Sommer, 2002). The Neuse River estuary is a subestuary within the 

APS system. 

To estimate the value of reductions in nitrogen loads, the APS case study relied on 

economic value estimates obtained from two related studies—Kaoru (1995) and Kaoru, Smith, 

and Liu (1995). Both studies used recreational data obtained from a 1981–1982 intercept survey 

of recreational fishermen conducted at 35 boat ramps or marinas within the APS estuary. 

Kaoru (1995) used a three-level nested random utility model (RUM), which broke the 

recreational fishing decision into three stages: a decision on the duration of the trip (1, 2, 3, or 

more than 3 days), a decision on which of the five regions to visit, and a decision on which of the 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

individual sites within the region to visit. The impact of nitrogen (and phosphorus) loadings was 

specifically investigated in the second stage of the decision process (regional choice). A 25% 

reduction in nitrogen loadings for the entire APS estuary resulted in a benefit estimate of $4.70 

(in 1982 dollars) per person-trip. 

Kaoru, Smith, and Liu (1995) also used a RUM approach to estimate the value of 

improving water quality. First, a household production function (HPF) was estimated to predict 

expected catch rates for individuals based on variables such as equipment used; effort exerted; 

and the physical characteristics of the fishing site, including pollutant loadings. Second, the HPF 

model was used to predict the impact of a 36% reduction in nitrogen loadings on expected catch 

rates. The estimated values ranged from $0.76 to $6.52 (in 1982 dollars) per person-trip. 

Based on a systematic review of the value estimates reported in these studies, the CAFO 

case study selected three estimates to include in the benefit transfer model—$4.70 per persontrip, for a 25% reduction in nitrogen loads (Kaoru, 1995) and $3.95 and $6.52 per person, for a 

36% reduction (Kaoru, Smith, and Liu, 1995). 

To apply these estimates, they were converted to comparable units. First, they were 

converted to 2007 dollars using the CPI. Second, they were rescaled to values per 1% reduction 

in loadings (i.e., dividing by 25 and 36, respectively). The resulting three unit values are $0.40, 

$0.24, and $0.39 per person-trip per 1% reduction in nitrogen loads to the APS. 

A further adjustment is necessary to convert these values into per-ton units. According to 

Kaoru (1995), the average nitrogen load to the APS estuary at the time the study was conducted 

was 1,741 tons per bordering county per year, which translates to a total of 22,633 tons of 

nitrogen loadings per year because of the 13 counties bordering the APS estuary in North 

Carolina. The resulting three unit values are $0.0018, $0.0010, and $0.0017 per person-trip per 

1-ton reduction in nitrogen loads to the APS. 

To estimate the aggregate annual recreational fishing benefits of total reductions in 

nitrogen loads to the APS estuary, the following benefit transfer equation was specified: 

AggBAPSfish = V× ∆L × T, (4.12) 

where 

AggBAPSfish = the aggregate annual recreational fishing benefits from reductions in 

nitrogen loads to the APS estuary (in 2007 dollars), 

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Appendix 8 - 96 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

V = the annual per trip value per unit (either in tons per year or percentage) 

reduction in nitrogen (in 2007 dollars), 

∆L = reduction in nitrogen loadings (either in tons per year or percentage) to 

the APS estuary, and 

T = the total number of annual fishing trips to the APS estuary (person-trips 

per year). 

Although the unit value (V) estimates derived from Kaoru (1995) and Kaoru, Smith, and 

Liu (1995) are based on data only for boating anglers, it was assumed that they apply to all

recreational fishing trips (T) in the APS. Data on visitation rates for recreational anglers in the 

APS estuary are available from the MRFSS, which contains information on the number, type, 

and destination of recreational fishers for several coastal regions in the United States. For 2006, 

the MRFSS data provide an estimate of 753,893 person-trips to the APS for recreational fishing. 

4.2.2.2.1 Results: Aggregate Recreational Fishing Benefits 

As noted above, the findings of the Neuse River/Neuse Estuary Case Study indicate that 

eliminating atmospheric deposition of nitrogen to the Neuse watershed would reduce nitrogen 

loads to the Neuse estuary (and, thus, the APS estuary as well) by 1.15 million kg per year, 

which is equivalent to 1,268 tons of nitrogen per year. Assuming that annual recreational fishing 

levels in the APS remain at 2006 levels and applying Equation (4.12), the resulting aggregate 

annual benefits (AggBAPSfish) of such a reduction are estimated to be between $1.0 million and 

$1.7 million. 

If the Neuse case study results regarding the portion of nitrogen loadings attributable to 

atmospheric deposition (26%) are extended to the entire APS system, then this extrapolation 

implies that eliminating all atmospheric nitrogen loads to the APS watershed would also reduce 

annual nitrogen loads to the APS estuary by 26%. Applying Equation (4.11) to this scenario 

suggests that the aggregate recreational fishing benefits of zeroing out nitrogen deposition in the 

entire APS watershed would be between $4.6 million and $7.9 million. 

4.2.2.2.2 Limitations and Uncertainties 

The following limitations and uncertainties should be considered when interpreting these 

recreational fishing benefit estimates. First, the value estimates are based on fishing activity data 

Final Risk and Exposure Assessment September 2009 

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Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

that are more than 2 decades old. The analysis assumes that the benefits of water quality changes 

have remained constant (in real terms) over this period. 

Second, the value estimates obtained from the two existing studies were based on 

percentage reductions in nutrients that were uniform across the APS estuary. By converting these 

estimates into per-ton terms and applying them only to the Neuse River nitrogen load reductions, 

the analysis implicitly assumes that average per-trip benefits do not vary with respect to the 

spatial distribution of the loadings reductions. 

Third, the original value estimates are based on data only from boat fishermen; however, 

the analysis assumes that these values are appropriate for both boat and nonboat fishers. 

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in Fisheries.” Journal of Bioeconomics 4:163-181. 

Krupnick, A. 1988. “Reducing Bay Nutrients: An Economic Perspective.” Maryland Law 

Review 47(2):453-480. 

Leeworthy, V.R., and P.C. Wiley. 2001. Current Participation Patterns in Marine Recreation. 

Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric 

Administration, National Ocean Service, Special Projects. 

Lipton, D. W. 1999. “Pfiesteria’s Economic Impact on Seafood Industry Sales and Recreational 

Fishing.” In Proceedings of the Conference, Economics of Policy Options for Nutrient 

Management and Pfiesteria. B. L. Gardner and L. Koch, eds., pp. 35–38. College Park, 

MD: Center for Agricultural and Natural Resource Policy, University of Maryland. 

Lipton, D. 2004. “The Value of Improved Water Quality to Chesapeake Bay Boaters.” Marine 

Resource Economics 19:265-270. 

Lipton, D. 2006. “Economic Impact of Maryland Boating in 2005.” University of Maryland Sea 

Grant Extension Program. Available at 

ftp://ftp.mdsg.umd.edu/Public/MDSG/rec_boat05.pdf. 

Lipton, D. 2008. “Economic Impact of Maryland Boating in 2007.” University of Maryland Sea 

Grant Extension Program. Available at 

ftp://ftp.mdsg.umd.edu/Public/MDSG/rec_boat07.pdf. 

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Lipton, D.W., and R.W. Hicks. 1999. “Linking Water Quality Improvements to Recreational 

Fishing Values: The Case of Chesapeake Bay Striped Bass.” In Evaluating the Benefits of 

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105-110. Vancouver: University of British Columbia. 

Lipton, D.W., and R.W. Hicks. 2003. “The Cost of Stress: Low Dissolved Oxygen and the 

Economic Benefits of Recreational Striped Bass Fishing in the Patuxent River.” Estuaries

26(2A):310-315. 

Massey, M., S. Newbold, and B. Gentner. 2006. “Valuing Water Quality Changes Using a 

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Economics and Management 52:482-500. 

Millennium Ecosystem Assessment (MEA). 2005. Ecosystems and Human Well-being: 

Wetlands and Water. Synthesis. A Report of the Millennium Ecosystem Assessment. 

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Mistiaen, J.A., I.E. Strand, and D. Lipton. 2003. “Effects of Environmental Stress on Blue Crab 

(Callinectes sapidus) Harvests in Chesapeake Bay Tributaries.” Estuaries 26(2a): 316-

322. 

Morgan, C., and N. Owens. 2001. “Benefits of Water Quality Policies: The Chesapeake Bay.” 

Ecological Economics 39:271-284. 

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Commercial Landing Statistics.” Available at http://www.st.nmfs.noaa.gov/ 

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National Oceanic and Atmospheric Administration (NOAA). “Recreational Fisheries.” 

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updated February 12, 2009. 

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Coastal Management 46(9-10):845-858. 
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5.0 TERRESTRIAL ENRICHMENT 

Terrestrial enrichment occurs when terrestrial ecosystems receive nitrogen loadings in 

excess of natural background levels, either through atmospheric deposition or direct application. 

Evidence presented in the Integrated Science Assessment (Environmental Protection agency 

[EPA], 2008) supports a causal relationship between atmospheric nitrogen deposition and 

biogeochemical cycling and fluxes of nitrogen and carbon in terrestrial systems. Furthermore, 

evidence summarized in the report supports a causal link between atmospheric nitrogen 

deposition and changes in the types and number of species and biodiversity in terrestrial systems. 

The Terrestrial Nutrient Enrichment Case Study focuses on the coastal sage scrub (CSS) 

ecosystem and San Bernardino and Sierra Nevada mixed conifer forests (MCF), both located in 

California. CSS is a unique and endemic ecosystem that provides habitat to several threatened 

and endangered species. Additionally, CSS is generally less fire prone than the nitrophyllous 

species that tend to amass dominance in abundance and richness with increased nutrient 

enrichment. MCF provide habitat for animals as well as contribute other ecosystem services such 

as timber, recreation, and water cycling. Nitrogen enrichment occurs over a long time period; as 

a result, it may take as much as 50 years or more to see changes in ecosystem conditions and 

indicators. This long time scale also affects the timing of the ecosystem service changes. 

The Terrestrial Nutrient Enrichment Case Study differs from the other case studies in that 

it focuses on geographic information system (GIS) analyses and existing nitrogen loading 

threshold investigations as the basis for describing endpoints. The CSS investigation analyzed 

GIS data in conjunction with the results from Community Multiscale Air Quality (CMAQ) 2002 

modeling to assess the relationship between atmospheric nitrogen deposition and changes in the 

CSS ecosystem. In the San Bernardino and Sierra Nevada MCF, nitrogen loading thresholds 

obtained in situ and through simulation modeling were investigated for potential endpoints and 

applicability to the San Bernardino and Sierra Nevada MCF system. 

5.1 OVERVIEW OF AFFECTED ECOSYSTEM SERVICES 

The ecosystem service impacts of terrestrial nutrient enrichment include primarily 

cultural and regulating services. In CSS, concerns focus on a decline in CSS and an increase in 

nonnative grasses and other species, impacts on the viability of threatened and endangered 

species associated with CSS, and an increase in fire frequency. Changes in MCF include changes 

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in habitat suitability and increased tree mortality, increased fire intensity, and a change in the 

forest’s nutrient cycling that may affect surface water quality through nitrate leaching (EPA, 

2008). 

Both CSS and MCF are located in areas of California valuable for housing, recreation, 

and development. CSS runs along the coast through densely populated areas of California (see 

Figure 5.1-1). From Figure 5.1-2, MCF covers less densely populated areas that are valuable for 

recreation. The proximity of CSS and MCF to population centers and recreational areas and the 

potential value of these landscape types in providing regulating ecosystem services suggest that 

the value of preserving CSS and MCF to California could be quite high. The value that 

California residents and the U.S. population as a whole place on CSS and MCF habitats is 

reflected in the various federal, state, and local government measures that have been put in place 

to protect these habitats. Threatened and endangered species are protected by the Endangered 

Species Act. The State of California passed the Natural Communities Conservation Planning 

Program (NCCP) in 1991, and CSS was the first habitat identified for protection under the 

program (see www.dfg.ca.gov/habcon/nccp). Figure 5.1-3 shows the boundaries of the NCCP 

region and subregions for CSS. Private organizations such as The Nature Conservancy, the 

Audubon Society, and local land trusts also protect and restore CSS and MCF habitat. According 

to the 2005 National Land Trust Census Report (Land Trust Alliance, 2006), California has the 

most land trusts of any state with a total of 1,732,471 acres either owned, under conservation 

easement, or conserved by other means. 

5.1.1 Cultural 

The primary cultural ecosystem services associated with CSS and MCF are recreation, 

aesthetic, and nonuse values. The possible ecosystem service benefits from reducing nitrogen 

enrichment in CSS and MCF are discussed below, and a general overview of the types and 

relative magnitude of the benefits is provided. 

CSS, once the dominant landscape type in the area, is a unique ecosystem that provides 

cultural value to California and the nation as a whole. Culturally, the remaining patches of CSS 

contain a number of threatened and endangered species, and patches of CSS are present in a 

number of parks and recreation areas. More generally the patches of CSS represent the iconic 

landscape type of Southern California and serve as a reminder of what the area looked like 

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predevelopment. Changes that might impact cultural ecosystem services in CSS resulting from 

nutrient enrichment potentially include 

 decline in CSS habitat, shrub abundance, and species of concern; 

 increased abundance of nonnative grasses and other species; and 

 increase in wildfires. 

Figure 5.1-1. Coastal Sage Scrub Areas and Population 

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Figure 5.1-2. Mixed Conifer Forest Areas and Population 

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Figure 5.1-3. Boundaries of the NCCP Region and Subregions for Coastal Sage Scrub 

(Source: California Department of Fish and Game, n.d.). 

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For MCF, the changes from nutrient enrichment that might impact cultural ecosystem 

services include 

 change in habitat suitability and increased tree mortality and 

 decline in MCF aesthetics. 

5.1.1.1 Recreation 

CSS and MCF are found in numerous recreation areas in California. Three national parks 

and monuments in California contain CSS, including Cabrillo National Monument, Channel 

Islands National Park, and Santa Monica National Recreation Area. All three parks showcase 

CSS habitat with educational programs and information provided to visitors, guided hikes, and 

research projects focused on understanding and preserving CSS. Together a total of 1,456,879 

visitors traveled through these three parks in 2008. MCF is highlighted in Sequoia and Kings 

Canyon National Park, Yosemite National Park, and Lassen Volcanic National Park, where a 

total of 5,313,754 people visited in 2008. Figure 5.1-4 maps national and state parkland against 

MCF areas. 

In addition, numerous state and county parks encompass CSS and MCF habitat. Visitors 

to these parks engage in activities such as camping, hiking, attending educational programs, 

horseback riding, wildlife viewing, water-based recreation, and fishing. For example, 

California’s Torrey Pines State Natural Reserve protects CSS habitat (see 

http://www.torreypine.org/). 

Table 5.1-1 reports the results from the 2006 National Survey of Fishing, Hunting, and 

Wildlife Associated Recreation (FHWAR) for California (DOI, 2007) on the number of 

individuals involved in fishing, hunting, and wildlife viewing in California. Millions of people 

are involved in just these three activities each year. The quality of these trips depends in part on 

the health of the ecosystems and their ability to support the diversity of plants and animals found 

in important habitats. Based on estimates from Kaval and Loomis (2003), in the Pacific Coast 

region of the United States, a day of fishing has an average value of $48.86 (in 2007 dollars), 

based on 15 studies. For hunting and wildlife viewing in this region, average day values were 

estimated to be $50.10 and $79.81 from 18 and 23 studies, respectively. Multiplying these 

average values by the total participation days reported in Table 5.1-1, the total benefits in 2006 

from fishing, hunting, and wildlife viewing away from home in California were approximately 

$947 million, $169 million, and $3.59 billion, respectively. 

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In addition, data from California State Parks (2003) indicate that in 2002 68.7% of adult 

residents participated in trail hiking for an average of 24.1 days per year. Applying these same 

rates to Census estimates of the California adult population in 2007 suggests that residents in 

California hiked roughly 453 million days in 2007. According to Kaval and Loomis (2003), the 

average value of a hiking day in the Pacific Coast region is $25.59, based on a sample of 49 

studies. Multiplying this average-day value by the total participation estimate indicates that the 

aggregate annual benefit for California residents from trail hiking in 2007 was $11.59 billion. 

Figure 5.1-4. Mixed Conifer Forest Areas and National and State Park 

Boundaries 

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Table 5.1-1. Recreational Activities in California in 2006 by Residents and Nonresidents 

Activities in California by Residents and Nonresidents

Fishing 

Anglers ............................................................................................................................................... 1,730,000 

Days of fishing .................................................................................................................................. 19,394,000 

Average days per angler ................................................................................................................................. 11 

Hunting 

Hunters ................................................................................................................................................. 281,000 

Days of hunting .................................................................................................................................. 3,376,000 

Average days per hunter ................................................................................................................................. 12 

Wildlife Watching 

Total wildlife-watching participants ............................................................................................... 6,270,000 

Away-from-home participants ............................................................................................................ 2,894,000 

Around-the-home participants ............................................................................................................ 5,259,000 

Days of participation away from home ............................................................................................. 45,010,000 

Average days of participation away from home ............................................................................................. 16 

Activities in California by Residents 

Fishing 

Anglers ............................................................................................................................................... 1,578,000 

Days of fishing .................................................................................................................................. 18,310,000 

Average days per angler ................................................................................................................................. 12 

Hunting 

Hunters ................................................................................................................................................. 274,000 

Days of hunting .................................................................................................................................. 3,339,000 

Average days per hunter ................................................................................................................................. 12 

Wildlife Watching 

Total wildlife-watching participants ............................................................................................... 5,704,000 

Away-from-home participants ............................................................................................................ 2,328,000 

Around-the-home participants ............................................................................................................ 5,259,000 

Days of participation away from home ............................................................................................. 41,436,000 

Average days of participation away from home ............................................................................................. 18 

Source: U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce, U.S. Census 

Bureau, 2007. 

The potential impacts of an increase in wildfires on recreation are discussed in Section 

5.1.2, Regulating. 

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5.1.1.2 Aesthetic 

Beyond the recreational value, the CSS landscape and MCF provide aesthetic services to 

local residents and homeowners who live near CSS or MCF. Aesthetic services not related to 

recreation include the view of the landscape from houses, as individuals commute, and as 

individuals go about their daily routine in a nearby community. Studies find that scenic 

landscapes are capitalized into the price of housing. Although no studies came to light that look 

at the value of housing as a function of the view in landscapes that include CSS or MCF, other 

studies document the existence of housing price premia associated with proximity to forest and 

open space (Acharya and Bennett, 2001; Geoghegan, Wainger, and Bockstael, 1997; Irwin, 

2002; Mansfield, et al., 2005; Smith, Poulos, and Kim, 2002; Tyrvainen and Miettinen, 2000). 

The CSS landscape itself is closely associated with Southern California, which should increase 

the aesthetic value of the landscape in general. Figure 5.1-5 presents home values in 2000 by 

Census block and CSS areas. CSS areas border a number of areas along the coast near large 

cities with very high home values, as well as areas between the cities where home values are 

lower. 

5.1.1.3 Nonuse Value 

Nonuse value, also called existence value or preservation value, encompasses a variety of 

motivations that lead individuals to place value on environmental goods or services that they do 

not use. The values individuals place on protecting rare species, rare habitats, or landscape types 

that they do not see or visit and that do not contribute to the pleasure they get from other 

activities are examples of nonuse values. 

While measuring the public’s willingness to pay (WTP) to protect endangered species 

poses theoretical and technical challenges, it is clear that the public places a value on preserving 

endangered species and their habitat. Data on charitable donations, survey results, and the time 

and effort different individuals or organizations devote to protecting species and habitat suggest 

that endangered species have intrinsic value to people beyond the value derived from using the 

resource (recreational viewing or aesthetic value). CSS and MCF are home to a number of 

important and rare species and habitat types. CSS displays richness in biodiversity with more 

than 550 herbaceous annual and perennial species. Of these herbs, nearly half are endangered, 

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sensitive, or of special status (Burger et al., 2003). Additionally, avian, arthropod, herpetofauna, 

and mammalian species live in CSS habitat or use the habitat for breeding or foraging. 

Figure 5.1-5. Coastal Sage Scrub Areas and Housing Values 

Figure 5.1-6 shows communities of CSS and three important federally endangered 

species. MCF is home to one federally endangered species and a number of state-level sensitive 

species. Figure 5.1-7 provides a map of MCF habitat and two threatened and endangered 

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species. The Audubon Society lists 28 important bird areas in CSS habitat and at least 5 in MCF 

in California (http://ca.audubon.org/iba/index.shtml).1

Figure 5.1-6. Presence of Three Threatened and Endangered Species in 

California’s Coastal Sage Scrub Ecosystem 

 

1 Important bird areas are sites that provide essential habitat for one or more species of bird. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Figure 5.1-7. Presence of Two Threatened and Endangered Species in 

California’s Mixed Conifer Forest 

To the authors’ knowledge, only one study has specifically estimated values for 

protecting CSS habitat in California. Stanley (2005) uses a contingent valuation (CV) survey to 

measure WTP to support recovery plans for endangered species in Southern California. The 

survey of Orange County, California, residents asked respondents to value the recovery of a 

single species (the Riverdale fairy shrimp) and a larger bundle of 32 species found in the county. 

The acquisition of critical habitat and implementation of the recovery plan were the specific 

goods being valued in the WTP question and the programs would be financed by an annual tax 

payment. The average WTP for fairy shrimp recovery was roughly $29 (in 2007 dollars) and for 

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all 32 species was $61 per household, depending on the model used. Aggregating benefits 

(multiplying average household WTP by the number of households in the county) results in total 

estimated WTP of over $27 million annually for protecting fairy shrimp and $57 million 

annually for all 32 species. 

In a more general study valuing endangered species protection, Loomis and White (1996) 

synthesize key results from 20 threatened and endangered species valuation studies using metaanalysis methods. They find that annual WTP estimates range from a low of $11 for the Striped 

Shiner fish to a high of $178 for the Northern Spotted Owl (in 2007 dollars). None of the studies 

summarized by Loomis and White are found in CSS or MCF, but the study provides another 

indication of the value that the public places on preserving endangered species in general. 

5.1.2 Regulating 

Excessive nitrogen deposition upsets the balance between CSS and nonnative plants, 

changing the ability of an area to support the biodiversity found in CSS. The composition of 

species in CSS changes fire frequency and intensity, as nonnative grasses fuel more frequent and 

more intense wildfires. More frequent and intense fires also reduce the ability of CSS to 

regenerate after a fire and increase the proportion of nonnative grasses (EPA, 2008). A healthy 

MCF ecosystem supports native species, promotes water quality, and helps regulate fire 

intensity. Excess nitrogen deposition leads to changes in the forest structure, such as increased 

density and loss of root biomass, which in turn can result in more intense fires and water quality 

problems related to nitrate leaching (EPA, 2008). 

The importance of CSS and MCF as homes for sensitive species and their aesthetic 

services are discussed in Section 5.1.1. Here the contribution of CSS and MCF to fire regulation 

and water quality are discussed. 

5.1.2.1 Fire Regulation 

The Terrestrial Nutrient Enrichment Case Study identified fire regulation as a service that 

could be affected by enrichment of the CSS and MCF ecosystems. Wildfires represent a serious 

threat in California and cause billions of dollars in damage. Over the 5-year period from 2004 to 

2008, Southern California experienced, on average, over 4,000 fires a year burning, on average, 

over 400,000 acres (National Association of State Foresters [NASF], 2009). Improved fire 

regulation leads to short-term and long-term benefits. The short-term benefits include the value 

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of avoided residential property damages, avoided damages to timber, rangeland, and wildlife 

resources; avoided losses from fire-related air quality impairments; avoided deaths and injury 

due to fire; improved outdoor recreation opportunities; and savings in costs associated with 

fighting the fires and protecting lives and property. For example, the California Department of 

Forestry and Fire Protection (CAL FIRE) estimated that average annual losses to homes due to 

wildfire from 1984 to 1994 were $163 million per year (CAL FIRE, 1996) and were over $250 

million in 2007 (CAL FIRE, 2008). In fiscal year 2008, CAL FIRE’s costs for fire suppression 

activities were nearly $300 million (CAL FIRE, 2008). Therefore, even a 1% reduction in these 

damages and costs would imply benefits of over $5 million per year. 

Figure 5.1-8 is a map of the overlap between fire threat and CSS habitat. CSS overlaps 

with areas of very to extremely high fire threat. MCF is found in some areas closer to the coast 

with extremely high fire threat and in areas up in the mountains also under very high fire threat, 

as seen in Figure 5.1-9. 

In the long term, decreased frequency of fires could result in an increase in property 

values in fire-prone areas. Mueller, Loomis, and González-Cabán (2007) conducted a hedonic 

pricing study to determine whether increasing numbers of wildfires affect house prices in 

southern California. They estimated that house prices would decrease 9.71% ($30,693 in 2007 

dollars) after one fire and 22.7% ($71,722; $102,417 cumulative) after a second wildfire within 

1.75 miles of a house in their study area. After the second fire, the housing prices took between 5 

and 7 years to recover. The results come from a sample of 2,520 single-family homes located 

within 1.75 miles of one of five fires during the 1990s. 

Long-term decreases in wildfire risks are also expected to provide outdoor recreation 

benefits. The empirical literature contains several articles measuring the relationship between 

wildfires and recreation values; however, very few address fires in California, particularly in 

CSS areas. One exception is Loomis et al. (2002), which estimates the changes in deer harvest 

and deer hunting benefits resulting from controlled burns or prescribed fire in the San Bernardino 

National Forest in Southern California. Using a CV survey of deer hunters in California, they 

estimated that the net economic value of an additional deer harvested is on average $122 (in 

2007 dollars). Based on predicted changes in deer harvest in response to a prescribed fire, they 

estimated annual economic benefits for an additional 1,000 acres of prescribed burning ranges 

from $3,328 to $3,893. 

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Figure 5.1-8. Coastal Sage Scrub Areas and Fire Threat 

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Figure 5.1-9. Mixed Conifer Forest Areas and Fire Threat 

5.1.2.2 Water Quality 

In the MCF case study, maintaining water quality emerged as a regulating service that 

can be upset by excessive nitrogen. When the soil becomes saturated, nitrates may leach into the 

surface water and cause acidification. Several large rivers and Lake Tahoe cut through MCF 

areas, presented in Figure 5.1-10. Additional nitrogen from MCF areas could further degrade 

waters that are already stressed by numerous other sources of nutrients and pollution. 

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Figure 5.1-10. Mixed Conifer Forest Areas and Major Lakes and Rivers 

5.2 VALUE OF COASTAL SAGE SCRUB AND MIXED CONIFER 

FOREST ECOSYSTEM SERVICES 

The CSS and MCF were selected as case studies for terrestrial enrichment because of the 

potential that these areas could be adversely affected by excessive nitrogen deposition. To date, 

the detailed studies needed to identify the magnitude of the adverse impacts due to nitrogen 

deposition have not been completed. Based on available data, this report provides a qualitative 

discussion of the services offered by CSS and MCF and a sense of the scale of benefits 

associated with these services. California is famous for its recreational opportunities and 

beautiful landscapes. CSS and MCF are an integral part of the California landscape, and together 

the ranges of these habitats include the densely populated and valuable coastline and the 

mountain areas. Through recreation and scenic value, these habitats affect the lives of millions of 

California residents and tourists. Numerous threatened and endangered species at both the state 

and federal levels reside in CSS and MCF. Both habitats may play an important role in wildfire 

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frequency and intensity, an extremely important problem for California. The potentially high 

value of the ecosystem services provided by CSS and MCF justify careful attention to the longterm viability of these habitats. 

5.3 REFERENCES 

Acharya, G., and L.L. Bennett. 2001. “Valuing Open Space and Land-Use Patterns in Urban 

Watersheds.” Journal of Real Estate Finance and Economics 22(2/3):221-237. 

Burger, J.C., R.A. Redak, E.B. Allen, J.T. Rotenberry, and M.F. Allen. 2003. “Restoring 

Arthropod Communities in Coastal Sage Scrub.” Conservation Biology 17(2):460-467. 

CAL FIRE (California Department of Forestry and Fire Protection). 1996. California Fire Plan. 

Available at http://cdfdata.fire.ca.gov/fire_er/fpp_planning_cafireplan. 

CAL FIRE (California Department of Forestry and Fire Protection). 2008. CAL FIRE 2007 

Wildland Fire Summary. 

California Department of Fish and Game. n.d. 

http://www.dfg.ca.gov/habcon/nccp/images/region.gif.

Geoghegan, J., L.A. Wainger, and N.E. Bockstael. 1997. “Spatial Landscape Indices in a 

Hedonic Framework: An Ecological Economics Analysis Using GIS.” Ecological 

Economics 23:251-264. 

Irwin, E.G. 2002. “The Effects of Open Space on Residential Property Values.” Land Economics

78(4):465-480. 

Kaval, P., and J. Loomis. 2003. Updated Outdoor Recreation Use Values With Emphasis On 

National Park Recreation. Final Report October 2003, under Cooperative Agreement CA 

1200-99-009, Project number IMDE-02-0070. 

Land Trust Alliance. 2006. The 2005 National Land Trust Census Report. Washington, D.C.: 

Land Trust Alliance, November 30, 2006. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 121 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 122 

Loomis, J., D. Griffin, E. Wu, and A. González-Cabán. 2002. “Estimating the Economic Value 

of Big Game Habitat Production from Prescribed Fire Using a Time Series Approach.” 

Journal of Forest Economics 2:119-29. 

Loomis, J.B., and D.S. White. 1996. “Economic Benefits of Rare and Endangered Species: 

Summary and Meta-Analysis.” Ecological Economics 18(3):197-206. 

Mansfield, C.A., S.K. Pattanayak, W. McDow, R. MacDonald, and P. Halpin. 2005. “Shades of 

Green: Measuring the Value of Urban Forests in the Housing Market.” Journal of Forest 

Economics 11(3):177-199. 

Mueller, J., J. Loomis, and A. González-Cabán. 2007. “Do Repeated Wildfires Change 

Homebuyers’ Demand for Homes in High-Risk Areas? A Hedonic Analysis of the Short 

and Long-Term Effects of Repeated Wildfires on House Prices in Southern California.” 

Journal of Real Estate Finance and Economics, 1-18. 

National Association of State Foresters (NASF). 2009. Quadrennial Fire Review 

2009.Washington, DC: NASF. Quadrennial Fire and Fuel Review Final Report 2009. 

National Wildfire Coordinating Group Executive Board January 2009. 

Smith, V.K., C. Poulos, and H. Kim. 2002. “Treating Open Space as an Urban Amenity.” 

Resource and Energy Economics 24:107-129. 

Tyrvainen, L., and A. Miettinen. 2000. “Property Prices and Urban Forest Amenities.” Journal of 

Economics and Environmental Management 39:205-223. 

U.S. Department of the Interior, Fish and Wildlife Service, and U.S. Department of Commerce, 

U.S. Census Bureau. 2007. 2006 National Survey of Fishing, Hunting, and WildlifeAssociated Recreation. 

U.S. Environmental Protection Agency (EPA). 2008. Integrated Science Assessment for Oxides 

of Nitrogen and Sulfur–Environmental Criteria.EPA/600/R-08/082. U.S. Environmental 

Protection Agency, Office of Research and Development, National Center for 

Environmental Assessment – RTP Division, Research Triangle Park, NC. 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

6.0 CONCLUSION 

This report has identified, characterized, and, to the extent possible, quantified the 

ecosystem services that are primarily affected by changes in nitrogen and sulfur deposition and 

associated ecological indicators. The discussion has focused on four main categories of 

ecosystem effects—aquatic and terrestrial acidification and aquatic and terrestrial nutrient 

enrichment—and on three main categories of ecosystem services—provisioning, cultural, and 

regulating services. 

The report demonstrates that nitrogen and sulfur deposition have wide-ranging 

detrimental effects on the services provided by ecosystems across the United States; however, 

there continues to be significant uncertainty regarding the overall magnitude of these effects. To 

partially address this uncertainty, where data and scientific evidence permit, this study has 

estimated how reducing nitrogen and sulfur deposition in specific areas would affect the value of 

selected ecosystem services. These estimates are summarized in Table 6-1. 

6.1 BENEFITS FROM ENHANCED PROVISIONING SERVICES 

Provisioning services are derived from goods and commodities whose production 

depends directly on inputs from healthy ecosystems. Two main examples of provisioning 

services that are constrained by nitrogen and sulfur deposition are the production and 

consumption of forest products and seafood. 

Terrestrial acidification has been shown to cause forest damages, and much of the 

specific evidence has focused on two tree species—sugar maples and red spruce. The value of 

commercial harvests from these two species in 2006 was roughly $400 million, but the more 

relevant question is how much would the value of these services increase with reductions in 

nitrogen and sulfur deposition? This study estimates that eliminating the growth suppression 

effects of terrestrial acidification on sugar maples and red spruce would generate market benefits 

of about $684,000 per year. 

Aquatic enrichment resulting from excess inputs of nitrogen is also known to contribute 

to eutrophic conditions in surface waters, which limits the growth and abundance of commercial 

fish species. Evidence regarding the magnitude of these effects is limited, largely due to the 

complexities involved in modeling the dynamic ecosystem processes and links between fish 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 123 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

stocks and commercial fishing behaviors. One exception is a model of commercial blue crab 

fishing in the Neuse River estuary (Smith, 2007). Using the results of this study, it is estimated 

that eliminating the contribution of atmospheric nitrogen deposition to the Neuse would generate 

market benefits from blue crab fishery ranging between $0.1 million and $1 million per year. 

Table 6-1. Summary of Aggregate Benefit Estimates for Selected Ecosystem Services and Areas 

(Zero Out of Nitrogen and Sulfur Deposition)a

Ecosystem Effect 

Ecosystem Service 

Area 

Range (in millions of 2007 dollars/year) 

Low High 

Aquatic Acidification 

Recreational Fishing 

Adirondack Lakes 3.9 9.3 

New York Lakes 4.5 130.0 

General 

Adirondack Lakes 291.2 1097.2 

Terrestrial Acidification 

Commercial Timber (Sugar Maple and Red 

Spruce) 

Northeastern U.S. 0.684 0.684 

Aquatic Enrichment 

Commercial Fishing (Blue Crab) 

Neuse 0.1 1.0 

Recreational Fishing 

Chesapeake 42.6 217.0 

APS 1.0 7.9 

Recreational Boating 

Chesapeake 2.9 8.2 

Recreational Beach Use 

Chesapeake 124.0 124.0 

Aesthetic (Nearshore Residents) 

Chesapeake 38.7 102.2 

Nonuse 

Chesapeake 159.1 270.4 

a

 Because of overlaps in the services covered, the value estimates reported in the table should not be added together. 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 124 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

6.2 BENEFITS FROM ENHANCED CULTURAL SERVICES 

Cultural services are derived from the nonmaterial benefits that individuals receive from 

ecosystems, including spiritual enrichment, cognitive development, reflection, recreation, and 

aesthetic experiences. As this report discusses, acidification and enrichment effects from 

nitrogen and sulfur deposition have the potential to affect a wide variety of these services; 

however, most of the available evidence concerns recreation and aesthetic services. 

As discussed in Sections 3 and 5, much of the evidence of adverse terrestrial ecosystem 

impacts from acidification and enrichment centers on forests in the northeastern portion of the 

United States and coastal sage scrub (CSS) and mixed conifer forest (MCF) ecosystems in the 

west. These ecosystems support a wide variety of land-based outdoor recreational activities, 

including hunting, wildlife viewing, and hiking, worth several billions of dollars each year to the 

general public. Unfortunately, relatively little evidence is available to quantity how the benefits 

of these recreational services are affected by terrestrial acidification or enrichment due to 

nitrogen and sulfur deposition. 

As discussed in Sections 2 and 4, aquatic ecosystem impacts due to the acidification and 

nutrient enrichment of surface waters also adversely affect a broad and valuable range of outdoor 

recreation services. In contrast to terrestrial effects, however, the impacts of these aquatic effects 

on recreational services are relatively easier to quantify (at least for selected activities and 

geographic areas). For example, based on the results of an Aquatic Acidification Case Study of 

the Adirondacks, the benefits to recreational anglers in New York from zeroing out the 

acidification effects of nitrogen and sulfur deposition on Adirondack lakes are estimated to be 

roughly equivalent to $4 million to $9 million per year. If the zero-out conditions are extended to 

all New York lakes, the annual benefits could be an order of magnitude higher. 

This study also used the results of the aquatic enrichment case studies to estimate the 

value of enhancements to several recreational activities in the Chesapeake and AlbemarlePamlico estuaries, as a result of zeroing out nitrogen deposition to their watersheds. In the 

Chesapeake, the benefits to striped bass and summer flounder anglers were estimated to be 

roughly $43 million per year. Extending the estimation methodology to all recreational species 

implies benefits of nearly $220 million, but with a much higher degree of uncertainty. For 

Chesapeake boaters and beach users, the main benefit estimates were $8 million and $124 

million per year, respectively. In the Albemarle and Pamlico Sounds (APS), the benefits to 

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 125 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8 - 126 

recreational anglers were $1 million if the nitrogen loading reductions only occurred in the 

Neuse and almost $8 million if they applied to the entire APS. 

In addition to the benefits from enhanced recreational services, this report also examines 

benefits to other cultural ecosystem services as a result of reduced aquatic acidification and 

nutrient enrichment effects. Using the results of the Resources for the Future (RFF) contingent 

valuation ( CV) study of New York residents, total annual benefits (assumed to primarily be for 

improved cultural services, including recreational fishing services) of between $291 million and 

$1.1 billion per year for a zero out of nitrogen and sulfur deposition were estimated. In the 

Chesapeake Bay, benefits to nearshore residents (assumed to be mainly from improved aesthetic 

and recreation services) of $39 million to $102 million were estimated. Total nonuse benefits of 

between $159 million and $271 million per year were also estimated. 

6.3 BENEFITS FROM ENHANCED REGULATING SERVICES 

Terrestrial and aquatic ecosystems provide a variety of regulating services, such as fire, 

flood, and erosion control and hydrological and climate regulation; however, there is relatively 

little evidence regarding the magnitude of impairments to these services due to the effects of 

nitrogen and sulfur deposition. Therefore, this report provides more of a qualitative assessment 

of these services. It describes how aquatic acidification and enrichment can affect biological food 

chain control services through their effects on the growth and mortality of fish species. It also 

describes the regulating services provided by forests, including erosion and sedimentation 

control, water storage, and carbon sequestration, which may be adversely affected by nitrogen 

and sulfur deposition. Finally, it describes potential changes in wildfire risks and fire regulation 

services as a result of changes in CSS ecosystems that have been altered through nitrogen 

enrichment. 
ATTACHMENT A 

ANNUAL RECREATIONAL FISHING BENEFIT ESTIMATES 

FOR REDUCTIONS IN NEW YORK LAKE ACIDIFICATION 

LEVELS, 2002–2100 

Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 1 

Table A-1. Adirondack Region—20 μeq/L Threshold 

Year Per Capita Benefit Population Undiscounted Benefit 

2002 0.0000 8,333,023 $0 

2003 0.0000 8,391,727 $0 

2004 0.0000 8,440,400 $0 

2005 0.0000 8,471,658 $0 

2006 0.0000 8,511,465 $0 

2007 0.0000 8,551,313 $0 

2008 0.0000 8,665,048 $0 

2009 0.0000 8,595,000 $0 

2010 0.0000 8,630,872 $0 

2011 0.0680 8,661,363 $588,805 

2012 0.1360 8,685,935 $1,180,951 

2013 0.2039 8,706,365 $1,775,593 

2014 0.2719 8,721,517 $2,371,578 

2015 0.3399 8,729,057 $2,967,035 

2016 0.4079 8,731,768 $3,561,548 

2017 0.4759 8,731,621 $4,155,069 

2018 0.5438 8,730,281 $4,747,921 

2019 0.6118 8,733,574 $5,343,426 

2020 0.6798 8,733,759 $5,937,266 

2021 0.6760 8,731,785 $5,903,042 

2022 0.6723 8,728,824 $5,868,169 

2023 0.6685 8,725,132 $5,832,830 

2024 0.6647 8,720,953 $5,797,195 

2025 0.6610 8,716,339 $5,761,304 

2026 0.6572 8,712,372 $5,725,873 

2027 0.6534 8,709,340 $5,691,082 

2028 0.6497 8,707,278 $5,656,945 

2029 0.6459 8,705,815 $5,623,210 

2030 0.6421 8,705,075 $5,589,950 

2031 0.6384 8,705,075 $5,557,169 

2032 0.6346 8,705,075 $5,524,387 

2033 0.6309 8,705,075 $5,491,606 

2034 0.6271 8,705,075 $5,458,824 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 2 

Table A-1. Adirondack Region—20 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2035 0.6233 8,705,075 $5,426,042 

2036 0.6196 8,705,075 $5,393,261 

2037 0.6158 8,705,075 $5,360,479 

2038 0.6120 8,705,075 $5,327,698 

2039 0.6083 8,705,075 $5,294,916 

2040 0.6045 8,705,075 $5,262,134 

2041 0.6007 8,705,075 $5,229,353 

2042 0.5970 8,705,075 $5,196,571 

2043 0.5932 8,705,075 $5,163,790 

2044 0.5894 8,705,075 $5,131,008 

2045 0.5857 8,705,075 $5,098,226 

2046 0.5819 8,705,075 $5,065,445 

2047 0.5781 8,705,075 $5,032,663 

2048 0.5744 8,705,075 $4,999,882 

2049 0.5706 8,705,075 $4,967,100 

2050 0.5668 8,705,075 $4,934,318 

2051 0.5647 8,705,075 $4,915,346 

2052 0.5625 8,705,075 $4,896,373 

2053 0.5603 8,705,075 $4,877,400 

2054 0.5581 8,705,075 $4,858,428 

2055 0.5559 8,705,075 $4,839,455 

2056 0.5538 8,705,075 $4,820,482 

2057 0.5516 8,705,075 $4,801,510 

2058 0.5494 8,705,075 $4,782,537 

2059 0.5472 8,705,075 $4,763,565 

2060 0.5450 8,705,075 $4,744,592 

2061 0.5429 8,705,075 $4,725,619 

2062 0.5407 8,705,075 $4,706,647 

2063 0.5385 8,705,075 $4,687,674 

2064 0.5363 8,705,075 $4,668,701 

2065 0.5341 8,705,075 $4,649,729 

2066 0.5320 8,705,075 $4,630,756 

2067 0.5298 8,705,075 $4,611,783 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 3 

Table A-1. Adirondack Region—20 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2068 0.5276 8,705,075 $4,592,811 

2069 0.5254 8,705,075 $4,573,838 

2070 0.5232 8,705,075 $4,554,865 

2071 0.5211 8,705,075 $4,535,893 

2072 0.5189 8,705,075 $4,516,920 

2073 0.5167 8,705,075 $4,497,947 

2074 0.5145 8,705,075 $4,478,975 

2075 0.5123 8,705,075 $4,460,002 

2076 0.5102 8,705,075 $4,441,029 

2077 0.5080 8,705,075 $4,422,057 

2078 0.5058 8,705,075 $4,403,084 

2079 0.5036 8,705,075 $4,384,111 

2080 0.5014 8,705,075 $4,365,139 

2081 0.4993 8,705,075 $4,346,166 

2082 0.4971 8,705,075 $4,327,193 

2083 0.4949 8,705,075 $4,308,221 

2084 0.4927 8,705,075 $4,289,248 

2085 0.4906 8,705,075 $4,270,275 

2086 0.4884 8,705,075 $4,251,303 

2087 0.4862 8,705,075 $4,232,330 

2088 0.4840 8,705,075 $4,213,357 

2089 0.4818 8,705,075 $4,194,385 

2090 0.4797 8,705,075 $4,175,412 

2091 0.4775 8,705,075 $4,156,439 

2092 0.4753 8,705,075 $4,137,467 

2093 0.4731 8,705,075 $4,118,494 

2094 0.4709 8,705,075 $4,099,521 

2095 0.4688 8,705,075 $4,080,549 

2096 0.4666 8,705,075 $4,061,576 

2097 0.4644 8,705,075 $4,042,603 

2098 0.4622 8,705,075 $4,023,631 

2099 0.4600 8,705,075 $4,004,658 

2100 0.4579 8,705,075 $3,985,685 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 4 

Table A-2. Adirondack Region—50 μeq/L Threshold 

Year Per Capita Benefit Population Undiscounted Benefit 

2002 0.0000 8,333,023 $0 

2003 0.0000 8,391,727 $0 

2004 0.0000 8,440,400 $0 

2005 0.0000 8,471,658 $0 

2006 0.0000 8,511,465 $0 

2007 0.0000 8,551,313 $0 

2008 0.0000 8,665,048 $0 

2009 0.0000 8,595,000 $0 

2010 0.0000 8,630,872 $0 

2011 0.1232 8,661,363 $1,066,847 

2012 0.2463 8,685,935 $2,139,747 

2013 0.3695 8,706,365 $3,217,170 

2014 0.4927 8,721,517 $4,297,025 

2015 0.6159 8,729,057 $5,375,925 

2016 0.7390 8,731,768 $6,453,113 

2017 0.8622 8,731,621 $7,528,505 

2018 0.9854 8,730,281 $8,602,686 

2019 1.1086 8,733,574 $9,681,672 

2020 1.2317 8,733,759 $10,757,642 

2021 1.2310 8,731,785 $10,748,533 

2022 1.2302 8,728,824 $10,738,213 

2023 1.2294 8,725,132 $10,727,000 

2024 1.2287 8,720,953 $10,715,194 

2025 1.2279 8,716,339 $10,702,859 

2026 1.2271 8,712,372 $10,691,327 

2027 1.2264 8,709,340 $10,680,945 

2028 1.2256 8,707,278 $10,671,759 

2029 1.2248 8,705,815 $10,663,309 

2030 1.2241 8,705,075 $10,655,746 

2031 1.2233 8,705,075 $10,649,090 

2032 1.2226 8,705,075 $10,642,433 

2033 1.2218 8,705,075 $10,635,777 

2034 1.2210 8,705,075 $10,629,121 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 5 

Table A-2. Adirondack Region—50 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2035 1.2203 8,705,075 $10,622,464 

2036 1.2195 8,705,075 $10,615,808 

2037 1.2187 8,705,075 $10,609,151 

2038 1.2180 8,705,075 $10,602,495 

2039 1.2172 8,705,075 $10,595,839 

2040 1.2164 8,705,075 $10,589,182 

2041 1.2157 8,705,075 $10,582,526 

2042 1.2149 8,705,075 $10,575,869 

2043 1.2141 8,705,075 $10,569,213 

2044 1.2134 8,705,075 $10,562,557 

2045 1.2126 8,705,075 $10,555,900 

2046 1.2118 8,705,075 $10,549,244 

2047 1.2111 8,705,075 $10,542,587 

2048 1.2103 8,705,075 $10,535,931 

2049 1.2096 8,705,075 $10,529,275 

2050 1.2088 8,705,075 $10,522,618 

2051 1.2079 8,705,075 $10,514,670 

2052 1.2070 8,705,075 $10,506,723 

2053 1.2061 8,705,075 $10,498,775 

2054 1.2051 8,705,075 $10,490,827 

2055 1.2042 8,705,075 $10,482,879 

2056 1.2033 8,705,075 $10,474,931 

2057 1.2024 8,705,075 $10,466,983 

2058 1.2015 8,705,075 $10,459,035 

2059 1.2006 8,705,075 $10,451,087 

2060 1.1997 8,705,075 $10,443,139 

2061 1.1987 8,705,075 $10,435,191 

2062 1.1978 8,705,075 $10,427,243 

2063 1.1969 8,705,075 $10,419,296 

2064 1.1960 8,705,075 $10,411,348 

2065 1.1951 8,705,075 $10,403,400 

2066 1.1942 8,705,075 $10,395,452 

2067 1.1933 8,705,075 $10,387,504 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 6 

Table A-2. Adirondack Region—50 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2068 1.1924 8,705,075 $10,379,556 

2069 1.1914 8,705,075 $10,371,608 

2070 1.1905 8,705,075 $10,363,660 

2071 1.1896 8,705,075 $10,355,712 

2072 1.1887 8,705,075 $10,347,764 

2073 1.1878 8,705,075 $10,339,817 

2074 1.1869 8,705,075 $10,331,869 

2075 1.1860 8,705,075 $10,323,921 

2076 1.1851 8,705,075 $10,315,973 

2077 1.1841 8,705,075 $10,308,025 

2078 1.1832 8,705,075 $10,300,077 

2079 1.1823 8,705,075 $10,292,129 

2080 1.1814 8,705,075 $10,284,181 

2081 1.1805 8,705,075 $10,276,233 

2082 1.1796 8,705,075 $10,268,285 

2083 1.1787 8,705,075 $10,260,338 

2084 1.1777 8,705,075 $10,252,390 

2085 1.1768 8,705,075 $10,244,442 

2086 1.1759 8,705,075 $10,236,494 

2087 1.1750 8,705,075 $10,228,546 

2088 1.1741 8,705,075 $10,220,598 

2089 1.1732 8,705,075 $10,212,650 

2090 1.1723 8,705,075 $10,204,702 

2091 1.1714 8,705,075 $10,196,754 

2092 1.1704 8,705,075 $10,188,806 

2093 1.1695 8,705,075 $10,180,859 

2094 1.1686 8,705,075 $10,172,911 

2095 1.1677 8,705,075 $10,164,963 

2096 1.1668 8,705,075 $10,157,015 

2097 1.1659 8,705,075 $10,149,067 

2098 1.1650 8,705,075 $10,141,119 

2099 1.1641 8,705,075 $10,133,171 

2100 1.1631 8,705,075 $10,125,223 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 7 

Table A-3. Adirondack Region—100 μeq/L Threshold 

Year Per Capita Benefit Population Undiscounted Benefit 

2002 0.0000 8,333,023 $0 

2003 0.0000 8,391,727 $0 

2004 0.0000 8,440,400 $0 

2005 0.0000 8,471,658 $0 

2006 0.0000 8,511,465 $0 

2007 0.0000 8,551,313 $0 

2008 0.0000 8,665,048 $0 

2009 0.0000 8,595,000 $0 

2010 0.0000 8,630,872 $0 

2011 0.1307 8,661,363 $1,131,871 

2012 0.2614 8,685,935 $2,270,164 

2013 0.3920 8,706,365 $3,413,256 

2014 0.5227 8,721,517 $4,558,928 

2015 0.6534 8,729,057 $5,703,587 

2016 0.7841 8,731,768 $6,846,430 

2017 0.9148 8,731,621 $7,987,367 

2018 1.0454 8,730,281 $9,127,018 

2019 1.1761 8,733,574 $10,271,769 

2020 1.3068 8,733,759 $11,413,319 

2021 1.3056 8,731,785 $11,400,066 

2022 1.3044 8,728,824 $11,385,532 

2023 1.3031 8,725,132 $11,370,052 

2024 1.3019 8,720,953 $11,353,948 

2025 1.3007 8,716,339 $11,337,287 

2026 1.2995 8,712,372 $11,321,479 

2027 1.2982 8,709,340 $11,306,893 

2028 1.2970 8,707,278 $11,293,575 

2029 1.2958 8,705,815 $11,281,036 

2030 1.2946 8,705,075 $11,269,438 

2031 1.2934 8,705,075 $11,258,798 

2032 1.2921 8,705,075 $11,248,159 

2033 1.2909 8,705,075 $11,237,519 

2034 1.2897 8,705,075 $11,226,879 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 8 

Table A-3. Adirondack Region—100 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2035 1.2885 8,705,075 $11,216,240 

2036 1.2872 8,705,075 $11,205,600 

2037 1.2860 8,705,075 $11,194,961 

2038 1.2848 8,705,075 $11,184,321 

2039 1.2836 8,705,075 $11,173,681 

2040 1.2824 8,705,075 $11,163,042 

2041 1.2811 8,705,075 $11,152,402 

2042 1.2799 8,705,075 $11,141,763 

2043 1.2787 8,705,075 $11,131,123 

2044 1.2775 8,705,075 $11,120,484 

2045 1.2762 8,705,075 $11,109,844 

2046 1.2750 8,705,075 $11,099,204 

2047 1.2738 8,705,075 $11,088,565 

2048 1.2726 8,705,075 $11,077,925 

2049 1.2714 8,705,075 $11,067,286 

2050 1.2701 8,705,075 $11,056,646 

2051 1.2674 8,705,075 $11,033,188 

2052 1.2647 8,705,075 $11,009,730 

2053 1.2621 8,705,075 $10,986,272 

2054 1.2594 8,705,075 $10,962,814 

2055 1.2567 8,705,075 $10,939,356 

2056 1.2540 8,705,075 $10,915,897 

2057 1.2513 8,705,075 $10,892,439 

2058 1.2486 8,705,075 $10,868,981 

2059 1.2459 8,705,075 $10,845,523 

2060 1.2432 8,705,075 $10,822,065 

2061 1.2405 8,705,075 $10,798,607 

2062 1.2378 8,705,075 $10,775,149 

2063 1.2351 8,705,075 $10,751,691 

2064 1.2324 8,705,075 $10,728,233 

2065 1.2297 8,705,075 $10,704,775 

2066 1.2270 8,705,075 $10,681,317 

2067 1.2243 8,705,075 $10,657,859 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 9 

Table A-3. Adirondack Region—100 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2068 1.2216 8,705,075 $10,634,400 

2069 1.2189 8,705,075 $10,610,942 

2070 1.2162 8,705,075 $10,587,484 

2071 1.2135 8,705,075 $10,564,026 

2072 1.2109 8,705,075 $10,540,568 

2073 1.2082 8,705,075 $10,517,110 

2074 1.2055 8,705,075 $10,493,652 

2075 1.2028 8,705,075 $10,470,194 

2076 1.2001 8,705,075 $10,446,736 

2077 1.1974 8,705,075 $10,423,278 

2078 1.1947 8,705,075 $10,399,820 

2079 1.1920 8,705,075 $10,376,362 

2080 1.1893 8,705,075 $10,352,903 

2081 1.1866 8,705,075 $10,329,445 

2082 1.1839 8,705,075 $10,305,987 

2083 1.1812 8,705,075 $10,282,529 

2084 1.1785 8,705,075 $10,259,071 

2085 1.1758 8,705,075 $10,235,613 

2086 1.1731 8,705,075 $10,212,155 

2087 1.1704 8,705,075 $10,188,697 

2088 1.1677 8,705,075 $10,165,239 

2089 1.1650 8,705,075 $10,141,781 

2090 1.1623 8,705,075 $10,118,323 

2091 1.1597 8,705,075 $10,094,865 

2092 1.1570 8,705,075 $10,071,406 

2093 1.1543 8,705,075 $10,047,948 

2094 1.1516 8,705,075 $10,024,490 

2095 1.1489 8,705,075 $10,001,032 

2096 1.1462 8,705,075 $9,977,574 

2097 1.1435 8,705,075 $9,954,116 

2098 1.1408 8,705,075 $9,930,658 

2099 1.1381 8,705,075 $9,907,200 

2100 1.1354 8,705,075 $9,883,742 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 10 

Table A-4. New York State—20 μeq/L Threshold 

Year Per Capita Benefit Population Undiscounted Benefit 

2002 0.0000 8,333,023 $0 

2003 0.0000 8,391,727 $0 

2004 0.0000 8,440,400 $0 

2005 0.0000 8,471,658 $0 

2006 0.0000 8,511,465 $0 

2007 0.0000 8,551,313 $0 

2008 0.0000 8,665,048 $0 

2009 0.0000 8,595,000 $0 

2010 0.0000 8,630,872 $0 

2011 0.0777 8,661,363 $673,415 

2012 0.1555 8,685,935 $1,350,651 

2013 0.2332 8,706,365 $2,030,742 

2014 0.3110 8,721,517 $2,712,368 

2015 0.3887 8,729,057 $3,393,391 

2016 0.4665 8,731,768 $4,073,334 

2017 0.5442 8,731,621 $4,752,142 

2018 0.6220 8,730,281 $5,430,186 

2019 0.6997 8,733,574 $6,111,264 

2020 0.7775 8,733,759 $6,790,438 

2021 0.7727 8,731,785 $6,747,422 

2022 0.7680 8,728,824 $6,703,667 

2023 0.7632 8,725,132 $6,659,383 

2024 0.7585 8,720,953 $6,614,765 

2025 0.7537 8,716,339 $6,569,857 

2026 0.7490 8,712,372 $6,525,479 

2027 0.7442 8,709,340 $6,481,834 

2028 0.7395 8,707,278 $6,438,935 

2029 0.7347 8,705,815 $6,396,496 

2030 0.7300 8,705,075 $6,354,599 

2031 0.7252 8,705,075 $6,313,245 

2032 0.7205 8,705,075 $6,271,891 

2033 0.7157 8,705,075 $6,230,538 

2034 0.7110 8,705,075 $6,189,184 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 11 

Table A-4. New York State—20 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2035 0.7062 8,705,075 $6,147,830 

2036 0.7015 8,705,075 $6,106,477 

2037 0.6967 8,705,075 $6,065,123 

2038 0.6920 8,705,075 $6,023,769 

2039 0.6872 8,705,075 $5,982,416 

2040 0.6825 8,705,075 $5,941,062 

2041 0.6777 8,705,075 $5,899,708 

2042 0.6730 8,705,075 $5,858,355 

2043 0.6682 8,705,075 $5,817,001 

2044 0.6635 8,705,075 $5,775,647 

2045 0.6587 8,705,075 $5,734,294 

2046 0.6540 8,705,075 $5,692,940 

2047 0.6492 8,705,075 $5,651,586 

2048 0.6445 8,705,075 $5,610,233 

2049 0.6397 8,705,075 $5,568,879 

2050 0.6350 8,705,075 $5,527,525 

2051 0.6327 8,705,075 $5,508,117 

2052 0.6305 8,705,075 $5,488,710 

2053 0.6283 8,705,075 $5,469,302 

2054 0.6261 8,705,075 $5,449,894 

2055 0.6238 8,705,075 $5,430,487 

2056 0.6216 8,705,075 $5,411,079 

2057 0.6194 8,705,075 $5,391,671 

2058 0.6171 8,705,075 $5,372,264 

2059 0.6149 8,705,075 $5,352,856 

2060 0.6127 8,705,075 $5,333,448 

2061 0.6105 8,705,075 $5,314,041 

2062 0.6082 8,705,075 $5,294,633 

2063 0.6060 8,705,075 $5,275,225 

2064 0.6038 8,705,075 $5,255,818 

2065 0.6015 8,705,075 $5,236,410 

2066 0.5993 8,705,075 $5,217,002 

2067 0.5971 8,705,075 $5,197,595 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 12 

Table A-4. New York State—20 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2068 0.5948 8,705,075 $5,178,187 

2069 0.5926 8,705,075 $5,158,779 

2070 0.5904 8,705,075 $5,139,372 

2071 0.5882 8,705,075 $5,119,964 

2072 0.5859 8,705,075 $5,100,556 

2073 0.5837 8,705,075 $5,081,149 

2074 0.5815 8,705,075 $5,061,741 

2075 0.5792 8,705,075 $5,042,333 

2076 0.5770 8,705,075 $5,022,926 

2077 0.5748 8,705,075 $5,003,518 

2078 0.5726 8,705,075 $4,984,110 

2079 0.5703 8,705,075 $4,964,703 

2080 0.5681 8,705,075 $4,945,295 

2081 0.5659 8,705,075 $4,925,887 

2082 0.5636 8,705,075 $4,906,480 

2083 0.5614 8,705,075 $4,887,072 

2084 0.5592 8,705,075 $4,867,664 

2085 0.5569 8,705,075 $4,848,257 

2086 0.5547 8,705,075 $4,828,849 

2087 0.5525 8,705,075 $4,809,441 

2088 0.5503 8,705,075 $4,790,034 

2089 0.5480 8,705,075 $4,770,626 

2090 0.5458 8,705,075 $4,751,218 

2091 0.5436 8,705,075 $4,731,811 

2092 0.5413 8,705,075 $4,712,403 

2093 0.5391 8,705,075 $4,692,995 

2094 0.5369 8,705,075 $4,673,588 

2095 0.5347 8,705,075 $4,654,180 

2096 0.5324 8,705,075 $4,634,772 

2097 0.5302 8,705,075 $4,615,365 

2098 0.5280 8,705,075 $4,595,957 

2099 0.5257 8,705,075 $4,576,549 

2100 0.5235 8,705,075 $4,557,142 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 13 

Table A-5. New York State—50 μeq/L Threshold 

Year Per Capita Benefit Population Undiscounted Benefit 

2002 0.0000 8,333,023 $0 

2003 0.0000 8,391,727 $0 

2004 0.0000 8,440,400 $0 

2005 0.0000 8,471,658 $0 

2006 0.0000 8,511,465 $0 

2007 0.0000 8,551,313 $0 

2008 0.0000 8,665,048 $0 

2009 0.0000 8,595,000 $0 

2010 0.0000 8,630,872 $0 

2011 0.4224 8,661,363 $3,658,144 

2012 0.8447 8,685,935 $7,337,045 

2013 1.2671 8,706,365 $11,031,452 

2014 1.6894 8,721,517 $14,734,201 

2015 2.1118 8,729,057 $18,433,674 

2016 2.5341 8,731,768 $22,127,279 

2017 2.9565 8,731,621 $25,814,723 

2018 3.3788 8,730,281 $29,498,013 

2019 3.8012 8,733,574 $33,197,783 

2020 4.2235 8,733,759 $36,887,209 

2021 4.2077 8,731,785 $36,740,406 

2022 4.1918 8,728,824 $36,589,530 

2023 4.1759 8,725,132 $36,435,696 

2024 4.1601 8,720,953 $36,279,955 

2025 4.1442 8,716,339 $36,122,540 

2026 4.1284 8,712,372 $35,967,946 

2027 4.1125 8,709,340 $35,817,317 

2028 4.0967 8,707,278 $35,670,765 

2029 4.0808 8,705,815 $35,526,718 

2030 4.0649 8,705,075 $35,385,658 

2031 4.0491 8,705,075 $35,247,618 

2032 4.0332 8,705,075 $35,109,578 

2033 4.0174 8,705,075 $34,971,538 

2034 4.0015 8,705,075 $34,833,498 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 14 

Table A-5. New York State—50 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2035 3.9857 8,705,075 $34,695,458 

2036 3.9698 8,705,075 $34,557,418 

2037 3.9539 8,705,075 $34,419,378 

2038 3.9381 8,705,075 $34,281,337 

2039 3.9222 8,705,075 $34,143,297 

2040 3.9064 8,705,075 $34,005,257 

2041 3.8905 8,705,075 $33,867,217 

2042 3.8747 8,705,075 $33,729,177 

2043 3.8588 8,705,075 $33,591,137 

2044 3.8429 8,705,075 $33,453,097 

2045 3.8271 8,705,075 $33,315,057 

2046 3.8112 8,705,075 $33,177,016 

2047 3.7954 8,705,075 $33,038,976 

2048 3.7795 8,705,075 $32,900,936 

2049 3.7637 8,705,075 $32,762,896 

2050 3.7478 8,705,075 $32,624,856 

2051 3.7216 8,705,075 $32,397,187 

2052 3.6955 8,705,075 $32,169,517 

2053 3.6693 8,705,075 $31,941,848 

2054 3.6432 8,705,075 $31,714,179 

2055 3.6170 8,705,075 $31,486,509 

2056 3.5909 8,705,075 $31,258,840 

2057 3.5647 8,705,075 $31,031,171 

2058 3.5386 8,705,075 $30,803,502 

2059 3.5124 8,705,075 $30,575,832 

2060 3.4863 8,705,075 $30,348,163 

2061 3.4601 8,705,075 $30,120,494 

2062 3.4340 8,705,075 $29,892,824 

2063 3.4078 8,705,075 $29,665,155 

2064 3.3816 8,705,075 $29,437,486 

2065 3.3555 8,705,075 $29,209,816 

2066 3.3293 8,705,075 $28,982,147 

2067 3.3032 8,705,075 $28,754,478 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 15 

Table A-5. New York State—50 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2068 3.2770 8,705,075 $28,526,808 

2069 3.2509 8,705,075 $28,299,139 

2070 3.2247 8,705,075 $28,071,470 

2071 3.1986 8,705,075 $27,843,800 

2072 3.1724 8,705,075 $27,616,131 

2073 3.1463 8,705,075 $27,388,462 

2074 3.1201 8,705,075 $27,160,793 

2075 3.0940 8,705,075 $26,933,123 

2076 3.0678 8,705,075 $26,705,454 

2077 3.0416 8,705,075 $26,477,785 

2078 3.0155 8,705,075 $26,250,115 

2079 2.9893 8,705,075 $26,022,446 

2080 2.9632 8,705,075 $25,794,777 

2081 2.9370 8,705,075 $25,567,107 

2082 2.9109 8,705,075 $25,339,438 

2083 2.8847 8,705,075 $25,111,769 

2084 2.8586 8,705,075 $24,884,099 

2085 2.8324 8,705,075 $24,656,430 

2086 2.8063 8,705,075 $24,428,761 

2087 2.7801 8,705,075 $24,201,092 

2088 2.7540 8,705,075 $23,973,422 

2089 2.7278 8,705,075 $23,745,753 

2090 2.7017 8,705,075 $23,518,084 

2091 2.6755 8,705,075 $23,290,414 

2092 2.6493 8,705,075 $23,062,745 

2093 2.6232 8,705,075 $22,835,076 

2094 2.5970 8,705,075 $22,607,406 

2095 2.5709 8,705,075 $22,379,737 

2096 2.5447 8,705,075 $22,152,068 

2097 2.5186 8,705,075 $21,924,398 

2098 2.4924 8,705,075 $21,696,729 

2099 2.4663 8,705,075 $21,469,060 

2100 2.4401 8,705,075 $21,241,390 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 16 

Table A-6. New York State—100 μeq/L Threshold 

Year Per Capita Benefit Population Undiscounted Benefit 

2002 0.0000 8,333,023 $0 

2003 0.0000 8,391,727 $0 

2004 0.0000 8,440,400 $0 

2005 0.0000 8,471,658 $0 

2006 0.0000 8,511,465 $0 

2007 0.0000 8,551,313 $0 

2008 0.0000 8,665,048 $0 

2009 0.0000 8,595,000 $0 

2010 0.0000 8,630,872 $0 

2011 1.8336 8,661,363 $15,881,141 

2012 3.6671 8,685,935 $31,852,394 

2013 5.5007 8,706,365 $47,890,966 

2014 7.3342 8,721,517 $63,965,753 

2015 9.1678 8,729,057 $80,026,316 

2016 11.0014 8,731,768 $96,061,405 

2017 12.8349 8,731,621 $112,069,745 

2018 14.6685 8,730,281 $128,060,056 

2019 16.5021 8,733,574 $144,121,906 

2020 18.3356 8,733,759 $160,138,854 

2021 18.3111 8,731,785 $159,888,275 

2022 18.2865 8,728,824 $159,619,755 

2023 18.2620 8,725,132 $159,338,035 

2024 18.2374 8,720,953 $159,047,621 

2025 18.2129 8,716,339 $158,749,471 

2026 18.1883 8,712,372 $158,463,334 

2027 18.1638 8,709,340 $158,194,349 

2028 18.1392 8,707,278 $157,943,136 

2029 18.1147 8,705,815 $157,702,858 

2030 18.0901 8,705,075 $157,475,737 

2031 18.0656 8,705,075 $157,262,020 

2032 18.0410 8,705,075 $157,048,303 

2033 18.0165 8,705,075 $156,834,587 

2034 17.9919 8,705,075 $156,620,870 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 17 

Table A-6. New York State—100 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2035 17.9674 8,705,075 $156,407,153 

2036 17.9428 8,705,075 $156,193,436 

2037 17.9183 8,705,075 $155,979,720 

2038 17.8937 8,705,075 $155,766,003 

2039 17.8691 8,705,075 $155,552,286 

2040 17.8446 8,705,075 $155,338,569 

2041 17.8200 8,705,075 $155,124,852 

2042 17.7955 8,705,075 $154,911,136 

2043 17.7709 8,705,075 $154,697,419 

2044 17.7464 8,705,075 $154,483,702 

2045 17.7218 8,705,075 $154,269,985 

2046 17.6973 8,705,075 $154,056,269 

2047 17.6727 8,705,075 $153,842,552 

2048 17.6482 8,705,075 $153,628,835 

2049 17.6236 8,705,075 $153,415,118 

2050 17.5991 8,705,075 $153,201,401 

2051 17.5589 8,705,075 $152,851,672 

2052 17.5187 8,705,075 $152,501,943 

2053 17.4786 8,705,075 $152,152,214 

2054 17.4384 8,705,075 $151,802,485 

2055 17.3982 8,705,075 $151,452,756 

2056 17.3580 8,705,075 $151,103,027 

2057 17.3179 8,705,075 $150,753,298 

2058 17.2777 8,705,075 $150,403,569 

2059 17.2375 8,705,075 $150,053,840 

2060 17.1973 8,705,075 $149,704,111 

2061 17.1572 8,705,075 $149,354,382 

2062 17.1170 8,705,075 $149,004,652 

2063 17.0768 8,705,075 $148,654,923 

2064 17.0366 8,705,075 $148,305,194 

2065 16.9965 8,705,075 $147,955,465 

2066 16.9563 8,705,075 $147,605,736 

2067 16.9161 8,705,075 $147,256,007 

(continued) 
Analysis of Ecosystem Services Impacts for the NOx/SOx Secondary NAAQS Review

Final Risk and Exposure Assessment September 2009 

Appendix 8, Attachment A - 18 

Table A-6. New York State—100 μeq/L Threshold (continued) 

Year Per Capita Benefit Population Undiscounted Benefit 

2068 16.8759 8,705,075 $146,906,278 

2069 16.8358 8,705,075 $146,556,549 

2070 16.7956 8,705,075 $146,206,820 

2071 16.7554 8,705,075 $145,857,091 

2072 16.7152 8,705,075 $145,507,362 

2073 16.6751 8,705,075 $145,157,633 

2074 16.6349 8,705,075 $144,807,903 

2075 16.5947 8,705,075 $144,458,174 

2076 16.5545 8,705,075 $144,108,445 

2077 16.5144 8,705,075 $143,758,716 

2078 16.4742 8,705,075 $143,408,987 

2079 16.4340 8,705,075 $143,059,258 

2080 16.3938 8,705,075 $142,709,529 

2081 16.3537 8,705,075 $142,359,800 

2082 16.3135 8,705,075 $142,010,071 

2083 16.2733 8,705,075 $141,660,342 

2084 16.2331 8,705,075 $141,310,613 

2085 16.1930 8,705,075 $140,960,883 

2086 16.1528 8,705,075 $140,611,154 

2087 16.1126 8,705,075 $140,261,425 

2088 16.0724 8,705,075 $139,911,696 

2089 16.0323 8,705,075 $139,561,967 

2090 15.9921 8,705,075 $139,212,238 

2091 15.9519 8,705,075 $138,862,509 

2092 15.9117 8,705,075 $138,512,780 

2093 15.8716 8,705,075 $138,163,051 

2094 15.8314 8,705,075 $137,813,322 

2095 15.7912 8,705,075 $137,463,593 

2096 15.7510 8,705,075 $137,113,864 

2097 15.7109 8,705,075 $136,764,134 

2098 15.6707 8,705,075 $136,414,405 

2099 15.6305 8,705,075 $136,064,676 

2100 15.5903 8,705,075 $135,714,947 

United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication No