August 2008

EPA/600/R-08/083



# Annexes for the

Integrated Science Assessment

for Oxides of Nitrogen and

Sulfur – Environmental Criteria

(Second External Review Draft)EPA/600/R-08/083

August 2008



# Annexes for the

Integrated Science Assessment

for Oxides of Nitrogen and Sulfur –

Environmental Criteria

National Center for Environmental Assessment-RTP Division

Office of Research and Development

U.S. Environmental Protection Agency

Research Triangle Park, NC

## DISCLAIMER

This document is a second external review draft being released for review purposes only and

does not constitute U.S. Environmental Protection Agency policy. Mention of trade names or

commercial products does not constitute endorsement or recommendation for use.

August 2008 ii DRAFT—DO NOT QUOTE OR CITE

# Table of Contents

List of Tables

_______________________________________________________________________________________ viii

List of Figures

_______________________________________________________________________________________ xi

Acronyms and Abbreviations__________________________________________________________________________ xiii

Annex A. Ecosystem Monitoring and Models_____________________________________________________________ A­1

A.1. Introduction

______________________________________________________________________________________________ A­1

A.2. Ecosystem Monitoring______________________________________________________________________________________ A­2

A.2.1. Environmental Monitoring and Assessment Program

_________________________________________________________ A­3

A.2.2. Surface Water Chemistry Monitoring

_____________________________________________________________________ A­4

A.2.2.1. TIME Project

__________________________________________________________________________________ A­4

A.2.2.2. Long­Term Monitoring Project

____________________________________________________________________ A­6

A.2.3. Forest Inventory and Analysis___________________________________________________________________________ A­8

A.2.3.1. Lichens

______________________________________________________________________________________ A­9

A.2.3.2. Soil Quality

__________________________________________________________________________________ A­11

A.2.4. USGS Monitoring Programs

___________________________________________________________________________ A­12

A.2.4.1. National Water Quality Assessment Program

_______________________________________________________ A­12

A.2.4.2. Hydrologic Benchmark Network

__________________________________________________________________ A­13

A.2.4.3. New York City Water Quality Network

_____________________________________________________________ A­14

A.2.4.4. Catskill Long­Term Monitoring Sites_______________________________________________________________ A­14

A.2.4.5. Buck Creek, New York

_________________________________________________________________________ A­14

A.2.5. NSF Long­Term Ecological Research Network

____________________________________________________________ A­15

A.2.5.1. Hubbard Brook Experimental Forest

______________________________________________________________ A­18

A.2.5.2. Coweeta

____________________________________________________________________________________ A­21

A.2.5.3. Walker Branch

_______________________________________________________________________________ A­23

A.2.6. Water, Energy, and Biogeochemical Budgets Program

______________________________________________________ A­24

A.2.6.1. Sleepers River

_______________________________________________________________________________ A­24

A.2.6.2. Loch Vale

___________________________________________________________________________________ A­26

A.2.7. Other Monitoring Programs

____________________________________________________________________________ A­28

A.2.7.1. Bear Brook

__________________________________________________________________________________ A­28

A.2.7.2. Shenandoah Watershed Study___________________________________________________________________ A­29

A.2.7.3. Fernow

_____________________________________________________________________________________ A­32

A.2.7.4. National Ecological Observatory Network

__________________________________________________________ A­34

A.3. Modeling

_______________________________________________________________________________________________ A­34

A.3.1. Principal Ecosystem Models Used in the U.S.

_____________________________________________________________ A­34

A.3.1.1. MAGIC

_____________________________________________________________________________________ A­35

A.3.1.2. NuCM Model

_________________________________________________________________________________ A­39

A.3.1.3. PnET­BGC

__________________________________________________________________________________ A­41

A.3.1.4. DayCent­Chem

_______________________________________________________________________________ A­44

A.3.1.5. SPARROW

__________________________________________________________________________________ A­46

A.3.1.6. WATERSN

__________________________________________________________________________________ A­49

A.3.2. Additional Effects Models Used Widely in Europe

__________________________________________________________ A­54

A.3.2.1. The Very Simple Dynamic Model

_________________________________________________________________ A­54

A.3.2.2. SMART

_____________________________________________________________________________________ A­55

A.3.2.3. SAFE_______________________________________________________________________________________ A­55

A.3.3. Other Models_______________________________________________________________________________________ A­55

A.3.3.1. Current Long­term Monitoring Data Sets Developed through the Hubbard Brook Ecosystem Study

_____________ A­62

ANNEX A ­ References

_______________________________________________________________________________ A­63

Annex B. Acidification Effects

_________________________________________________________________________ B­1

B.1. Effects on Biogeochemical Processes along Acidification Pathways__________________________________________________ B­1

August 2008 iii DRAFT—DO NOT QUOTE OR CITEB.1.1. Atmospheric Deposition and Canopy Interaction

____________________________________________________________ B­1

B.1.2. Interactions with Soil

__________________________________________________________________________________ B­1

B.1.2.1. Sulfur Retention and Release_____________________________________________________________________ B­2

B.1.2.2. Base Cation Depletion

__________________________________________________________________________ B­3

B.1.2.3. Aluminum Mobilization

__________________________________________________________________________ B­7

B.1.2.4. Soil Acidification

_______________________________________________________________________________ B­8

B.1.2.5. N Saturation

__________________________________________________________________________________ B­9

B.1.2.6. Nitrate Leaching

______________________________________________________________________________ B­16

B.1.3. Interactions with Transitional Ecosystems

________________________________________________________________ B­16

B.1.3.1. S Storage and Release in Transitional Ecosystems___________________________________________________ B­16

B.1.3.2. Organic Acidity in Transitional Ecosystems and Downstream Surface Waters

______________________________ B­17

B.2. Factors That Determine Ecosystem Sensitivity

_________________________________________________________________ B­23

B.2.1. Transitional Ecosystems

______________________________________________________________________________ B­23

B.2.1.1. Wetlands and Peatlands________________________________________________________________________ B­23

B.2.1.2. Ponds

______________________________________________________________________________________ B­24

B.2.2. Streams and Lakes

__________________________________________________________________________________ B­24

B.2.3. Other Types of Ecosystems

___________________________________________________________________________ B­25

B.3. Distribution and Extent of Ecosystem Effects

___________________________________________________________________ B­25

B.3.1. Terrestrial Ecosystems

_______________________________________________________________________________ B­25

B.3.2. Transitional Ecosystems

______________________________________________________________________________ B­28

B.3.3. Aquatic Ecosystems

_________________________________________________________________________________ B­28

B.3.3.1. Status of Surface Waters

– Regional Overview

______________________________________________________ B­29

B.3.3.2. Recent Changes in Surface Water Chemistry

_______________________________________________________ B­31

B.3.4. Regional Assessments

_______________________________________________________________________________ B­33

B.3.4.1. Northeastern Surface Waters

____________________________________________________________________ B­33

B.3.4.2. Southeastern Surface Waters____________________________________________________________________ B­47

B.3.4.3. Upper Midwest

_______________________________________________________________________________ B­55

B.3.4.4. West

_______________________________________________________________________________________ B­58

B.3.4.5. Temporal Variability in Water Chemistry

___________________________________________________________ B­64

B.4. Effects on Biota

__________________________________________________________________________________________ B­76

B.4.1. Types of Effects of Acidification on Biota

_________________________________________________________________ B­77

B.4.1.1. Individual Condition Factor

______________________________________________________________________ B­77

B.4.1.2. Species Composition

__________________________________________________________________________ B­80

B.4.1.3. Taxonomic Richness___________________________________________________________________________ B­80

B.4.1.4. Community Structure

__________________________________________________________________________ B­86

B.4.1.5. Indices of Ecological Effects

_____________________________________________________________________ B­87

B.4.2. Timing of Effects

____________________________________________________________________________________ B­87

B.4.2.1. Life Stage Differences in Sensitivity

_______________________________________________________________ B­87

B.4.2.2. Biological Effects of Episodes____________________________________________________________________ B­90

B.4.2.3. Timing of Recovery from Acidification

_____________________________________________________________ B­95

B.4.3. Effects by Ecosystem Type

____________________________________________________________________________ B­99

B.4.3.1. Terrestrial Ecosystems

_________________________________________________________________________ B­99

B.4.3.2. Aquatic Ecosystem

___________________________________________________________________________ B­102

B.5. Effects on Watersheds and Landscapes

_____________________________________________________________________ B­112

B.5.1. Interactions among Terrestrial, Transitional, and Aquatic Ecosystems

_________________________________________ B­112

B.5.2. Interactions with Land Use and Disturbance______________________________________________________________ B­113

B.5.2.1. Timber Harvest

______________________________________________________________________________ B­115

B.5.2.2. Insect Infestation_____________________________________________________________________________ B­117

B.5.3. Wind or Ice Storm Damage

___________________________________________________________________________ B­119

B.5.3.1. Fire

_______________________________________________________________________________________ B­120

B.5.3.2. Multiple Stress Response

______________________________________________________________________ B­120

B.6. Ecological indicators of acidification

_________________________________________________________________________ B­121

B.6.1. Biological Indicators

________________________________________________________________________________ B­121

B.6.1.1. Phytoplankton

_______________________________________________________________________________ B­122

B.6.1.2. Zooplankton

________________________________________________________________________________ B­124

B.6.1.3. Benthic Invertebrates

_________________________________________________________________________ B­125

B.6.1.4. Fish

_______________________________________________________________________________________ B­127

B.6.1.5. Amphibians

_________________________________________________________________________________ B­132

B.6.1.6. Fish­Eating Birds_____________________________________________________________________________ B­133

August 2008 iv DRAFT—DO NOT QUOTE OR CITEANNEX B ­ References

______________________________________________________________________________ B­160

Annex C. Nutrient Enrichment Effects from N

____________________________________________________________ C­1

C.1. Effects on Biogeochemical Pathways and Cycles

________________________________________________________________ C­1

C.1.1. N Cycling in Terrestrial Ecosystems

______________________________________________________________________ C­1

C.1.1.1. N Deposition Effects on DON Leaching

_____________________________________________________________ C­1

C.1.1.2. Interactions between snow melt and nitrate leaching___________________________________________________ C­1

C.1.1.3. Denitrification: NO and N 2 O Flux

__________________________________________________________________ C­1

C.1.1.4. Climate and N 2 O Interactions

_____________________________________________________________________ C­3

C.1.2. N Cycling in Transitional Ecosystems_____________________________________________________________________ C­4

C.1.2.1. Denitrification: Measurement Techniques

___________________________________________________________ C­4

C.1.2.2. N Deposition Effects on Methane

__________________________________________________________________ C­4

C.1.3. N Cycling in Estuarine Ecosystems

______________________________________________________________________ C­5

C.1.3.1. Denitrification and Anammox in Estuarine Ecosystems

_________________________________________________ C­5

C.1.3.2. N Budgets

____________________________________________________________________________________ C­6

C.1.4. Timing of Chemical Change

____________________________________________________________________________ C­8

C.1.4.1. Interannual Change: Nitrate Leaching

______________________________________________________________ C­8

C.1.4.2. Episodic Change_______________________________________________________________________________ C­8

C.1.4.3. Reversibility of Impacts__________________________________________________________________________ C­9

C.1.5. Tables Supporting Cross Ecosystem Evaluation of N 2 O, CH 4 and CO 2 Flux_______________________________________ C­9

C.2. Terrestrial Ecosystems

____________________________________________________________________________________ C­15

C.2.1. General C cyling

____________________________________________________________________________________ C­15

C.2.2. Forest Growth Interactions with Herbivores

_______________________________________________________________ C­15

C.2.3. Forest Growth Interactions with Herbivores

_______________________________________________________________ C­16

C.2.4. Southern California Coniferous Forest

___________________________________________________________________ C­16

C.2.5. Boreal forests

______________________________________________________________________________________ C­17

C.2.6. Alpine

____________________________________________________________________________________________ C­18

C.2.7. Arctic Tundra_______________________________________________________________________________________ C­20

C.2.8. Arid Land__________________________________________________________________________________________ C­21

C.2.9. Lichens

___________________________________________________________________________________________ C­21

C.3. Transitional Ecosystems___________________________________________________________________________________ C­23

C.4. Aquatic Ecosystems

______________________________________________________________________________________ C­24

C.4.1. History of Evaluating N Enrichment in Freshwater Aquatic Ecosystems

_________________________________________ C­25

C.4.2. Interactions between N and P loading

___________________________________________________________________ C­25

C.4.3. Aquatic Species Affected

_____________________________________________________________________________ C­27

C.4.3.1. Phytoplankton and Plants_______________________________________________________________________ C­27

C.4.4. Seasonal N Input and Cyanobacteria

____________________________________________________________________ C­29

C.4.5. Nitrate Toxicity: Invertebrates

__________________________________________________________________________ C­29

C.4.6. Nitrate Toxicity: Amphibians and Fish____________________________________________________________________ C­30

C.5. Estuary and Coastal Ecosystems

____________________________________________________________________________ C­33

C.5.1. Interacting Factors with Productivity

_____________________________________________________________________ C­35

C.5.2. Hydrology Interactions with Phytoplankton Biomass

________________________________________________________ C­35

C.6. Effects on Watersheds and Landscapes

______________________________________________________________________ C­36

C.6.1. Interactions among Terrestrial, Transitional, and Aquatic Ecosystems

__________________________________________ C­36

C.6.2. Interactions with Land Use and Disturbance

______________________________________________________________ C­41

C.6.3. Timber Harvest and Fire

______________________________________________________________________________ C­42

C.6.4. Insect Infestation and Disease

_________________________________________________________________________ C­44

C.6.5. Urbanization

_______________________________________________________________________________________ C­46

C.6.6. Agriculture

_________________________________________________________________________________________ C­46

C.6.7. Other Disturbances

__________________________________________________________________________________ C­47

C.6.8. Multiple Stress Response

_____________________________________________________________________________ C­47

ANNEX C ­ References

_______________________________________________________________________________ C­50

Annex D. Critical Loads_______________________________________________________________________________ D­1

D.1. Background______________________________________________________________________________________________ D­1

D.1.1. The Critical Load Process

______________________________________________________________________________ D­3

D.1.2. Organization of this Annex

_____________________________________________________________________________ D­4

D.2. Definitions and Conceptual Approach

_________________________________________________________________________ D­5

D.2.1. Critical Load Definitions

_______________________________________________________________________________ D­5

D.2.2. Critical Load Analysis Procedures

_______________________________________________________________________ D­6

D.2.3. Target Load Definition________________________________________________________________________________ D­10

August 2008 v DRAFT—DO NOT QUOTE OR CITED.3. Time Frame of Response

__________________________________________________________________________________ D­11

D.3.1. Steady State Critical Loads____________________________________________________________________________ D­12

D.3.2. Dynamic Critical Loads

_______________________________________________________________________________ D­12

D.3.3. Receptor Responses_________________________________________________________________________________ D­13

D.3.4. Deposition Schedules

________________________________________________________________________________ D­14

D.3.5. Long­Term Implications_______________________________________________________________________________ D­18

D.3.6. Steady State and Dynamic Critical Loads

– Complementary Information

________________________________________ D­19

D.4. Calculation of Critical Loads

________________________________________________________________________________ D­20

D.4.1. Empirical Models____________________________________________________________________________________ D­20

D.4.2. Acidification Effects of Sulfur and N

_____________________________________________________________________ D­21

D.4.3. Nutrient Effects of N

_________________________________________________________________________________ D­21

D.4.4. Process­Based Models

_______________________________________________________________________________ D­21

D.4.5. Steady State Models

_________________________________________________________________________________ D­22

D.4.6. Dynamic Models

____________________________________________________________________________________ D­22

D.5. Use of Critical Loads in the U.S.

– Current Status

_______________________________________________________________ D­24

D.5.1. Current Recommendations on Critical Loads Uses in the U.S.

________________________________________________ D­25

D.5.2. Questions and Limitations Regarding Critical Loads Uses in the U.S.

___________________________________________ D­26

D.5.3. Critical Loads Research and Monitoring Needs

____________________________________________________________ D­26

D.5.4. Emissions and Deposition_____________________________________________________________________________ D­26

D.5.5. Soils______________________________________________________________________________________________ D­27

D.5.6. Surface Waters

_____________________________________________________________________________________ D­27

D.5.7. Biological Effects____________________________________________________________________________________ D­28

D.5.8. Critical Loads Models

________________________________________________________________________________ D­28

ANNEX D ­ References

_______________________________________________________________________________ D­30

Annex E. Effects of NO

Y , NH

X , and SO

X on Structures and Materials

__________________________________________ E­1

E.1. Introduction

______________________________________________________________________________________________ E­1

E.2. Environmental Exposures of Materials

_________________________________________________________________________ E­2

E.2.1. Mechanisms of Materials Damage

_______________________________________________________________________ E­2

E.2.2. Deposition Processes

_________________________________________________________________________________ E­2

E.2.3. Chemical Interactions of N and S Oxide Species

____________________________________________________________ E­3

E.2.4. Materials Damage Experimental Techniques

_______________________________________________________________ E­4

E.3. Effects on Dyes and Textiles_________________________________________________________________________________ E­5

E.3.1. Fading of Dyes

______________________________________________________________________________________ E­5

E.3.2. Degradation of Textile Fibers

___________________________________________________________________________ E­5

E.4. Effects on Plastics and Elastomers____________________________________________________________________________ E­5

E.5. Effects on Metals__________________________________________________________________________________________ E­6

E.5.1. Role of NO Y

, NH X

, and SOX in the Corrosion Process________________________________________________________ E­6

E.5.2. Effect on Economically Important Metals

__________________________________________________________________ E­7

E.5.3. Effects on Electronics

_________________________________________________________________________________ E­9

E.6. Effects on Paints

__________________________________________________________________________________________ E­9

E.7. Effects on Stone and Concrete

______________________________________________________________________________ E­10

E.8. Effects of NO X on Paper and Archival Materials_________________________________________________________________ E­12

E.9. Costs of Materials Damage from NO

Y , NH X

, and SOX

___________________________________________________________ E­13

E.10. Summary______________________________________________________________________________________________ E­13

ANNEX E ­ References

_______________________________________________________________________________ E­17

Annex F. Valuation of the Environmental Effects of N and S (non­materials)___________________________________ F­1

F.1. Introduction

______________________________________________________________________________________________ F­1

F.1.1. Valuation in the Context of NO X and SO X

__________________________________________________________________ F­1

F.1.1.1. Ecosystem Services

____________________________________________________________________________ F­2

F.1.1.2. Use of the Valuation Literature to Define Adversity

____________________________________________________ F­4

F.1.1.3. Methods for Selecting Literature for this Assessment

__________________________________________________ F­5

F.2. Conceptual Framework

_____________________________________________________________________________________ F­7

F.2.1. Taxonomy of Values for Environmental Goods and Services___________________________________________________ F­7

F.2.2. Welfare Economics

___________________________________________________________________________________ F­9

F.2.3. Benefit Estimation Approaches

_________________________________________________________________________ F­11

F.3. Valuation of forests and terrestrial ecosystems

_________________________________________________________________ F­15

F.3.1. Use Values

________________________________________________________________________________________ F­15

F.3.2. Total Values________________________________________________________________________________________ F­18

August 2008 vi DRAFT—DO NOT QUOTE OR CITEF.3.3. National­Scale Valuation

______________________________________________________________________________ F­19

F.3.4. Valuation of Degrees of Injury

__________________________________________________________________________ F­19

F.3.5. Limitations and Uncertainties

__________________________________________________________________________ F­21

F.4. Valuation of Transitional Ecosystems

_________________________________________________________________________ F­23

F.4.1. Use and Non­Use Values

_____________________________________________________________________________ F­23

F.4.2. Limitations and Uncertainties

__________________________________________________________________________ F­25

F.5. Valuation of Aquatic Ecosystems

____________________________________________________________________________ F­25

F.5.1. Acidification

________________________________________________________________________________________ F­27

F.5.2. Eutrophication

______________________________________________________________________________________ F­28

F.5.2.1. Recreation___________________________________________________________________________________ F­29

F.5.2.2. Commercial Fisheries

__________________________________________________________________________ F­31

F.5.2.3. Water Clarity

_________________________________________________________________________________ F­32

F.5.3. Avoided Costs

______________________________________________________________________________________ F­33

F.5.4. Limitations and Uncertainties

__________________________________________________________________________ F­33

F.6. Summary

_______________________________________________________________________________________________ F­34

ANNEX F ­ Glossary

_________________________________________________________________________________ F­45

ANNEX F ­ References

_______________________________________________________________________________ F­52

Au gust 2008 vii DRAFT—DO NOT QUOTE OR CITE

# List of Tables

Table A-3. Parameter Estimates, Probability Levels, and Regression Results for the Chesapeake Bay

Total Nitrogen Sparrow Model_____________________________________________________ A-47

Table A-4. Effect of spatial referencing on measures of regression model performance for predicting total

N flux using the sparrow model. ___________________________________________________ A-48

Table A-5. Summary of N retention rates used in recent WATERSN studies. __________________________ A-52

Table A-6. Some examples of models that could contribute to development of a better understanding of

the ecological efforts of atmospheric S and N deposition, but that are not explicitly addressed

in this Annex.__________________________________________________________________ A-56

Table B-1. N-saturated forests in North America, including estimated N inputs and outputs. _____________ B-136

Table B-2. Summary of measured ANC, pH, and Al concentrations compared with reference values in the

six high-interest areas. _________________________________________________________ B-137

Table B-3. Sources of data and sample sizes for datasets analyzed by Stoddard et al. (2003), along with

estimates of the condition of surface waters in each region in the 1980s.___________________ B-138

Table B-4. Estimates of change in number and proportion of acidic surface waters in acid-sensitive

regions of the North and East, based on applying current rates of change in Gran ANC to

past estimates of population characteristics from probability surveys. _____________________ B-138

Table B-5. Regional trend results for long-term monitoring sites for the period 1990 through 2000. ________ B-139

Table B-6. Slopes of trends in Gran ANC in acidic, low ANC and moderate ANC lakes and streams for

the period 1990–2000. _________________________________________________________ B-139

Table B-7. Changes in key chemical characteristics during periods of record in aquatic systems in Maine. __ B-140

Table B-8. Projected changes (μeq/L) in median values of streamwater chemistry at the regional

modeling sites from 1995 to 2040 in each of the three emissions control strategies, stratified

into two segments of the SAMI region (northeast and southwest) and by physiographic

province. ____________________________________________________________________ B-140

Table B-9. Population estimates of water chemistry percentiles for selected lake populations in the

a western U.S.

________________________________________________________________ B-141

Table B-10. Population estimates of the percentage of lakes in selected subregions of the West with ANC

− and NO 3 within defined ranges. __________________________________________________ B-141

Table B-11. Median streamwater ANC and watershed area of streams in Shenandoah National Park that

have water chemistry and fish species richness data.__________________________________ B-142

Table B-12. Reference levels for the Acidic Stress Index (ASI) based on logistic regression of fish

presence as a function of the sensitive intermediate and tolerant ASI values for brown

bullhead, brook trout, lake trout, and common shiner.__________________________________ B-142

Table B-13. General summary of biological changes anticipated with surface water acidification,

expressed as a decrease in surface water pH. _______________________________________ B-143

Table B-14. Estimated percentage of Adirondack lakes with and Acidic Stress Index exceeding the

reference levels for effects on fish populations, based on diatom-inferred historical (preindustrial) chemistry and present-day measured and inferred acid-base chemistry.___________ B-143

Table B-15. Estimated percentage of Adirondack lakes with acid-base chemistry unsuitable for fish

population survival, based on diatom-inferred historical (pre-industrial) chemistry and

present-day measured and inferred acid-base chemistry._______________________________ B-144

August 2008 viii DRAFT—DO NOT QUOTE OR CITETable B-16. Estimated percentage of the lakes in the Northeast and Upper Midwest, ELS/NSWS target

population with an Acidic Stress Index exceeding the reference levels for fish populations

defined in Table C-12.__________________________________________________________ B-144

Table B-17. Estimated percentage of lakes in the Northeast, ELS/NSWS target populations with acid-

base chemistry unsuitable for fish population survival. _________________________________ B-145

Table B-18. Distribution of acidic stress index values among the NSS-1 Target populations for the mid-

Appalachian region.____________________________________________________________ B-146

Table B-19. Distribution of acidic stress index values among the NSS-1 target populations for the interior

Southeast region. _____________________________________________________________ B-147

Table B-20. Comparison of solution and tissue chemistries at threshold treatment levels where significant

impacts on tree growth or nutrient content were first observed. __________________________ B-147

Table B-21. Overview of selected major processes by which landscape change can alter drainage

water acid-base chemistry_______________________________________________________ B-150

Table B-22. Observed relationships between zooplankton species richness (R) and lakewater ANC. ______ B-150

Table B-23. Threshold response of increased mortality of fish to low pH listed from least sensitive to most

sensitive. ____________________________________________________________________ B-151

Table B-24. Threshold values of pH for various taxa and effects. __________________________________ B-152

Table B-25. Threshold values of Al for various species and effects (form of A not specified for most

studies)._____________________________________________________________________ B-153

2+ Table B-26. The effects of increasing Ca to ameliorate low pH and high Al. _________________________ B-154

Table B-27. Brook trout acidification response categories developed by Bulger et al. (XXXXXXXXXX) for

streams in Virginia (2000). ______________________________________________________ B-154

Table B-28. Partial listing of bioassays demonstrating decreased fish survival in waters with low pH and

(or) elevated aluminum._________________________________________________________ B-155

Table B-29. Mills et al., 1987. Shows effect of various pH on fish forage fish and lake trout.______________ B-156

Table B-30. Range of minimum pH of fish species occurrence in 11 lake surveys. _____________________ B-156

a Table B-31. Studies that either did (yes) or did not (no) yield evidence that acidic deposition affected

certain species of birds _________________________________________________________ B-157

Table B-32. Predicted habitat suitability for lakes in the Algona Model Dataset________________________ B-158

Table B-33. Summary statistics of biological data layers for mercury (Hg) concentrations in fish and

wildlife (μg/g) in the northeastern U.S. and southeastern Canada. ________________________ B-158

Table B-34. Mercury concentrations in avian eggs and tissues and related effects. ____________________ B-159

Table C-1. Estimated percent of total N load to Delaware Bay and Hudson River/Raritan Bay contributed

by atmospheric deposition. ________________________________________________________ C-7

Table C-2. The study site, experimental condition, ecosystem type, N form, amount of N addition and

citations is presented for all studies used in NEE, EC, CH 4 uptake, CH 4 emission and N 2 O

emission meta analyses. _________________________________________________________ C-10

Table C-3. Principal Air Quality Indicator Lichen Species in Oregon and Washington____________________ C-22

Table C-4. Contribution of fens to support of plant species diversity in selected states ___________________ C-23

Table C-5. Summary of additional evidence for N limitation on productivity of freshwater ecosystems. ______ C-27

Table C-6. Summary of effects of N enrichment on aquatic biota in freshwater ecosystems. ______________ C-31

Table C-7. Essential ecological attributes and reporting categories__________________________________ C-37

August 2008 ix DRAFT—DO NOT QUOTE OR CITETable C-8. Primary goods and services provided by ecosystems. ___________________________________ C-40

Table C-9. Ecological effects of N deposition described for study sites in the Western U.S. _______________ C-48

Table D-1. An example of the matrix of information that must be considered in the definition and

calculation of critical loads. ________________________________________________________ D-7

Table D-2. Biological indicators for the effects of elevated N deposition and related empirical critical loads

for major ecosystem types (according to the Eunis classification) occurring in Europe. _________ D-29

Table E-1. Studies on corrosive effects of NO Y /NH 3 /SO X effects on metals. ___________________________ E-14

Table E-2. Studies on corrosive effects of NO /NH 3 /SO X onstone.__________________________________ E-15

Y

Table F-2. Economic effects of ozone and other pollutants on agriculture, as reported in the 1996 ozone

criteria document. ______________________________________________________________ F-36

Table F-3. Economic effects of ozone on marketable benefits from forests. ___________________________ F-37

Table F-4. Forecasted average values for select activities, per day per person in 1996. __________________ F-37

Table F-5. Typical impacts of specific pollutants on the visual quality of forests. ________________________ F-37

Table F-6. Economic valuation studies related to forest aesthetics. __________________________________ F-38

Table F-7. Summary of the monetized estimates of the annual value of forest quality changes ____________ F-39

Table F-8. Estimated value of avoiding forest damage in the U.S. ___________________________________ F-40

Table F-9. Ecological wetland functions, economic goods and services, types of value, and applicable

valuation methods. _____________________________________________________________ F-40

August 2008 x DRAFT—DO NOT QUOTE OR CITE

# List of Figures

Figure A-1. Location of acid-sensitive regions of the northern and eastern U.S. _________________________ A-5

Figure A-2. Location LTM sites used in the 2003 Surface Water report. _______________________________ A-7

2− Figure A-3. Long-term record of SO 4 concentration in streamwater and precipitation at Watershed 6 of

HBEF. _______________________________________________________________________ A-21

Figure A-4. Location of sampling stations in Sleepers River watershed, Vermont. ______________________ A-25

Figure A-5. SWAS-VTSS Program Study Sites._________________________________________________ A-30

Figure A-6. Conceptual structure of the MAGIC model showing major pools and fluxes included in

simulation of effects of S and N deposition. __________________________________________ A-37

Figure A-7. Structure of the PnET-BGC model illustrating the compartments and flow paths of carbon and

nutrients (C/Nut) within the model. _________________________________________________ A-43

Figure A-8. Mathematical form of the SPARROW model. _________________________________________ A-46

Figure A-9. Schematic diagram of the WATERSN approach to estimate the contribution made by different

N sources to the total N inputs an estuary. ___________________________________________ A-50

Figure A-10. WATERSN model estimates of anthropogenic N inputs to the estuaries of the northeastern

U.S., in kilograms per hectare per year. _____________________________________________ A-53

Figure B-1. MAGIC model hindcast estimates of pre-industrial pH versus diatom-inferred pH for 33

statistically selected Adirondack lakes: ______________________________________________ B-20

Figure B-3. Location and percentage of acidic surface waters in U.S. high-interest subpopulations with

respect to acidic deposition effects._________________________________________________ B-31

Figure B-4. Measured concentration of SO 4 2− in selected representative lakes and streams in six regions

of the U.S. during the past approximately 15 years. ____________________________________ B-41

Figure B-5. Summary of regional trends in surface water chemistry from 1990 through 2000 in regions

covered by the Stoddard et al. (2003) report. _________________________________________ B-42

Figure B-6. Estimated time series of S deposition at one example watershed in the SW Adirondack

Mountains used by Sullivan et al. (2006) as input to the MAGIC model for projecting past and

future changes in lakewater chemistry attributable to acidic deposition. _____________________ B-45

Figure B-7. Simulated cumulative frequency distributions of lakewater ANC at three points in time for the

population of Adirondack lakes. ___________________________________________________ B-46

Figure B-8. Map showing simulated changes in streamwater ANC from 1995 to 2040 in response to the

SAMI A2 emmissions control strategy, representing existing emissions control regulations. _____ B-52

Figure B-9. Major geomorphic units and locations of lakes sampled in the Western Lake Survey. Those

areas known to contain sensitive lake resources are shaded with cross-hatching._____________ B-59

Figure B-10. Estimated percent changes in the total deposition of sulfur, reduced N, and nitrate-N at

MAGIC modeling sites from 1995 to 2040 under each of the emissions control strategies. ______ B-61

Figure B-11. Relationship between mean summer and spring ANC values at LTM sites in New England,

the Adirondacks, and the Northern Appalachian Plateau.________________________________ B-69

Figure B-12. Minimum streamwater ANC sampled at each site during each year versus median spring

ANC for all samples collected at that site during that spring season. _______________________ B-71

Figure B-13. Relationship between ANC and runoff for streamwater samples collected at intensively

studied sites in Shenandoah National Park. __________________________________________ B-73

August 2008 xi DRAFT—DO NOT QUOTE OR CITEFigure B-14. Decrease in ANC and pH and increase in dissolved aluminum in response to a sharp

increase in streamflow in three watersheds within Shenandoah National Park during a

hydrological episode in 1995. _____________________________________________________ B-74

Figure B-15. Length-adjusted condition factor (K), a measure of body size in blacknose dace ( Rhinichthys

atratulus ) compared with mean stream pH among 11 populations (n = 442) in Shenandoah

National Park. _________________________________________________________________ B-79

Figure B-16. Mean residual number of species per lake for lakes in Ontario, by pH interval. ______________ B-82

The residual number of species for a lake is the deviation of the observed number from the number

predicted by lake area. __________________________________________________________ B-82

Figure B-17. Number of fish species per lake or stream versus acidity statues, expressed either as pH or

ANC. (A) Adirondack lakes (Sullivan et al., 2006a).; (B) streams in Shenandoah National

Park (Bulger, 1999). The data for the Adirondacks are presented as mean and range of

species richness within 10 μeq/L ANC categories, based on data collected by the Adirondack

Lakes Survey Corporation. _______________________________________________________ B-83

Figure B-18. Number of fish species among 13 streams in Shenandoah National Park. Values of ANC are

means based on quarterly measurements, 1987–94. ___________________________________ B-84

Figure B-19. Life stages of brook trout. _______________________________________________________ B-88

Figure B-20. Example model application.______________________________________________________ B-98

2− − 2+ Figure B-21. Time series data for SO 4 , NO 3 , base cations [Ca +Mg 2+ ], Gran ANC, pH, and DOC in

example Long Term Monitoring Lakes and streams that have relatively low ANC.____________ B-106

Figure C-1. Schematic representation of the response of vegetation to nutrient addition. _________________ C-16

Figure C-2. Distribution of alpine vegetation in three western regions that are in close proximity to urban

and agricultural sources of atmospheric N emissions: a) the Denver-Fort Collins region of

Colorado, b) the Seattle-Tacoma region of Washington, and c) the Fresno-Los Angeles area

of California (the blue line on the map is the California/Nevada border). Alpine vegetation in

these areas is sensitive to nutrient enrichment effects from atomospheric N deposition. ________ C-19

Figure C-3. Number of nationally rare species versus standing crop in each of 401 quadrants from

wetlands in Ontario, Quebec, and Nova Scotia. _______________________________________ C-23

Figure C-5 Linkages among various ecosystem goods and services (food, water, biodiversity, forest

products) and other driving forces (climate change).____________________________________ C-41

Figure C-6. Effect of watershed defoliation by the gypsy moth caterpillar on NO 3 − flux in streamwater. ______ C-45

Figure D-1. Conceptual patterns of pollutant deposition effects on a chemical indicator and a

corresponding biological indicator during increasing and decreasing deposition. ______________ D-15

Figure D-2. Pollutant deposition patterns for defining the temporal parameters of dynamic critical loads

analyses. _____________________________________________________________________ D-16

Figure F-1. Illustration chart of the assessment.__________________________________________________ F-2

Figure F-2. Reviewed studies by ecosystem addressed. ___________________________________________ F-7

Figure F-3. Reviewed studies by ecological endpoint. _____________________________________________ F-8

Figure F-4. Taxonomy of values for environmental goods and services. _______________________________ F-8

Figure F-5. Linkages from emissions to forest aesthetics. _________________________________________ F-16

Figure F-6. Geographic distribution of forested areas historically affected by air pollution. ________________ F-17

August 2008 xii DRAFT—DO NOT QUOTE OR CITE

# Acronyms and Abbreviations

ACCENT Atmospheric Composition Change: the European Network of

excellence

AIRMoN Atmospheric Integrated Research Monitoring Network

AIRS Atmospheric Infrared Sounder (instrument)

Al aluminum

Al 3+ aluminum ion

Al i inorganic aluminum

Al n+ aluminum ion

Al

o organic aluminum

Al(OH) 3 aluminum hydroxide

ALSC Adirondack Lake Survey Corporation

ALTM Adirondack Long Term Monitoring

AMD acid mine drainage

ANC acid neutralizing capacity

AOD aerosol optical depth

AQCD Air Quality Criteria Document

AQEG Air Quality Expert Group

AQI Air Quality Index

AQS Air Quality System (database)

Ar argon

ARP Acid Rain Program

ARS Agricultural Research Service

As arsenic

ASI Acid Stress Index

asl above sea level

ATMOS Atmospheric Trace Molecule Spectroscopy

ATTILA type of Lagrangian model

AUSPEX Atmospheric Utility Signatures, Predictions, and Experiments

AVIRIS Airborne Visible and Infrared Imaging Spectrometer

Ba barium

BBW Bear Brook Watershed

BBWM Bear Brook Watershed, Maine

BC black carbon

BCS base-cation surplus

BGC BioGeoChemical (model)

B-IBI benthic index of biological integrity

BMPs best management practices

BNF bacterial nitrogen fertilization

Br bromine

Br ! bromine ion

Br 2 molecular bromine

BrCl bromine chloride

August 2008 xiii DRAFT—DO NOT QUOTE OR CITEBrO bromine oxide

BUV Backscatter Ultraviolet Spectrometer

BUVD Beneficial Use Values Database

C

12 C

13 C carbon; concentration

carbon-12, stable isotope of carbon

carbon-13, stable isotope of carbon

C

a ambient air concentration

Ca calcium

2+ Ca calcium ion

CAA Clean Air Act

CAAA Amendments to the Clean Air Act

CAAAC Clean Air Act Advisory Committee

CaCl 2 calcium chloride

CaCO 3 calcium carbonate

CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation

(satellite)

Ca(NO 3 ) 2 calcium nitrate

Ca(OH) 2 calcium hydroxide

CAPMoN Canadian Air and Precipitation Monitoring Network

CaSO 4 · 2H 2 O gypsum

CASTNet Clean Air Status and Trends Network

CB4 Carbon Bond 4 (model)

Cd cadmium

CEC cation exchange capacity

CENTURY model that simulates carbon, nitrogen, phosphorus, sulfur, and water

dynamics in the soil-plant system at monthly intervals over time scales

of centuries and millennia

CFCs chlorinated fluorocarbons

CG cloud-to-ground (lightning flash)

chl a chlorophyll a

CH 4 methane

C 2 H 4 ethene

C 2 H 6 ethane

C 5 H 8 isoprene

CH 3 CHO acetaldehyde

CH 3 C(O) acetyl radical

CH 3 C(O)OO acetyl peroxy radical

CH 2 I 2 diiodomethane

CH 2 O formaldehyde

CH 3 OOH methyl hydroperoxide

CH 3 -S-CH 3 dimethylsulfide, DMS

CH 3 -S-H methyl mercaptan

(CH 3 ) 2 SO dimethyl sulfoxide, DMSO

CH 3 SO 3 H methanesulfonic acid

CH 3 -S-S-CH 3 dimethyl disulfide, DMDS

C i interstitial air concentration

A ugust 2008 xiv DRAFT—DO NOT QUOTE OR CITECL critical load

Cl chlorine

Cl ! chlorine ion

Cl 2 molecular chlorine

CLaMS type of Lagrangian model

CloudSat NASA Earth observation satellite

ClNO 2 nitryl chloride

CMAQ Community Multiscale Air Quality (modeling system)

CMSA consolidated metropolitan statistical area

CO carbon monoxide

CO 2 carbon dioxide

! CO 3 carbonate

CONUS continental United States

CPUE catch per unit effort

CRREL U.S. Army Cold Regions Research and Engineering Laboratory

CS Consumer surplus

CS 2 carbon disulfide

CSS coastal sage scrub (ecosystem)

CTM chemical transport model

Cu copper

CV contingent valuation

CVM contingent valuation method

) difference; change

DayCent model for daily biogeochemistry for forest, grassland, cropland, and

savanna systems

DayCent-Chem combination of DayCent-Chem and PHREEQC models

DC dichotomous choice

DDRP Direct Delayed Response Project

DDT Damage Delay Time

DECOMP decomposition model based on soil-plant system dynamics

DEP Department of Environmental Protection

DIC dissolved inorganic carbon

DIN dissolved inorganic nitrogen

DMDS dimethyl disulfide, CH 3 -S-S-CH 3

DMS dimethyl sulfide, CH 3 -S-CH 3

DMSO dimethylsulfoxide

DNDC Denitrification-Decomposition (model)

DO dissolved oxygen

DOC dissolved organic carbon

DON dissolved organic nitrogen

EBB East Bear Brook

EC elemental carbon

EEAs Essential Ecological Attributes

ELA Experimental Lakes Area

ELS Eastern Lakes Survey

EMAP Environmental Monitoring and Assessment Program

A ugust 2008 xv DRAFT—DO NOT QUOTE OR CITEEMEFS Eulerian Model Evaluation Field Study

EMEP Co-operative Programme for Monitoring and Evaluation of the Longrange Transmission of Air Pollutants in Europe

EMF ectomycorrhizal fungi

EOS Earth Observation System

EPA U.S. Environmental Protection Agency

EPT Ephemeroptera-Plecoptera-Tricoptera (index)

ERP Episodic Response Project

ESA European Space Agency

EVRI Environmental Valuation Reference Inventory

F flux

F ! fluorine ion

FAB First-order Acidity Balance model

FACE free-air CO 2 enrichment (studies)

Fe iron

FePO 4 iron phosphate

FeS iron sulfide

F-factor fraction of the change in mineral acid anions that is neutralized by base

cation release

FHM Forest Health Monitoring

FIA Forest Inventory and Analysis (program)

FISH Fish in Sensitive Habitats (project)

FLEXPART type of Lagrangian model

ForSAFE three-component model using nitrogen, carbon cycling, and soil

chemistry

FRM Federal Reference Method

FTIR Fourier Transform Infrared Spectroscopy

FW2 black carbon soot

F

x flux

γ N 2 O 5 reaction potential coefficient for N 2 O 5

GAW Global Atmospheric Watch (program)

GCE Goddard Cumulus Ensemble (model)

GDP gross domestic product

GEOS Goddard Earth Observing System

GEOS-Chem Goddard Earth Observing System (with global chemical transport

model)

GEOS-1DAS Goddard Earth Observing System Data Assimilation System

GFED Global Fire Emissions Database

GHG greenhouse gas

GOES Geostationary Operational Environmental Satellites

GOME Global Ozone Monitoring Experiment

g

s stomatal conductance

GtC global ton carbon

Gton global ton

GWP global warming potential

H hydrogen; hydrogen atom

2 H hydrogen-2, deuterium, stable isotope of hydrogen

August 2008 xvi DRAFT—DO NOT QUOTE OR CITEH + proton, hydrogen ion; relative acidity

ha hectare

HAPs hazardous air pollutants

HBEF Hubbard Brook Experimental Forest

HBES Hubbard Brook Ecosystem Study

HBN Hydrologic Benchmark Network

HC hydrocarbon

HCHO formaldehyde

HCl hydrochloric acid

Hg mercury

HNO 2 , HONO nitrous acid

HNO 3 , HOONO nitric acid

HNO 4 pernitric acid

HO 2 hydroperoxyl radical

H 2 O 2 hydrogen peroxide

HO 2 NO 2 peroxynitric acid

HOBr hypobromous acid

HOCl hypochlorous acid

HOX hypohalous acid

HP hedonic pricing

HPVM

! HSO 3 bisulfate ion

! HSO 4 sulfuric acid ion

H 2 S hydrogen sulfide

H 2 SO 3 sulfurous acid

H 2 SO 4 sulfuric acid

HTC

h v photon with energy at wavelength v

I iodine

I 2 molecular iodine

IA Integrated Assessment

IADN Integrated Atmospheric Monitoring Deposition Network

IC intracloud (lightning flash)

ICARTT International Consortium for Atmospheric Research on Transport and

Transformation

ILWAS Integrated Lake-Watershed Acidification Study

IPC International Cooperative Programme

IEc Industrial Economics

IIASA International Institute for Applied Systems Analysis

IMPROVE Interagency Monitoring of Protected Visual Environments

ICARTT International Consortium for Atmospheric Research on Transport and

Transformation

INO 3 iodine nitrate

INTEX-NA Intercontinental Chemical Transport Experiment - North America

IO iodine oxide

IPCC Intergovernmental Panel on Climate Change

A ugust 2008 xvii DRAFT—DO NOT QUOTE OR CITEIPCC-AR4 Intergovernmental Panel on Climate Change 4th Assessment Report

IPCC-TAR Intergovernmental Panel on Climate Change 3rd Assessment Report

IQR interquartile range

IR infrared

ISA Integrated Science Assessment

J flux from a leaf

JPL Jet Propulsion Laboratory

JRGCE Jasper Ridge Global Climate Change Experiment

K potassium

K + potassium ion

K

a dissociation constant

K

b dissociation constant

K

H Henry’s Law constant in M atm-1

KNO 3 potassium nitrate

K

w ion product of water

LAF Lake Acidification and Fisheries

LAR leaf-area ratio

LB laboratory bioassay

LC 0.01 lethal concentration at which 0.01% of exposed animals die

LD 33 lethal dose at which 33% of exposed animals die

LDH lactic acid dehydrogenase

LIDAR Light Detection and Ranging (remote sensing system)

LIF laser-induced fluorescence

LIMS Limb Infrared Monitor of the Stratosphere

LOD limit of detection

LP long-path

LRTAP Long Range Transport of Air Pollution

LTER Long-Term Ecological Research (program)

LTM Long-Term Monitoring (project)

M air molecule

MA Millennium Ecosystem Assessment

MAGIC Model of Acidification of Groundwater in Catchments (model)

MAHA Mid-Atlantic Highlands Assessment of streams

MAQSIP Multiscale Air Quality Simulation Platform (model)

MAT moist acidic tundra

MAX-DOAS multiple axis differential optical absorption spectroscopy

MBL marine boundary layer

MDN Mercury Deposition Network

MeHg methylmercury

MEM model ensemble mean

μeq microequivalent

Mg magnesium

Mg 2+ magnesium ion

MIMS membrane inlet mass spectrometry

MM5 National Center for Atmospheric Research/Penn State Mesoscale

Model, version 5

August 2008 xviii DRAFT—DO NOT QUOTE OR CITEMn manganese

MOBILE6 Highway Vehicle Emission Factor Model

MODIS Moderate Resolution Imaging Spectroradiometer

MOPITT Measurement of Pollution in the Troposphere

MOZAIC Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft

MOZART Model for Ozone and Related Chemical Tracers

MPAN peroxymethacrylic nitrate

MSA metropolitan statistical area

Mt million tons

N nitrogen

N, n number of observations

14 N nitrogen-14, stable isotope of nitrogen

15 N nitrogen-15, stable isotope of nitrogen

N 2 molecular nitrogen; nonreactive nitrogen

NA not available; insufficient data

Na sodium

+ Na sodium ion

NAAQS National Ambient Air Quality Standards

NaCl sodium chloride

NADP National Atmospheric Deposition Program

Na 2 MoO 4 sodium molybdate

NAMS National Air Monitoring Stations

NANI Net anthropogenic nitrogen inputs

NAPAP National Acid Precipitation Assessment Program

NASQAN National Stream Quality Accounting Network

NARSTO program formerly known as North American Regional Strategy for

Atmospheric Ozone

NAS National Academy of Sciences

NASA National Aeronautics and Space Administration

Na 2 SO 4 sodium sulfate

NASQAN National Stream Quality Accounting Network

NATTS National Air Toxics Trends (network)

NAWQA National Water Quality Assessment (program)

NCore National Core Monitoring Network

NEE net ecosystem exchange

NEG/ECP New England Governors and Eastern Canadian Premiers

NEI National Emissions Inventory

NEON National Ecological Observatory Network

NEP net ecosystem productivity

NFI net factor income

NH 3 ammonia

NH 2 amino (chemical group)

+ NH 4 ammonium ion

NH 4 Cl ammonium chloride

NH 4 NO 3 ammonium nitrate

(NH 4 ) 2 SO 4 ammonium sulfate

A ugust 2008 xix DRAFT—DO NOT QUOTE OR CITENH X + category label for NH 3 plusNH 4

NH Y total reduced nitrogen

Ni nickel

NILU Norwegian Institute for Air Research

NITREX NITRogen saturation EXperiments

nitro-PAH nitro-polycyclic aromatic hydrocarbon

NLCD National Land Cover Data

NMOC nonmethane organic compound

NO nitric oxide

NO 2 nitrogen dioxide

! NO 2 nitrite

! NO 3 nitrate

N 2 O nitrous oxide

N 2 O 5 dinitrogen pentoxide

NOAA U.S. National Oceanic and Atmospheric Administration

NOAA-ARL U.S. National Oceanic and Atmospheric Administration Air Resources

Laboratory

NOAEL no-observed-adverse-effect level

NOEC no-observed-effect concentration

NO X sum of NO and NO 2

NO Y sum of NO X and NO Z ; odd nitrogen species; total oxidized nitrogen

NO Z sum of all inorganic and organic reaction products of NO X (HONO,

HNO 3 , HNO 4 , organic nitrates, particulate nitrate, nitro-PAHs, etc.)

NPOESS National Polar-orbiting Operational Environmental Satellite System

NPP net primary production

NPS National Park Service

N

r reactive nitrogen

NRC National Research Council

NS nonsignificant

NSF National Science Foundation

NSS National Stream Survey

nss non-sea salt

NSTC National Science and Technology Council

NSWS National Surface Water Survey

NTN National Trends Network

NuCM nutrient cycling model

O 2 molecular oxygen

O 3 ozone

16 O oxygen-16, stable isotope of oxygen

18 O oxygen-18, stable isotope of oxygen

19 O oxygen-19, radioactive isotope of oxygen

OC organic carbon

OCO Orbiting Carbon Observatory

OCS carbonyl sulfide

O( 1 D) electronically excited oxygen atom

OH hydroxyl radical

A ugust 2008 xx DRAFT—DO NOT QUOTE OR CITEO M I O z o n e M o n ito r in g I n s tr u m e n t

O ( 3 P ) g r o u n d

- s ta te o x y g e n a to m

P p h o s p h o r u s

P

, p p r o b a b ilit y v a l u e

P 1 1 s t p e r c e n tile

P 5 5 th p e r c e n tile

P 9 5 9 5 th p e r c e n tile

P 9 9 9 9 th p e r c e n tile

P

A H s p o ly c y c lic a r o m a tic h y d r o c a r b o n s

P

A M S P h o to c h e m ic a l A s s e s s m e n t M o n ito r in g S ta tio n s

P

A N p e r o x y a c e t y l n itr a te

P

A N s p e r o x y a c y l n itr a te s

P

A R A S O L P o la r iz a tio n a n d A n i s o tr o p y o f R e f le c ta n c e s f o r A t m o s p h e r i c S c ie n c e s

c o u p le d w it h O b s e r v a t io n s f r o m a L id a r ( s a te llite )

P b le a d

P B L p la n e ta r y b o u n d a r y la y e r

P C p a y m e n t c a r d

P C B s p o ly c h lo r in a te d b ip h e n y l c o m p o u n d s

p H r e la tiv e a c id it y

P ( H N O 3 ) p r o d u c tio n o f n itr ic a c id

P H R E E Q C m o d e l f o r s o il a n d w a te r g e o c h e m ic a l e q u ilib r i u m

P I R L A P a le o c o lo g ic a l I n v e s ti g a tio n o f R e c e n t L a k e A c id i f ic a tio n ( p r o j e c ts )

p K

a d is s o c ia tio n c o n s ta n t

P M p a r tic u la te m a t te r

P M 2

. 5 p a r tic u la te m a t te r w ith a e r o d y n a m ic d ia m e te r o f # 2

. 5 μ m

P M 1 0 p a r tic u la te m a t te r w ith a e r o d y n a m ic d ia m e te r # 1 0 μ m

P M 1 0

- 2

. 5 p a r tic u la te m a t te r w ith a e r o d y n a m ic d ia m e te r b e t w e e n 1 0 a n d 2

. 5 μ m

P M

- C A M x C o m p r e h e n s i v e A ir Q u a li t y M o d e l w ith e x te n s io n s a n d w i th p a r tic u la te

m a tte r c h e m i s tr y

P n E T P h o to s y n t h e s is a n d E v a p o T r a n s p ir a tio n ( m o d e l)

P n E T

- B G C P h o to s y n t h e s is a n d E v a p o T r a n s p ir a tio n

- B io G e o C h e m ic a l ( m o d e l)

P n E T

- C N P h o to s y n t h e s is a n d E v a p o T r a n s p ir a tio n m o d e l o f C

, w a te r

, a n d N

b a la n c e s

P n E T

- N

- D N D C P h o to s y n t h e s is a n d E v a p o T r a n s p ir a tio n

- D e n itr i f ic a tio n

- D e c o m p o s i tio n

( m o d e l)

p N O 3 ! p a r tic u la te n itr a te

P ( O 3 ) p r o d u c tio n o f O 3

P O 4 !

, P O 4 3

! p h o s p h a te

P O P s p e r s is te n t o r g a n ic p o llu ta n ts

p p b p a r ts p e r b illio n

P P N p e r o x y p r o p io n y l n itr a te

p p t p a r ts p e r tr illio n

P R B p o lic y r e le v a n t b a c k g r o u n d

P R E

- S T O R M P r e lim i n a r y R e g io n a l E x p e r i m e n t f o r S T O R M

P R O F I L E m o d e l u s i n g s o il m in e r a lo g y a s in p u t

P S p r o d u c e r s u r p lu s

A ugust 2008 xxi DRAFT—DO NOT QUOTE OR CITE2

! pSO 4 particulate sulfate

2

! )P(SO 4 production of sulfate

Q flow rate; discharge

Q 10 temperature coefficient

QAPP Quality Assurance Project Plan

R generic organic group attached to a molecule

R 2 coefficient of determination

2 r correlation coefficient

R

a aerodynamic resistance

R b boundary layer resistance

R

c internal resistance

RADM Regional Acid Deposition Model

RAMS Regional Atmospheric Modeling System

RAPS Regional Air Pollution Study

RCOO-s strongly acidic organic anions

RC(O)OO organic peroxy radical

RDT Recovery Delay Time

REMAP Regional Environmental Monitoring and Assessment Program

RH relative humidity

RLTM Regional Long-Term Monitoring

RMCC Research and Monitoring Coordinating Committee

RMSE root mean squared error

RO 2 organic peroxyl; organic peroxy

RONO 2 organic nitrate

RO 2 NO 2 peroxynitrate

RP revealed preferences

RR X lognormal-transformed response ratio

RuBisCO ribulose-1,5-bisphosphate carboxylase/oxygenase

S sulfur

32 S sulfur-32, stable isotope of sulfur

34 S sulfur-34, stable isotope of sulfur

35 S sulfur-35, radioactive isotope of sulfur

SAA sum of mineral acid anion concentrations

SAFE Soil Acidification in Forest Ecosystems (model)

SAMAB Southern Appalachian Man and the Biosphere (program)

SAMI Southern Appalachian Mountains Initiative

SAO Smithsonian Astrophysical Observatory

SAPRAC Statewide Air Pollution Research Center

SBC sum of base cation concentrations

SBUV Solar Backscatter Ultraviolet Spectrometer

SC safe concentration

SCAQS Southern California Air Quality Study

SCIAMACHY Scanning Imaging Absorption Spectrometer for Atmospheric

Chartography

Se selenium; standard error

A ugust 2008 xxii DRAFT—DO NOT QUOTE OR CITESEARCH Southeastern Aerosol Research and Characterization Study (monitoring

program)

Si silicon

SIP State Implementation Plan

SJAQS San Joaquin Valley Air Quality Study

SLA specific leaf area

SLAMS State and Local Air Monitoring Stations

SMART Simulation Model for Acidification’s Regional Trends (model)

SMB Simple Mass Balance (model)

SO sulfur monoxide

SO 2 sulfur dioxide

SO 3 sulfur trioxide

2

! SO 3 sulfite

2

! SO 4 sulfate ion

S 2 O disulfur monoxide

SONEX Subsonics Assessment Ozone and Nitrogen Oxides Experiment

SOS Southern Oxidant Study

SOS/T State of Science/Technology (report)

SO X sulfur oxides

SP stated preferences

SPARROW SPAtially Referenced Regressions on Watershed Attributes (model)

Sr strontium

86 Sr strontium-86, stable isotope of strontium

87 Sr strontium-87, stable isotope of strontium

SRB sulfate-reducing bacteria

SRP soluble reactive phosphorus

SSWC Steady State Water Chemistry (model)

STE stratospheric-tropospheric exchange

STN Speciation Trends Network

SUM06 seasonal sum of all hourly average concentrations $ 0.06 ppm

SVOC semivolatile organic compound

SWAS Shenandoah Watershed Study

τ atmospheric lifetime

T time; duration of exposure

TAF Tracking and Analysis Framework (model)

T air air temperature

TAMM Timber Assessment Market Model

TAR Third Assessment Report

TC total carbon; travel cost

TCM travel cost method

TDLAS Tunable Diode Laser Absorption Spectrometer

Tg teragram

TIME Temporally Integrated Monitoring of Ecosystems (program)

TN total nitrogen

TOMS Total Ozone Mapping Spectrometer

TOR tropospheric ozone residual

A ugust 2008 xxiii DRAFT—DO NOT QUOTE OR CITETP total phosphorus

TRACE-P Transport and Chemical Evolution over the Pacific

TSI timber-stand improvement

TSS total suspended solids

T

water water temperature

UMD-CTM University of Maryland Chemical Transport Model

UNECE United Nations Economic Commission for Europe

USDA U.S. Department of Agriculture

USFS U.S. Forest Service

USGS U.S. Geological Survey

UV ultraviolet

UV-A ultraviolet radiation of wavelengths from 320 to 400 nm

UV-B ultraviolet radiation of wavelengths from 280 to 320 nm

V d deposition rate

VOC volatile organic compound

VSD Very Simple Dynamic (soil acidification model)

VTSSS Virginia Trout Stream Sensitivity Study

WARMS Waterfowl Acidification Response Modeling System

WATERSN Watershed Assessment Tool for Evaluating Reduction Scenarios for

Nitrogen

WBB West Bear Brook

WEBB Water, Energy, and Biogeochemical Budgets

WFPS water-filled pore space

WGE Working Group on Effects

WLS Western Lakes Survey

WMO World Meteorological Organization

WMP Watershed Manipulation Project

WSA Wadeable Stream Assessment (survey)

wt % percent by weight

WTA willingness-to-accept

WTP willingness-to-pay

XNO 3 nitrate halogen-X salt

XO halogen-X oxide

Zn zinc

ZnO zinc oxide

August 2008 xxiv DRAFT—DO NOT QUOTE OR CITE

# A.1. Introduction

A tremendous amount of research has been conducted in the U.S., and elsewhere, over the past

three decades on the ways in which atmospheric deposition of sulfur (S) and nitrogen (N) affect the

health, condition, and vitality of aquatic, transitional, and terrestrial ecosystems. Much of this work has

focused on developing a better understanding of acidification and nutrient enrichment processes. Some of

this work has been highly quantitative, allowing researchers to determine key process rates in multiple

ecosystem compartments. Nevertheless, quantification of overall ecosystem response requires a higher

level of process rate aggregation. It is important to develop quantitative understanding of the extent of

past ecosystem effects in response to atmospheric S and N deposition, the extent to which conditions will

worsen or recover under continued or reduced deposition levels, and the sustained loads of deposition that

would be required to prevent further ecosystem damage and to allow damaged ecosystems to recover.

This kind of quantitative understanding cannot evolve directly out of process-based research. It requires

development of mathematical models that encode process knowledge and link it in such a way as to

produce quantitative estimates of change in resource conditions over time in response to changes in the

major forcing functions, including atmospheric deposition, climate, and landscape disturbance. As

described in this Annex, many such models have been developed and used to estimate past and future

changes in ecosystem condition. Such models cannot be validated, per se

, because environmental systems

are never closed and because important processes yield conflicting, often opposing, results. Therefore, a

model can produce the right answer for the wrong reason (cf. Oreskes, 1994). Similarly, a particular

process may not be important at a particular site where a model is tested, but assume much greater

importance elsewhere. For these reasons, it is critical that environmental models be tested and confirmed

at multiple locations that exhibit differing conditions and pollutant loads before they are used as the

foundation for public policy (Sullivan, 2000).

Some of the best data with which to test and confirm environmental models are derived from

long-term monitoring sites. These are locations where one or more attributes of a natural ecosystem

compartment (i.e., surface water, soil, plants) is periodically sampled and analyzed over a long period of

time. Such data are often especially valuable for sites which experience rather large changes in one of the

forcing functions (often atmospheric deposition). This enables evaluation of the extent to which the model

accurately captures the dynamics of ecosystem response(s) that occur. Because many environmental 

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# Annex A. Ecosystem Monitoring

and Models

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32 attributes undergo rather substantial intra- and inter-annual variability in response to climatic variation

and other changes, a long period of record is required before a monitoring data set can be used for

evaluation of ecosystem response or for model confirmation.

Long-term monitoring data provide not only data with which to test model projections, but also a

reality check on scientific understanding of damage and recovery processes. If observed (monitored)

changes are not in agreement with process understanding, it is possible or perhaps likely that one or more

key processes is not well-understood or well-formulated in the model.

This Annex summarizes the primary long-term monitoring sites and programs in the U.S., and the

principal mathematical models used to simulate environmental responses to atmospheric S a nd N

deposition. Quantitative data derived from the model projections and from trends analyses of the

monitoring data provide an important part of the foundation for evaluating the past, current, and future

effects of S and N deposition, and expected recovery as emissions levels decrease in the future.



# A.2. Ecosystem Monitoring

The effects of acidic and N nutrient deposition on ecosystems require long-term study. Changes in

ecosystems often occur gradually, and sustained monitoring of key variables provides the principal record

of change over time. Monitoring data are also useful for establishing a baseline of resource conditions and

determining if short-term events were unusual or extreme (Lovett, 2007). There are limited monitoring

programs and data to document ecosystem responses to changes in atmospheric deposition in the past. It

is often difficult to sustain funding for ecosystem monitoring, perhaps because results are produced

slowly and because results are seldom viewed as novel. Nevertheless, monitoring data provide some of

the best means for evaluating the completeness of the scientific knowledge base and for testing how

robust our projections of future conditions might be. This section describes some of the more important

and useful monitoring programs for evaluating the effects of N and S d eposition on ecosystems in the

U.S.

There are long-term monitoring sites scattered throughout the U.S. where samples are periodically

collected and analyzed to determine the condition of aquatic, transitional, or terrestrial ecosystem

elements. Some have been in operation for only a short period of time; others have continued for decades.

None extend back far enough to have documented resource conditions prior to the advent of high levels of

atmospheric S a nd N deposition. Some of the monitoring sites exist as an individual entity, or small

collection of sites, often established primarily for research purposes. Despite the research focus, many of

these long-term research sites include collection of monitoring data. Other long-term monitoring sites

exist as part of large regional programs with a specific focus on long-term monitoring. The most

significant individual monitoring sites and networks are discussed below.

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32 Lovett et al. (2007) reviewed the characteristics of successful environmental monitoring programs,

and argued that monitoring is a fundamental part of environmental science and policy. Their analysis

underscored the fact that environmental monitoring costs little relative to the value of the resources that it

protects and the policy that it informs. Monitoring data also have substantial added value because they

can be used for multiple purposes, including various research objectives.

Ecosystems also require long-term study because most changes occur slowly. When more rapid

change does occur, for example in response to an extreme event, a long-term record is needed to put the

effects of the extreme event into proper context.



# A.2.1. Environmental Monitoring and Assessment Program

The EPA Environmental Monitoring and Assessment Program (EMAP) began regional surveys of

the nation’s surface waters in 1991 with a survey of Northeastern U.S. 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 (fish, macroinvertebrates, zooplankton, and periphyton), water chemistry, and physical

habitat.

Of particular interest with respect to acidic 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 the states of New York, New Jersey, Vermont, New Hampshire, Maine,

Rhode Island, Connecticut, and Massachusetts (Whittier, 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

Adirondacks 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, 2000).

Sampling was done during spring baseflow. Sample sites were restricted to first through third order

streams as depicted on the U.S. Geological Survey (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.

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# A.2.2. Surface Water Chemistry Monitoring

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, 2003) and the Long-term Monitoring (LTM) program

(Ford, 1993; Stoddard, 1998). These efforts focus on portions of the U.S. most affected by the acidifying

influence of S a nd N 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 and LTM projects 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.



## A.2.2.1. TIME Project

At the core of the TIME project is the concept of probability sampling, whereby each sampling site

is chosen statistically from a pre-defined target population. Collectively, the monitoring data collected at

the sites are representative of the target population of lakes or streams in each study (Figure A-1). The

target populations in these regions include lakes and streams likely to be responsive to changes in acidic

deposition, defined in terms of acid neutralizing capacity (ANC), which represents an estimate of the

ability of water to buffer acid. It can be either calculated (calculated ANC = sum of base cations

– sum of

mineral acid anions, where all concentrations are in μeq/L) or titrated in the laboratory (Gran ANC).

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 less than 100 μeq/L. In the Mid-Atlantic, the target population is upland streams with Gran ANC

less than 100 μeq/L. In both regions, the sample sites selected for future monitoring were selected from

the EMAP survey sites in the region (Section A.2.1) that met the TIME target population definition.

August 2008 A­4 DRAFT—DO NOT QUOTE OR CITESource: Stoddard et al. (2003).



### Figure A­1. Location of acid­sensitive regions of the northern and eastern U.S. These are regions

for which statistical survey data are available in the 1990s, and locations of individual TIME sites

used in trend analysis.

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33 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, 1993; Larsen, 1994; Stoddard, 1996; Urquhart, 1998).

TIME sites were selected using the methods developed by the EMAP (Paulsen, 1991; Herlihy, 2000). The

TIME project began sampling northeastern lakes in 1991. Data from 43 Adirondack lakes 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 1830 lakes with surface area greater than 1 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 km of low-ANC stream length out of a total stream length of 42,000 km).

The initial 1993−1995 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.



## A.2.2.2. Long­Term Monitoring Project

As a complement to the statistical lake and stream sampling in TIME, the LTM project samples a

subset of generally acid-sensitive lakes and streams that have long-term data, many dating back to the

early 1980s (Figure A-2). These sites are sampled 3 to 15 times per year. This information is used to

characterize how some of the most sensitive of 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 greater than 100 μeq/L) sites are also sampled, and help

separate temporal changes due to acidic deposition from those attributable to other disturbances (e.g.,

climate, land use change). Because of the availability of long-term records (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 (DOC), and dissolved

inorganic carbon (DIC). The field protocols, laboratory methods, and 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.

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19 Source: Stoddard et al. (2003).



### Figure A­2. Location LTM sites used

in the 2003 Surface Water report.

New E ngland Lakes: The LTM project collects quarterly data from lakes in Maine (sampled by the

University of Maine; Kahl, 1991; Kahl, 1993) and Vermont (data collected by the Vermont Department of

Environmental Conservation`; \Stoddard, 1993; Stoddard, 1998). Data from 24 New England lakes were

available for the trend analysis reported by Stoddard et al. (2003) for the time 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 (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, 1993; Driscoll, 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 seepage and drainage lakes, most with Gran

ANC values in the range of –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, 1993, and five streams in Pennsylvania (collected by Pennsylvania State

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32 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 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, 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, 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 greater than 100 μeq/L.



# A.2.3. Forest Inventory and Analysis

The USDA Forest Service’s Forest Inventory and Analysis (FIA) program authority is mandated

under the Forest and Rangeland Renewable Resources Research Act of 1978 (PL 95–307). Earlier, the

program was known as the Forest Survey and has been run continuously since the 1930s. The FIA

Program collects, analyzes, and reports information on the status and trends of America’s forests: how

much forest exists, where it exists, who owns it, and how it is changing, as well as how the trees and other

forest vegetation are growing and how much has died or has been removed in recent years. (See

http://www.fia.fs.fed.us for more information).

Currently, FIA plots are divided into three phases. Phase 1 establishes approximately three million

samples via remote sensing (aerial photographs, digital orthoquads, and satellite imagery). These samples

are used to classify land as forest or nonforest. As defined by the U.S. Forest Service, forest land is any

land ≥ 1 acre in size that is at least 10% stocked by forest trees of any size. Phase 2 establishes a subset of

the Phase 1 plots for ground sampling, approximately one field sample site for every 6,000 acres for a

total of about 125,000 plots. The forest characteristics measured include forest type, site attributes, tree

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32 species, tree size, and overall tree condition. Phase 3 plots are the Forest Health Monitoring (FHM) plots,

and are a subset of Phase 2 plots. Phase 2 and Phase 3 plots are remeasured every 5 years. Each year,

different plots are sampled within defined regions, so that statistical trends for the overall region can be

developed on an annual basis.

Phase 2 measurements consist primarily of basic vegetation measurements and general site

description. From these measurements information is obtained on tree diameter, tree length, tree quality

for use as lumber, tree damage, stocking and seedling and sapling counts.

Approximately one of every 16 Phase 2 plots is measured for forest health attributes in addition to

Phase 2 attribute measurements. These plots are known as Phase 3 plots. There is approximately one

Phase 3 plot for every 96,000 acres, for a total of roughly 7,800 plots. Measurements for Phase 3 plots

include tree crown conditions, lichen community composition, understory vegetation, down woody

debris, and soil attributes. O 3 injury to vegetation is also monitored at some Phase 3 plots. Comprehensive

reviews of the FIA sampling strategy may be found in Brand et al. (2000) and McRoberts et al. (2004).

Goodale et al. (Goodale, 2002) used FIA data to generate estimates of carbon sequestration in forest

ecosystems in the U.S. in 1990 and 1991. The home page for the National FIA program can be found at:

http://fia.fs.fed.us./



## A.2.3.1. Lichens

Lichens are organisms consisting of both fungi and algae. Lichens are very responsive to

environmental stressors in forests, including changes in forest structure, air quality, and climate. Many

studies have documented the close relationship between lichen communities and air pollution, especially

sulfur dioxide (SO 2 ) and acidifying or fertilizing N- and S-based pollutants. The composition of an

epiphytic lichen community is one of the best biological indicators of air pollution in forests, because

epiphytic lichens rely totally on atmospheric sources of nutrition.

In several studies (e.g.`, \Muir, 1988), lichens have given much clearer responses to N and S

pollutants (in terms of diversity, total abundance, and community composition) than either leaf symptoms

or tree growth, and have been one of the few components of terrestrial ecosystems to show a clear

relationship to gradients of acidic deposition in the eastern U.S.

Although trees may respond to moderate, chronic levels of air pollution deposition, all of the other

influences on tree growth, such as variation in soils, make the responses of trees to pollutants difficult to

measure in the field. Epiphytic lichens may be used to assess potential air quality impacts on forest

ecosystem health and productivity that are difficult to measure directly. Long-term observation of lichen

community change provides early indication of improving or deteriorating air quality. Epiphytic lichens

also have important biological roles in many forests, including N fixation, food for animals (deer, caribou,

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35 voles, and flying squirrels), and nesting material for small mammals and birds. Typically, there are 10 to

50 lichen species per FIA plot.

Lichen community information contributes to the investigation of several key forest ecosystem

concerns: the contamination of natural resources, biodiversity, and productivity/sustainability. Lichen

community data are collected by personnel that receive FIA training on field procedures at the beginning

of each field season. They are trained to observe the presence or absence of lichen species, to estimate the

abundance of each species, and to collect lichen specimens for identification by a specialist, a

lichenologist. Identifications by the lichenologist are part of quality assurance and quality control

procedures. Errors are further minimized by audits of the field. The method has two parts that are

performed simultaneously. (1) In each standard 0.38 ha FIA plot (see http://www.fia.fs.fed.us/library/

field-guides-methods-proc/docs/2006/p3_3_0_sec10_10_2005.pdf for plot design and field methods) the

field crew searches for macrolichens on woody plants and collects samples of each lichen believed to be a

distinct species. Tree and shrub bases below 0.5 m are excluded from sampling. Lichens on fallen

branches and other lichen litter may be included. Given the large plot area, fallen branches typically

provide an excellent sample of the canopy lichens. The collection represents the species diversity of

macrolichens in the plot as fully as possible, with a maximum time limit of 2 hours. (2) The field crew

estimates the abundance of each species using a four-step scale: 1 = rare (<3 individuals in plot); 2 =

uncommon (4–10 individuals in plot); 3 = common (>10 individuals in plot but less than half of the boles

and branches have that species present); and 4 = abundant (more than half of boles and branches in the

plot have the subject species present). As plots are finished, specimens are sent to specialists for

identification. Note that the field crew need not accurately assign species names to the lichens, but must

be able to distinguish among species, and be able to estimate abundances accurately.

Two procedures are used for constructing plot-level indices: (1) Species richness: A component of

diversity, species richness is the total number of epiphytic macrolichen species found in the lichen plot.

(2) Community gradients: The dominant gradients across the region are determined using accepted

statistical methods. Relationships of these gradients to forest structure, climate and air quality are then

analyzed. Scores for air quality and climate are calculated for each plot and are used to answer questions

about air quality and forest productivity/sustainability and biodiversity. Data and more information on

FIA lichen surveys can be found at: http://fia.fs.fed.us/lichen/

.

The lichen community indicator was developed in the Southeast in 1990–93 by Bruce McCune and

Jonathan Dey, funded jointly by EPA and the USDA Forest Service (EMAP Program). FHM pilot projects

were run in 1994–96 in a variety of eastern states and Colorado, Oregon, Washington and California in

the west. The lichen community indicator was included in regular permanent plot surveys starting in 1997

(FHM 1997–1999, FIA after 1999). Regional gradient models have been developed for the southeast

(1994), the northeast (1997), and Colorado (1998), and are being developed for the Midatlantic States

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�

�

�

� B u l k d e n s i t y

, w a t e r c o n t e n t

, a n d c o a r s e f r a g m e n t ( > 2

– m m ) c o n t e n t

p H ( w a t e r a n d 0 .0 1 M C a C l 2 )

T o t a l c a r b o n

T o t a l i n o r g a n i c c a r b o n ( c a r b o n a t e s ) ( p H > 7 .5 s o i l s o n l y ) (1999–2000) the Pacific Northwest (2001), and California (2003). Lichen communities are now a Phase 3

indicator in the FIA.



## A.2.3.2. Soil Quality

Another type of forest health data that the FIA program collects on their Phase 3 plots is the Soil

Quality Indicator. The FIA program began consistent sampling related to the Soil Quality Indicator in

2001. The Soil Quality Indicator collects information through field measurements on FIA sample plots

and laboratory analyses. Soil condition indicators such as erosion, compaction, and soil chemistry are

monitored over time and used to demonstrate trends. Details of the Soil Quality Indicator measurements

of FIA can be found here: http://nrs.fs.fed.us/ fia/topics/soils/.

FIA field personnel collect soil data during the Phase 3 field season, which begins in early June and

ends in September. Soil samples are sent to the laboratory immediately after collection where they are

stabilized by air drying. Laboratory analyses are conducted throughout the fall and winter following the

field season. On-plot measurements include soil compaction and bare soil observations. Soil compaction,

the percentage of the soil surface exhibiting evidence of soil compaction as well as the type of

compaction, is measured by ocular estimation. The relative amount of bare soil is also estimated. Field

measurements related to erosion and compaction estimates are made on all four subplots on the Phase 3

field plot. Soil samples are collected on FIA sample plots along soil sampling lines adjacent to subplots 2,

3, and 4. Soils are collected if the soil sampling location is in a forested condition. A total of five samples

are collected on each plot (three forest floor, two mineral soil). The entire forest floor layer is sampled

from a known area after measuring the thickness of the litter and duff layers at the north, south, east, and

west edges of a 12–inch diameter sampling frame. Only organic material that is < one-fourth-inch

diameter is collected; rocks and larger woody materials are discarded prior to collection.

Once the forest floor has been removed, mineral and organic soils are sampled volumetrically by

collecting cores from two depths: 0 to 4 inches and 4 to 8 inches. The texture of each layer is estimated in

the field and characterized as organic, loamy, clayey, sandy, or coarse sandy. Following soil sampling, the

depth to any restrictive horizon within the top 20 inches is estimated using a soil probe. Soil samples are

mailed to the regional laboratory for physical and chemical analysis.

In the lab, mineral soil samples collected from FIA plots are analyzed for a suite of physical and

chemical properties including:

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� Exchangeable cations (Na, K, Mg, Ca, Al, Mn)

� Extractable sulfur and trace metals (Sr, Ba, Mn, Ni, Cu, Zn, Cd, Pb)

� Extractable P (Bray 1 method for pH < 6 soils, Olsen method for pH > 6 soils)



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23 Fo rest floor an d l itter samples are analyzed for:

�

�

� Bulk density and water content

To tal carbon

Total N

S o il chemical and p hysical properties can be highly variable in the field and are expensive to

analyze. As a result, interpretation of soil chemical data is confounded by spatial variability within the

plot. In addition, depending upon the soil type, both the number of samples and the methods used in

collecting these samples may vary between plots, complicating compilation and estimation procedures.

Finally, soil samples reflect conditions only in the forest floor and upper 20 cm of the soil. In many

systems, the upper portion of the soil profile is likely to be more responsive to disturbance, providing a

useful index for monitoring changes in soil properties over time.



# A.2.4. USGS Monitoring Programs

A.2.4.1. National Water Quality Assessment Program

The National Water Quality Assessment (NAWQA) Program was created in 1991 by the USGS t o

assess the nation’s water quality in 51 study units defined primarily by major drainage divides. These

units comprise approximately 50% of the conterminous U.S. Its major of the program are to determine (1)

the condition of the nation’s streams, rivers, and ground water, (2) whether these conditions are changing

over time, and (3) how these conditions are affected by natural features and human activities.

The major priority of the NAWQA Program since its inception has been on watersheds that have

experienced impacts from agriculture and various forms of development. The location of sites, sampling

frequency, and types of measurements taken, all reflect this priority. Each study unit runs on a 9–year

cycle, with approximately one-third of the study units beginning the cycle every 3 years. Each 9–year

cycle is comprised of 3 years of intensive data collection and 6 years of low-level assessment. Three types

of sampling sites are established within each study unit: integrator sites, indicator sites, and synoptic sites.

Integrator sites are located on major rivers at points that drain much or the entire study unit. Indicator

sites drain large fractions of the study unit that are representative of a particular landscape or land use

type. Some indicator sites are also located to evaluate point sources of water pollution, and some are

located downstream of undisturbed drainages to provide reference, or background conditions. Reference

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33 indicator sites are generally located too low in the drainage basin for assessment of surface water

acidification. Sites associated with synoptic studies are chosen for the purpose of improving spatial

resolution of data collection within the study unit. The strategies for site selection, sampling, and analysis

for synoptic sites are issue-specific and keyed to hydrologic conditions, times, and places of specific

interest for the targeted water quality issue.

During the 3-year intensive sampling period, integrator and indicator sites are sampled multiple

times, both periodically and in association with high flows. The sampling approach for synoptic studies

varies depending on the issue of interest, but is usually done within the second or third year of the

intensive sampling period. During the six years of the low-level assessment, sampling usually involves

base-flow sampling of high priority integrator sites, and possibly some sampling of indicator sites.

Water quality measurements vary among study units, but usually include pathogens, nutrients

(including N and S), trace elements, pesticides, industrial organics, suspended sediment, salinity,

temperature, acidity, and dissolved O 2

.

NAWQA studies have resulted in over 1000 reports on an extensive list of water quality issues,

including freshwater and marine eutrophication associated with N pollution. None deal with acidification,

however. Program details and access to publications can be obtained at http://water.usgs.gov/nawqa/.



## A.2.4.2. Hydrologic Benchmark Network

The Hydrologic Benchmark Network (HBN) was started in 1963 by the USGS a nd gradually grew

to include 57 river gauging stations and 1 lake-stage station in 39 states by 1990. Most of the stations

have been established at the outlet of watersheds that were virtually free of human activities, located in

places such as in national parks and forests, wilderness areas, or nature preserves. Streamflow was

initially monitored continuously at each station, and samples were collected every month for waterquality analyses that included concentrations of nutrients and all major ions. The frequency of water

sampling at HBN stations was decreased to quarterly in 1986 because of budgetary restrictions. Sampling

was discontinued in October 1997, except for a small study in the eastern U.S. that focused on the initial

response of rivers to decreases in industrial emissions mandated by the Clean Air Act Amendments of

1990 (http://www.epa.gov/oar/oaq_caa.html/).

All HBN watersheds were evaluated in 2002 to determine whether upstream development had

made them unsuitable as reference watersheds. The 36 sites that best met the network criteria were

selected for continued streamflow monitoring, and water sampling was reinitiated at 15 of those 36 sites.

In 2003, 15 of the original HBN stations were equipped with refrigerated, automated samplers and

telemetry systems that allow program coordinators to monitor stream conditions and adjust sampling

frequency and capture unique stream conditions or special sampling needs. The automated sampling

system is designed to collect samples through a wide range of flow conditions and to transmit data by

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28 satellite. About 25 water samples are collected annually at each HBN water quality station and

refrigerated on site until retrieved by field personnel who visit the sites regularly. The most recent trends

analysis was done by Clow and Mast (1999) to evaluate long-term trends in stream chemistry with respect

to the Clean Air Act. The program is further described in a fact sheet that can be found at:

http://ny.water.usgs.gov/pubs/fs/fs20053135/.



## A.2.4.3. New York City Water Quality Network

The New York District of the U.S. Geological Survey operates a water quality network throughout

the water supply watershed for New York City, in the Catskill Mountains region. The purpose of the

network is to provide stream flow and water quality data at key locations within the watershed. There are

currently 34 sites throughout the network at which stream flow data are collected, and at thirteen of those

sites stream water quality data are also collected. The water quality network is composed of paired

“nodes” consisting of one or more “upper nodes” that provide water quality of undeveloped, forested

watersheds, and “lower nodes” that provide downstream water quality data that may reflect some level of

development within the watershed.

Water quality sampling for this program began at the 13 sites in 1998–99. Water samples are

collected biweekly and during high flow for approximately 6 storms per year. All water samples are

analyzed for concentrations of nutrients and major ions. Because streams in this area are also affected by

acidic deposition, acid-neutralizing capacity and 3 forms of Al are also measured. Further details on the

program are available at: http://ny.cf.er.usgs.gov/ nyc/unoono.cfm.



## A.2.4.4. Catskill Long­Term Monitoring Sites

Within the Catskill Mountains region of New York State, stream samples are collected and stream

flow is measured at three locations within the Neversink River basin, and at one site on Rondout Creek.

Water samples are collected biweekly and during most storms. These sites are currently part of the EPA

LTM program, but also are affiliated with other programs. Sampling at two of the four sites began in the

mid 1980s, whereas sampling at the remaining two sites began in 1991. The primary purpose of these

sites is to monitor effects of acidic deposition on stream chemistry. The full suite of analytes needed to

assess acidic deposition effects are measured on these water samples.



## A.2.4.5. Buck Creek, New York

Stream flow and water chemistry are monitored at three locations within Buck Creek watershed, in

the western Adirondack Region of New York. Samples are collected biweekly and during most storms at

each location. Sampling began in 1998 at two sites and 2001 at the third site. The full suite of analytes

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Table A­1. LTER site locations and basic site description information.

Site Landsat WRS Institutional Affiliations Principal Biome/

Main Communities Research Topics

H.J. Andrews

Experimental Forest

(AND) 44.2,

−122.2

Arctic Tundra

(ARC) 68.6,

−149.6

Baltimore

Ecosystem

Study (BES)

39.1, −76.3

Bonanza Creek

Experimental Forest

(BNZ) 64.8,

−148.0 Path 46 Row 29;

Lat/Long: 44°14'N/

122°11'W

Path 73, Row 12;

Lat/Long: 68°38'N/

149°34'W

Path 15, Row 33;

Lat/Long 38°54" 04'

76°52" 04' (W)

Path 69, Row 15;

Lat/Long: 64°45'N/

148°00'W (N), Oregon State University;

USDA Forest Service Pacific

Northwest Research Station

The Ecosystem Center,

Marine Biological

Laboratory; Universities of

Alaska, Massachusetts,

Minnesota, Cincinnati, and

Kansas; Clarkson University

Institute of Ecosystem

Studies; USDA Forest

Service, Johns Hopkins

University; University of

Maryland; Baltimore County

and College Park; University

of North Carolina; Parks

and People Foundation; US

Geological Survey; Yale

University

University of Alaska;

Institute of Northern

Forestry, USDA Forest

Service, Pacific Northwest

Research Station Temperate coniferous forest.

Douglas­fir/western hemlock/

western red cedar; true fir and

mountain hemlock; streams

Arctic tundra, lakes, streams.

Tussock tundra; heath tundra;

riverine willows; oligotrophic

lakes; headwater streams

Eastern deciduous forest/

Suburban Agriculture fringe,

urban parks, residential and

commercial patches, riparian and

stream habitats

Taiga. Areas of boreal forest

including permafrost­free uplands

and permafrost­dominated north

slopes and lowlands; floodplain

seres Successional changes in ecosystems;

forest­stream interactions; population

dynamics of forest stands; patterns and

rates of decomposition; disturbance

regimes in forest landscapes

Research topics: Movement of nutrients

from land to stream to lake; changes

due to anthropogenic influences;

controls of ecological processes by

nutrients and by predation

Patch dynamics of built, social,

biological, and hydrological components

of the metropolitan area; feedback's

between social, economic, and

ecological components of an urban

ecosystem; effect of infrastructure and

development on fluxes of nutrients,

energy, and water in upland, stream,

and coastal regions of metropolitan

Baltimore

Successional processes associated with

wildfire and floodplains; facilitative and

competitive interactions among plant

species throughout succession; plantmediated changes in resource and

energy availability for decomposers;

herbivorous control of plant species

composition; hydrologic regime and

stream ecology needed to assess acidic deposition effects is measured on these water samples. Measurements of ANC and

pH were also collected at one site weekly for the period of 1991 to 2001 (Lawrence, 2004). Recent data

from Buck Creek are presented in Lawrence et al. (2007). Buck Creek is the only stream within the

acidified region of the Adirondacks where base flow and storm samples are collected in conjunction with

flow monitoring.



# A.2.5. NSF Long­Term Ecological Research Network

The Long-Term Ecological Research (LTER) program constitutes a loose network of 26 sites

(Table A-1), funded by the National Science Foundation (NSF). There is increasing concern over such

globally significant problems as loss of biodiversity, climate change, destruction of forests, depletion of

stratospheric ozone, regional air and water pollution, and soil erosion. The research conducted at the

various LTER sites has examined and continues to examine aspects of these problems and provides

scientific information which has been invaluable in the formation of public policy. Site locations and

research activities are summarized in (Table A-1). A few of the sites that have been used most extensively

for evaluation of long-term effects of N and sulfur deposition are discussed in greater detail below.

August 2008 A­15 DRAFT—DO NOT QUOTE OR CITESite Landsat WRS Institutional Affiliations Principal Biome/

Main Communities Research Topics

Cedar Creek Path 27, Row 28; University of Minnesota Eastern deciduous forest and Successional dynamics; primary

Natural History Lat/Long: 45°24'N/ tallgrass prairie. Old fields; oak productivity and disturbance patterns;

Area (CDR) 93°12'W savanna and forest, conifer bog; nutrient budgets and cycles; climatic

45.4, −93.2 lakes; pine forest; wetland marsh variation and the wetland/upland

and carr boundary; plant­herbivore dynamics

Central Path 36, Row 37 and Arizona State University Sonoran Desert scrub. Urban Interactions of ecological and socioArizona

­ Path 36, Row 36° These (Main and West) parks, residential, interior economic systems in an urban

Phoenix (CAP) have been used by CAP remnant desert patches, environment; influence of land use

33.5, −11.2 although Path 37, Row 37 commercial and industrial change on ecological patterns and

is centered more closely patches, urban fringe, regulated processes; movement of nutrients

in the City of Phoenix river and floodplain (dry), through highly manipulated, urban

effluent­dominated river flowpaths; interactions of introduced

and native species in urban

environment; millennium­ and centuryscale geomorphic change in landforms

and interaction with engineered

landscapes

Coweeta Path 18, Row 36; University of Georgia; USDA Eastern deciduous forest. Long­term dynamics of forest

Hydrologic Lat/Long: 35°00'N/ Forest Service, Hardwood forests and white pine ecosystems including forest disturbance

Laboratory 83°30'W Southeastern Forest plantations and stress along an environmental

(CWT) 35.0, Experiment Station gradient; stream ecosystems along an

−83.5 environmental gradient; and the riparian

zone as a regulator of terrestrial­aquatic

linkages

Harvard Forest Path 13, Row 30; Harvard University; Eastern deciduous forest. Long­term climate change, disturbance

(HFR) Lat/Long: 42°32'N/ Universities of New Hardwood­white­pine­hemlock history and vegetation dynamics;

42.5, −72.2 72°10'W Hampshire and forest; spruce swamp forest; comparison of community, population,

Massachusetts; The conifer plantations and plant architectural responses to

Ecosystem Center, Marine human and natural disturbance; forestBiological Laboratory atmosphere trace gas fluxes; organic

matter accumulation, decomposition and

mineralization; element cycling, fine root

dynamics and forest microbiology

Hubbard Brook Path 13, Row 29; Yale, Cornell, and Syracuse Eastern deciduous forest. Vegetation structure and production;

Experimental Lat/Long: 43°56'N/ Universities; Institute of Northern hardwood forests in dynamics of detritus in terrestrial and

Forest (HBR) 71°45'W Ecosystem Studies; USDA various developmental stages, aquatic ecosystems; atmosphere43.9, −71.8 Forest Service, spruce­fir forests; streams and terrestrial­aquatic ecosystem linkages;

Northeastern Forest lakes heterotroph population dynamics;

Experiment Station effects of human activities on

ecosystems

Jornada Path 33, Row 37; New Mexico State Hot desert. Playa, piedmont, and Desertification; factors affecting primary

Experimen­ Lat/Long: 32°30'N/ University; USDA ARS swale; bajada, basin, mountain production; animal­induced soil

tal Range 106°45'W Jornada Experimental and swale shrubland; mesquite disturbances; direct and indirect

(JRN) 32.5, Range; Duke University; dunes consumer effects; vertebrate and

−106.8 NOAA, RTP, NC; University invertebrate population dynamics;

of New Mexico; Dartmouth grazing effects on ecosystem structure

College, NH; Oregon Gradu­ and function; biodiversity and

ate Center; Texas ecosystem function; small mammal

Technological University; effects on soil and vegetation

SUNY Buffalo; University of heterogeneity; soil microbial processes; Keele, UK; Kings College, surface hydrology; trace gas emissions

London, UK; EPA­EMAP, Las from soils; eolian processes

Vegas, NV

W.K. Kellogg Path 21, Row 31; Michigan State University, Row­crop agriculture. Ecological interactions underlying the

Biological Lat/Long: 85°24'W/ Michigan Agricultural Conventional and organic­based productivity and environmental impact

Station (KBS) 42°24'N Experiment Station corn­soybean­wheat cultivation; of production­level cropping systems;

42.4, −5.4 perennial biomass cultivation; patterns, causes, and consequences of

native successional communities microbial, plant, and insect diversity in

agricultural landscapes; gene transfer,

community dynamics, biogeochemical

fluxes

Konza Prairie Path 28, Row 33; Kansas State University Tallgrass prairie. Tallgrass Effects of fire, grazing and climatic

Research Lat/Long: 39°05'N/ prairie; gallery forest; prairie variability on ecological patterns and

Natural 96°35'W stream processes in tallgrass prairie

Area(KNZ) ecosystems, use of remotely sensed

39.1, −94.6 data and geographic information

systems to evaluate grassland structure

and dynamics

August 2008 A­16 DRAFT—DO NOT QUOTE OR CITESite Landsat WRS Institutional Affiliations Principal Biome/

Main Communities Research Topics

Luquillo Path 4, Row 47 and 48; Center for Energy and Tropical rainforest. Tabonuco Patterns of and ecosystem response to

Experimental Lat/Long: 18°18'N/ Environment Research, forest; palo Colorado forest; palm different patterns of disturbance; landForest (LUQ) 65°47'W University of Puerto Rico; brake; dwarf forest and montane stream interactions; effect of

18.3, −65.8 Institute of Tropical streams management on ecosystem properties;

Forestry, USDA Forest integration of ecosystem models and

Service, Southern geographic information systems

Experiment Station

McMurdo Dry Path 56, Row 116 Desert Research Institute, Polar desert oases Microbial ecosystem dynamics in arid

Valleys

­ Reno, Nevada; U.S. soils, ephemeral streams, and closed

Antarctica Geological Survey, Boulder, basin lakes; resource and environmental

(MCM) −78.0, Colorado controls on terrestrial, stream and lake

+165.0 ecosystems; material transport between

aquatic and terrestrial ecosystems;

ecosystem response to greater

hydrologic flux driven by warming

climate

Niwot Ridge/ Path 34, Row 32; Institute of Arctic and Alpine tundra; Fellfield; meadow; Patterns and controls of nutrient cycling;

Green Lakes Lat/Long: 40°03'N/ Alpine Research, University herbaceous and shrub tundras; trace gas dynamics, plant primary

Valley (NWT) 105°37'W of Colorado cliffs and talus; glacial lakes; productivity and species composition;

40.1, −105.6 streams and wetlands geomorphology, and paleoecology

North Path 25, Row 28 and Center for Limnology, Northern temperate lakes in Physical, chemical and biological

Temperate Path 24, Row 30 University of Wisconsin­ glacial landscapes in urban, limnology; hydrology and geochemistry;

Lakes (NTL) Lat/Long: 46°00'N/ Madison, Wisconsin agricultural and forested climate forcing; producer and consumer

46.0, −89.7 89°40'W and watersheds. Oligotrophic, ecology; ecology of invasions;

and 43.1, 89.4 89°24/ 43°06 dystrophic and eutrophic lakes; ecosystem variability; lakescape and

temporary forest ponds; warm landscape ecology

and cold streams; sphagnumleatherleaf bog; conifer swamp;

mixed deciduous and coniferous

forests

Palmer Station Path 219

, Row 105; University of California, Polar marine. Coastal and open Oceanic­ice circulation and models; sea­ (PAL) Lat/Long: 64°40'S/ 64°W Santa Barbara; Old ocean pelagic communities; ice dynamics; biological/physical

Antarctica Dominion University seabird nesting areas interactions; effect of sea ice on primary

−64.7, −64.0 production, consumer populations and

apex predators; bio­optical models of

primary production; spatial distribution

and recruitment in consumer populations; seabird population

dynamics and reproductive ecology

Plum Island Path 12, Row 30; Lat/ The Ecosystems Center, Coastal estuary Linkages between land and coastal

Sound (PIE) Long: 42°40'/ 70°59' Site Marine Biological waters involving organic carbon and

42.67, −70.99 has the following X and Y Laboratory; Universities of organic N inputs to estuarine

bounds in decimal South Carolina and New ecosystems from watersheds with

coordinates: X min = Hampshire; Massachusetts various land covers and uses

−71.22 X max = −70.75. Audubon; Wells, Maine,

Y min = 42.50 Y max = NERRS

42.83. The total area is

approximately 37 km x 37

2 km or 1369 km

Sevilleta Path 33, Row 36; University of New Mexico; Multiple: intersection of subalpine Landscape and organism population

National

Wildlife Refuge

(SEV) 34.3,

−106.8 To acquire entire site

area, Path 32, Row 36,

Path 32, Row 37 and

Path 33, Row 37 are also

needed. Lat/ Long:

34°19'/ 106°62'W U.S. Fish and Wildlife

Service mixed­conifer forest/meadow,

riparian cottonwood forest, dry

mountainland, grassland, cold

desert, hot desert. Conifer

savanna; creosote bush; desert

grassland; mesquite and sand

dunes; Great Basin shrub and dynamics in a biome tension zone;

semiarid watershed ecology; climate

change; biospheric/atmospheric

interactions; paleobotany/ archaeology;

microbial role in gas flux; and control of

landscape heterogeneity; scale effects

on spatial and temporal variability

shortgrass steppes; tallgrass

swales; riparian communities

Shortgrass Path 33, Row 32; Colorado State University; Floodplain; shrubland; Soil water; above­ and belowground net

Steppe (SGS) Lat/Long: 40°49'N/ USDA Forest Service; USDA saltmeadow primary production; plant population

40.8, −104.8 104°46'W Agricultural Research and community dynamics; effects of

Service livestock grazing; soil organic matter

accumulation and losses, soil nutrient

dynamics; and ecosystem recovery from

cultivation

August 2008 A­17 DRAFT—DO NOT QUOTE OR CITESite Landsat WRS Institutional Affiliations Principal Biome/

Main Communities Research Topics

Virginia Coast Path 14, Row 34; University of Virginia Coastal barrier islands. Sandy Holocene barrier island geology; salt

Reserve (VCR) Lat/Long: 37°30'N intertidal; open beach; marsh ecology, geology, and hydrology;

37.5, −74.8 75°40'W shrubthicket; mature pine forest; ecology/ evolution of insular

salt marsh; estuary vertebrates; primary/ secondary

succession; life­form modeling of

succession



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## A.2.5.1. Hubbard Brook Experimental Forest

The Hubbard Brook Ecosystem Study (HBES) at Hubbard Brook Experimental Forest (HBEF) is

the longest-running precipitation and stream chemistry (1963 to present) monitoring program in the U.S.

(see http://www.hubbardbrook.org ). HBEF w as established in 1955 as a major center for hydrologic

research in New England. The site is located within the boundaries of the White Mountain National Forest

in central New Hampshire. The 3138–ha, bowl-shaped valley has hilly terrain, ranging from 222 to 1015

m elevation. The HBES o riginated in 1960 with the intention of applying the small watershed approach to

the study of element fluxes and cycles. The goal of the study is to develop a better understanding of

ecological patterns and processes that characterize the northern forest in eastern North America, and its

response to both natural and human disturbances. In 1987, HBEF joined the NSF’s LTER network

(http://www.lternet.edu). Hubbard Brook is renowned for its long-term record of measurements,

landscape-scale experiments of whole watersheds, and the involvement of scientists from diverse

disciplines and institutions.

The HBEF i s entirely forested, mainly with deciduous northern hardwoods: sugar maple ( Acer

saccharum ), beech ( Fagus grandifolia ), and yellow birch ( Betula allegheniensis ), and some white ash

( Fraxinus americana ) on the lower and middle slopes. Other less abundant species include mountain

maple ( Acer spicatum ), striped maple ( Acer pensylvanicum ), and trembling aspen ( Populus tremuloides ).

Red spruce ( Picea rubens ), balsam fir ( Abies balsamea ), and white birch ( Betula papyrifera var.

cordifolia ) are abundant at higher elevations and on rock outcrops. Hemlock ( Tsuga canadensis ) is found

along the main Hubbard Brook. Pin cherry ( Prunus pensylvanica ), a shade intolerant species, dominates

all sites for the first decade following a major forest disturbance. Logging operations ending around

1915–1917 removed large portions of the conifers and better quality, accessible hardwoods. The present

second-growth forest is even-aged and composed of about 80 to 90% hardwoods and 10 to 20% conifers.

The HBEF i s an oblong basin about 8 km long by 5 km wide. Hubbard Brook is the single major

stream draining the basin. Numerous smaller tributary streams of varying size drain into Hubbard Brook

including Watershed 6 (WS-6), which is the biogeochemical reference watershed.

One of the strengths of the HBES i s the long-term monitoring program. Section A.3.3.1 lists the

major parameters included in the HBES l ong-term monitoring study. The monitoring data illustrate that

short-term observations can be misleading and that decades of monitoring may be required to detect real

August 2008 A­18 DRAFT—DO NOT QUOTE OR CITEchanges in complex ecosystems. The long-term record at the HBEF p rovides: (1) insight into ecosystem

function; 2) empirical data for testing models and generating hypotheses; 3) a record of extreme or

unusual events; and (4) information that is relevant to regional national and global environmental issues.

Some of the monitoring is done on experimentally manipulated watersheds. There are nine gaged

watersheds at the HBEF, four of which have been treated experimentally. A tenth ungauged watershed

was also treated. Table A-2 includes summary data on the various watersheds. Datasets for long-term

monitoring can be found at http://www.hubbardbrook.org/ data/dataset_search.php. The datasets most

often used to examine ecosystem response to ambient deposition of N and S are from WS-6 and Mirror

Lake, since they have not been experimentally manipulated. Watershed 6 is the biogeochemical reference

catchment at HBEF w here monitoring began in June 1963. Measured stream chemistry parameters

include major anions and cations, pH, silica, dissolved organic and inorganic carbon, specific

conductance, dissolved O 2 , ANC, and PO 4

. Stream chemistry data can be accessed at

http://www.hubbardbrook.org/ data/dataset.php?id=8. The normal sampling interval for WS-6 is weekly,

with more frequent samples taken at times of increased discharge. 

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22 Table A­2. Study watersheds at HBEF

WS Area (ha) Slope (°) Aspect Elevation (m) Gauge Type Initial Yr.

1 11.8 18.6 S22°E 488–747 V­notch weir 1956

2 15.6 18.5 S31°E 503−716 V­notch weir 1957

3 42.4 12.1 S23°W 527–732 V­notch wir 1957

4 36.1 15.6 S40°E 442–747 V­notch weir 1960

5

6

7

8

9 21.9

13.2

77.4

59.4

68.4 15.4

15.8

12.4

14.0 S24°E

S32°E

N16°W

N12°W

NE 488–762

549–792

619–899

610–905

685–910 V­notch

V­notch

V­notch

V­notch

V­notch weir,

weir,

weir,

weir,

weir San

San

San

San Dimas

Dimas

Dimas

Dimas flume

flume

flume

flume 1962

1963

1965

1968

1995

10 12.1 SE 470–595 None 1970

Monitoring o f streamflow and water chemistry has shown that the study watersheds have similar

characteristics. Within each watershed there are a variety of soils, vegetation, microtopographical

features, and micro-climate. Nevertheless, the composition of these variables seems to be similar from

watershed-to-watershed. Thus, the effects of experimental manipulations of watersheds can be adequately

evaluated by comparison with neighboring unmanipulated watersheds.

The most conspicuous streamflow characteristic is the seasonal shift from large volume of flow in

spring to very low flow in late summer and early autumn. These yearly highs and lows reflect seasonal

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35 spring snowmelt that often occurs over a few days or weeks and the slow progressive decrease in flow

from the transpirational draft in summer, respectively. The numerous streams in the HBEF r ange from

small ephemeral channels that often dry up during summer to a large perennial 5th-order stream (Hubbard

Brook).

Mirror Lake is a 15–ha oligotrophic clearwater lake adjacent to HBEF. The lake normally mixes in

spring and fall, and is ice-covered from about December 1st to April 15th each year. Part of the drainage

to the lake originates in the Experimental Forest. The lake water is dilute, slightly acidic, and quite clear,

with low productivity and low concentrations of nutrients in the water. Numerous studies have been

conducted on Mirror Lake since the mid-1960s, including extensive physical, chemical, biological, and

paleoecological research (cf. Likens, 1985). Data are available since 1967 for lakewater concentrations at

discrete depths for base cations, pH, and dissolved O 2

. Ammonium, major anions, phosphate, and

dissolved silica have been measured routinely since 1970, although some data are available prior to these

dates for each solute. Other standard monitoring data include temperature and specific conductance at

each depth. Prior to 1990, not all records had complete solute arrays. Since 1990, DIC and ANC have also

been measured on a routine basis, although some prior data do exist for those parameters. The usual

sampling interval for Mirror Lake is four to six times each year, especially at times of maximum and

minimum thermal stratification. Data for Mirror Lake and inlet and outlet streams can be found at:

http://www.hubbardbrook.org/data/ dataset_search.php.

The soils, vegetation, and climate at the HBEF a re characteristic of the northern hardwood forest

complex, which spans much of the north-central and northeastern U.S. and southeastern Canada.

Streamflow and stream chemistry reflect the landscape characteristics of the drainage area. Consequently,

results from the relatively small watersheds at the HBEF a re to a first approximation representative of a

much larger regional area. During the scientific debate that occurred prior to passage of the Clean Air Act

Amendments of 1990, the trends in sulfate (SO 2−

4 ) concentrations in streamwater and precipitation at

HBEF w ere very influential in convincing scientists and policy makers that decreasing S e missions would

yield large decreases in the concentration of SO 2−

4 in precipitation and streamwater in the northeastern

U.S. (Lovett, 2007). Monitoring data collected since 1963 (Figure A-3) played a major role in

development of the watershed-ecosystem concept and methods for analyzing and understanding

watershed biogeochemical cycles (Bormann, 1967; Likens, 1978; Likens, 1995; Lovett, 2007).

An extensive effort has been made to bring together some of the results of research that had been

done at Hubbard Brook over the last several decades. Over the duration of the HBES t here have been six

books and more than 1,000 papers published. In addition, more than 500 abstracts were published and

more than 100 graduate theses completed. A complete list of titles is available at

http://www.hubbardbrook.org/pubs/pub_search.php. To date, four synthesis volumes have been completed

(Likens, 1977; Bormann, 1979; Likens, 1985; Likens, 1992).

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13 Source: Lovett et al. (2007) ; updated from Likens et al. (2002).



### Figure A­3. Long­term record of SO 2−

4 

### concentration in streamwater and precipitation at Watershed

6 of HBEF.



## A.2.5.2. Coweeta

The Coweeta LTER research program (http://coweeta.ecology.uga.edu/) in North Carolina is based

in the eastern deciduous forest of the Blue Ridge Physiographic Province of the southern Appalachian

Mountains. The program entails long-term cooperation between the University of Georgia and the U.S.

Department of Agriculture (USDA) Forest Service Coweeta Hydrologic Laboratory. The research

program centers on the effects of disturbance and environmental gradients on biogeochemical cycling,

and the underlying watershed ecosystem processes that regulate and respond to those cycles. Coweeta

represents one of the longest continuous environmental studies of any North American landscape.

The research at Coweeta focuses largely on how water, soil, and forest resources respond to

management practices, natural disturbances, and the atmospheric environment. It also aims to identify

practices that mitigate impacts on these watershed resources. Current topics of emphasis include (1)

analyses of long-term changes in hydrology, nutrient cycling, and productivity in response to management

practices and natural disturbances; 2) assessment of prescribed burning effects on the forest environment;

3) interdisciplinary implementation of ecosystem management on the national forests; 4) effects of

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34 climatic change on productivity; 5) impacts of atmospheric deposition on forest processes and

ecosystems; 6) cumulative effects of land use practices on water quality; 7) physiological studies of forest

carbon balance and competition; and (8) biodiversity.

Investigators at the Coweeta Hydrologic Laboratory have recorded N dynamics of streams and

precipitation in mature mixed hardwood-covered watersheds since 1972. Research has been conducted on

responses to management practices such as clearcutting, selective cutting, conversion of native hardwood

to coniferous forest, and old-field succession. Reference watersheds were characterized as in a transition

phase between stage 0 and stage 1 of watershed N saturation. Evidence for stage 3 of N saturation, where

the watershed is a net source of N rather than a N sink, was found for the most disturbed watershed at

Coweeta.

The Coweeta Basin comprises 2185 hectares within the Blue Ridge geologic province in North

Carolina. The laboratory has been dedicated to forest hydrology research since its establishment in 1933.

Elevations range from 679 to 1592 m. More than 50 km of streams drain the area.

Coweeta is the first major mountain range contacted by air masses moving over the industrialized

Piedmont region to the south. Analyses of precipitation chemistry have shown the influence of both local

and regional activities on nutrient inputs to forest ecosystems.

Since Coweeta was established, 32 weirs have been installed on streams. Seventeen of these weirs

are currently operational. Stream gauging was initiated on most watersheds between 1934 and 1938, and

stream chemistry measurements date back to 1968.

Research has been conducted on eight mixed hardwood control areas and 13 catchments where

forest management prescriptions have been applied. Past treatments have included varying intensities of

cutting, ranging from light selection through clear-cutting; conversion of hardwoods to grass and

subsequent succession to hardwoods; multiple-use management; mountain farming; and the application of

herbicides and fertilizers.

Research and monitoring data from Coweeta has been extensively analyzed and reported in the

scientific literature. Example recent publications include Swank and Vose (1997, Grossman et al. (1998,

Schofield et al. (2001, and Scott and Helfman (2001).

Long-term changes in soils have been identified in reference and managed watersheds over two

decades (Knoepp, 1994). For example, changes in exchangeable soil cation content varied with aspect:

concentrations decreased on a north-facing slope but were stable on a south-facing slope. The

demonstrated impacts of forest management practices have varied considerably. Soils in a white pine

plantation showed stable C levels, but cations declined.

Commercial sawlog harvest resulted in large increases in soil C and cation concentrations, which

remained elevated for 17 years. Whole-tree harvest resulted in decreased soil C for the next 14 years.

August 2008 A­22 DRAFT—DO NOT QUOTE OR CITE

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25 �

�

�

�

�

� w a t e r s h e d h y d r o l o g y a n d f o r e s t n u t r i e n t d y n a m i c s

,

f o r e s t m i c r o m e t e o r o l o g y

,

a t m o s p h e r i c d e p o s i t i o n

,

I n t e r n a t i o n a l B i o l o g i c a l P r o g r a m E a s t e r n D e c i d u o u s F o r e s t B i o m e P r o j e c t

,

t r a c e e l e m e n t c y c l i n g a n d s t r e a m n u t r i e n t s p i r a l i n g

, a n d

e f

f e c t s o f a c i d i c d e p o s i t i o n o n c a n o p y p r o c e s s e s a n d s o i l c h e m i s t r y

.

�

�

�

� D a i l y c l i m a t e d a t a

M o n t h l y c l i m a t e d a t a

P r e c i p i t a t i o n

A t m o s p h e r i c d e p o s i t i o n Clearly soil response to harvest varies with type of harvest and site. Long-term studies like these have

proven useful in guiding ecosystem management projects in the southern Appalachians (Meyer, 1996).



## A.2.5.3. Walker Branch

Walker Branch Watershed is located on the U.S. Department of Energy’s Oak Ridge Reservation in

Tennessee. The 97.5 ha Walker Branch watershed has been the site of long-term, intensive environmental

studies since the late-1960s (see http://walkerbranch.ornl.gov/ ).

The forest soils are acidic, very cherty, infertile, and permeable. They are formed over dolomitic

bedrock, but retain little evidence of their carbonate parent material. The forest vegetation is primarily

oak-hickory with scattered pine on the ridges and mesophytic hardwoods in the valleys.

Initially, the research and monitoring of Walker Branch centered primarily on the geologic and

hydrologic processes that control the amounts and chemistry of water moving through the watershed. Past

projects have included:

These projects ha v e all contributed to a more complete understanding of how forest watersheds

function and have provided insights into the solution of energy-related problems associated with air

pollution, contaminant transport, and forest nutrient dynamics. Available long-term data at this site

include:

August 2008 A­23 DRAFT—DO NOT QUOTE OR CITE� Stream discharge and annual runoff

� Stream chemistry

� Vegetation 

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28 

# A.2.6. Water, Energy, and Biogeochemical Budgets Program

The Water, Energy, and Biogeochemical Budgets (WEBB) Program was started in 1991 at five

small watersheds in the U.S. to examine water, energy, and biogeochemical fluxes and to determine the

effects of atmospheric deposition, climatic variables, and human influences on watershed processes. The

five sites are at Loch Vale, Colorado; Luquillo Experimental Forest, Puerto Rico; Panola Mountain,

Georgia; Sleepers River, Vermont; and Trout Lake, Wisconsin. These sites are supported, in part, by other

programs in the USGS, other Federal and State Agencies, and Universities. Two of these sites, Loch Vale

and Sleepers River, have been used extensively to evaluate the effects of atmospheric sulfur and N

deposition, and are described here. Each of those sites is also part of the LTER network.



## A.2.6.1. Sleepers River

The Sleepers River Research Watershed in northeastern Vermont was established by the

Agricultural Research Service (ARS) of the USDA in 1959 and is now operated jointly by the USGSand

the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL), with collaboration from

several other Federal agencies and universities (see http://nh.water.usgs.gov/ projects/sleepers/index.htm).

The USGS u ses hydrologic measurements and chemical and isotopic tracing techniques to determine how

water moves from the hillslope to the stream, and what processes cause chemical changes, including the

neutralization of acid rain. Research results provide insights on how pollutants move through ecosystems,

and how ecosystems may respond to climatic change.

The watershed is covered by 1 to 4 m of glacial till, a compacted fine silty material that formed

underneath glacial ice as it moved overland. The till was formed primarily from local bedrock, which is a

calcareous granulite/quartz-mica phyllite. About 60 to 80 cm of soil has developed in the till. Weathering

of calcite in the till and bedrock causes highly buffered streamflow, compared to most streams in New

England, and a nutrient-rich biological environment. Sleepers River is, therefore, an end member in

regional biogeochemical cycling studies (Hornbeck, 1997).

The Sleepers River area has reverted from a predominately cleared, agricultural landscape to a

mostly forested one. A Northern Hardwood forest, dominated by sugar maple, white ash, yellow birch,

and beech, with lesser amounts of red spruce and balsam fir, now covers two-thirds of the area; the

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Figure A­4. Location of sampling stations in Sleepers River watershed, Vermont. remaining open land is primarily pasture and hayfields. Dairy farming and logging are the primary human

enterprises in the watershed. The average annual temperature is 6 °C and the average annual precipitation

is 1.1 m, 20% to 30% of which falls as snow.

Sleepers River has one of the longest historical hydrologic and climatologic data bases for a coldregion area in the U.S., featuring measurements of precipitation and streamflow since 1959, snow depth

and corresponding water content since 1960, soil frost depth since 1984 (Shanley, 1999, and ground-water

levels since 1991. These and other measurements constitute a valuable resource for hydrologic modeling

and for the evaluation of climatic changes. Sampling site locations are shown in Figure A-4.

August 2008 A­25 DRAFT—DO NOT QUOTE OR CITERecent research findings include the following:

�

�

�

� Precipitation is acidic, but streamflow is well-buffered from calcite weathering in till and

bedrock.

Infiltrating snowmelt causes ground water to rise into the permeable soil zone, where it moves

rapidly downslope.

Naturally occurring isotopic and chemical tracers indicate that “old” water dominates

streamflow, and that water acquires solutes from weathering and biogeochemical processes

along both deep and shallow flowpaths.

Nitrate (NO 3 − ) in streamflow is supplied primarily by mineralization and nitrification in the

soil, rather than directly by the N content of precipitation. 

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29 The fate of NO − 3 in the forest ecosystem is being investigated by analysis of both the N and O

isotopes of the NO − 3 ion. The isotopic composition of NO − 3 in streamflow matches that of NO − 3 produced

by mineralization and nitrification in the soil, indicating that streamflow NO − 3 is derived from the soil and

not from the rain or snowmelt that causes the high flow (Kendall, 1995). This finding suggests that most

incoming atmospheric N is incorporated at least temporarily in the soil where it is utilized by the biota.



## A.2.6.2. Loch Vale

The Loch Vale Watershed is a 661–ha alpine/subalpine basin located in the south-central Rocky

Mountains, about 100 km northwest of Denver, Colorado. The basin is in a roadless area in Rocky

Mountain National Park and is accessed by a 5–km hike or ski from the trailhead near Bear Lake. The

western boundary of the basin is the Continental Divide; streams drain to the northeast. Basin elevations

range from 4192 m (13,153 ft) at Taylor Peak to 3110 m (10,200 ft) at the outlet. There are two main

subbasins in Loch Vale: Andrews Creek drains the northern subbasin, and Icy Brook drains the southern

subbasin. These two creeks join above The Loch, which is the lowest of three lakes in the basin. Stream

gauges are operated on Andrews Creek, Icy Brook, and at The Loch outlet. Water chemistry monitoring

occurs at The Loch and on both inlet streams (see http://nh.water.usgs.gov/projects/sleepers/index.htm ).

Large glaciers that covered much of Rocky Mountain National Park during the late Pleistocene

sculpted the basin into characteristic glacial landforms, including steep U-shaped valleys, cirques, and

arêtes. When the glaciers retreated about 12,500 years ago, they deposited till of varying thickness, which

is confined mostly to the forested, lower part of the basin. Smaller, more recent glacial advances left

younger till, talus, and rock deposits in the upper parts of the basin. The younger glacial and periglacial

deposits are largely unvegetated.

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24 � Precipitation: quantity — 3 sites continuous; chemistry, 1 site, biweekly.

� Stream discharge, 2 sites (Andrews Creek-Loch Vale, Icy Brook-Loch Vale).

� Stream chemistry, (Andrews Creek, Icy Brook-Loch Vale).

� Spring discharge, conductance, and temperature, 3 sites continuous.

� Spring water chemistry, 3 sites biweekly, 20–30 sites once during low-flow season.

� Soil lysimeters, 5 sites, biweekly to monthly during summer and fall.

� Snowpack amount and chemistry (depth, snow-water equivalent) basin-wide survey at

maximum accumulation, index sites biweekly to monthly.

� Selected microenvironment runoff, e.g., rock outcrop, talus fields, weekly to monthly.

� Meteorology: 3 sites (wind speed, wind direction, air temperature, incoming and outgoing

radiation, relative humidity), continuous.

� Gas flux (CO 2 and CH 4 ) in wetland, forest, and talus soils, weekly to monthly; CO 2

concentrations in surface waters at 10–15 sites several times annually.

� Snowmelt lysimeter discharge and chemistry, monitored for three years, currently inactive. Av ailable water che m istry data include major ions, nutrients, DIC, DOC, and, for selected samples,

a range of isotopes including 2 H/H, 18 O/ 16 O, 15 N/ 14 N and 18 O/ 16 O in nitrate ion, 35 S, 34 S/ 32 S, 87 Sr/ 86 Sr, 13 C,

12 C). Monitoring of precipitation and hydrology include the following elements:

Atmospheric dep o sition of N to Loch Vale is high compared to most other sites in the Rockies,

although considerably lower than most impacted sites in eastern North America and Europe. The

alpine/subalpine ecosystem at Loch Vale exhibits symptoms of advanced watershed N saturation,

indicating sensitivity to N deposition. Talus landscapes contribute substantially to N export in streamflow,

and soil microbial processes are important in cycling N, even in areas such as talus that have little soil

development. Research at this site indicates that N export is a function of both deposition and internal Ncycling processes that are affected by variability in climate.

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28 � Identify and quantify the major processes that control surface water acidity, with a major

2− emphasis on (1) the role of excess SO 4 and nitrate provided via atmospheric deposition and

experimental application, and (2) the rate of cation supply from chemical weathering and

cation desorption.

� Assess the quantitative and qualitative responses at the watershed level to different (both

increased and decreased) levels of acidic deposition.

� Evaluate the ability of existing models of water acidification to predict short- and long-term

chemical variations in surface water chemistry and to predict watershed soil responses to

increased and decreased loading of strong acids. 

# A.2.7. Other Monitoring Programs

A.2.7.1. Bear Brook

The Bear Brook Watershed (BBW) is located in eastern Maine (44°52'15" latitude, 68°06'25"

longitude), approximately 60 km from the Atlantic coastline. The BBW is a paired watershed study

funded by EPA since 1987 as part of The Watershed Manipulation Project (WMP) within the National

Acid Precipitation Assessment Program (NAPAP) (see http://hydromodel.com/bbwm.htm ;

http://www.umaine.edu/DrSoils/bbwm/bbwm.html ). As a long-term research watershed, the BBW

includes bench-scale, micro-site, plot, and whole watershed investigations. The major purposes of the

BBW project are to:

The watershed in c ludes two first order streams: East Bear Brook (EBB) and West Bear Brook

(WBB). On each stream, a catchment outlet was selected and gauged so that both streams have about the

same catchment area (EBB = 10.7 ha and WBB = 10.2 ha). Since streams are close and face the same

slope direction, the watersheds are geographically similar and are appropriate for a paired watershed

zintervals. Both watersheds have a maximum discharge of about 0.01 mm/ha/sec or 0.15 m 3 /s. Annual

water yield relative to incoming precipitation for WBB ranges from 68 to 77% and EBB ranges from 62–

68%.

Stream channels in each watershed are well defined. Each stream bed is approximately 1 m wide at

the weir and water flows over exposed bedrock in places. Elsewhere, the streambeds are comprised of

boulders and gravel. Both streams have undergone intermittent dry periods during summer over the

course of the study. One V-notch weir was constructed on each of the streams during winter 1987–1988.

Mean discharge in each stream is about 0.13 cfs.

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32 Sampling frequency at the weirs was every three weeks during the winter of 1986–1987 and at

least weekly thereafter. On the basis of sampling conducted prior to beginning the manipulation

experiment (1987–1989), the streams had the following characteristics: ANC, −5 to 90 μeq/L; airequilibrated pH, 4.7 to 7.2; specific conductance of approximately 26 μS/cm; and DOC of 1 to 4 mg C/l.

Soils in the Bear Brook watersheds are primarily Spodosols. The average depth of the overburden

in the watersheds is 0.5 m, with a range of 0 to 5.2 m. Soil pH (0.01M CaCl 2 ) values ranged from 2.9 in

the O horizon, to 3.9 in the B horizon, to 4.4 in the C horizon. The bedrock is primarily metamorphosed

and folded politic graded beds and quartzites, with granitic dikes. The surficial material is till.

The forest is comprised primarily of deciduous species with areas of conifers. Tree species include

American beech ( Fagus grandifolia ), birch ( Betula sp.), maple ( Acer sp.), red spruce ( Picea rubens ),

balsam fir ( Abies balsamea ), white pine ( Pinus strobus ), and hemlock ( Tsuga canadensis ). Coniferous

stands, which occupy approximately 17% of the total watershed area, occur more commonly in the upper,

steeper portions of the watersheds.

Although the Bear Brook project was intended as an experimental manipulation of West Bear

Brook, there is also great value in the long-term monitoring data collected at East Bear Brook, the nonmanipulated reference watershed. This two-decade long monitoring record provides information on the

response of an acid-sensitive low-order stream in Maine to changes that have occurred in atmospheric

deposition since 1986.

Results of the Bear Brook project have been widely published (Norton, 1994; cf. Kahl, 1993;

Norton, 1999; Norton, 1999).



## A.2.7.2. Shenandoah Watershed Study

The Shenandoah Watershed Study (SWAS) program is a monitoring and research network focused

on low-order, high-gradient streams associated with public lands in western Virginia (see

http://swas.evsc.virginia.edu/ ). The objectives of the program are to increase understanding of factors that

govern biogeochemical conditions and stressor-response relationships in forested mountain watersheds of

the central Appalachian region. Success in addressing these scientific and problem-oriented objectives has

been achieved through development of a data collection network that accounts for spatial gradients, as

well as temporal variation, in the chemical composition of the region’s relatively undisturbed headwater

streams.

The program is notable for the length of the continuous data record that has been obtained,

including the longest-running record (28 years) of stream water composition and discharge in the National

Park System. The SWAS c omponent of the program, which now includes 14 streams in Shenandoah

National Park, was initiated in 1979. The Virginia Trout Stream Sensitivity Study (VTSSS) component,

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17 Figure A­5. SWAS­VTSS Program Study Sites. The length of the stream water chemical composition

record for SWAS­VTSSS study watersheds is 20 to 28 years. SWAS sites are located in Shenandoah

National Park (shaded blue). VTSSS sites are mainly located in Virginia’s National Forests (shaded

green). which now includes 51 streams in National Forests and other conservation lands, was initiated in 1987.

The distribution of SWAS-VTSSS s tudy sites in relation to public lands is shown in Figure A-5.

The SWAS-VTSSS program has been maintained as a cooperative effort involving the Department

of Environmental Sciences at the University of Virginia, the National Park Service, the EPA, the USDA

Forest Service, the U.S. Geological Survey, the Virginia Department of Game and Inland Fisheries, and

Trout Unlimited. The monitoring sites account for ecological variation among the region’s forested

mountain watersheds with a data-collection strategy that represents: (1) spatial variation through the

distribution of hydrochemical monitoring within a lithologic classification system; and (2) temporal

variation through long-term data collection at fixed locations sampled at different frequencies.

The l ithologic classification system includes 6 classes based on the physical and chemical

properties of bedrock formations in the region. ANC and concentrations of related acid-base constituents

in stream waters, as well as other biotic and abiotic properties of watersheds, differ among the lithologic

classes.

The SWAS-VTSSS data collection framework is most-well developed in the Blue Ridge Mountains

Province within Shenandoah National Park, where stream water composition data are collected seasonally

at 14 sites, weekly at 6 sites, and every four hours during episodic high-flow conditions at 3 sites with

continuous discharge gauging. Stream water composition data are collected on a seasonal basis at an

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32 additional 51 sites located outside of the Park, in both the Blue Ridge Mountains and Ridge and Valley

Provinces.

Stream water samples collected through the SWAS a nd VTSSS p rograms are analyzed for ANC,

pH, and the major anions (SO 2−

4 , nitrate, and chloride) and cations (calcium, magnesium, potassium, and

sodium) by methods appropriate for low-ionic strength natural waters. Both the SWAS a nd VTSSS

sample streams were selected based on geographic distribution, representation of the major bedrock types

underlying the mountain ridges in the region, and minimization of recent watershed disturbance. All but a

few of the sample streams currently support reproducing populations of native brook trout. All of the

sample streams supported brook trout populations historically.

Sustained data collection in a network constructed of intensively studied sites nested within a

geographically extensive set of less intensively studied sites has allowed detection and interpretation of

change that has occurred in a context of multiple time scales and stressors. Responses to multi-year

changes in acidic deposition have been reflected in long-term trends in quarterly concentrations of SO 2−

4 ,

ANC, and other acid-base constituents of streams in the network. Expectations for southeastern

watersheds with soils that retain sulfur, for example, have been confirmed by the lack of regional

improvement in stream water quality following reductions in acidic deposition mandated by the Clean Air

Act. The acid-base chemistry of streams in the network also varies seasonally and on shorter time scales.

Weekly and higher-frequency automated stream water sampling during periods of high runoff have

supported the study of episodically more-acidic conditions, including the study of fish sensitivity with instream bioassays and development of models to predict severity and recurrence intervals.

By accounting for significant spatial gradients and temporal patterns in the region, the SWASVTSSS h ydrochemical data collection program provides a basis for both observing and interpreting

watershed-scale change, as well as an informed foundation for process-oriented research. Monitoring data

and research findings obtained through the SWAS-VTSSS p rogram have contributed to increased

scientific understanding, as well as to policy formulation and implementation.

The mathematical model, Model of Acidification of Groundwater in Catchments (MAGIC), was

first calibrated using data obtained for White Oak Run, a SWAS-VTSSS study stream in Shenandoah

National Park. MAGIC is the most widely used acid-base chemistry model in the U.S. and Europe and the

principal model used by the National Acid Precipitation Assessment Program in the 1980s to estimate

future damage to lakes and streams in the eastern U.S. The MAGIC model has since been applied in a

number of regional assessments that relied extensively on stream water and soils data obtained through

the SWAS-VTSSS p rogram. Among these are:

August 2008 A­31 DRAFT—DO NOT QUOTE OR CITE� The Southern Appalachian Mountain Initiative, a multi-state effort to evaluate alternative

approaches to solving regional air-pollution problems. MAGIC projections indicated that even

ambitious emission control strategies would not result in near-term recovery of the region’s

most acidified surface waters – a consequence of base-cation depletion in soils exposed to

decades of acidic deposition.

� The Shenandoah Assessment, an assessment of acidification effects on aquatic systems in

Shenandoah National Park. MAGIC reconstructions indicated that Park streams associated

with base-poor bedrock lost about 70 μeq/L between 1900 and 1990. MAGIC projections

indicated that some streams may recover given prospective reductions in acidic deposition, but

others will not. 

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31 Data and findings provided through the SWAS-VTSSS program have also proven relevant to the

evaluation and implementation of national air pollution control policies. The SWAS-VTSSS program

provides data for the EPA’s long-term monitoring of surface water response to legislated reductions in

sulfur emissions. Whereas SO 2−

4 concentrations in surface water declined during the 1990–2000 period

for four northeastern regions with sensitive surface waters, the SWAS-VTSSS study region, in contrast,

experienced increasing stream-water SO 2−

4 concentrations and continuing acidification.

Recent publications that were based on analyses of SWAS-VTSSS d ata include Cosby et al. (1991,

Stoddard et al. (1993, Sullivan et al. (Sullivan, 2003), 2004), and Webb et al. (2004).



## A.2.7.3. Fernow

The Fernow Experimental Forest, established in 1934, is located just south of the city of Parsons in

the most mountainous region of West Virginia. It is surrounded by the Monongahela National Forest,

which comprises about 900,000 acres of rugged, hilly terrain. Most research at Fernow is focused on

improvement of forest management (see http://www.fs.fed.us/ne/parsons/fefhome.htm ).

Scientists at Fernow are developing information and techniques for sustainably managing

hardwood forests in the central Appalachians. The mixed hardwood forest covers about 78% of West

Virginia and supplies important timber products, provides recreational opportunities, and supports a

diverse assemblage of wildlife and plant species.

The Fernow Experimental Forest was heavily logged between 1905 and 1911. The forest now

contains about 1900 ha of second- and third-growth Appalachian hardwood stands, which are

representative of average to better than average sites found on approximately 4 million ha of the forest

type in West Virginia and surrounding states. At the lowest elevations, the original forests consisted

mainly of hardwoods, with eastern hemlock ( Tsuga canadensis [L.] Carr.) along stream bottoms and on

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34 north slopes. Forests at the higher elevations were dominated by red spruce ( Picea rubens Sarg.) and

hemlock. Small patches of pure spruce occurred on the tops of the mountains.

Elevations in the Fernow range from 533 to 1112 m, with slopes of 10% to 60%. A rock layer

composed of fractured hard sandstone and shale underlies most of the Fernow. A majority of the soils are

of the Calvin and Dekalb series, which originated from these rocky materials (loamy-skeletal mixed

mesic Typic Dystrochrepts). On the southeastern part of the forest, Greenbrier limestone outcrops to

produce a midslope zone of limestone soil of the Belmont series (fine-loamy mixed mesic Typic

Hapludalfs). Almost all Fernow soils, including the sandstone, shale, and limestone soils, are welldrained, medium textured loams and silt loams. Average soil depth is about 1 m, and average soil pH is

about 4.5.

A rainy, cool climate is typical on the Experimental Forest. Precipitation, which averages about 145

cm per year, is evenly distributed throughout the year. Mean annual temperature is about 9 °C, and the

length of the growing season is approximately 145 days.

The forest types and conditions today reflect the site qualities and past history of the area. Oaks

( Quercus spp.) are most common and are found on all sites along with American beech ( Fagus

grandifolia Ehrh

. ) and sweet birth ( Betula lenta L.). Excellent sites in coves and on north slopes support

primarily northern red oak ( Quercus rubra L.), sugar maple ( Acer saccharum Marsh.), yellow-poplar

( Liriodendron tulipifera L.), black cherry ( Prunus serotina Ehrh.), white ash ( Fraxinus americana L.),

basswood ( Tilia americana L.), cucumbertree ( Magnolia acuminata L.), and beech. Fair sites on south

and east slopes usually support oak stands composed of red oak, white oak ( Quercus alba L.), chestnut

oak ( Quercus prinus L.), and scarlet oak ( Quercus coccinea Muenchh.). Other fair site species include red

maple ( Acer rubrum L.), sweet birch, black gum ( Nyssa sylvatica Marsh.), sassafras ( Sassafras albidum

Nutt.), and sourwood ( Oxydendrum arboreum [L.] DC.). Good sites commonly support a mixture of

excellent and fair site species. Black locust ( Robinia pseudoacacia L.), sweet birch, and Fraser magnolia

( Magnolia fraseri Walt.) are consistent but generally minor components of the forest on all sites.

American chestnut was a major forest component until it was eliminated by the chestnut blight.

The Fernow Experimental Forest encompasses practically the entire Elk Lick Run drainage, which

is about 5.8 km long and 3.5 km across at the widest point. Elk Lick Run has seven major tributaries

including Big Spring, which drains a headwater limestone formation. Headwater areas on two of these

tributaries have been gauged to show how forest management influences streamflow.

Research on the Fernow Experimental Forest by the Timber and Watershed Project scientists is

done in cooperation with the Monongahela National Forest, West Virginia University, Marshall

University, Pennsylvania State University, Virginia Tech, and the West Virginia Division of Natural

Resources.

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27 Scientific studies on the Fernow have followed two lines of research, with considerable overlap.

Silvicultural research, focused mostly on mixed hardwood stands, addresses questions relating to

regenerating, growing, tending, and harvesting trees and stands. Watershed research has addressed some

of the more basic questions about water use by forests and forest hydrology, as well as critical issues

affecting roads, best management practices, and forest management effects on water and soil resources.

The Fernow also has been in the forefront of research on acidic deposition and N saturation. A wholewatershed acidification study has been conducted since 1989. Recently, research on threatened and

endangered species has assumed a more prominent role, due to the presence of Indiana bat and running

buffalo clover on the Fernow.



## A.2.7.4. National Ecological Observatory Network

The National Ecological Observatory Network (NEON) is a continental-scale research platform

that is primarily focused on discovering and understanding the impacts of climate change, land-use

change, and invasive species on ecology. It will also generate data that will be useful for assessing effects

of NO X and SO X deposition on ecosystems. NEON has not yet been implemented; it is described here

because it represents an ambitious monitoring program that is expected to be very useful in the near

future. NEON will gather long-term data on ecological responses of the biosphere to changes in land use

and climate, and on feedbacks with the geosphere, hydrosphere, and atmosphere. NEON is proposed as a

national observatory, consisting of distributed sensor networks and experiments, linked by advanced

cyber infrastructure to record and archive ecological data for at least 30 years. Using standardized

protocols and an open data policy, NEON is intended to gather essential data for developing scientific

understanding and theory required to manage the nation’s ecological challenges. The program description

is found at www.neoninc.org/.



# A.3. Modeling

A.3.1. Principal Ecosystem Models Used in the U.S.

It is particularly difficult to study endpoints at the larger levels of biological organization (e.g., at

the population, community, biogeochemical, and ecosystem-level) with monitoring studies. Geographic

areas are larger, and timeframes are longer, rendering it difficult to obtain data in sufficient quantity to

detect impacts unless they are exceptionally severe. Therefore, the most common approach to study

endpoints at these scales is to develop and apply a model. Models may be calibrated using data from

monitoring, survey, or laboratory or field experiments and are useful tools in predicting larger-scale,

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32 longer-term impacts. However, verifying the predictions and assessing the overall validity of the model

can be challenging.

Some of the most frequently used ecosystem models designed to quantify effects of atmospheric N

and S d eposition are discussed below. It is important to note that the ecosystem models are parameterized

for specific areas and may not be readily applicable to other locations without significant reparameterization.

There are four principal models that are currently being used in the U.S. to assess the effects of S

and N deposition on terrestrial and freshwater aquatic ecosystems: MAGIC, NuCM, PnET/BGC, and

DayCent-Chem. Two models, SPARROW and WATERSN, are commonly used to evaluate N loading to

large river systems and to estuaries. These six models are briefly reviewed in the following sections. Each

review begins with a summary of the provenance and conceptual basis of the model and contains

references to some of the published applications. This is followed by a more detailed description of the

processes included in the model, the inputs required, and the output variables simulated by the model.

The ranges of process complexity, temporal resolution and spatial discretization represented in

these models are considerable. These ranges make comparative summaries of inputs, outputs, and

processes across the models problematic. The models are all currently in use because they are, in a sense,

complementary to each other, with each providing an approach or satisfying requirements unique to their

own structure and intended applications. As a result, there is no good way to develop satisfying

comparative equivalences among the components of the various structures. It is also beyond the scope of

this document to present the level of detail necessary to run any of the models. The descriptions below

must of necessity be brief. References to appropriate texts designed to provide more detail are given for

each model.

Following the discussion of the four models most frequently used in the U.S., there are brief

descriptions of the most important models of S and N deposition effects that are being used in Europe and

elsewhere.



## A.3.1.1. MAGIC

The MAGIC model (Cosby, 1985; Cosby, 1985; Cosby, 1985) is a mathematical model of soil and

surface water acidification in response to atmospheric deposition based on process-level information

about acidification. MAGIC has been applied extensively in North America and Europe to both individual

sites and regional networks of sites, and has also been used in Asia, Africa and South America. The utility

of MAGIC for simulating a variety of water and soil acidification responses at the laboratory, plot,

hillslope, and catchment scales has been tested using long-term monitoring and experimental

manipulation data.

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16 MAGIC has been widely used in policy and assessment activities in the U.S. and in several

countries in Europe (Cosby, 1985; Cosby, 1990; Cosby, 1995; Cosby, 1996; Ferrier, 2001; Jenkins, 1990;

Clair, 2004; Sullivan, 1998; Moldan, 1998; Hornberger, 1989; Whitehead, 1997; Whitehead, 1988;

Wright, 1994; Wright, 1998; e.g.`, \Beier, 1995; Sullivan, 2006).



### MAGIC Model Structure

MAGIC is a lumped-parameter model of intermediate complexity, developed to predict the longterm effects of acidic deposition on surface water chemistry (see Figure A-6). The model simulates soil

solution chemistry and surface water chemistry to predict the monthly and annual average concentrations

of the major ions in these waters. MAGIC consists of: (1) a section in which the concentrations of major

ions are assumed to be governed by simultaneous reactions involving SO 2−

4 adsorption, cation exchange,

dissolution-precipitation- speciation of aluminum, and dissolution-speciation of inorganic carbon; and (2)

a mass balance section in which the flux of major ions to and from the soil is assumed to be controlled by

atmospheric inputs, chemical weathering, net uptake and loss in biomass and losses to runoff. At the heart

of MAGIC is the size of the pool of exchangeable base cations in the soil. As the fluxes to and from this

pool change over time owing to changes in atmospheric deposition, the chemical equilibria between soil

and soil solution shift to give changes in surface water chemistry. The degree and rate of change of

surface water acidity thus depend both on flux factors and the inherent characteristics of the affected soils.

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15 Figure A­6. Conceptual structure of the MAGIC model showing major pools and fluxes included in

simulation of effects of S and N deposition.

Cation exchange is modeled using equilibrium (Gaines-Thomas) equations with selectivity

coefficients for each base cation and aluminum. SO 2−

4 adsorption is represented by a Langmuir isotherm.

Aluminum dissolution and precipitation are assumed to be controlled by equilibrium with a solid phase of

aluminum trihydroxide. Aluminum speciation is calculated by considering hydrolysis reactions as well as

complexation with SO 2−

4 , fluoride and dissolved organic compounds. Effects of carbon dioxide 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 rates are assumed to be constant. Two

alternate mechanisms are offered for simulation of nitrate and ammonium in soils and water: (1) first

order equations representing net uptake and retention; or (2) a set of equations and compartments

describing process-based N dynamics controlled by C and N pools and fluxes in the compartments.

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

the model. These inputs are generally assumed to be uniform over the catchment. Atmospheric fluxes are

calculated from concentrations of the ions in precipitation and the rainfall volume into the catchment. The

atmospheric fluxes of the ions must be corrected for dry deposition of gas, particulates and aerosols and

for inputs in cloud/fog water. The volume discharge for the catchment must also be provided to the model.

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35 In general, the model is implemented using average hydrologic conditions and meteorological conditions

in annual or seasonal simulations, i.e., mean annual or mean monthly deposition, precipitation and lake

discharge are used to drive the model. Values for soil and surface water temperature, partial pressure of

carbon dioxide and organic acid concentrations must also be provided at the appropriate temporal

resolution.

The MAGIC model can be implemented as a one- or two-soil representation of a catchment with or

without wetlands. Atmospheric deposition enters the soil compartment(s) and the equilibrium equations

are used to calculate soil water chemistry. The water is then routed to the stream compartment, and the

appropriate equilibrium equations are reapplied to calculate runoff chemistry. Input-output mass balance

equations are provided for base cations and strong acid anions, and charge balance is required for all ions

in each compartment (for complete details of the model see \Cosby, 1985; Cosby, 1985; Cosby, 1985;

Cosby, 2001).

For most applications, model outputs for 15 stream water variables are used. These variables

consist of the concentrations of 10 ions (H; Ca; Mg; Na; K; NH 4 ; SO 2−

4 ; NO 3 ; Cl; and total inorganic Al),

the stream discharge (Q), stream pH, sum of base cation (SBC) concentrations (SBC = Ca + Mg + Na + K

+ NH 4 ), sum of mineral acid anion (SAA) concentrations (SAA = Cl + SO 2−

4 + NO 3 ) and the charge

balance acid neutralizing capacity (ANC = SBC

– SAA). These variables are expressed in units of m/yr

(or m/mo) for Q, μmol/L for inorganic Al, and μeq/L for all other variables. In addition, model output for

7 soil and soilwater variables are frequently used, the total base saturation and individual cation

saturations for Ca, Mg, Na, and K, the soilwater pH and the Ca/Al ratio in soil water.

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

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

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

used for calibration is not arbitrary. Model output will be more reliable if the annual flux estimates used in

calibration are based on a number of years rather than just one year. There is a lot of year-to-year

variability in atmospheric deposition and catchment runoff. Averaging over a number of years reduces the

likelihood that an “outlier” year (very dry, etc.) is used to specify the primary data on which model

forecasts are based. On the other hand, averaging over too long a period may remove important trends in

the data that need to be simulated by the model.

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

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

needed for calibration are the concentrations of the individual base cations (Ca, Mg, Na, and K) and acid

anions (Cl, SO 2−

4 , and NO 3 ) and the pH. The soil data used in the model include soil depth and bulk

density, soil pH, soil cation-exchange capacity, and exchangeable bases in the soil (Ca, Mg, Na, and K).

The atmospheric deposition inputs to the model must be estimates of total deposition, not just wet

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33 deposition. In some instances, direct measurements of either atmospheric deposition or soil properties

may not be available for a given site with stream water data. In these cases, the required data can often be

estimated by: (a) assigning soil properties based on some landscape classification of the catchment; and

(b) assigning deposition using model extrapolations from some national or regional atmospheric

deposition monitoring network.

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

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

individual soil horizons at that sampling site can be aggregated based on horizon, depth, and bulk density

to obtain single vertically aggregated values for the site, or the stratified data can be used directly in the

model.

Calibration of the model (and estimation of historical changes at the modeled sites) requires a

temporal sequence of historical anthropogenic deposition. Current understanding of ecosystem responses

to acidic deposition suggests that future ecosystem responses can be strongly conditioned by historical

acid loadings. Thus, as part of the model calibration process, the model should be constrained by some

measure of historical deposition to the site. However, such long-term, continuous historical deposition

data may not exist. The usual approach is to use historical emissions data as a surrogate for deposition.

The emissions for each year in the historical period can be normalized to emissions in a reference year (a

year for which observed deposition data are available). Using this scaled sequence of emissions, historical

deposition can be estimated by multiplying the total deposition estimated for each site in reference year

by the emissions scale factor for any year in the past to obtain deposition for that year.



## A.3.1.2. NuCM Model

The current NuCM model is based on the original Integrated Lake Watershed Acidification Study

(ILWAS) model of the 1980s (cf. Chen, 1984; Goldstein, 1984; Gherini, 1985). NuCM was developed as

an extension to the ILWAS m odel by investigators in the Integrated Forest Study see (Johnson, 1992), and

the model code was written by Tetra-Tech, Inc. (Liu, 1991). NuCM was developed to explore potential

effects of atmospheric deposition, fertilization and harvesting in forest ecosystems. Because NuCM was

designed primarily for simulating the effects of atmospheric deposition on nutrient cycling processes, its

construction emphasizes soil and soil solution chemistry (Liu, 1991). As a stand-level model, NuCM

incorporates all major nutrient cycling processes (uptake, translocation, leaching, weathering, organic

matter decay, and accumulation). Vegetation is divided into leaf, bole and root compartments for underand overstory vegetation. NuCM simulates the cycling of N, P, K, Ca, Mg, Na, and S b ased on expected

optimal growth rates (input by the user and reduced in the event of nutrient limitation), user-defined

litterfall, weathering, N and S m ineralization rates, soil minerals composition, initial litter, soil organic

matter pools, and C/N ratios.

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33 The model has been calibrated for different vegetation types, including a loblolly pine ( Pinus taeda

L.) stand at Duke University (Johnson, 1995), a mixed deciduous stand at Walker Branch (Johnson, 1993)

and a red spruce ( Picea rubens Sarg. ) stand in the Great Smoky Mountains (Johnson, 1996). The NuCM

model was used as part of the Southern Appalachian Mountain Assessment (Sullivan, 2002).



### NuCM Model Structure

In NuCM, the ecosystem is represented as a series of vegetation and soil components. The

overstory consists of one generic conifer and one generic deciduous species of specified biomass and

nutrient concentration (foliage, branch, bole, roots). For mixed species stands, average values for biomass

and nutrient concentration by component must be used. NuCM also includes an understory, which can be

divided into canopy, bole, and roots. Maximum potential vegetative growth in the model is defined by the

user and is constrained in the model by the availability of nutrients and moisture. The forest floor is

simulated from litterfall inputs and litter decay. Litterfall mass inputs are defined by the user, and litter

decay is represented as a four stage process where: (1) coarse litter decays to fine litter; 2) fine litter

decays to humus and cations; 3) humus decays to organic acids, NH + 2− +

4 , SO 4 , H , and CO 2 ; and (4)

organic acids decay to NH + 4 , SO 2−

4 , H +

, and CO 2

. Each stage is represented as a first-order equation.

The soil includes multiple layers (up to 10), and each layer can have different physical and

chemical characteristics. The user defines bulk density, cation exchange capacity, exchangeable cations,

adsorbed phosphate and SO 2−

4 , and four soil minerals and their composition. These inputs define the

initial soil exchangeable/adsorbed pools and total pools. Initial total soil N pools are simulated from

litterfall and decay, as described above, and user-defined C/N ratios. Vegetation, litter, and soil pools

change over a simulation in response to growth, litterfall and decomposition, and nutrient fluxes via

deposition, leaching and weathering.

The processes that govern interactions among these pools include translocation, uptake, foliar

exudation and leaching, organic matter decay, nitrification, anion adsorption, cation exchange and mineral

weathering. Translocation, defined as the removal of nutrients from foliage prior to litterfall, is userspecified. Maximum uptake is calculated from biomass and nutrient concentrations; actual uptake is equal

to this maximum value when sufficient nutrients are available and reduced when nutrients become

limiting. Reduced uptake first allows reduced nutrient concentrations in plant tissues, then causes a

reduction in growth. Foliar exudation and leaching rates are simulated as proportional to foliar

concentrations using user-defined coefficients.

Mineral weathering reactions are described in the model using rate expressions with dependencies

on the mass of mineral present and solution-phase hydrogen-ion concentration taken to a fractional power.

Cation exchange is represented by the Gapon equation. The model simulates a tri-protic organic acid with

a fixed charge density. Nitrification is represented in the form of a Michaelis–Menton rate expression.

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4 adsorption is represented by a

Langmuir adsorption isotherm.

Climate inputs to the NuCM model are through input meteorological files (typically 1 to 5 years

long), which are repeated in order to generate long-term simulations. The meteorological files contain

daily values for precipitation quantity, maximum and minimum air temperature, cloud cover, dewpoint,

atmospheric pressure, and wind speed. Monthly soil temperature data are also required.

Precipitation is routed through the canopy and soil layers and evapotranspiration, deep seepage,

and lateral flow are simulated. The movement of water through the system is simulated using the

continuity equation, Darcy’s equation for permeable media flow, and Manning’s equation for free surface

flow. Percolation occurs between layers as a function of layer permeability’s and differences in moisture

content. Nutrient pools associated with soil solution, the ion exchange complex, minerals, and soil organic

matter are all tracked explicitly by NuCM.

Wet deposition is calculated from precipitation amounts and user-input air quality files which

define precipitation concentrations on a monthly basis. Dry deposition is calculated from air

concentrations in the air quality files combined with user-defined deposition velocities and simulated leaf

areas. Leaching is calculated from soilwater percolation and simulated soil solution concentrations using

the soil chemical and biological algorithms defined above for each soil horizon.

The only processes in the NuCM model that are explicitly temperature-dependent are evaporation,

occurrence of precipitation as rainfall versus snowfall, snowpack melting, litter decay, and nitrification.

Temperature affects processes such as cation exchange, mineral weathering, and uptake only indirectly.

Precipitation effects are manifested strictly through the hydrologic simulations; none of the nutrient

processes are dependent explicitly upon moisture.



## A.3.1.3. PnET­BGC

PnET-BGC is an integrated dynamic biogeochemical model that simulates chemical transformations of vegetation, soil and drainage water. The PnET-BGC model was formulated by linking two

submodels (vegetation and biogeochemical) to allow for the simultaneous simulation of major element

cycles in forest and interconnected aquatic ecosystems. The vegetation submodel is based on PnET-CN

(Aber, 1992; Aber, 1997; Aber, 1997), a simple generalized model of monthly carbon, water, and N

balances that provides estimates of net primary productivity, N uptake, and water balances. The

biogeochemical submodel BGC (Gbondo-Tugbawa, 2001, expands PnET to include vegetation and

organic matter interactions of other elements (Ca 2+

, Mg 2+

, K +

, Na +

, Si, S, P, Al 3+

, Cl

−

, and F

− ), abiotic soil

processes, solution speciation, and surface water process.

PnET-BGC was initially developed for and applied to the northern hardwood forest ecosystem. The

model has been tested using vegetation, soil and water chemistry data from the Hubbard Brook

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13 Experiment Forest (HBEF) (Gbondo-Tugbawa, 2001). The model has subsequently been applied to

intensively studied watersheds in the Adirondack and Catskill regions of New York and applied regionally

to the Adirondacks (Chen, 2005) and northern New England (Chen, 2005; Chen, 2005). PnET-BGC has

also been used to evaluate the effects of current and future atmospheric deposition scenarios (GbondoTugbawa, 2002; Sullivan, 2006).



### PnET­BGC Model Structure

PnET/BGC simulates major biogeochemical processes, such as forest canopy element

transformations, hydrology, soil organic matter dynamics, N cycling, geochemical weathering, and

chemical equilibrium reactions in solid and solution phases, and allows for simulations of land

disturbance (Gbondo-Tugbawa, 2001) (see Figure A-7). The model uses mass transfer relationships to

describe weathering, canopy interactions and surface water processes. Chemical equilibrium relationships

describe anion adsorption, cation exchange and soil solution and surface water speciation. Soil solution

equilibrium reactions are described using the tableau approach (Morel, 1993). A more detailed description

of the model can be found in Gbondo-Tugbawa et al., 2001a.

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7 Source: Gbondo­Tugbawa et al. (2001).



### Figure A­7. Structure of the PnET­BGC model illustrating the compartments and flow paths of

carbon and nutrients (C/Nut) within the model.

The model operates on a monthly time step and is applied at the stand to small-watershed scale.

The process of photosynthesis, growth and productivity, litter production and decay, mineralization of

organic matter, immobilization, and nitrification in PnET have been described in Aber and Federer (Aber,

1992) and Aber et al. (1997). The BGC submodel uses the Gaines–Thomas formulation (White, 1986) to

describe cation exchange reactions within the soil. The exchangeable cations considered in the model

include Ca 2+

, Mg 2+

, Na +

, H +

, Al 3+

, K +

, and NH + 4

. A pH-dependent adsorption isotherm is used to describe

the SO 2−

4 adsorption process. The speciation of monomeric aluminum is calculated in the model,

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31 including both organic and inorganic forms. Organic acids are described using a triprotic analogue

(Driscoll, 1994, and the total amount of organic acids is estimated as a certain fraction (based on the

charge density) of DOC. The model simulates ANC in surface waters as an analogue to ANC measured by

Gran plot analysis, by considering the contributions of DIC, organic anions and Al complexes (Driscoll,

1994).

The PnET/BGC model requires inputs of climate, wet and dry deposition chemistry, and

weathering data. Climate inputs consist of minimum and maximum air temperature, solar radiation, and

precipitation. The model uses a constant dry-to-wet deposition ratio by default, but a variable ratio can

also be applied (Chen, 2004). The model inputs utilize canopy enhancement factors to depict the

increased dry deposition observed in coniferous and mixed forest stands compared to hardwood forests.

Deposition and weathering fluxes for all major elements are required as model inputs. Weathering rates

are assumed to remain constant over time.

Calibration of PnET-BGC is based on empirical relationships and observations. The model uses

historical reconstructions of climate, atmospheric deposition, and land disturbance in order to construct

hindcasts of the response of forests to past acidic deposition. The model can also be used to predict the

response of acid-sensitive forest ecosystems to future changes in acidic deposition, for example in

response to controls on atmospheric emissions. A detailed description of the model, including a detailed

uncertainty analysis of parameter values, is available in Gbondo-Tugbawa et al. (2001).



## A.3.1.4. DayCent­Chem

DayCent-Chem links two widely accepted and tested models, one of daily biogeochemistry for

forest, grassland, cropland, and savanna systems, DayCent (Parton, 1998), and the other of soil and water

geochemical equilibrium, PHREEQC (Parkhurst, 1999). The linked DayCent/PHREEQC model was

created to capture the biogeochemical responses to atmospheric deposition and to explicitly consider

those biogeochemical influences on soil and surface water chemistry. The linked model expands on

DayCent’s ability to simulate N, P, S, and C ecosystem dynamics by incorporating the reactions of many

other chemical species in surface water.

Hartman et al. (2007) used DayCent-Chem to investigate how wet and dry deposition affect biological assimilation, soil organic matter composition, ANC and pH of surface waters, and also Al mobilization, soil base cation depletion, and base cation flux. Model results were tested against a long-term data

set available from Andrews Creek in Loch Vale Watershed, Rocky Mountain National Park, Colorado.



### DayCent­Chem Model Structure

DayCent is the daily time-step version of CENTURY, a non-spatial, lumped parameter model that

simulates C, N, P, S, and water dynamics in the soil-plant system at a monthly timestep over time scales

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35 of centuries and millennia (Parton, 1994). CENTURY can represent a grassland, crop, forest, or savanna

system with parameters that describe the site-specific plant community and soil properties. DayCent, the

daily timestep version of CENTURY, adds layered soil temperature, a trace gas submodel, a more detailed

soil hydrology submodel, and explicitly represents inorganic N as either NO − or + 3 NH 4 (Kelly, 2000) Del

Grosso, 2001; Parton, 1998). DayCent 5 is an object-oriented model written in the C++ programming

language that implements a layered soil structure and algorithms to manage soil layers. The model is

initialized with an organic soil depth and up to 10 soil layers, where each layer has a specified thickness,

texture, bulk density, field capacity, wilting point, and saturated hydraulic conductivity.

PHREEQC is a model based on equilibrium chemistry of aqueous solutions interacting with

minerals, gases, exchangers, and sorption surfaces. The model is written in the C programming language

and has an extensible chemical database. Version 2.7 of PHREEQC is used in the linked DayCent-Chem

model to compute aqueous speciation, ion-exchange equilibria, fixed-pressure gas-phase equilibria,

dissolution and precipitation of mineral phases to achieve equilibrium, and irreversible aqueous mineral

phase reactions. The aqueous model uses ion-association and Debye Huckel expressions. Ion-exchange

reactions are modeled with the Gaines-Thomas convention and equilibrium constants are derived from

Appelo and Postma (1993).

The DayCent-Chem model inputs are climate drivers consisting of daily precipitation, and minimum and maximum air temperatures. The model also requires daily atmospheric wet deposition concentrations for precipitation species Ca 2+

, Cl

−

, K +

, Mg 2+

, Na +

, NH + , NO

− 4 3 , SO 2−

4 , and H + and daily dry deposition amounts or dry/wet ratios for all precipitation species. Initial conditions for model simulations include: (1) initial snowpack water content and chemical composition; 2) initial soil solution concentrations; and (3) initial exchangeable cations in each soil layer. Potential annual denudation rates for each

mineral phase that could be dissolved in the soil, groundwater, or stream solutions must also be provided.

DayCent-Chem implements a geochemical submodel of layered pools and properties that provides

information exchange, such as of water fluxes and solute concentrations, between the coupled models,

and calculates daily geochemical outputs. The geochemical submodel defines soil layers and a

groundwater pool that correspond to those in Day-Cent 5’s original soil class. Surface water

concentrations are computed in a two-step process where solutes are first transported, and then

PHREEQC undertakes solution reactions. At each timestep, the model updates exchangeable base cation

pools and soil solutions in each soil layer, along with groundwater and stream solutions.

DayCent 5 output includes daily evapotranspiration; soilwater content; outflow; inorganic and

organic C, N, P, and S s tream fluxes; C, N, P, and S contents in soil and plant pools; net primary

production (NPP); nutrient uptake; trace gas flux; and heterotrophic respiration. In addition to standard

DayCent 5 outputs, at each daily timestep the model writes the solution chemistry for soil layers,

groundwater, and stream.

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15 Source: Preston and Brakebill (1999).



### Figure A­8. Mathematical form of the SPARROW model. 

## A.3.1.5. SPARROW

SPAtially Referenced Regressions on Watersheds (SPARROW) is a hybrid statistical/deterministic

model used to estimate pollutant sources and contaminant transport in surface waters. SPARROW can be

used to estimate pollutant loading to downstream receiving waters for a number of water quality

constituents. The model as constructed for evaluating N export to estuaries will be presented here.

SPARROW was first described by Smith et al. (1997) as a water quality model designed to reduce

problems with interpreting watershed data as a result of sparse sampling, network bias, and basin heterogeneity. SPARROW combines regression techniques and process information regarding contaminant

transport and retention in watershed and riverine systems. Literature values for watershed retention rates

are used; in-stream retention of N is estimated by a first-order decay function (Smith, 1997).

Others have developed similar regression models relating in-stream water quality measurements to

watershed nutrient sources and basin attributes (Howarth, 1996; Mueller, 1997; Jaworski, 1997). These

simple correlative models assume that contaminate sources and sinks are homogenously distributed and

do not make a distinction between watershed and in-stream loss processes. SPARROW is distinct from

these methods by incorporating spatial representation of basin attributes in the model. Model correlations

between basin attributes and water quality measurements are strengthened by incorporating these spatial

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Table

Total A­3. Parameter Estimates,

Nitrogen Sparrow Model Probability Levels, and Regression Results for the Chesapeake Bay

Explanatory Variables Parameter Estimates Probability Level

Nitrogen sources

Point Sources $

1.496 <0.005

Urban area (acres) 7.008 0.010 references (Alexander, 2001; Smith, 1997). Spatially referenced basin attributes include land use, point

and non-point N sources, temperature, soil permeability, and stream density, among others (Figure A-8).

(Preston, 1999) shows the mathematical form of the SPARROW model (Figure A-8). Smith et al. (1997)

provided an example of SPARROW model development for application to the conterminous U.S. Their

exploratory model included five N sources and eight land surface characteristics as potential factors that

deliver N from land to water. In-stream decay coefficients for three stream size classes were also tested

for significance (Table A-3).

The final model resulted in the inclusion of each of the five N sources and three (temperature, soil

permeability, and stream density) of the eight land to water delivery factors. Parameter selection was

primarily based on statistical significance. Further discussion regarding the exclusion of precipitation and

irrigated land, both of which were determined to be significant, can be found in Smith et al. (1997).

Parameter estimates are evaluated for robustness through the use of bootstrap analysis.

The bootstrap procedure involves randomly selecting, with replacement, M monitored loads and

associated predictor variables from among the observations in the data set ( M is the number of monitored

reaches in the reach network). Where a sampled observation has an upstream monitored load as one of its

predictors, the monitored value is used, regardless of whether the upstream station appears in the

bootstrap sample. Coefficient values are estimated from the bootstrap sample. The bootstrap process is

repeated 200 times, resulting in 200 estimates of each coefficient. From these estimates, the mean

coefficient value (called the bootstrap estimate), minimum confidence interval, and probability that the

estimated coefficient has the wrong sign are determined (Smith, 1997).

Spatial referencing in the model occurs in two ways: (1) land surface polygons are mapped in

conjunction with nonpoint contaminant sources and the land-water delivery variables (temperature, soil

permeability, stream density, etc.) and (2) the stream reach network is mapped along with point sources,

channel transport characteristics, and measured transport rates. The positive impacts of this spatial

referencing can be quantified by eliminating the channel decay coefficients from the model and creating a

new model with only the contaminant sources and land-water delivery variables in the original model

(Smith, 1997). Removing this spatial reference provided by the reach network results in a model with

significantly higher mean squared error and lower predictive capacity (Table A-4).Explanatory Variables Parameter Estimates Probability Level

Fertilizer application (lb/yr)

Livestock waste production (lb/yr)

Atmospheric deposition (lb/yr)

Land-to-water delivery

Temperature ( E F)

Precipitation (in)

Avg slope (%)

Soilpermeability (in/h)

Stream density (l/mi)

Wetland (%)

Instream loss (days) a

T1(Q # 200 ft 3 /s)

T2 (200 ft 3 /s < Q # 1,000 ft 3 /s)

T3(Q > 1,000 ft 3 /s)

Tu(reservoir retention)

R-squared

Mean square error

Number of observations 0.2790

0.3361

1.024

α

–

–

–

0.0754

–

–

δ

0.7595

0.3021

0.0669

0.4145

0.961

0.1669

79 <0.005

<0.005

<0.005

–

–

–

0.095

–

–

<0.005

<0.005

<0.005

<0.005

a T, travel time

Q, stream discharge

lb/yr, pounds per yr

E F, degrees Fahrenheit

in/h, inches per hour mi, mile;

in, inches

%, percent

ft 3 /s, cubic feet per second

–, value not statistically significant



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Table A­4. Effect of spatial referencing on measures

total N flux using the sparrow model. of regression model performance for predicting

Model Components Mean Square Error R2

Includes full spatial referencing (SPARROW)a 0.4544 0.8743

Excludes in­stream decay and reservoir retention 0.9659b 0.7307 SPARROW has also been applied to spatially identify N sources at the scale of the Chesapeake Bay

watershed (Preston, 1999). A similar set of N sources, land-to-water delivery parameters, and in-stream

loss rates to those used in Smith et al. (1997) were considered for this model (Preston, 1999). Only

estimates for parameters that were used in the final model are given in Table A-5.

The final model included five N sources, one land-to-water deliver parameter (soil permeability),

and four in-stream loss rates (including reservoir retention). Comparisons between predicted and observed

N loading provided an r 2 value of 0.961 (Preston, 1999). Because the data that are used in SPARROW are

spatially referenced, model results can be mapped.

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## A.3.1.6. WATERSN

The Watershed Assessment Tool for Evaluating Reduction Strategies for Nitrogen (WATERSN)

model is a steady-state numerical N budgeting model that estimates the amount of N exported to rivers

and estuaries from forest, agricultural and urban land uses. The model is intended to provide an

understanding of the relative contribution of N export from these land uses to estuaries, and to evaluate N

export reduction strategies that are specific to each land use type (Driscoll, 2007).

Figure A-9 shows a conceptual diagram of the N budgeting system used in WATERSN. A detailed

description of the original model calculations is provided in Castro et al. (2001). Subsequent model

applications (2003; Castro, 2002) Driscoll, 2003; Whitall, 2004) have developed modifications to the

approach originally described in Castro et al. (2001).

WATERSN uses calculations described in Jordan and Weller (1996) to estimate N inputs to the

watershed/estuary system. Estimated anthropogenic sources of N inputs to the modeled watershed/estuary

system include: (1) crop and lawn fertilizer application, (2) biotic N fixation by leguminous crops and

pastures, (3) atmospheric deposition of wet and dry inorganic N (NH + 4 , NO

− 3 ), (4) net N import of food

for human consumption, and (5) net N import of feed for livestock (Castro, 2002).



### Agricultural Areas

N available for water-borne export to estuaries from agricultural lands is determined as the

difference between N inputs and outputs (Castro, 2001). Modeled N inputs to agricultural lands consist of

wet and dry atmospheric NH + and NO − 4 3 deposition, N fertilization, biotic N fixation, and livestock waste

(Castro, 2002). Wet and dry deposition are derived from NADP and CASTNET data. Average annual wet

deposition rates of NH + NO − 4 and 3 are taken from NADP sites in or near the study watersheds. Dry

deposition of NH + 4 and NO − 3 is calculated as an average of all CASTNET sites nearest to the study

watersheds. WATERSN assumes that dry deposition of both NH + 4 and NO − 3 to the estuary surface is 25%

less than dry deposition to the watershed (Castro, 2002). Meyers et al. (2000) described the uncertainty of

estimates of wet and dry deposition and considered it to be no less than a factor of 2. Estimates of N

fertilization are taken from agricultural census data. WATERSN assumes that all fertilizer sold in a county

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### Figure A­9. Schematic diagram of the WATERSN approach to estimate the contribution made by

different N sources to the total N inputs an estuary.

is applied in that county. This is considered to be the most certain N input to the model (±25%) (Castro,

2002). WATERSN estimates both non-symbiotic and symbiotic N fixation for crops, pastures, hay fields

and upland forests. Non-symbiotic rates were taken from literature values for crops, orchards, upland

forests, and non-wooded pastures (Hendrickson, 1990; Stevenson, 1982; Woodmansee, 1978). Symbiotic

rates of N fixation are based on type of legume, crop N harvest, N in unharvested portions of crops, soil N

availability, and fertilization rate. These estimates are less certain than for N fertilization, but are noted as

being a relatively minor N source in most of the study watersheds. Livestock waste was calculated as the

difference between livestock consumption of N in feed and production of N in meat, milk, and eggs for

human consumption (Jordan, 1996).

N outputs from agricultural land include crop harvest, pasture grazing, volatilization of NH 3 and

denitrification. Data regarding crop harvest are obtained from agricultural census. N removed through

crop harvest is estimated by multiplying the crop harvest by the percent N in each crop. Estimates for

grazing are based on sheep, cattle, and horse populations (USDA online database in \Castro, 2002, their

dietary N requirements, and proportion of dietary N obtained from grazing (Jordan, 1996). NH 3

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31 volatilization is assumed to be 10% of the N input from fertilizer and atmospheric deposition, and 20% of

livestock manure inputs (Schlesinger, 1992). Denitrification rates were originally estimated as 10 to 30%

of the N inputs from fertilizer and atmospheric deposition and 20% of livestock N waste. Subsequent

applications of WATERSN (Castro, 2003) modified denitrification rates from agricultural lands to vary

with the mean watershed temperature and are based on a denitrification activity Q 10 value of 2 (Maag,

1997; Stanford, 1975). A Q 10 value of 2 suggests that the denitrification rate used by the model will

change by a factor of 2 for every 10 degree change in temperature based on a direct relationship between

temperature and denitrification.



### Urban Areas

N inputs to urban areas include atmospheric and non-atmospheric sources. The total atmospheric N

deposition input to urban areas is taken as the total (wet + dry) inorganic (NO − 3 and NH + 4 ) N deposition

rate to the watershed multiplied by the total urban area in the watershed. Non-atmospheric sources include

point sources (primarily waste water treatment plants) and non-point sources (septic systems and

pervious/impervious surface runoff) of N in urban areas (Castro, 2002).

N outputs from urban areas include waste water treatment plant effluent, septic system leachate,

and total N runoff from pervious and impervious lands. Measured total N data are used to calculate N

export for wastewater treatment plants that have available data. A strong regression relationship between

measured total N discharged from wastewater treatment plants and human populations that use

wastewater treatment facilities is used to estimate total N discharges from wastewater treatment plants

that do not have total N monitoring data available. Septic system output is determined by multiplying

watershed specific human per capita N excretion rates by the human population of the watershed.

WASTERSN assumes that 75% of this N is exported to the estuary (Castro, 2002). The soil water

assessment tool (SWAT) is used to estimate non-point source non-atmospheric total N runoff from

pervious and impervious urban lands. SWAT is a distributed parameter, continuous time model applicable

at the watershed scale. Required inputs to SWAT include climatic variables, soil properties, elevation,

vegetation information, and land use. SWAT is designed to predict land use and land management impacts

on water, sediment, and agricultural yields in large watersheds (Castro, 2002). The model assumes that

75% of atmospheric N inputs to urban areas is exported to the estuary (Fisher, 1991). Alternatively, this N

export term can be modified.



### Upland Forests

N inputs to forests are assumed to be in the form of atmospheric deposition and non-symbiotic N

fixation. Outputs from forests are estimated with a non-linear regression relationship between wet

deposition of inorganic N and stream water export of dissolved inorganic nitrogen (DIN) developed using

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20 Table A­5. Summary of N retention rates used in recent WATERSN studies.

Study Retention Type % N Retention

Castro

Castro

Castro et al. (Castro, 2001)

and Driscoll (Castro,

et al. (2003) 2002) In Stream*

Agriculture

In Stream*

Agriculture

Agriculture

Septic System

In Stream 30%

50%

30%

60%

40%

40%

Adjusted until predicted N flux matched observed fluxes

*In­stream N retention was only applied to lengths of river located above the “fall line.” Below fall line N inputs to streams were assumed to not be

attenuated due to the relatively short travel time to the estuary. The “fall line” is defined as the boundary between the Piedmont and Coastal Plain

physiographic provinces in the eastern U.S. Source: Castro and Driscoll (Castro, 2002). results from a multitude of forest watershed studies. Exported dissolved organic nitrogen (DON) was

assumed to be equal to 50% of the inorganic N export (Castro, 2002).



### Watershed and In­Stream N Retention

Model validation efforts using measured N fluxes from the USGS N ational Stream Quality

Accounting Network (NASQAN) have shown that WATERSN tends to overestimate N export from

watersheds to estuaries (Castro, 2002). These differences are not unexpected since WATERSN does not

account for watershed and in-stream N sinks. Attempts have been made to improve flux estimates by

accounting for watershed and in-stream N retention (Castro, 2002; Castro, 2001; Castro, 2003; Castro,

2003). A summary of the N retention rates applied to WATERSN in these studies is given in Castro and

Driscoll (2002) assumed that 30% of the total N that entered rivers above the fall line was lost during

transport to the fall line and that inputs that enter the river below the fall line were not attenuated because

of the relatively short travel times to the estuary See Table A-5. This 30% in-stream N retention value

represents the median retention value obtained in previous studies of northeastern U.S. rivers (Castro,

2001) and falls within the range of retention values estimated by Howarth et al. (1996) and Alexander

et al. (2000). Castro and Driscoll (2002) also incorporated watershed N retention fractions specific to

individual land uses. They assumed that 60% of the excess N from agricultural land and septic systems

was lost (retained within the watershed) due to watershed processes. Support for this value of N retention

was given by several reports of riparian N removal rates from agricultural land, ranging from about 50 to

90% (Jordan, 1993; Jacobs, 1985; Peterjohn, 1984; Lowrance, 1983). After incorporating these

assumptions, predicted fluxes closely matched (r 2

= 0.909) measured fluxes.

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### Figure A­10. WATERSN model estimates of anthropogenic N inputs to the estuaries of the

northeastern U.S., in kilograms per hectare per year.

Driscoll et al. (2003) applied WATERSN to investigate anthropogenic N loading to estuaries in the

northeastern U.S. The objectives of the study were to apply WATERSN to (1) quantify the inputs of

reactive N to the region ( Error! Reference source not found. ), (2) discuss the ecological effects of

regional elevated anthropogenic reactive N inputs, and (3) evaluate management options aimed at

mitigating the effects of these elevated anthropogenic N inputs. Modeled N reduction scenarios included

reductions atmospheric N emissions, increased N removal efficiencies of wastewater treatment plants,

offshore pumping of wastewater, reductions in agricultural N runoff to surface waters, and an integrated

management scenario consisting of a combination of N reductions from multiple sources. Other studies

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U.S. (Whitall, 2004; Castro, 2003) Whitall, 2006).



# A.3.2. Additional Effects Models Used Widely in Europe

The models of the effects of S a nd N deposition described below have been used primarily in

Europe. These descriptions are derived in part from the UNECE Convention of Long-Range

Transboundary Air Pollution Modelling and Mapping manual (Posch, 2003).



## A.3.2.1. The Very Simple Dynamic Model

The Very Simple Dynamic (VSD) soil acidification model is frequently used in Europe to simulate

acidification effects in soils when observed data are sparse. It only includes weathering, cation exchange,

N immobilization processes, and a mass balance for cations, sulfur and N. It resembles the model

presented by Reuss (1980) which, however, did not consider N processes. In the VSD model, the various

ecosystem processes have been limited to a few key processes. Processes that are not taken into account

include (1) canopy interactions, (2) nutrient cycling processes, (3) N fixation and NH 4 adsorption, (4)

SO 2−

4 transformations (adsorption, uptake, immobilization, and reduction), (5) formation and protonation

of organic anions, and (6) complexation of Al.

The VSD model consists of a set of mass balance equations, describing the soil input-output

relationships, and a set of equations describing the rate-limited and equilibrium soil processes. The soil

solution chemistry in VSD depends solely on the net element input from the atmosphere (deposition

minus net uptake minus net immobilization) and the geochemical interaction in the soil (CO 2 equilibria,

weathering of carbonates and silicates, and cation exchange). Soil interactions are described by simple

rate-limited (zero-order) reactions (e.g., uptake and silicate weathering) or by equilibrium reactions (e.g.,

cation exchange). It models the exchange of Al, H, and Ca + Mg + K with Gaines-Thomas or Gapon

equations.

Solute transport in VSD is described by assuming complete mixing of the element input within one

homogeneous soil compartment with a constant density and a fixed depth. Since VSD is a single layer soil

model neglecting vertical heterogeneity, it predicts the concentration of the soil water leaving this layer

(mostly the rootzone). The annual water flux percolating from this layer is taken as being equal to the

annual precipitation excess. The time step of the model is one year, and therefore seasonal variations are

not considered. A detailed description of the VSD model can be found in Posch and Reinds (2003).

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## A.3.2.2. SMART

The Simulation Model for Acidification’s Regional Trends (SMART) model is similar to the VSD

model, but somewhat extended. It is described in De Vries et al. (1989) and Posch et al. (1993). As with

the VSD model, the SMART model consists of a set of mass balance equations, describing soil inputoutput relationships, and a set of equations describing the rate-limited and equilibrium soil processes. It

includes most of the assumptions and simplifications given for the VSD model; and justifications for

them can be found in De Vries et al. (1989).

SMART models the exchange of Al, H, and divalent base cations using Gaines Thomas equations.

Additionally, SO 2−

4 adsorption is modeled using a Langmuir equation (as in MAGIC) and organic acids

can be described as mono-, di-, or tri-protic. Furthermore, it does include a balance for carbonate and Al,

thus allowing application to a range of site conditions, from calcareous soils to completely acidified soils

that do not have an Al buffer left. Recently, a description of the complexation of aluminum with organic

acids has been included. The SMART model has been developed with regional applications in mind, and

an early example of an application to Europe can be found in De Vries et al. (1994).



## A.3.2.3. SAFE

The Soil Acidification in Forest Ecosystems (SAFE) model has been developed at the University of

Lund (Warfvinge, 1993) and a recent description of the model can be found in Alveteg and Sverdrup

(2002). The main differences between the SMART and MAGIC models are: (a) weathering of base

cations is not a model input, but it is modeled with the PROFILE (sub-)model, using soil mineralogy as

input (Warfvinge, 1992) ; b) SAFE is oriented to soil profiles in which water is assumed to move

vertically through several soil layers (usually 4); and c) Cation exchange between Al, H, and (divalent)

base cations is modeled with Gapon exchange reactions, and the exchange between soil matrix and the

soil solution is diffusion-limited.

The standard version of SAFE does not include SO 2−

4 adsorption although a version, in which

SO 2−

4 adsorption is dependent on SO 2−

4 concentration and pH has recently been developed (Martinson,

2003). The SAFE model has been applied to many sites and more recently also regional applications have

been carried out for Sweden (Alveteg, 2002) and Switzerland (Kurz, 1998).



# A.3.3. Other Models

There are scores of models that can be useful in the context of developing a better understanding of

the ecological effects of atmospheric S and N deposition. In the preceding sections, we have attempted to

summarize a relatively small number of models that are most commonly used for this purpose in the U.S.

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Table A­6. Some examples of models that

understanding of the ecological efforts of

explicitly addressed in this Annex. could contribute to development

atmospheric S and N deposition, of a

but better

that are not

Model Name 1 Type 2 Support 3 Reference Notes

QUAL2K

WASP7

CE­QUAL­RIV1;

CE­QUAL­R1;

CE­QUAL­W2; CEQUAL­ICM

RCA

WARMS Water Quality Analysis

Simulation Program

Water quality models

(river, reservoir, and

estuary/ coastal)

supported by USACE

Row Column AESOP

W aterfowl Acidification

R esponse M odeling System A

A

A

A

A S

S

N

N

N 1

2

3

4

McNicol

(1995),

(2002) et al.

McNicol Q UAL2K is one dimensional river and stream water quality

model. QUAL2K assumes: that he channel is well­mixed

vertically and laterally; steady state hydraulics; diurnal

water­quality kinetics. QUAL2K addresses point and nonpoint loads, BOD/DO, non­living particulate organic matter

(detritus); denitrification; sediment­water interactions;

bottom algae; pH (both alkalinity and total inorganic

carbon).

WASP is a dynamic c ompartment­modeling program for

aquatic systems, i ncluding bo th the water column a nd the

underlying benthos. WASP allows the user to i nvestigate 1,

2, and 3 dimensional systems, and a variety of pollutant

t ypes. T he time varying processes of advection, dispersion,

po int and diffuse mass l oading a nd boundary exchange are represented in t he model. WASP also can be linked with

h ydrodynamic and sediment transport models that can provide flows, depths velocities, temperature, salinity and

sediment fluxes.

CE­QUAL­R1 i s a one­dimensional, vertical reservoir model

a nd CE­QUAL­W2 i s a two­dimensional (vertical a nd

longitudinal), laterally averaged, hydrodynamic a nd water

quality model. These two models are widely u sed by the

Corps of Engineers, other f ederal and state agencies, t he

pr ivate s ector, and agencies i n other countries. C E­QUALRIV1 was developed for highly unsteady flow conditions,

such as storm water flows a nd streams below pe aking

hydropower dams. CE­QUAL­ICM run in a 2D mode. This

approach h as been used for large, shallow waterways,

h arbors, a nd embayments.

RCA evaluates the fate and transport of conventional a nd

toxic pollutants in s urface waterbodies in one, two, or t hree

dimensions. RCA has been linked to various hydrodynamic

models. Subroutines h ave been de veloped to model

coliforms, pathogens, BOD/DO, simple a nd a dvanced

eutrophication, w etland systems, and toxic c ontaminants. A

sediment nutrient flux subroutine permits the coupling of the

water c olumn a nd sediment bed.

WARMS includes a n acidification model linked to fish a nd

waterfowl models. W ARMS uses pH, area, dissolved organic

c arbon, total P

, a nd presence of fish t o e stimate

pr eacidification, present a nd eventual s teady­state values for

pH

, fish pr esence and waterfowl breeding parameters under

pr oposed SO emission s cenarios.

2 and Europe, in particular those that contribute to substantive conclusions presented in the ISA. There are

many other models that are not covered in the discussion presented in this Annex. Several are highlighted

in Table A-6.

August 2008 A­56 DRAFT—DO NOT QUOTE OR CITEModel Name 1 Type 2 Support 3 Reference Notes

GT­MEL Georgia Tech hydrologic

model and the Multiple I N 5 GT­MEL is a spatially distributed, process­based

ecohydrology model that links a land surface hydrology

Element Limitation model model with a terrestrial biogeochemistry model. GT­MEL

differs from other available ecohydrology models in its

simplicity, flexibility, and theoretical foundation. The coupled

GT­MEL simulates the cycling and transport of water and

nutrients (C, N and P) within hillslopes and watersheds. The

model runs on a daily time step and can be applied to user­

2 defined landscape units that may vary in shape and size (m

to km 2 ). Thus, GT­MEL can provide detailed spatial and

temporal information on nutrient acquisition and turnover in

plants and soils, and terrestrial flow pathways and discharge

of water and nutrients to surface waters. The same set of

model equations applies to any terrestrial ecosystem

– agricultural crops, forests, grasslands, wetlands, tundra, etc.

GT­MEL simulates the effects of multiple interacting

stressors, including changes in land use, land cover, climate,

atmospheric CO 2 and N deposition.

ILWAS Integrated Lake­Watershed

Acidification Study I N Gherini

(1985) et al. ILWAS was developed to predict changes in surface water

acidity given changes in the acidity of precipitation and dry

deposition. The model routes precipitation through the

forest canopy, soil horizons, streams and lakes using mass

balance concepts and equations which relate flow to

hydraulic gradients. The physical­chemical processes which

change the acid­base characteristics of the water are

simulated by rate (kinetic) and equilibrium expressions and

include mass transfers between gas, liquid and solid phases.

THMB/IBIS I N 6, 7 THMB is a mechanistic simulation model of large river

systems that has been used recently in combination with a dynamic terrestrial ecosystem model IBIS to quantify nitrate

flux in the Mississippi River Basin. The coupled models

simulate time­varying flow and storage of water and N in

rivers, wetlands, and reservoirs, based on major source

inputs, subsurface drainage and N leaching, topography,

and precipitation and evaporation. Evaluations of the model

in the Mississippi basin indicated that the model accurately

simulated inter­annual variability in the water and N budget

from 1960 to 1994.

BIOME­BGC Biome­BGC is a multibiome generalization of T N 8 Biome­BGC is a computer program that estimates fluxes and

storage of energy, water, carbon, and N for the vegetation

FOREST­BGC and soil components of terrestrial ecosystems. The primary

model purpose is to study global and regional interactions

between climate, disturbance, and biogeochemical cycles.

DNDC Denitrificationdecomposition model T N 9 DNDC was initially developed to quantifying nitrous oxide

(N O) emissions from agricultural soils in the U.S. The

2 capability of the model to simulate soil biogeochemistry also

allows DNDC to model emissions from other ecosystems

through linkages with vegetation models; the model can be

applied from field site to regional scales. The core of DNDC

is a soil biogeochemistry model.

EPIC Agricultural dynamic

simulation model T N 11 EPIC is a widely used dynamic simulation model that

describes the influence of agricultural management on crop productivity and erosion. The model has been used in

studies of climate change, agricultural management and

policy, and water­quality. EPIC simulates N cycling processes

in soils­including mineralization, nitrification, immobilization,

NH 3 volatilization and denitrification, runoff and subsurface

leaching based on physical principles and parameter values

derived from extensive model testing and specific field

validation.

August 2008 A­57 DRAFT—DO NOT QUOTE OR CITEModel Name 1 Type 2 Support 3 Reference Notes

GLEAMS Groundwater Loading

Effects of Agricultural T N 12 GLEAMS was developed from both EPIC and CREAMS and

employs a more explicit description of soil water content. In

Management Systems GLEAMS, the concentration of nitrate­N removed via

denitrification is a function of the factors describing the soil

water content, the soil temperature, and the organic C

content. Under this formulation, denitrification only occurs if

the soil water content is greater than a parameter related to

the soil water content at field capacity and saturation. The

fraction of soil nitrate­ N lost to denitrification increases

quickly as soil water content increases beyond the field

capacity. The EPIC and GLEAMS method of simulating

denitrification neglects denitrification that may occur in

anaerobic micro­zones when the soil is not at field capacity

or saturation.

Hole­in­the­pipe Hole­in­the­pipe T N Davidson et al.

(2000) The Hole­in­Pipe model relates the emissions of nitrous

oxides to common soil processes. It regulates soil emissions

of NO and N O at two levels: 1st, the rate of N cycling

2 through ecosystems, which is symbolized by the amount of

N flowing through the pipes, affects total emissions of NO

and N O; 2nd, soil water content and perhaps other factors

2 affect the ratio of N O:NO emissions, symbolized by the

2 relative sizes of the holes through which nitric oxide and

nitrous oxide "leak.” Soil water content is so important

because it controls the transport of O 2 into soil and the

transport of NO, N O, and N

2 2

MERLIN Model of Ecosystem

Retention and Loss of

Inorganic Nitrogen T N Cosby et al.

(1997), Kjonaas

and Wright (1998) MERLIN is a catchment­scale mass­balance model of linked

carbon and N cycling in ecosystems for simulating leaching

losses of inorganic N. It considers linked biotic and abiotic

processes affecting the cycling and storage of N. The model

is aggregated in space and time and contains compartments

intended to be observable and/or interpretable at the plot or

catchment scale. The structure of the model includes the

inorganic soil, a plant compartment, and two soil organic

compartments. Fluxes in and out of the ecosystem and

between compartments are regulated by atmospheric

deposition, hydrological discharge, plant uptake, litter

production, wood production, microbial immobilization,

mineralization, nitrification, and denitrification. N fluxes are controlled by carbon productivity, the C:N ratios of organic

compartments and inorganic N in soil solution. Inputs

include time series, constants, rates, source terms, and soil

characteristics (Cosby, 1997).

NLM Waquoit Bay Nitrogen

Loading Model T N RTI International

(2001) The Waquoit Bay Nitrogen Loading model estimates inputs

from different N sources to defined land use categories and

then estimates losses of N in various compartments of the

watershed ecosystem, including the groundwater. This

empirical N loading model produces long­term avg output. It

is not currently endorsed by a federal agency but has been

published in peer­reviewed journals. Most applications of the

model have focused on the Cape Cod area of Massachusetts.

The empirical data for this model are specific to that area

and the model simulates N transport exclusively in the

subsurface (i.e., overland transport is not considered).

August 2008 A­58 DRAFT—DO NOT QUOTE OR CITEModel Name 1 Type 2 Support 3 Reference Notes

Simple Mass

Balance

Method/Steady

State Mass

Balance “Mass balance approach” T N Bhattacharya

et al. (2004),

Likens et al.

(1996), Rodriguez

and Macias (2006) Examples of two mass balance approaches are:

Simple Mass Balance: This model is based on a balance of

inputs and outputs of N according to the equation: Ndep +

Nfix

= Ni + Nu + Nad + Nde + Nfire + Neros + Nvol + Nle

where the subscripts denote: dep (deposition); fix (fixation);

i (immobilization); u (uptake); ad (adsorption); de

(denitrification); fire (N loss during combustion); eros (erosion); vol (volatilization); le (leaching).

Steady State Mass Balance: This method is the most

commonly used method for analysis of critical loads of acid

deposition. Its basic principle is based on identifying the

long­term avg sources of acidity and alkalinity in order to

determine the maximum acid input that will balance the

system at a biogeochemical safe­limit. Several assumptions

have been made in the steady state calculations. First, it is

assumed that ion exchange is at steady state and there is no

net change in base saturation or no net transfer of ANC from

soil solution to the ion exchange matrix. It is assumed that

for N there is no net denitrification, adsorption or desorption

and the N cycle is at steady state. Sulfate is also assumed to

be at steady state: no sulfide oxidation, sulfate uptake,

sulfate permanent fixation or sulfate reduction are significant. Simple hydrology is assumed where there is

straight infiltration through the soil profile.

HSPF/LSPC Hydrological Simulation

Program

­ FORTRAN T* S 12, 13 HSPF simulates watershed hydrology and water quality for

conventional and toxic pollutants. HSPF incorporates

watershed­scale ARM and NPS models into a basin­scale

analysis framework that includes fate and transport in

stream channels. It is a comprehensive model of watershed

hydrology and water quality that allows the integrated

simulation of land and soil contaminant runoff processes

with In­stream hydraulic and sediment­chemical

interactions. LSPC is a simplified version of HSPF. Although

LSPC was designed to provide a less data intensive

alternative to HSPF for modeling very large scale

watersheds, it can also be used to model smaller, more

detailed watersheds. The primary disadvantage of this

simplified version of HSPF is that the developers eliminated

the atmospheric deposition routines found in HSPF. For each

model run, it automatically generates comprehensive textfile output by subwatershed for all land­layers, reaches, and

simulated modules, which can be expressed on hourly or daily intervals. Output from LSPC has been linked to other

model applications such as EFDC, WASP, and CE­QUAL­W2.

PLOAD Pollutant Loading Model T* S 14 PLOAD is part of EPA’s BASINS (Better Assessment Science

Integrating Point and Nonpoint Sources) program and

estimates nonpoint pollution sources on an annual basis.

PLOAD can be combined with geographic information

system (GIS)–based data coverages to rapidly estimate N

loading to the bay using pass­through rates based on land

uses from EPA guidance documents, literature, or other

studies. This model is not precipitation driven; it does it

include N speciation. However, the model does capture

differences in N transport for different land uses in the

watersheds.

SWAT Soil and Water Assessment

Tool T* S van Griensven and

Bauwens (2001),

15 SWAT is a public domain river basin scale model actively

developed and primarily supported by the USDA (and

included within EPA's BASINS framework), that quantifies

the impact of land management practices in large, complex

watersheds. SWAT is a physically based model that applies

to all land uses and to include stormwater runoff in its

calculations. The model simulates NH 3 , nitrate, and organic

N throughout the waterbodies and vegetation in the

modeled system. At this time SWAT only accepts the nitrate

concentration in the rain as the N atmospheric component.

WARMF Watershed Analysis Risk

Management Framework T* S 16 WARMF includes a GIS­based watershed model that

calculates daily runoff, shallow groundwater flow, hydrology

and water quality of a river basin. A river basin is divided

into a network of land catchments (including canopy and soil

layers), stream segments, and lake layers for hydrologic and

water quality simulations. Inputs include meteorology, air

quality, point source, reservoir release, and flow diversion

data. WARMF also includes two watershed approach

modules for Consensus building and TMDL calculation.

August 2008 A­59 DRAFT—DO NOT QUOTE OR CITEModel Name 1 Type 2 Support 3 Reference Notes

DRAINMOD T* N 17 DRAINMOD quantifies N losses and transport from

agricultural lands with shallow water tables where artificial

drainage systems are used. Watershed­scale versions of

DRAINMOD have been developed and evaluated based on

data collected on a NC coastal plain. DRAINMOD is based on

water balances in the soil and at the soil surface. It uses

functional methods to quantify infiltration, subsurface

drainage, surface drainage, evapotranspiration, seepage, freezing, thawing, snowmelt, and seepage. The model

predicts the water Table depth and soil water contents

above the water Table, drainage rates and the other

hydrologic components on an hourly and daily basis for long

periods of hydrologic record. Hydrologic predictions of the

model have been tested and found to be reliable for a wide

range of soil, crop, and climatological conditions.

INCA T* N Wade et

(2005) al. INCA is a water and N mass balance simulation model; it

estimates the integrated effects of point and diffuse N

sources on stream nitrate and ammonium concentrations

and loads and also estimates the N loads related to

processes in the plant/soil system. It has been most

commonly applied to watersheds within the UK, but more recently has been modified for use in other European

watersheds. INCA quantifies plant uptake of nitrate and

ammonium, nitrification, denitrification, and mineralization

and immobilization within each land­use type and

subcatchment. Biogeochemical reactions are limited to the

soil zone from which water and N are leached to deeper

groundwater.

LWWM Linked

Watershed/Waterbody T* N 18 The original release of the LWWM coupled the RUNOFF

Block of the EPA’s SWMM model (Version 4.21) with the

Model EPA’s Water Quality Analysis Program (WASP5). All

components were accessed via a user­friendly operating

shell. The LWWM included a GIS interface based on

Arc/INFO to automate the reduction of spatial data within a watershed (i.e., land use and soils) for input into the

RUNOFF Block of SWMM. The LWWM included preprocessors for inputting data into the RUNOFF Block of

SWMM, WASP5 (Eutro and Toxi), and two hydrodynamic

models associated with WASP5 (RIVMOD and DYNHYD5), as

well as a graphical post­processor for the review of output

from all model components. The post­processor was also the

means by which nonpoint source loading files from RUNOFF

were mapped to WASP segments.

ReNuM a Regional Nutrient

Management Model T* N 19 ReNuMa is based on the Generalized Watershed Loading

Function (GWLF) model that has been used widely for

purposes such as TMDL development. ReNuMa improves on

GWLF by incorporating Net Anthropogenic N Inputs (NANI)

accounting system. The model now considers atmospheric

deposition, fertilizer application, septic system effluents, N

fixation, and denitrification.

RHESS T* N Boyer et

(2006) al. RHESS has been used to explore N dynamics at the

watershed scale. RHESSys simulates the coupled effects of

C, N, and hydrological processes by coupling biogeochemical

dynamics from the BIOME_BGC and the NGAS model used in

DAYCENT. Streamflow is based on the implementation of

variable source­area concepts based on topography,

quantifying routing of water through the landscape from

patch to patch using either a lumped topographic approach

adapted from TOPMODEL or a distributed approach adapted

from the DHSVM model

August 2008 A­60 DRAFT—DO NOT QUOTE OR CITEModel Name 1 Type 2 Support 3 Reference Notes

1 Type: A = aquatic; I = integrated aquatic/terrestrial; T = terrestrial; T*

= watershed

2 Support: S = currently supported by EPA; N = currently not supported by EPA

3 Websites:

1: River and Stream Water Quality Model (QUAL2K); http://www.epa.gov/athens/wwqtsc/html/qual2k.html

2: U.S.EPA's WASP Website; http://epawasp.com/

3: U.S. Army Core of Engineers Environmental Laboratory

­ Water Quality Models;

http://el.erdc.usace.army.mil/products.cfm?Topic=model&Type=watqual

4: Hydroqual: Row Column AESOP (RCA) Modeling Code Description and Technical Capabilities;

http://www.hydroqual.com/pdf/RCA_Desc_doc.pdf

5: Woods Hole Marine Lab, Ecosystems Center MEL home page http://ecosystems.mbl.edu/Research/Models/mel/welcome.htmll

6: IBIS (Integrated Biosphere Simulator); http://water.usgs.gov/software/hspf.html

7: THMB (Terrestrial Hydrology Model with Biogeochemistry)

­ formerly HYDRA; http://www.sage.wisc.edu/download/HYDRA/hydra.html

8: Biome­BGC: Terrestrial Ecosystem Process Model, Version 4.1.1; http://www.daac.ornl.gov/MODELS/guides/biome­bgc_guide.html

9: Denitrification Modeling Workshop: Model Summary; http://marine.rutgers.edu/BGC/RCNsite/WS1/WS1models/DNDC­2.pdf

10: EPIC Fact Sheet; http://www.brc.tamus.edu/epic/epfact2004.htm

11: GLEAMS Y2K Update Website; http://www.tifton.uga.edu/sewrl/Gleams/gleams_y2k_update.htm

12: U.S. EPA’s HSPF Website; http://www.epa.gov/ceampubl/swater/hspf/

13: USGS’s Water Resources Applications Software: HSPF Website; http://water.usgs.gov/software/hspf.htm

14: U.S. EPA's Better Assessment Science Integrating Point & Nonpoint Sources (BASINS) Website; http://www.epa.gov/waterscience/basins/

15: Soil & Water Assessment Tool; http://www.brc.tamus.edu/swat/

16: US EPA's Watershed Analysis Risk Management Framework (WARMF) Website; http://www.epa.gov/athens/wwqtsc/html/warmf.html

17: DRAINMOD Download Website; http://www.bae.ncsu.edu/soil_water/drainmod/

18: Linked Watershed Waterbody Model at the Southwest Florida Water Management District;

http://www.swfwmd.state.fl.us/software/lwwm.htm

19: Regional Nutrient Management (ReNuMa) at Cornell University College of Agriculture and Life Sciences;

http://www.eeb.cornell.edu/biogeo/nanc/usda/renuma.htm

August 2008 A­61 DRAFT—DO NOT QUOTE OR CITE

## A.3.3.1. Current Long­term Monitoring Data Sets Developed through the Hubbard

Brook Ecosystem Study

CURRENT LONG-TERM MONITORING DATA SETS DEVELOPED THROUGH

THE HUBBARD BROOK ECOSYSTEM STUDY

Physical/Hydrologic Monitoring

Instantaneous streamflow (9 stations)

Dailyprecipitation (24 stations)

Class A weather station data

Weekly snow depth on snow courses

Daily soil temperature and moisture

Air Chemistry

(SO 2 

### , HNO 3 

### , particulates, ozone)

Mirror Lake

Instantaneous streamflow (3 inlets,

outlet)

Dailyprecipitation (2 stations)

Weekly chemistry (3 inlets, outlet)

Bi-monthly limnology (temp, chemistry,

plankton) Solution Chemistry

Weekly bulk precipitation (6-10

stations)

Monthly soil solution WS5, WS6

Weekly stream at weirs of WS19

Monthly stream within WS5, WS6

Organisms

Birdpopulations

Phytophagous insect populations

WS2, WS4, WS5, WS6

Vegetation biomass, chemistry

Soils

Forest floor mass, chemistry (WS6,

WS5; 5-yr intervals)

Chemical and physical properties

from soil pits (WS5)

Chemical and physical properties

fromsoil bags

August 2008 A­62 DRAFT—DO NOT QUOTE OR CITE

# ANNEX A ­ References

Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N deposition on N

cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11: 639­

648.

Aber, J. D.; Federer, C. A. (1992) A generalized, lumped-parameter model of photosynthesis,

evapotranspiration and net primary production in temperate and boreal forest ecosystems.

Oecologia 92: 463-474.

Aber, J. D.; Ollinger, S.; Driscoll, C. (1997) Modeling nitrogen saturation in forest ecosystems in

response to land use and atmospheric deposition. Ecol. Model. 101: 61-78.

Alexander, R. B.; Smith, R. A.; Schwarz, G. E. (2000) Effect of stream channel size on the

delivery of nitrogen to the Gulf of Mexico. Nature 403: 758-761.

Alexander, R. B.; Smith, R. A.; Schwarz, G. E.; Preston, S. D.; Brakebill, J. W.; Srinivasan, R.;

Pacheco, P. A. (2001) Atmospheric nitrogen flux from the watersheds of major estuaries

of the United States: An application of the SPARROW watershed model. In: Valigura,

R.; Alexander, R.; Castro, M.; Meyers, T.; Paerl, H.; Stacey, P.; Turner, R. E., eds.

Nitrogen loading in coastal water bodies: an atmospheric perspective. Washington, DC:

American Geophysical Union; pp. 119-170.

Alveteg, M.; Sverdrup, H. (2002) Manual for regional assessments using the SAFE model (draft

version 8 April 2002). Lund, Sweden: Lund University, Department of Chemical

Engineering II.

Appelo, C. A. J.; Postma, D. (1993) Geochemistry, groundwater, and pollution. Rotterdam, The

Netherlands: Balkema.

Beier, C.; Hultberg, H.; Moldan, F.; Wright, R. F. (1995) MAGIC applied to roof experiments

(Risdalsheia, N; Gårdsjön, S.; Klosterhede, D. K.) to evaluate the rate of reversibility of

acidification following experimentally reduced acid deposition. Water Air Soil Pollut.

85: 1745-1751.

Bhattacharya, A.; Mudgal, R.; Taneja, A. (2004) Acid deposition and critical load analysis in

Agra, India. J. Hazard. Mat. B 106: 157-160.

Bormann, F. H.; Likens, G. E. (1967) Nutrient cycling. Science 155: 424-429.

Bormann, F. H.; Likens, G. E. (1979) Pattern and process in a forested ecosystem. New York,

NY: Springer-Verlag.

Boyer, E. W.; Alexander, R. B.; Parton, W. J.; Li, C.; Butterbach-Bahl, K.; Donner, S. D.;

Skaggs, R. W. (2006) Modeling denitrification in terrestrial and aquatic ecosystems at

regional scales. Ecol. Appl. 16: 2123-2142.

Brand, G. J.; Nelson, M. D.; Wendt, D. G.; Nimerfro, K. K. (2000) The hexagon/panel system

for selecting FIA plots under an annual inventory. In: McRoberts, R. E.; Reams, G. A.;

Van Deusen, P. C., eds. Proceedings of the first annual forest inventory and analysis

symposium. St. Paul, MN: Department of Agriculture, Forest Service, North Central

Research Station Gen. Tech. Rep. NC-213 U.S.; pp. 8-13. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

August 2008 A­63 DRAFT—DO NOT QUOTE OR CITE

### Castro, M. S.; Driscoll, C. T. (2002) Atmospheric nitrogen deposition to estuaries in the MidAtlantic and Northeastern United States. Environ. Sci. Tech. 36: 3242-3249.

Castro, M. S.; Driscoll, C. T.; Jordan, T. E.; Reay, W. G.; Boynton, W. R.; Seitzinger, S. P.;

Styles, R. V.; Cable, J. E. (2001) Contribution of atmospheric depostition to the total

nitrogen loads to thirty-four estuaries on the Atlantic and Gulf Coasts of the United

States. In: Valigura, R. A.; Alexander, R. B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.;

Stacey, P. E.; Turner, R. E., eds. Nitrogen loading in coastal water bodies: an atmospheric

perspective. Washington, DC: American Geophysical Union; pp. 77-106.

Castro, M. S.; Driscoll, C. T.; Jordan, T. E.; Reay, W. G.; Boynton, W. R. (2003) Sources of

nitrogen to estuaries in the United States. Estuaries 26: 803-814.

Chen, L.; Driscoll, C. T. (2004) An evaluation of processes regulating spatial and temporal

patterns in lake sulfate in the Adirondack region of New York. Glob. Biogeochem.

Cycles 18(GB3024): 10-10.

Chen, L.; Driscoll, C. T. (2005a) A two-layered model to simulate the seasonal variations in

surface water chemistry draining a northern forest watershed. Water Resour. Res.

41(W09425): 10.1029/2004WR003625.

Chen, L.; Driscoll, C. T. (2005b) Regional assessment of the response of acid-base status of lakewatersheds in the Adirondack region of New York to changes in atmospheric deposition

using PnET-BGC. Environ. Sci. Tech. 39: 787-794.

Chen, L.; Driscoll, C. T. (2005c) Regional application of an integrated biogeochemical model to

northern New England and Maine. Ecol. Appl. 15: 1783-1797.

Chen, C. W.; Gherini, S. A.; Dean, J. D.; Hudson, R. J. M.; Goldstein, R. A. (1984)

Development and calibration of the Integrated lake-Watershed Acidification Model. In:

Schnoor, J. L., ed. Modeling of Total Acid Precipitation Impacts. Stoneham, MA:

Butterwoirth Publishers; pp. 175-203.

Clair, T. A.; Dennis, I. F.; Amiro, P. G.; Cosby, B. J. (2004) Past and future chemistry changes in

acidified Nova Scotian Atlantic salmon (Salmo salar) rivers: a dynamic modeling

approach. Can. J. Fish. Aquat. Sci. 61: 1965-1975.

Clow, D. W.; Mast, M. A. (1999) Long-term trends in stream water and precipitation chemistry

at five headwater basins in the northeastern United States. Water Resour. Res. 35: 541­

554.

Cosby, B. J.; Hornberger, G. M.; Galloway, J. N.; Wright, R. F. (1985a) Modeling the effects of

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

chemistry. Water Resour. Res. 21: 51-63.

Cosby, B. J.; Hornberger, G. M.; Galloway, J. N.; Wright, R. F. (1985b) Time scales of

catchment acidification: a quantitative model for estimating freshwater acidification.

Environ. Sci. Tech. 19: 1144-1149.

Cosby, B. J.; Wright, R. F.; Hornberger, G. M.; Galloway, J. N. (1985c) Modelling the effects of

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

catchment. Water Resour. Res. 21: 1591-1601. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

August 2008 A­64 DRAFT—DO NOT QUOTE OR CITE

### Cosby, B. J.; Jenkins, A.; Ferrier, R. C.; Miller, J. D.; Walker, T. A. B. (1990) Modelling stream

acidification in afforested catchments: longterm reconstructions at two sites in central

Scotland. J. Hydrol. 120: 143-162.

Cosby, B. J.; Ryan, P. F.; Webb, J. R.; Hornberger, G. M.; Galloway, J. N.; Charles, D. F. (1991)

Mountains of western Virginia. In: Charles, D. F., ed. Acidic deposition and aquatic

ecosystems: regional case studies. New York, NY: Springer-Verlag; pp. 297-318.

Cosby, B. J.; Wright, R. F.; Gjessing, E. (1995) An acidification model (MAGIC) with organic

acids evaluated using whole-catchment manipulations in Norway. J. Hydrol. 170: 101­

122.

Cosby, B. J.; Norton, S. A.; Kahl, J. S. (1996) Using a paired catchment manipulation

experiment to evaluate a catchment-scale biogeochemical model. Sci. Total Environ. 183:

49-66.

Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Emmett, B. A.; Wright, R. F.; Tietema, A. (1997)

Modelling the ecosystem effects of nitrogen deposition: model of ecosystem retention

and loss of inorganic nitrogen (MERLIN). Hydrol. Earth Syst. Sci. 1: 137-158.

Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Wright, R. F. (2001) Modeling the effects of acid

deposition: refinements, adjustments and inclusion of nitrogen dynamics in the MAGIC

model. Hydrol. Earth Syst. Sci. 5: 499-517.

Davidson, E. A.; Keller, M.; Erickson, H. E.; Verchot, L. V.; Veldkamp, E. (2000) Testing a

conceptual model of soil emissions of nitrous and nitric oxides. BioScience 50: 667-680.

De Vries, W.; Posch, M.; Kämäri, J. (1989) Simulation of the long-term soil response to acid

deposition in various buffer ranges. Water Air Soil Pollut. 48: 349-390.

De Vries, W.; Reinds, G. J.; Posch, M.; Kämäri, J. (1994) Simulation of soil response to acidic

deposition scenarios in Europe. Water Air Soil Pollut. 78: 215-246.

Del Grosso, S. J.; Parton, W. J.; Mosier, A. R.; Hartman, M. D.; Brenner, J.; Ojima, D. S.;

Schimel, D. S. (2001) Simulated interaction of carbon dynamics and nitrogen trace gas

fluxes using the DAYCENT model. In: Shaffer, M. J.; Ma, L.; Hansen, S., eds. Modeling

Carbon and Nitrogen Dynamics for Soil Management. Boca Raton, FL: Lewis

Publishers; pp. 303-332.

DeWalle, D. R.; Swistock, B. R. (1994) Differences in oxygen-18 content of throughfall and

rainfall in hardwood and coniferous forests. Hydrol. Process. 8: 75-82.

Driscoll, C. T.; Van Dreason, R. (1993) Seasonal and long-term temporal patterns in the

chemistry of Adirondack lakes. Water Air Soil Pollut. 67: 319-344.

Driscoll, C. T.; Lehtinen, M. D.; Sullivan, T. J. (1994) Modeling the acid-base chemistry of

organic solutes in Adirondack, New York, lakes. Water Resour. Res. 30: 297-306.

Driscoll, C. T.; Postek, K. M.; Kretser, W.; Raynal, D. J. (1995) Long-term trends in the

chemistry of precipitation and lake water in the Adirondack region of New York, USA.

Water Air Soil Pollut. 85: 583-588.

Driscoll, C. T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C. L.;

Groffman, P.; Hopkinson, C.; Lambert, K.; Lawrence, G.; Ollinger, S. (2003) Nitrogen

pollution in the northeastern United States: sources, effects, and management options.

BioScience 53: 357-374. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

August 2008 A­65 DRAFT—DO NOT QUOTE OR CITE

### Driscoll, C. T.; Lambert, K. F.; Chen, L. (2007) Acidic deposition: sources and effects. In:

Visgilio, G. R.; Whitelaw, D. M., eds. Acid in the Environment. New York, NY:

Springer; pp. 332.

Ferrier, R. C.; Helliwell, R. C.; Cosby, B. J.; Jenkins, A.; Wright, R. F. (2001) Recovery from

acidification of lochs in Galloway, south-west Scotland, UK: 1979-1998. Hydrol. Earth

Syst. Sci. 5: 421-431.

Fisher, D. C.; Oppenheimer, M. (1991) Atmospheric nitrogen deposition and the Chesapeake

Bay Estuary. Ambio 20: 102-108.

Ford, J.; Stoddard, J. L.; Powers, C. F. (1993) Perspectives in environmental monitoring: an introduction to the U.S. EPA Long-Term Monitonring (LTM) project Water Air Soil

Pollut. 67: 247-255.

Gbondo-Tugbawa, S. S.; Driscoll, C. T. (2002) Evaluation of the effects of future controls on

sulfur dioxide and nitrogen oxide emissions on the acid-base status of a northern forest

ecosystem. Atmos. Environ. 36: 1631-1643.

Gbondo-Tugbawa, S. S.; Driscoll, C. T.; Aber, J. D.; Likens, G. E. (2001) Evaluation of an

integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem.

Water Resour. Res. 37: 1057-1070.

Gherini, S. A.; Mok, L.; Hudson, R. J.; Davis, G. F.; Chen, C. W.; Goldstein, R. A. (1985) The

ILWAS model: formulation and application. Water Air Soil Pollut. 26: 425-459.

Goldstein, R. A.; Gherini, S. A.; Chen, C. W.; L. Mok; Hudson, R. J. M. (1984) Integrated

acidification study (ILWAS): a mechanistic ecosystem analysis. Phil. Trans. R. Soc.

Lond. Ser. B, 305: 409-425.

Goodale, C.; Lajtha, K.; Nadelhoffer, K. J.; Boyer, E. W.; Jaworski, N. A. (2002) Forest nitrogen

sinks in large eastern U.S. watersheds: estimates from forest inventory and an ecosystem

model. Biogeochemistry 57/58: 239-266.

Gran, G. (1952) Determination of the equivalence point in potentiometric titrations. Int. Congr.

Anal. Chem. 77: 661-671.

Grossman, G. D.; Ratajczak, R. E., Jr.; Crawford, M. K.; Freeman, M. C. (1998) Assemblage of

organization in stream fishes: effects of environmental variation and interspecific

interactions. Ecol. Monogr. 68: 395-420.

Hartman, M. D.; Baron, J. S.; Ojima, D. S. (2007) Application of a coupled ecosystem-chemical

equilibrium model, DayCent-Chem, to stream and soil chemistry in a Rocky Mountain

watershed. Ecol. Model. 200: 493-510.

Hendrickson, O. Q. (1990) Asymbiotic nitrogen fixation and soil metabolism in three Ontario

forests. Soil Biol. Biochem 22: 967-971.

Herlihy, A. T.; Larsen, D. P.; Paulsen, S. G.; Urquhart, N. S.; Rosenbaum, B. J. (2000)

Designing a spatially balanced, randomized site selection process for regional stream

surveys: the EMAP mid-Atlantic pilot study. Environ. Monitor. Assess. 63: 95-113.

Hornbeck, J. W.; Bailey, S. W.; Buso, D. C.; Shanley, J. B. (1997) Streamwater chemistry and

nutrient budgets for forested watersheds in New England" variability and management

implications. For. Ecol. Manage. 93: 73-89. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

August 2008 A­66 DRAFT—DO NOT QUOTE OR CITE

### Hornberger, G. M.; Cosby, B. J.; Wright, R. F. (1989) Historical reconstructions and future

forecasts of regional surface water acidification in southernmost Norway. Water Resour.

Res. 25: 2009-2018.

Howarth, R. W.; Billen, G.; Swaney, D.; Townsend, A.; Jaworski, N.; Lajtha, K.; Downing, J.

A.; Elmgren, R.; Caraco, N.; Jordan, T.; Berendse, F.; Freney, J.; Kudeyarov, V.;

Murdoch, P. S.; Zhao-Liang, Z. (1996) Regional nitrogen budgets and riverine N & P

fluxes for the drainages to the North Atlantic Ocean: natural and human influences.

Biogeochemistry 35: 75-139.

Jacobs, T. C.; Gilliam, J. W. (1985) Riparian losses of nitrate from agricultural drainage waters.

J. Environ. Qual. 14: 472-478.

Jaworski, N. A.; Howarth, R. S.; Hetling, L. J. (1997) Atmospheric deposition of nitrogen oxides

onto the landscape contributes to coastal eutrophication in the northeast United States.

Environ. Sci. Tech. 31: 1995-2004.

Jenkins, A.; Cosby, B. J.; Ferrier, R. C.; Walker, T. A. B.; Miller, J. D. (1990) Modelling stream

acidification in afforested catchments: an assessment of the relative effects of acid

deposition and afforestation. J. Hydrol. 120: 163–181.

Johnson, D. W.; Lindberg, S. E. (1992) Atmospheric deposition and forest nutrient cycling: a

synthesis of the integrated forest ecological series. New York, NY: Springer.

Johnson, D. W.; Swank, W. T.; Vose, J. M. (1993) Simulated effects of atmospheric sulfur

deposition on nutrient cycling in a mixed deciduous forest. Biogeochemistry 23: 169-196.

Johnson, D. W.; Binkley, D.; Conklin, P. (1995) Simulated effects of atmospheric deposition,

harvesting, and species change on nutrient cycling in a loblolly pine forest. For. Ecol.

Manage. 76: 29-45.

Johnson, D. W.; Susfalk, R. B.; Brewer, P. F. (1996) Simulated responses of red spruce forest

soils to reduced sulfur ad nitrogen deposition. J. Environ. Qual. 25: 1300-1309.

Jordan, T. E.; Weller, D. E. (1996) Human contributions to terrestrial nitrogen flux. BioScience

46: 655-664.

Jordan, T. E.; Correll, D. L.; Weller, D. E. (1993) Nutrient interception by a riparian forest

receiving inputs from adjacent cropland. J. Environ. Qual. 22: 467-473.

Kahl, J. S.; Norton, S. A.; Cronan, C. S.; Fernandez, I. J.; Bacon, L. C.; Haines, T. A. (1991)

Maine. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case

studies. New York, NY: Springer-Verlag; pp. 203-235.

Kahl, J. S.; Fernandez, I. J.; Nadelhoffer, K. J.; Driscoll, C. T.; Aber, J. D. (1993) Experimental

inducement of nitrogen saturation at the watershed scale. Environ. Sci. Tech. 27: 565­

567.

Kelly, R. H.; Parton, W. J.; Hartman, M. D.; Stretch, L. K.; Ojima, D. S.; Schimel, D. S. (2000)

Intra- and interannual variability of ecosystem processes in shortgrass steppe. J. Geophys.

Res. 105(D15): 20093–20100.

Kendall, C.; Silva, S. R.; Chang, C. C.; Campbell, D. H.; Burns, D. A.; Shanley, J. B. (1995) Use

of oxygen and nitrogen isotopes to trace sources of nitrate during snowmelt in forested

catchments. In: Tonnessen, K. A.; Williams, M. W.; Tranter, M., eds. Biogeochemistry of 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

August 2008 A­67 DRAFT—DO NOT QUOTE OR CITE

### Seasonally Snow-Covered Catchments. International Association of Hydrologic

Sciences; pp. 339-347.

Kjonaas, O. J.; Wright, R. F. (1998) Nitrogen leaching from N limited forest ecosystems: the

MERLIN model applied to Gardsjon, Sweden. Hydrol. Earth Syst. Sci. 2: 415-429.

Knoepp, J. D.; Swank, W. T. (1994) Long-term soil chemistry changes in aggrading forest

ecosystems. Soil Sci. Soc. Am. J. 58: 325-331.

Kurz, D.; Alveteg, M.; Sverdrup, H. (1998) Integrated assessment of soil chemical status. 2.

Application of a regionalized model to 622 forested sites in Switzerland. Water Air Soil

Pollut. 105: 11-20.

Larsen, D. P.; Urquhart, N. S. (1993) A framework for assessing the sensitivity of the EMAP

design. In: Larsen, D. P.; Christie, S. J., eds. EMAP-Surface Waters 1991 Pilot Report.

Corvallis, OR: U.S. Environmental Protection Agency; pp. 4.1-4.37.

Larsen, D. P.; Thornton, K. W.; Urquhart, N. S.; Paulsen, S. G. (1994) The role of sample

surveys for monitoring the condition of the nation's lakes. Environ. Monitor. Assess. 32:

101-134.

Lawrence, G. B.; Momen, B.; Roy, K. M. (2004) Use of stream chemistry for monitoring acidic

deposition effects in the Adirondack Region of New York. J. Environ. Qual. 33: 1002­

1009.

Lawrence, G. B.; Sutherland, J. W.; Boylen, C. W.; Nierzwicki-Bauer, S. A.; Momen, B.;

Baldigo, B. P.; Simonin, H. A. (2007) Acid rain effects on aluminum mobilization

clarified by inclusion of strong organic acids. Environ. Sci. Technol. 41: 93-98.

Likens, G. E. (1985) An ecosystem approach to aquatic ecology: Mirror Lake and its

environment. New York, NY: Springer.

Likens, G. E. (1992) The ecosystem approach: its use and abuse. Oldendorf/Luhe, Germany:

Ecology Institute.

Likens, G. E.; Bormann, F. H. (1995) Biogeochemistry of a forested ecosystem. 2nd ed. New

York, NY: Springer-Verlag.

Likens, G. E.; Bormann, F. H.; Pierce, R. S.; Eaton, J. S.; Johnson, N. M. (1977)

Biogeochemistry of a Forested Ecosystem. New York, NY: Springer Verlag.

Likens, G. E.; Bormann, F. H.; Pierce, R. S.; Reiners, W. A. (1978) Recovery of a deforested

ecosystem. Science 199: 492-496.

Likens, G. E.; Driscoll, C. T.; Buso, D. C. (1996) Long-term effects of acid rain: response and

recovery of a forest ecosystem. Science (Washington, DC) 272: 244-246.

Likens, G. E.; Driscoll, C. T.; Buso, D. C.; Mitchell, M. J.; Lovett, G. M.; Bailey, S. W.;

Siccama, T. G.; Reiners, W. A.; Alewell, C. (2002) The biogeochemistry of sulfur at

Hubbard Brook. Biogeochemistry 60: 235-316.

Liu, S.; Munson, R.; Johnson, D.; Gherini, S.; Summers, K.; Hudson, R.; Wilkinson, K.; Pitelka,

L. (1991) Application of a nutrient cycling model (NuCM) to a northern mixed hardwood

and a southern coniferous forest. Tree Physiol. 9: 173-184. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

August 2008 A­68 DRAFT—DO NOT QUOTE OR CITE

### Lovett, G. M.; Burns, D. A.; Driscoll, C. T.; Jenkins, J. C.; Mitchell, M. J.; Rustad, L.; Shanley,

J. B.; Likens, G. E.; Haeuber, R. (2007) Who needs environmental monitoring? Front.

Ecol. Environ. 5: 253-260.

Lowrance, R. R.; Todd, R. L.; Asmussen, L. E. (1983) Waterborne nutrient budgets for the

riparian zone of an agricultural watershed. Agric. Ecosyst. Environ. 10: 371-384.

Maag, M.; Malinovsky, M.; Nielson, S. M. (1997) Kinetics and temperature dependence of

potential denitrification in riparian soils. J. Environ. Sci. 26: 215-223.

Martinson, L.; Alveteg, M.; Warfvinge, P. (2003) Parameterization and evaluation of sulfate

adsorption in a dynamic soil chemistry model. Environ. Pollut. 124: 119-125.

McNichol, D. K. (2002) Relation of lake acidification and recovery to fish, common loon and

common merganser occurrence in Algoma Lakes. Water Air Soil Pollut. Focus 2: 151­

168.

McNichol, D. K.; Mallory, M. L.; Wedeles, C. H. R. (1995) Assessing biological recovery of

acid-sensitive lakes in Ontario, Canada. Water Air Soil Pollut. 85: 457-462.

McRoberts, R. E.; McWilliams, W. H.; Reams, G. A.; Schmidt, T. L.; Jenkins, J. C.; O’Neill, K.

P.; Miles, P. D.; Brand, G. J. (2004) Assessing sustainability using data from the Forest

Inventory and Analysis program of the United States Forest Service. J. Sustain. For. 18:

23-46.

Meyer, J. L.; Swank, W. T. (1996) Ecosystem management challenges ecologists. Ecol. Appl. 6:

738-740.

Meyers, T.; Sickles, J.; Dennis, R.; Russell, K.; Galloway, J.; Church, T., eds. (2000)

Atmospheric nitrogen deposition to coastal estuaries and their watersheds, p. 53–76. In:

Valigura, R. M.; Alexander, R. B.; Castro, M. S.; Greening, H.; Meyers, T.; Paerl, H.;

Turner, R. E. (eds.) An assessment of nitrogen loads to US estuaries with an atmospheric

perspective. Washington, D.C.: American Geophysical Union. (Coastal and Estuarine

Studies).

Moldan, F.; Wright, R. F.; Ferrier, R. C.; Andersson, B. I.; Hultberg, H. (1998) Simulating the

Gårdsjön covered catchment experiment with the MAGIC model. In: Hultberg, H.;

Skeffington, R. A., eds. Experimental reversal of acid rain effects. The Gårdsjön Roof

Project. Chichester, UK: Wiley and Sons; pp. p.351-362.

Morel, F. M. M.; Hering, J. G. (1993) Principles and Applications of Aquatic Chemistry. New

York, NY: Wiley-Interscience.

Mueller, D. K.; Ruddy, B. C.; Battaglin, W. A. (1997) Logistic model of nitrate in streams of the

upper-midwestern United States. J. Environ. Qual. 26: 1223-1230.

Muir, P. S.; McCune, B. (1988) Lichens, tree growth, and foliar symptoms of air pollution: are

the stories consistent? J. Environ. Qual. 17: 361-370.

Murdoch, P. S.; Stoddard, J. L. (1993) Chemical characteristics and temporal trends in eight

streams of the Catskill Mountains. Water Air Soil Pollut. 67: 367-395.

Norton, S. A.; Kahl, J. S.; Fernandez, I. J.; Rustad, L. E.; Scofield, J. P.; Haines, T. A. (1994)

Response of the West Bear Brook watershed, Maine, USA, to the addition of (NH 4 

### ) 2 

### SO 4 

### : 3-year results. For. Ecol. Manage. 68: 61-73. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

August 2008 A­69 DRAFT—DO NOT QUOTE OR CITE

### Norton, S.; Kahl, J.; Fernandez, I. (1999a) Altered soil-soil water interactions inferred from

stream water chemistry at an artificially acidified watershed at Bear Brook Watershed,

Maine USA. Environ. Monitor. Assess. 55: 97-111.

Norton, S.; Kahl, J.; Fernandez, I.; Haines, T.; Rustad, L.; Nodvin, S.; Schofield, J.; Strickland,

T.; Erickson, J.; Wignington, P., Jr.; Lee, J. (1999b) The Bear Brook Watershed, Maine

(BBWM), USA. Environ. Monitor. Assess. 55: 7-51.

Oreskes, N.; Shrader-Frechette, K.; Belitz, K. (1994) Verification, validation, and confirmation

of numerical models in the earth sciences. Science 263: 641-646.

Parkhurst, D. L.; Appelo, C. A. J. (1999) User’s guide to PHREEQC (version 2)—A computer

program for speciation, batch-reaction, one-dimensional transport, and inverse

geochemical calculations. Denver, CO: U.S. Geological Survey Water-Resources

Investigations Report.

Parton, W. J.; Schimel, D. S.; Ojima, D. S.; Cole, C. V. (1994) A general model for soil organic

matter dynamics: sensitivity to litter chemistry, texture and management. In: Bryant, R.

B.; Arnold, R. W., eds. Quantitative Modeling of Soil Forming Processes. Madison, WI:

Soil Sci. Soc. Am.; pp. 147-167.

Parton, W. J.; Hartman, M.; Ojima, D.; Schimel, D. (1998) DAYCENT and its land surface

submodel: description and testing. Global Planet Change 19: 35-48.

Paulsen, S. G.; Larsen, D. P.; Kaufmann, P. R.; Whittier, T. R.; Baker, J. R.; Peck, D.; McGue,

J.; Hughes, R. M.; McMullen, D.; Stevens, D.; Stoddard, J. L.; Lazorchak, J.; Kinney,

W.; Selle, A. R.; Hjort, R. (1991) EMAP - surface waters monitoring and research

strategy, fiscal year. Corvallis, OR: U.S. Environmental Protection Agency, Office of

Research and Development.

Peterjohn, W. T.; Correll, D. L. (1984) Nutrient dynamics in an agricultural watershed:

observation on the role of a riparian forest. Ecology 65: 1466-1475.

Posch, M.; Reinds, G. J. (2003) VSD - User manual of the very simple dynamic soil acidification

model. Bilthoven, The Netherlands: Coordination Center for Effects, RIVM.

Posch, M.; Reinds, G. J.; De Vries, W. (1993) SMART – a simulation model for acidification’s

regional trends: model description and user manual. Helsinki, Finland: Mimeograph

Series of the National Board of Waters and the Environment, no. 477.

Posch, M.; Hettelingh, J. P.; Slootweg, J. (2003) Manual for dynamic modelling of soil response

to atmospheric deposition. Bilthoven, The Netherlands: Coordination Center for Effects,

RIVM.

Preston, S. D.; Brakebill, J. W. (1999) Application of spatially reference regression modeling for

the evaluation of total nitrogen loading in the Chesapeake Bay Watershed. U.S.

Geological Survey.

Reuss, J. O. (1980) Simulation of soil nutrient losses due to rainfall acidity. Ecol. Model. 11: 15­

38.

Rodríguez, L.; Macías, F. (2006) Eutrophication trends in forest soils in Galicia (NW Spain)

caused by the atmospheric deposition of nitrogen compounds. Chemosphere 63: 1598­

1609. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

August 2008 A­70 DRAFT—DO NOT QUOTE OR CITE

### RTI International. (2001) Evaluation of Waquoit Bay models to estimate the water quality and

ecological improvements resulting from atmospheric nitrogen reduction regulatory

scenarios. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of

Air Quality Planning and Standards, contract no. 68-D-99-024.

Schlesinger, W. L.; Hartley, A. E. (1992) A global budget for atmospheric NH 3



### . Biogeochemistry 15: 191-211.

Schofield, K. A.; Pringle, C. M.; Meyer, J. L.; Sutherland, A. B. (2001) The importance of

crayfish in the breakdown of rhododendron leaf litter. Freshwater Biol. 46: 1-14.

Scott, M. C.; Helfman, G. S. (2001) Native invasions, homogenization, and the mismeasure of

integrity of fish assemblages. Fisheries 26: 6-15.

Shanley, J. B.; Chalmers, A. (1999) The effect of frozen soil on snowmelt runoff at Sleepers

River, Vermont. Hydrol. Process. 13: 1843-1857.

Smith, R. A.; Schwarz, G. E.; Alexander, R. B. (1997) Regional interpretation of water-quality

monitoring data. Water Resour. Res. 33: 2781-2798.

Stanford, G.; Dzienia, S.; Vander Pol, R. A. (1975) Effect of temperature on denitrification rate

in soils. Soil Sci. Soc. Am. Proc. 39: 867-870.

Stevenson, F. J., ed. (1982) Origin and distribution of nitrogen in soil. Nitrogen in agricultural

soils. Madison, WI: American Society of Agronomy.

Stoddard, J. L.; Kellogg, J. H. (1993) Trends and patterns in lake acidification in the state of

Vermont: evidence from the Long-Term Monitoring project. Water Air Soil Pollut. 67:

301-317.

Stoddard, J. L.; Urquhart, N. S.; Newell, A. D.; Kugler, D. (1996) The Temporally Interated

Monitoring of Ecosystems (TIME) project design 2. Detection of regional acidification

trends. Water Resour. Res. 32: 2529-2538.

Stoddard, J. L.; Driscoll, C. T.; Kahl, J. S.; Kellogg, J. H. (1998a) A regional analysis of lake

acidification trends for the northeastern U.S., 1982-1994. Environ. Monitor. Assess. 51:

399-413.

Stoddard, J. L.; Driscoll, C. T.; Kahl, S.; Kellogg, J. (1998b) Can site-specific trends be

extrapolated to the regional level? A lake acidification example for the northeastern U.S.

Ecol. Appl. 8: 288-299.

Stoddard, J.; Kahl, J. S.; Deviney, F. A.; DeWalle, D. R.; Driscoll, C. T.; Herlihy, A. T.;

Kellogg, J. H.; Murdoch, P. S.; Webb, J. R.; Webster, K. E. (2003) Response of surface

water chemistry to the Clean Air Act Amendments of 1990. Research Triangle Park, NC:

U.S. Environmental Protection Agency, Office of Research and Development, National

Health and Environmental Effects Research Laboratory. EPA 620/R-03/001.

Sullivan, T. J. (2000) Aquatic effects of acidic deposition. Boca Raton, FL.: Lewis Publishers.

Sullivan, T. J.; Cosby, B. J. (1998) Modeling the concentration of aluminium in surface waters.

Water Air Soil Pollut. 105: 643-659.

Sullivan, T. J.; Johnson, D. W.; Munson, R. K.; Joslin, J. D. (2002) Assessment of the effects of

acidic deposition on forest resources in the Southern Appalachian Mountains. Asheville,

NC: Southern Appalachian Mountains Initiative. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

August 2008 A­71 DRAFT—DO NOT QUOTE OR CITE

### Sullivan, T. J.; Cosby, B. J.; Laurence, J. A.; Dennis, R. L.; Savig, K.; Webb, J. R.; Bulger, A. J.;

Scruggs, M.; Gordon, C.; Ray, J.; Lee, H.; Hogsett, W. E.; Wayne, H.; Miller, D.; Kern,

J. S. (2003) Assessment of air quality and related values in Shenandoah National Park.:

U.S. Department of the Interior, National Park Service, Northeast Region.

NPS/NERCHAL/NRTR-03/090.

Sullivan, T. J.; Cosby, B. J.; Herlihy, A. T.; Webb, J. R.; Bulger, A. J.; Snyder, K. U.; Brewer,

P.; Gilbert, E. H.; Moore, D. L. (2004) Regional model projections of future effects of

sulfur and nitrogen deposition on streams in the southern Appalachian Mountains. Water

Resour. Res. 40(W02101): 10.1029/2003WR001998.

Sullivan, T. J.; Driscoll, C. T.; Cosby, B. J.; Fernandez, I. J.; Herlihy, A. T.; Zhai, J.; Stemberger,

R.; Snyder, K. U.; Sutherland, J. W.; Nierzwicki-Bauer, S. A.; Boylen, C. W.;

McDonnell, T. C.; Nowicki, N. A. (2006) Assessment of the extent to which intensivelystudied lakes are representative of the Adirondack Mountain Region, final report.

Prepared for: Albany, NY: New York State Energy Research and Development

Authority. Corvallis, OR: E&S Environmental Chemistry, Inc.; report no. 06-17.

Swank, W. T.; Vose, J. M. (1997) Long-term nitrogen dynamics of Coweeta forested watersheds

in the southeastern United States of America. Glob. Biogeochem. Cycles 11: 657-671.

Urquhart, N. S.; Paulsen, S. G.; Larsen, D. P. (1998) Monitoring for regional and policy-relevant

trends over time. Ecol. Appl. 8: 246-257.

U.S. Congress. (1990) Clean Air Act amendments of 1990, PL 101-549, November 15.

Washington, DC: U.S. Government Printing Office.

Van Griensven, A.; Bauwens, W. (2001) Integral water quality modelling of catchments. Water

Sci. Technol. 43: 321-328.

Wade, A. J.; Neal, C.; Whitehead, P. G.; Flynn, N. J. (2005) Modelling nitrogen fluxes from the

land to the coastal zone in European systems: a perspective from the INCA project. J.

Hydrol. (Amsterdam) 304: 413-429.

Warfvinge, P.; Sverdrup, H. (1992) Calculating critical loads of acid deposition with PROFILE ­

a steady-state soil chemistry model. Water Air Soil Pollut. 63: 119-143.

Warfvinge, P.; Falkengren-Greup, U.; Sverdrup, H.; Andersen, B. (1993) Modelling long-term

cation supply in acidified forest stands. Environ. Pollut. 80: 209-221.

Webb, J. R.; Cosby, B. J.; Galloway, J. N.; Hornberger, G. M. (1989) Acidification of native

brook trout streams in Virginia. Water Resour. Res. 25: 1367-1377.

Webb, J. R.; Cosby, B. J.; Deviney, F. A.; Galloway, J. N.; Maben, S. W.; Bulger, A. J. (2004)

Are brook trout streams in western Virginia and Shenandoah National Park recovering

from acidification? Environ. Sci. Technol. 38: 4091-4096.

Webster, K. E.; Brezonik, P. L.; Holdhusen, B. J. (1993) Temporal trends in low alkalinity lakes

of the upper Midwest (1983-1989). Water Air Soil Pollut. 67: 397-414.

Whitall, D.; Bricker, S. (2006) Assessment of eutrophication in estuaries: pressure-state-response

and source apportionment. USDA Forest Service Proceedings RMRS-P-42CD. Silver

Spring, MD: National Oceanic and Atmospheric Administration, National Centers for

Coastal Ocean Science, Center for Coastal Monitoring and Assessment. 1

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### Whitall, D.; Castro, M.; Driscoll, C. (2004) Evaluation of management strategies for reducing

nitrogen loadings to four US estuaries. Sci. Total Environ. 333: 25-36.

White, G. N.; Zelazny, L. W. (1986) Charge properties of soil colloids. In: Sparks, D. L., ed. Soil

Physical Chemistry. Boca Raton, FL: CRC Press; pp. 39–81.

Whitehead, P. G.; Reynolds, B.; Hornberger, G. M.; Neal, C.; Cosby, B. J.; Paricos, P. (1988)

Modeling long term stream acidification trends in upland Wales at Plynlimon. Hydrol.

Process. 2: 357-368.

Whitehead, P. G.; Barlow, J.; Haworth, E. Y.; Adamson, J. K. (1997) Acidification in three Lake

District tarns: historical long term trends and modelled future behaviour under changing

sulphate and nitrate deposition. Hydrol.Earth System Sci. 1: 197-204.

Whittier, T. R.; Paulsen, S. B.; Larsen, D. P.; Peterson, S. A.; Herlihy, A. T.; Kaufmann, P. R.

(2002) Indicators of ecological stress and their extent in the population of northeastern

lakes: a regional-scale assessment. BioScience 52: 235-247.

Woodmansee, R. G. (1978) Additions and losses of nitrogen in grassland ecosystems.

BioScience 28: 448-453.

Wright, R. F.; Cosby, B. J.; Flaten, M. B.; Reuss, J. O. (1990) Evaluation of an acidification

model with data from manipulated catchments in Norway. Nature 343: 53-55.

Wright, R. F.; Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Bulger, A. J.; Harriman, R. (1994)

Changes in the acidification of lochs in Galloway, southwestern Scotland, 1979-1988: the

MAGIC model used to evaluate the role of afforestation, calculate critical loads, and

predict fish status. J. Hydrol. 161: 257-285.

Wright, R. F.; Emmett, B. A.; Jenkins, A. (1998) Acid deposition, land-use change and global

change: MAGIC7 model applied to Risdalsheia, Norway (RAIN and CLIMEX projects)

and Aber, UK (NITREX project). Hydrol. Earth Syst. Sci. 2: 385­ 1

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# Annex B. Acidification Effects

B.1. Effects on Biogeochemical Processes along

Acidification Pathways

B.1.1. Atmospheric Deposition and Canopy Interaction

Inputs of N and S in wet, dry, and occult deposition first interact with the vegetative canopy. This

interaction can occur a few centimeters above the ground in some alpine or grassland ecosystems to over

100 m above the ground in some forest canopies. In the canopy, deposited pollutants (especially N) can be

taken up by the plants or by organisms that live within the canopy or on the leaf surface. Most of the

deposited S m oves as throughfall to the soil where it can be temporarily, or permanently, adsorbed on the

soil. Sulfur that is not adsorbed on the soil moves readily into drainage water.

Earlier reviews (i.e., Hosker and Lindberg, 1982; Taylor et al., 1988) summarized information on

the deposition of N to vegetation surfaces and interactions between pollutant deposition and canopy and

leaf surfaces. Deposited N that is not taken up within the canopy then falls to the ground as throughfall,

where plants, bacteria, and fungi compete for it. This competition for deposited N has long been known to

play an important role in determining the extent to which N deposition will stimulate plant growth and the

degree to which added N is retained within the ecosystem (U.S. Environmental Protection Agency, 1993).

The available surface area of vegetation, onto which N gasses readily diffuse, has a significant effect on

the dry deposition of N (Heil and Bruggink, 1987). Coniferous forests tend to increase deposition rates

(both dry and wet) relative to deciduous forests, and landscape features such as elevation, aspect, and

forest edge can play an important role in creating high levels of variability in deposition rates in complex

terrain (Weathers et al., 2000).



# B.1.2. Interactions with Soil

Air pollution is not the sole cause of soil acidity. High rates of soil acidification occur in lowdeposition regions of the western U.S. because of internal soil processes, including tree N uptake and

nitrification associated with extensive N fixation, for example on sites occupied by red alder trees ( Alnus

rubra ) (Johnson et al., 1991b). Acidic deposition is not a necessary condition for having acidic soils, as

evidenced by the common occurrence of acidic soils in unpolluted forests of the northwestern U.S. and

Alaska (Johnson et al., 1991b).

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## B.1.2.1. Sulfur Retention and Release

Soils in the U.S. that most effectively adsorb SO 2−

4 occur south of the maximum extent of

glaciation that occurred during the most recent ice age (Rochelle and Church, 1987; Rochelle et al.,

1987). Sulfate adsorption is strongly pH dependent, and a decrease in soil pH resulting from acidic

deposition can enhance the ability of soil to adsorb SO 2−

4 (Fuller et al., 1987).

Considerable effort in the 1980s went into the computation of S b udgets for watersheds and forest

plots, to evaluate S retention and release. These budgets were subject to complications from fluxes that

could not be measured directly, such as dry deposition and weathering, but generally indicated net S

retention at sites south of the line of glaciation—a result attributed to net adsorption of SO 2−

4 (Rochelle

et al., 1987; Cappellato et al., 1998). Through the 1990s little or no decrease in SO 2−

4 concentrations

occurred in streams below the glaciation line, despite regional decreases in atmospheric deposition of S

(Webb et al., 2004). This lack of response has been generally attributed to the net release of adsorbed

SO 2−

4 , resulting from a shift in equilibrium between the adsorbed and solution phases under conditions of

decreased atmospheric inputs of SO 2−

4

. This interpretation is supported by a decrease in concentrations of

adsorbed SO 2−

4 from 1982 to 1990 in a Piedmont soil in South Carolina that received decreasing levels of

S d eposition during this period (Markewitz et al., 1998). This same soil also experienced an increase in

adsorbed SO 2−

4 from 1962 to 1972 (Markewitz et al., 1998). The only published S budget more recent

than 1992 for an unglaciated site in the U.S. (Castro and Morgan, 2000) also suggests a net release of

SO 2−

4

. This upland Maryland watershed released 1.6 times more SO 2−

4 than measured in throughfall in

1996–97. Additional information was obtained in the German study of Martinson et al. (2005) in which a

“clean-roof” was used to exclude acidic deposition since 1989. Data collection enabled calibration of a

model that predicted elevated concentrations of desorbed SO 2−

4 in soil water for at least several decades.

Although decreased levels of deposition are most likely resulting in net SO 2−

4 desorption, limited research

is available on sulfate desorption over time periods relevant to the time scale of decreased levels of S

deposition (Johnson and Mitchell, 1998).

Numerous S b udgets were also compiled in the 1980’s for glaciated sites, and results generally

indicated that inputs approximately equaled outputs on an annual basis (Rochelle et al., 1987). Little or no

S r etention at glaciated sites was attributed to relatively low SO 2−

4 adsorption capacity in soils. Balanced

S b udgets implied that decreases in atmospheric deposition of S w ould lead directly to decreases in SO 2−

4

leaching, and the strong correlation between decreases in atmospheric deposition and decreases in SO 2−

4

concentrations in surface waters is widely recognized as an indication of this direct linkage (Stoddard

et al., 2003). However, considerable evidence also indicates that S inputs in glaciated ecosystems do not

behave conservatively, but instead are cycled through microbial and plant biomass (David et al., 1987;

Alewell and Gehre, 1999; Likens et al., 2002). As a result, large quantities of S a re stored in organic

forms within the soil. David et al. (1987) found that annual S deposition (wet plus dry) at a site in the

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33 central Adirondack region of New York was about 1% of the organic S p ool in the soil. Houle et al.

(2001) estimated that annual S d eposition at 11 sites in North America ranged from 1% to 13% of the

organic S p ool in soil.

Courchesne et al. (2005) measured a downward trend in water-soluble SO 4 from 1993 to 2002 in

glaciated soils in Quebec, and attributed this response to net desorption of SO 2−

4 rather than release of

organically associated S. However, during this period, deposition of SO 2−

4 was essentially unchanged.

They attributed this discrepancy to a delay in the release of adsorbed SO 2−

4 in response to a decrease in S

deposition over the previous decade. These authors did not provide a mechanism to explain how

desorption can continue under conditions of constant SO 2−

4 inputs, however. On the basis of the abundant

evidence of biological S c ycling, it seems more likely that the delay observed by Courchesne et al. (2005)

is the result of biological controls over the release of S.

Much of the organic S s tored in soil is in carbon–bonded forms that are relatively unreactive, but

can be mineralized to SO 2−

4 in oxic conditions, typically found in moderately well-drained to welldrained soils (Johnson and Mitchell, 1998). Furthermore, strong correlations have been shown between

levels of atmospheric deposition of S a nd concentrations of S in soil (Driscoll et al., 2001; Novák et al.,

2001). Long-term increases in concentrations of total S in soils that are at least partially attributable to

increases in organic S h ave also been documented (Knights et al., 2000; Lapenis et al., 2004). The study

of Houle et al. (2001) did not find a relation between these factors, however. A Swedish “clean-roof”

study also provides some insights into the role of organic S in possibly delaying recovery (Morth et al.,

2005). After 9 years of pre-industrial levels of S d eposition, the amount of S in runoff still exceeded

inputs by 30%. Most of the S i n runoff was attributed to mineralization of organic S i n the O horizon.



## B.1.2.2. Base Cation Depletion

Base cations are common in rocks and soils, but largely in forms that are unavailable to plants.

There is a pool of bioavailable base cations (termed exchangeable base cations) that are adsorbed to

negatively charged surfaces of soil particles. They can enter solution by exchanging with other dissolved

cations including acidic cations such as H + or Al 3+

. Base cations in this pool are gradually leached from

the soil in drainage water, but are constantly resupplied through weathering. Weathering slowly breaks

down rocks and minerals, releasing base cations to the pool of adsorbed base cations in the soil. The

balance between base cation supply and base cation loss determines whether the pool of available base

cations is increasing or decreasing in size. Net forest growth can also potentially lower exchangeable base

cation concentrations through uptake of nutrient cations (Ca, Mg, and K), but these cations remain in the

terrestrial ecosystem and can become available in the future through mineralization or canopy leaching. It

has long been known that leaching of base cations by acidic deposition might deplete the soil of

exchangeable bases faster than they are resupplied (Cowling and Dochinger, 1980). However, base cation

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35 depletion of soils had not been demonstrated at the time of the last SO X Air Quality Criteria Document

(AQCD) (U.S. Environmental Protection Agency, 1982).

Data that clearly showed soil base cation depletion in the U.S. did not become available until the

1990s, although decreases in exchangeable Ca 2+ concentrations between the periods 1947 to 1950 and

1987 to 1988 had been identified in European soils through repeated sampling (Billett et al., 1990;

Falkengren-Grerup and Eriksson, 1990). In the only repeated sampling in the U.S. in which the original

soil sample pre-dated acidic deposition, Johnson et al. (Johnson et al., 1994b) documented a decrease in

exchangeable Ca 2+ concentrations in both the O (combined Oa and Oe horizons) and B horizons from

1930 to 1984. Richter et al. (1994) also observed Ca 2+ depletion in the B horizon from 1960 to 1990, in

repeated sampling of Piedmont soil in South Carolina. The studies of Johnson et al. (Johnson et al.,

1994b) and Richter et al. (1994) acknowledged the potential role of acidic deposition in causing the loss

of Ca 2+ , but focused on net forest growth as the primary cause.

Through reanalysis of archived soils, Lawrence et al. (1995) measured decreases in concentrations

of exchangeable Ca 2+ and acid-extractable Ca 2+ in Oa horizons of spruce stands from 1969–70 to 1987–92

and presented relationships in soil chemistry that were not consistent with changes expected from

vegetation uptake effects, but that could be explained by acidic deposition. Drohan and Sharpe (1997)

also observed a decrease in Ca 2+ concentrations in Oa and A horizons at 11 sites across Pennsylvania that

were sampled in 1957 or 1959 and again in 1993, although effects of vegetation and acidic deposition

were not distinguished.

The most thorough soil re-sampling study in the U.S. was conducted by Bailey et al. (Bailey et al.,

2005) in northwestern Pennsylvania. Between 1967 and 1997, pronounced decreases, attributed largely to

acidic deposition, were measured in exchangeable Ca 2+ and Mg 2+ concentrations in Oa/A horizons and

throughout the B horizon. Courchesne et al. (2005) found higher concentrations of exchangeable Ca 2+ in

the O horizon (combined Oe and Oa horizons) in 2002 than in 1994 at one of three sampling areas within

a 5.1 ha watershed, but no significant differences at the other two locations. No significant differences

were found for exchangeable Mg 2+ at the three locations in the O horizon. In the upper 10 cm of the

B horizon, no significant differences were found in exchangeable Ca 2+ , but at two of three locations,

exchangeable Mg 2+ concentrations were lower in 2002 than in 1994. The 8–year interval between

sampling in this study is the shortest time in which changes in exchangeable base cations have been

reported for North American soils.

In a regionally designed assessment of changes in soil-exchange chemistry, Sullivan et al. (2006a)

found that base saturation and exchangeable Ca 2+ concentrations in the Adirondack region of New York

had decreased in the upper 10 cm of the B horizon between the mid 1980s and 2003, in watersheds of

lakes with ANC less than 200 μeq/L. Soil chemistry in 36 lake watersheds in the mid 1980s was

compared to soil chemistry in 32 lake watersheds in 2003. Although this study did not involve repeated

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35 sampling of the same sites, the comparison could be made on a regional basis because the sampling

locations were selected randomly in both the mid 1980s and in 2003, and a large and similar number of

sites were included in both samplings.

In a widely cited article, Likens et al. (1996) used a watershed mass balance approach to estimate

changes in ecosystem Ca 2+ pools at HBEF and found a sustained decrease in exchangeable Ca 2+

concentrations from 1963 to 1993. The maximum depletion rate occurred in 1972, at the estimated peak

in acidic deposition levels. The dependence on Ca:Na ratios to estimate Ca 2+ weathering fluxes in this

analysis adds uncertainty to the magnitude of changes reported for the exchangeable Ca 2+ pool (Bailey

et al., 2003). Two additional mass balance studies used Sr isotopes to evaluate changes in soil Ca 2+ pools

and fluxes. The study of Bailey et al. (1996) estimated substantial depletion rates in a watershed in the

White Mountains of New Hampshire. Miller et al. (1993) estimated that inputs from weathering and

atmospheric deposition approximately equaled leaching losses at a site in the Adirondack Mountains in

New York. The different findings in these two studies are related to differences in the mineralogical

composition of the respective soils. However, the Miller et al. (1993) study also estimated that 50 to 60%

of the Ca 2+ in vegetation and the forest floor was derived from the atmosphere, despite the fact that the

weathering flux was estimated to be three times the rate of atmospheric inputs. This result suggests that

Ca 2+ supply from weathering in the lower profile is not reaching the upper soil where most root activity

occurs, and that Ca 2+ depletion has occurred in the upper soil.

The study of Yanai et al. (1999) investigated changes in Ca 2+ and Mg 2+ concentrations and content

in northeastern hardwood stands over time intervals ranging from 10 to 21 years. The general conclusion

of this study was that little or no change in O horizon (Oi, Oe, and Oa horizons) exchange chemistry

occurred. However, a decrease in exchangeable Ca 2+ concentrations in the Oa horizon was observed in

this study at the HBEF from 1978 to 1997, although no change was observed in this soil in exchangeable

Mg 2+ concentrations, or Ca 2+ or Mg 2+ content in the Oa horizon. Results of this study were complicated

by high spatial variability and differences in field sampling techniques between the original collection and

the resampling (Yanai et al., 1999). Yanai et al. (2005) also found little difference over 15 years in

exchangeable and extractable Ca 2+ and Mg 2+ concentrations in Oa horizons at 6 sites, and in O horizons

(combined Oe and Oa horizons) at 13 sites, in hardwood stands in New Hampshire. In this study, it was

also estimated that a difference greater than 50% would be needed to be statistically detected due to a

large degree of spatial variability. Although most repeated sampling studies did identify decreases in

exchangeable base cations in the Oa or O horizon, the results of Yanai et al. (2005) indicated that this

change may not occur at all sites and may be difficult to detect in some soils due to inconsistencies in

identifying horizon separations during sampling.

Through direct and inferred evidence of Ca 2+ depletion, and additional research on soil processes, a

detailed understanding of the mechanisms of Ca 2+ depletion has developed over the past two decades.

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35 Ulrich (1983) explained Ca 2+ depletion as a three-stage process in which buffering of acidity in the

mineral soil is first accomplished by weathering of carbonates and other mineral forms that weather

relatively rapidly. Once these mineral forms are depleted, buffering is accomplished largely by cation

exchange, in which H + is substituted for base cations and concentrations of exchangeable base cations

decrease. Once the buffering capacity provided by cation exchange is depleted, acid neutralization is

accomplished by weathering of crystalline minerals that contain large amounts of silicon (Si) and Al and

relatively small amounts of base cations. At this stage, Al is mobilized within the soil and exchangeable

Al concentrations increase. The shift in acid buffering from base cation exchange to alumino-silicate

weathering and exchangeable Al was documented in Russian soils sampled three times over 75 years

(Lawrence et al., 1995).

The effect of decreasing concentrations of exchangeable base cations on cation leaching in mineral

soil was shown in simulation modeling by Reuss (1983). Below a base saturation of 20%, leaching of

Ca 2+ decreases substantially and becomes less sensitive to variations in acid inputs as base saturation

decreases further. This relationship was later shown experimentally by Lawrence et al. (1999a). Samples

from the upper B-horizon in nearly all of the Adirondack lake watersheds sampled by Sullivan et al.

(2006a) had base saturation values less than 20%, as did soils at 11 sites in New York, Vermont, New

Hampshire, and Maine in a regional study of mature spruce-fir forests (David and Lawrence, 1996).

Exchangeable Ca 2+ concentrations (expressed as a percentage of cation exchange capacity [CEC]) in the

regional spruce-fir study were weakly correlated with an estimate of the relative weathering potential of

parent material in the upper 10 cm of the B horizon (r 2 = 0.44). However, these factors were strongly

correlated in the Oa horizon (r 2 = 0.92) (Lawrence et al., 1997). Because mineral weathering in the

B horizon is the primary source of soil Ca 2+ , a strong relationship between weathering potential and

exchangeable Ca 2+ concentrations would be expected in this horizon. The weak correlation suggests that

concentrations of Ca 2+ had decreased into the Al-buffering range sometime in the past. The parent

material signature in the Oa horizon was likely maintained through vegetative recycling—uptake of Ca 2+

from the O and B horizons, followed by transport back into the O horizon in litterfall.

In summary, evidence from repeated sampling and studies of soil processes indicate that decreases

in exchangeable base cation concentrations in both Oa and B horizons are common and widespread in the

eastern U.S. Factors such as logging and net forest growth are likely to have contributed to this decrease

in varying degrees, but acidic deposition has played a major role (Lawrence et al., 1987; Huntington,

2000). The magnitudes and rates at which Ca 2+ depletion has occurred are less clear.

These base cation depletion issues relate directly to the chemical recovery potential of acidified

soils and surface waters. Replenishment of exchangeable base cation concentrations on soils will require

that inputs from weathering and atmospheric deposition exceed losses from leaching and vegetative

uptake. Inputs of Ca 2+ from atmospheric deposition decreased sharply in the east through the 1980s

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32 (Hedin et al., 1994), and have remained relatively stable since that time (http://nadp.sws.uiuc.edu/).

Atmospheric deposition of SO 2−

4 currently remains several factors higher than that of Ca 2+ even at sites

where SO 2−

4 levels are relatively low (http://nadp.sws.uiuc.edu/), so chemical recovery at current acidic

deposition levels will require inputs of base cations from weathering that are considerably greater than

inputs from the atmosphere.

Because of the importance of weathering to the base-cation status of soils, a great deal of effort has

been made to estimate in situ weathering flux with a variety of methods (Miller et al., 1993; Likens et al.,

1996; Bailey et al., 2003). The complexity and variability of factors that affect weathering flux rates, such

as soil mineralogy, particle surfaces, soil organic matter, moisture flux, and a host of other factors that are

difficult to quantify, add large uncertainties to weathering flux estimates. Weathering rates estimated in

geochemical models are generally assumed to be constant overtime, but lower weathering rates were

observed in a soil sampled in 1987 than in the same soil sampled and archived in 1949–50 (Zulla and

Billett, 1994). Further complexity in weathering flux rates results from the possible role of mycorrhizae in

penetrating silicate minerals to extract base cations while remaining isolated from the soil solution

(Van Breemen et al., 2000; Blum et al., 2002). Lastly, as yet unidentified sources of base cations may

exist in forest soils. Bailey et al. (2003) found that elevated rates of Ca 2+ loss from forest harvesting

continued for 30 years after disturbance, but the source of the additional Ca 2+ being lost could not be

identified. Until estimates of in situ weathering fluxes are better constrained and more data become

available from repeated soil sampling, predictions of recovery of exchangeable base cation concentrations

will be highly uncertain.



## B.1.2.3. Aluminum Mobilization

Through the natural process of podzolization, dissolved organic acids derived from partially

decomposed organic matter in the O horizon move into the mineral soil where they weather soil particles

and release Al into solution. As soil solution moves deeper into the profile, acidity is neutralized and Al is

deposited as a secondary mineral or more likely as an organic Al (Al

o ) complex (DeConinck, 1980). The

limited mobility of organic anions results in retention of most Al within the mineral soil (often in the

Bh horizon). Complexation with dissolved organic matter can increase the mobility of Al within the soil

and lead to transport of organic Al into surface waters from shallow soils that are high in organic matter

(Lawrence et al., 1986).

Increased concentrations of exchangeable Al in the mineral soil have been identified through

repeated sampling in the U.S. and Europe over periods ranging from 17 years to 41 years in studies by

Billet et al. (1990), Falkengren-Grerup and Eriksson (1990), Bailey et al. (Bailey et al., 2005), and

Lawrence et al. (Lawrence et al., 1995). In areas of Europe with excessively high acidic deposition levels,

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33 evidence of Al depletion in the mineral soil has also been found (Mulder et al., 1989; Lapenis et al.,

2004), but Al depletion has not been documented in the U.S.

Increases in exchangeable Al concentrations in the O horizon have been documented over periods

from 17 to 30 years (Lawrence et al., 1995; Drohan and Sharpe, 1997; (Bailey et al., 2005), although the

study of Yanai et al. (2005) did not find consistent changes in Oa horizons over 15 years.

Numerous papers have evaluated solubility controls on Al in both the mineral soil and the

O horizon. These papers have commonly related Al solubility to gibbsite (Al(OH) 3 ) or a gibbsite-like

mineral to determine if inorganic Al concentrations could be predicted from gibbsite solubility constants

and pH (e.g., Johnson et al., 1981; David and Driscoll, 1984; Cronan and Goldstein, 1989; Lawrence and

David, 1977). These efforts have shown that inorganic Al concentrations are often undersaturated with

respect to gibbsite and do not support Al-trihydroxide as the primary control in natural systems. Gibbsite

solubility should therefore be considered a useful point of reference in evaluating Al-solubility rather a

mineral form that is an important control of Al solubility in natural systems.

Through the 1990s, evidence accumulated to indicate that secondary Al in the mineral soil is in a

form associated with organic matter, and in some soils, imogolite (Dahlgren and Walker, 1993; Mulder

and Stein, 1994; Berggren and Mulder, 1995; Simonsson and Berggren, 1998; Skyllberg, 1999). Organic

matter also plays a major role in controlling Al solubility in O horizons. This interaction has been

described by Cronan et al. (1986) in O horizons through the bound Al ratio, which reflects the equivalents

of adsorbed Al per mol of carboxyl groups (Cronan et al., 1986). Tipping et al. (1995) described Al

solubility on organic and mineral soil horizons through equilibrium humic ion binding. Each of these

approaches has had success in describing dissolved Al concentrations in organic soils as a function of pH

through formulations that rely on concentrations of solid-phase organic bound Al. Further work has

shown these relationships to be specific to the particular horizon, and the pool sizes of Al and humic

substances (Lofts et al., 2001). However, inputs of acidity may alter concentrations of solid-phase organic

bound Al (Lawrence and David, 1977). Changes in atmospheric deposition levels may therefore shift

these relationships over time as soils further acidify or recover.



## B.1.2.4. Soil Acidification

In the B horizon of soils north of the maximum extent of glaciation, CEC is largely derived from

organic matter, whereas in older southern soils the surface charge of highly weathered clay minerals is the

primary source of CEC. The CEC derived from organic matter is pH-dependent. Decreases in pH result in

a decreases in CEC. In both cases, the CEC of the B horizon is much lower than in organic-rich

surface horizons (Oa or A horizons). Less acidity from organic matter and a limited capacity for buffering

due to low CEC makes the B horizon more susceptible to a lowering of pH from acidic deposition, and

decreases in pH lower the CEC, further reducing the acid-buffering capacity from cation exchange. Two

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33 studies in the U.S. have provided measurements to assess changes in soil pH in the B horizon from acidic

deposition. Bailey et al. (Bailey et al., 2005) found lower pH values in the upper B horizon in

northwestern Pennsylvania soils in 1967 than in 1997, at 50 cm depth (p < 0.001) and at 100 cm depth (p

< 0.001), which were largely attributable to acidic deposition. Markewitz et al. (1998) also found

pronounced decreases in soil pH down to 60 cm in highly weathered Piedmont soils from 1962 to 1990.

The latter study was conducted in a former cotton field in which loblolly pines were planted in 1956–57.

Forest regrowth undoubtedly played a large role in the soil pH changes that were measured, but

atmospheric deposition was estimated to account for 38% of the H + inputs during the 28 years that

elapsed between measurements.

Other studies in Europe have found similar decreases in soil pH of the B horizon that could be

attributed, at least in part, to acidic deposition. These include the study of Lawrence et al. (Lawrence et

al., 1995) in northwestern Russia, which documented decreases in soil pH in the B horizon down to 90

cm, from 1926 to 1964, and further decreases from 1964 to 2001. Acidic deposition was identified as the

probable primary cause of decreasing pH in this study. The study of Lawrence et al. (Lawrence et al.,

1995) also observed a decrease in CEC in this soil, as did a previous study of Russian soils (Lapenis et al.,

2004). The decrease in pH was likely to have contributed to the decreased CEC of these soils, but a more

important factor may have been a decrease in organic carbon concentrations that was also measured. To

our knowledge, data to assess possible changes in CEC in soils in the U.S. has not become available, but

change in CEC has implications for recovery potential of soils from acidic deposition effects (Sullivan

et al., 2006b). Increased CEC driven by increases in pH could foster soil recovery by increasing the

opportunity for adsorption of base cations, as soil solution becomes less acidic. Decreases in soil organic

matter driven by climate and/or vegetation changes, such as those seen in Russian soils, would result in a

decrease in acid-buffering capacity through cation-exchange. There are currently no data in the U.S. that

indicate increases in soil pH associated with recent declines in acidic deposition levels. These data

limitations make future projections of recovery of soil pH highly uncertain.



## B.1.2.5. N Saturation

Severe symptoms of N saturation, have been observed in high-elevation, nonaggrading spruce-fir

ecosystems in the Appalachian Mountains, as well as in the eastern hardwood watersheds at Fernow

Experimental Forest near Parsons, WV and throughout the northeastern U.S. Mixed conifer forests and

chaparral watersheds with high smog exposure in the Los Angeles Air Basin also are N-saturated and

exhibit the highest stream water NO − 3 concentrations documented within wildlands in North America

(Bytnerowicz and Fenn, 1996; Fenn et al., 1998).

Some examples of N-saturated forests in North America, including estimated inputs and outputs,

are shown in Table B-1 (Fenn et al., 1998). The Harvard Forest hardwood stand in western Massachusetts

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35 absorbed > 900 kg N/ha without significant NO 3 − leaching during an 8–year N amendment study (Fenn

et al., 1998). In contrast, NO 3 − leaching losses were high at the Harvard Forest pine sites. In the 8–year

experimental study, NO 3 − leaching was observed in the pine stand after the first year (1989) in the high-N

application plots, and further increases were observed in 1995 and 1996. The hardwood stand did not

show significant increases in NO 3 − leaching until 1996. The differences in response of the pine and

hardwood stands indicate that the mosaic of community types across the landscape must be considered

when evaluating landscape-scale responses to N deposition (Magill et al., 2000).

Utilization of N in the terrestrial ecosystem is accomplished through complex interactions between

plants and microbes that are not fully understood (Schimel and Bennett, 2004). Long-term N retention is

largely accomplished by incorporation of N into soil organic matter through biological assimilation (Aber

et al., 1998), and to a lesser extent by abiotic processes that are not well understood (Dail et al., 2001).

The forms in which N is assimilated by plants and microbes are determined by availability, as described

in Schimel and Bennett (2004). In the most N-limited ecosystems, competition between plants and

microbes is high and N is assimilated primarily in depolymerized organic forms, resulting in low

mineralization rates and minimal buildup of inorganic N in the soil. Increased availability of N increases

the mineralization rate, which enhances competition between plants and microbes for available NH 4 +

produced by mineralization. Further increase in the availability of N (for example by high levels of

atmospheric N deposition) lessens competition for NH 4 + between plants and microbes and leads to

increased production of NO 3 − by autotrophic nitrifying bacteria. Some of this NO 3 − can be taken up by

plants and microbes, but because much of the N demand is satisfied by NH 4 + under these conditions,

NO 3 − tends to be mobile within the soil, enabling it to leach to drainage water. Based on the definitions of

Aber et al. (1989, 1998) and Stoddard (1994), the first stage of N saturation is reached when competition

between plants and microbes for NH 4 + has decreased to the point that net nitrification occurs.

Substantial leaching of NO 3 − from forest soils to streamwater can acidify downstream waters

(Webb et al., 1995), eutrophy estuaries and marine waters (Fisher and Oppenheimer, 1991), and deplete

soils of nutrient base cations, especially Ca 2+ and Mg 2+ (Likens et al., 1998). Considerable evidence is

available to link N deposition to acidification of soils. Much of this evidence comes from the northeastern

U.S., where increased accumulation of N in soil is suggested by a strong positive correlation between

atmospheric deposition levels and total N concentration in the Oa horizon, at sites in New York, Vermont,

New Hampshire, and Maine (Driscoll et al., 2001b). Further evidence that atmospheric deposition has

increased availability of N in soil is shown by a strong negative correlation between atmospheric

deposition levels and the C:N ratio of the Oa horizon in this region (Aber et al., 2003). If the C:N ratio

falls below about 25, nitrification is stimulated, resulting in elevated NO 3 − in surface waters (Aber et al.,

2003). Similar results were found in Europe, where a C:N ratio of 24 was identified as the critical level

below which nitrification occurred (Emmett et al., 1998).

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35 Analyses have been conducted in the northeastern U.S. and Europe to examine the relationships

between N deposition and NO 3 − leaching to surface waters. The relationship between measured wet

deposition of N and streamwater output of NO 3 − was evaluated by Driscoll et al. (1989) for sites in North

America (mostly eastern areas), and augmented by Stoddard (1994). The resulting data showed a pattern

of N leaching at wet inputs greater than approximately 5.6 kg N/ha/yr. Stoddard (1994) presented a

geographical analysis of patterns of watershed loss of N throughout the northeastern U.S. He identified

approximately 100 surface water sites in the region with sufficiently intensive data to determine their N

status. Sites were coded according to their presumed stage of N retention, and sites ranged from Stage 0

(background condition) through Stage 2 (chronic effects). The geographic pattern in watershed N

retention depicted by Stoddard (1994) followed the geographic pattern of N deposition. Sites in the

Adirondack and Catskill Mountains in New York, where N deposition was about 11 to 13 kg N/ha/yr,

were typically identified as Stage 1 (episodic effects) or Stage 2. Sites in Maine, where N deposition was

about half as high, were nearly all Stage 0. Sites in New Hampshire and Vermont, which received

intermediate levels of N deposition, were identified as primarily Stage 0, with some Stage 1 sites. Based

on this analysis, a reasonable threshold of N deposition for transforming a northeastern site from the

“natural” Stage 0 condition to Stage 1 would correspond to the deposition levels found throughout New

Hampshire and Vermont, approximately 8 kg N/ha/yr. This agreed with Driscoll et al.’s (1989)

interpretation, which would probably correspond to total N inputs near 8 to10 kg N/ha/yr. This is probably

the approximate level at which episodic aquatic effects of N deposition would become apparent in many

watersheds of the eastern U.S.

Analysis of data from surveys of N outputs from 65 forested plots and catchments throughout

Europe were conducted by Dise and Wright (1995) and Tietema and Beier (1995). Below the throughfall

inputs of about 10 kg N/ha/yr, there was very little N leaching at any of the study sites. At throughfall

inputs greater than 25 kg N/ha/yr, the study catchments consistently leached high concentrations of

inorganic N. At intermediate deposition values (10 to 25 kg N/ha/yr), Dise and Wright (1995) observed a

broad range of watershed responses. Nitrogen output was most highly correlated with N input (r 2 = 0.69),

but also significantly correlated with S input, soil pH, percent slope, bedrock type, and latitude. A

combination of N input (positive correlation) and soil pH (negative correlation) explained 87% of the

variation in N output at the study sites (Dise and Wright, 1995).

The threshold level of atmospheric deposition that causes release of NO 3 − to surface waters was

identified by Aber et al. (Aber et al., 2003) as approximately 7 kg N/ha/yr for the northeastern U.S. In

watersheds receiving N deposition above this level, concentrations of NO 3 − in surface waters were

positively correlated with atmospheric deposition, whereas most watersheds with deposition less than

7 kg/ha/yr had little or no NO 3 − (undetectable at most sites) in their surface waters (Aber et al., 2003). The

threshold value of 7 kg/ha/yr was based on atmospheric deposition levels for the base of forested

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35 watersheds. When scaled to include higher deposition levels expected at upper elevations this value was

estimated to equal about 10 kg/ha/yr, similar to the European estimate of Dise et al. (1998).

The common deposition threshold for release of NO 3 − to surface waters in forested watersheds

found in the northeastern U.S. and Europe represents an important advance in relating N inputs to

ecosystem effects, but a considerable amount of variability in ecosystem response has also been

demonstrated. Lovett et al. (2000b) found that 39 watersheds in the Catskill region of New York State

retained from 49% to 90% of atmospheric N inputs. Castro and Morgan (2000) showed that NO 3 − export

from watersheds in eastern North America can range from nearly 0 to over 400 eq/ha/yr in watersheds

that receive similar levels of inorganic N in wet deposition in the range of 400 to 500 eq/ha/yr.

Experimental additions of N to plots and watersheds have also demonstrated variations in terrestrial

retention of N. Additions of N (approximately twice ambient deposition) to hardwood watersheds in

Maine (25 kg N/ha/yr) and West Virginia (35.5 kg N/ha/yr), which were releasing NO 3 − to surface waters

prior to the additions, resulted in substantial increases in NO 3 − concentrations in soil water and stream

water within the first treatment year (Kahl et al., 1993a; Peterjohn et al., 1996). Additions of 25 kg

N/ha/yr to spruce plots in Vermont (ambient bulk deposition 5.4 kg N/ha/yr), in which net nitrification did

not occur prior to treatment, triggered net nitrification in the second year of treatment, whereas

nitrification was not triggered until the third year in plots receiving 19.8 kg N/ha/yr (McNulty et al.,

1996). Similar results to these were seen in two studies from Colorado. Additions of 25 kg N/ha/yr to oldgrowth spruce plots in Loch Vale watershed (ambient bulk deposition ~4–5 kg N/ha/yr) doubled N

mineralization rates and stimulated nitrification, while the addition of the same amount to plots receiving

ambient bulk deposition of ~2.0 kg N/ha/yr in Fraser Experimental Forest elicited no microbial response

but significantly increased foliar and organic soil horizon N (Rueth et al., 2003). A comparison study of

old-growth spruce plots across a depositional gradient in Colorado found mineralization rates to be higher

where N deposition ranged from 3 to 5 kg N/ha/yr than where N deposition ranged from 1 to 2 kg

N/ha/yr, with measurable nitrification rates at sites with the highest deposition amounts (Rueth and Baron,

2002). In marked contrast to these results, concentrations of NO 3 − plus NH 4 + were not detected until the

seventh year in hardwood plots in Harvard Forest, which received additions of 150 kg N/ha/yr (Magill

et al., 2004). Concentrations of (NO 3 − + NH 4 + ) in hardwood plots receiving 50 kg N/ha/yr were not yet

detectable in the 15th year of treatments. The same treatments were applied to red pine ( Pinus resinosa )

plots, which exhibited elevated concentrations of (NO 3 − + NH 4 + ) in soil water after 1 year of 150 kg

N/ha/yr doses, and after 5 years of 50 kg N/ha/yr doses.

In general, deciduous forest stands in the eastern U.S. have not progressed toward N-saturation as

rapidly or as far as spruce-fir stands. Deciduous forests may have a greater capacity for N retention than

coniferous forests. In addition, deciduous forests tend to be located at lower elevation and receive lower

atmospheric inputs of N. Many deciduous forests have higher rates of N uptake and greater N requirement

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35 than spruce-fir forests. Decreased growth and increased mortality have more commonly been observed in

high-elevation coniferous stands than in lower elevation hardwood forests, and these differences have

been partially attributed to excess inputs of N (Aber et al., 1998). Indeed, many of the lower elevation

deciduous stands are N-deficient and are therefore likely to benefit (i.e., grow faster), at least up to a

point, with increased inputs of N.

There are examples of N saturation in lower-elevation eastern forests, especially in West Virginia.

For example, progressive increases in streamwater NO 3 − and Ca 2+ concentrations were measured at the

Fernow Experimental Forest in the 1970s and 1980s (Edwards and Helvey, 1991; Peterjohn et al., 1996;

Adams et al., 1997, 2000). This watershed has received higher N deposition (average throughfall input of

22 kg/ha/yr of N in the 1980s) than is typical for low-elevation areas of the eastern U.S., however (Eagar

et al., 1996), and this may help to explain the observed N saturation.

Varying responses to N additions reflect differences in N status of the treatment sites. These

variations have most often been attributed to disturbance history, dating back a century or more (Goodale

and Aber, 2001). Sites which have undergone disturbances that cause loss of soil N, such as logging, fire,

and agriculture, tend to be most effective at retaining atmospheric and experimental inputs of N. Nitrogen

retention capability often decreases with stand age, which suggests that older forests are more susceptible

than younger forests to becoming N-saturated (Hedin et al., 1995). Aber et al. (1998) surmised that land

use history may be more important than cumulative atmospheric deposition of N in determining the N

status of a forest ecosystem.

Although considerable progress has been made in understanding the factors that control N

retention, efforts to quantify net N retention through known processes have not been fully successful.

Assimilation of N by mycorrhizae followed by exudation as dissolved organic matter was proposed by

Aber et al. (1998) as a possible explanation for unaccounted conversion of inorganic N into soil organic

matter. However, Frey et al. (2004) found that elevated N inputs reduced active mycorrhizal biomass,

fungal diversity and fungal:bacterial biomass ratios. These results suggested a decreased role for

mycorrhizae in fixation of N under elevated N inputs.

Abiotic transformation of inorganic N into soil organic matter has also been proposed as a possible

mechanism to explain high rates of N retention in soil, and some evidence has been presented to support

this possibility. Dail et al. (2001) observed retention of 15 NO 3 − and 15 NO 2 in sterile soil, but the method of

sterilization may have increased dissolved organic carbon (DOC) concentrations and artificially increased

the opportunity for formation of soluble organic N compounds. Davidson et al. (2003) developed the

ferrous wheel hypothesis to explain incorporation of inorganic N into organic matter. The hypothesized

mechanism involves conversion of NO 3 − to NO 2 − through oxidation of Fe 2 + . Testing of this hypothesis in

situ was not found in the literature, but the small amount of Fe 2 + that typically occurs in the forest floor,

where presumably much of the conversion to organic N occurs, may limit the importance of this pathway.

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34 Fitzhugh et al. (2003) showed that NO 2 − produced in the first step of nitrification may be directly

converted to soluble organic N rather than becoming fully oxidized to NO 3 − . However, concentrations of

introduced 15 NO 2 in this experiment were several orders of magnitude higher than that normally seen in

forest soils. Therefore, the evidence at this time for abiotic retention of N is not fully convincing, and the

importance of this process requires further research.

In addition to our limited understanding of N retention mechanisms, there is no direct information

on ecosystem recovery from N saturation in the U.S. This may be at least partly because atmospheric

deposition of N has been relatively stable in the eastern U.S. over the past two to three decades. An

important source of information on N recovery responses has been provided by the European NITREX

study, which reduced ambient N deposition for 5 years with roofs constructed over experimental plots in

Germany and The Netherlands. At the German site, deposition was reduced from approximately 38 kg

N/ha/yr to levels that varied from 10 to 20 kg N/ha/yr. At the Dutch site, deposition was reduced from

45 kg N/ha/yr to levels that varied from 1 to 10 kg N/ha/yr. At both of these sites, deposition levels before

the experiment were approximately three to four times greater than the highest deposition levels

commonly found in the eastern U.S., whereas after the reduction, levels at the Dutch site fell within the

range of deposition in the eastern U.S. over the past two decades, and values at the German site were

somewhat higher than this range (Ollinger et al., 1993); Emmett et al., 1998). The decrease in ambient N

inputs resulted in a marked decrease in N outputs at each site within 2 to 3 years. The responses at the two

sites were somewhat different, however. At the Dutch site, outputs of N exceeded inputs both before and

after experimental reduction of inputs. At the German site, inputs exceeded outputs before and after

reduction of inputs, but outputs were more similar to inputs after the reduction. At both sites, outputs after

the reduction in deposition remained two to three times higher than outputs commonly measured in the

eastern U.S.

Thus, atmospheric deposition of N has increased N availability in soils, which has led to increased

nitrification and associated acidification of soil and soil water. The N retention capacity of soils is

strongly dependant on land-use history, however, so the relationships between N deposition and

ecosystem N status and percent of terrestrial retention are variable. In general, however, atmospheric

deposition of 10 kg N/ha/yr or higher is required for appreciable amounts of NO 3 − to leach to surface

waters in the eastern U.S. and northern Europe. Future projections of chemical recovery from N-driven

acidification are uncertain because (1) retention mechanisms are not fully understood, and (2) there are

only limited data on recovery responses. European experiments that reduced inputs of N found decreased

outputs of N within 2 to 3 years, which indicates a relatively rapid response to decreased deposition

levels. However, these studies are difficult to directly apply to the U.S. because deposition levels were

much higher at the European sites prior to the experiment, and the 5–year duration of the experiments

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35 only demonstrated recovery to levels of N saturation that are higher than the more heavily affected sites in

the eastern U.S.

High concentrations of lake or streamwater NO

− 3 , indicative of ecosystem saturation, have been

found at a variety of locations throughout the U.S., including the San Bernardino and San Gabriel

Mountains within the Los Angeles Air Basin (Fenn et al., 1996), the Front Range of Colorado (Baron

et al., 1994; Williams et al., 1996a,b), the Allegheny Mountains of West Virginia (Gilliam et al., 1996),

the Catskill Mountains of New York (Murdoch and Stoddard, 1992; Stoddard, 1994), the Adirondack

Mountains of New York (Wigington et al., 1996), and the Great Smoky Mountains in Tennessee (Cook

et al., 1994). All of these regions, except Colorado, received relatively high (more than about 10 kg

N/ha/yr) atmospheric deposition of oxidized N throughout the 1980s and 1990s. In contrast, the Front

Range of Colorado receives less than about 4 or 5 kg N/ha/yr of total (wet plus dry) deposition (Sullivan

et al., 2005), less than half of the total N deposition received at these other locations. The cause of Nsaturation at high-elevation western watersheds that receive low to moderate levels of atmospheric

deposition has been a subject of debate. High concentrations of NO − 3 in surface waters in the western

U.S. are not widespread. Nitrate concentrations during the fall sampling season were low in most western

lakes sampled in the Western Lakes Survey (WLS). Only 24 sampled lakes were found to have NO − 3

concentrations greater than 10 μeq/L. Of those, 19 lakes were situated at high elevation, most above 3,000

m. Cold temperatures in such lakes undoubtedly play an important role in maintaining high NO − 3

concentrations by limiting biological uptake processes. The high NO − 3 concentrations are likely to affect

acid-base chemistry only where ANC is low. Eight lakes showed high NO − 3 (>10 μeq/L) and low ANC

(<50 μeq/L), all of which occurred at elevations higher than 3,100 m. Four were located in Colorado, two

in Wyoming, and one each in California and Utah. In all cases, pH was above 6.5 and ANC was greater

than or equal to 15 μeq/L. Such lakes are sensitive to episodic pulses of NO − 3 acidity; such pulses have

been reported from Colorado Front Range lakes (Williams and Tonnessen, 2000). Episodic acidification

of western lakes could be important biologically.

In the Uinta Mountains of Utah and the Bighorn Mountains of central Wyoming, 19% of the lakes

included within the WLS had NO − 3 > 10 μeq/L. This suggests that N deposition in these areas may have

exceeded the capability of these systems to assimilate N. It is unknown if these concentrations of NO − 3

represent effects from anthropogenic sources or if this constituted a natural condition associated with

inhibited NO − 3 assimilation in cold alpine environments.

Williams et al. (1996a,b) contended that N-saturation is occurring throughout high-elevation

catchments of the Colorado Front Range. Many lakes in the Colorado Front Range have chronic NO − 3

concentrations greater than 10 μeq/L and concentrations during snowmelt are frequently much higher, due

at least in part to leaching from tundra, exposed bedrock, and talus areas. Although biological N demand

may be high in subalpine forests, uptake is limited in alpine areas by large N inputs from snowmelt, steep

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28 watershed gradients, rapid water flushing, extensive areas having little or no soil development, and

limitations on the growth of phytoplankton in some alpine lakes by factors other than N (e.g., phosphorus

[P], temperature) (Baron et al., 1994).



## B.1.2.6. Nitrate Leaching

Nitrate leaching losses from soils to drainage waters are governed by a complex suite of ecosystem

processes in addition to N inputs from atmospheric deposition. In particular, mineralization and

nitrification processes play important roles in regulating the quantity of, and temporal variability in, the

concentration of NO − 3 in soil solution, and consequently leaching losses from the rooting zone (Reuss and

Johnson, 1985; Joslin et al., 1987; Johnson et al., 1991b,c). Thus, NO − 3 leaching is mostly under

biological control and typically shows pronounced seasonal variability (Van Miegroet et al., 1993). Peak

concentrations of NO − 3 in soil solution appear to be largely responsible for the potentially toxic peaks in

Al concentration that sometimes occur in soil solution, although SO 2−

4 may also play a role by serving to

elevate chronic Al concentrations (Eagar et al., 1996).

High leaching of NO − 3 in soil water and streamwater draining high-elevation spruce-fir forests has

been documented in numerous studies in the Southern Appalachian Mountain region (cf. Joslin et al.,

1992; (Joslin et al., 1992)Van Miegroet et al., 1992a,b; Joslin and Wolfe, 1994; Nodvin et al., 1995). This

high NO − 3 leaching has been attributed to a combination of high N deposition, low N uptake by forest

vegetation, and inherently high N release from soils. Forest age is another major factor-affecting uptake,

with mature forests requiring minimal N for new growth and, hence, often exhibiting higher NO − 3

leaching that younger, faster growing stands (Goodale and Aber, 2001). Old-growth red spruce stands in

the Southern Appalachians have been demonstrated to have significantly slower growth rates than stands

younger than 120 years (Smith and Nicholas, 1999). The latter feature is associated with low C:N ratios in

mineral soil, high N mineralization potential and high nitrification (Joslin et al., 1992)Eagar et al., 1996).

In most terrestrial ecosystems in the U.S., N is strongly retained and there is limited mobility of

NO

− 3

. Exceptions to this pattern tend to occur in spatially limited regions that receive high levels of total

N deposition (higher than about 10 to 20 kg N/ha/yr) and in alpine and subalpine environments that have

little soil or vegetative development over substantial portions of the watersheds.



# B.1.3. Interactions with Transitional Ecosystems

B.1.3.1. S Storage and Release in Transitional Ecosystems

Although S i s generally mobile in upland soils in most parts of the U.S., wetlands act as both

sources and sinks of atmospherically deposited S. Wetlands retain and release S in response to variations

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32 in hydrology, which in turn affect oxidation and the reduction process in wetland soils. Ito et al. (2005)

evaluated the influence of land cover types on SO 2−

4 fluxes in Adirondack lake watersheds. They found

that SO 2−

4 concentration in drainage water decreased in association with increased wetland area within the

lake watershed (adj. r 2

= 0.58, p ≥ 0.001). They attributed this observed pattern to dissimilatory SO 2−

4

reduction in anaerobic wetland soils.

Sulfur storage in wetland soils to some degree prevents or delays the acidification of downstream

surface waters with mineral acidity. However, the water table in wetland areas typically drops during

drought conditions, and this allows development of aerobic conditions in surface wetland soils. Under

aerobic conditions, stored S is re-oxidized to SO 2−

4 , which can then be rapidly mobilized under high-flow

conditions that occur in response to rainfall or snowmelt. This can cause substantial episodic pulses of

acidity in surface waters that receive drainage water from wetlands. Thus, wetlands buffer downstream

receiving waters against chronic acidity to some degree, but can be an important source of periodic

episodes of more extreme acidity.



## B.1.3.2. Organic Acidity in Transitional Ecosystems and Downstream Surface Waters

Organic acids in fresh water originate from the degradation of biomass in upland areas, wetlands,

near-stream riparian zones, the water column, and stream and lake sediments (Hemond, 1994). The

watersheds of surface waters that have high concentrations of organic matter (DOC > about 400 μM)

often contain extensive wetlands and/or extensive organic-rich riparian areas (Hemond, 1990; Sullivan,

2000).

Organic acids contributed by wetlands to downstream drainage waters can influence surface water

acid-base chemistry, particularly in dilute waters having moderate to high (greater than about 400 μM)

DOC concentrations. Organic acids in surface waters include a mixture of functional groups having both

strong and weak acid character. Some lakes and streams are naturally acidic as a consequence of organic

acids contributed to solution by wetlands. The presence of organic acids also provides buffering to

minimize pH change in response to changes in the amount of SO 2− and NO − 4 3 derived from acidic

deposition.

There are many lakes and streams that are chronically acidic or low in ANC mainly due to the

presence of organic acids. In many cases, the principal source of these organic acids is the wetlands

within the watershed. The NAPAP (1991) concluded that about one-fourth of all acidic lakes and streams

surveyed in the National Surface Water Survey (NSWS) (Linthurst et al., 1986a; Kaufmann et al., 1988)

were acidic largely as a consequence of organic acids. A survey of 1400 lakes in the Adirondack

Mountains by the Adirondack Lake Survey Corporation (ALSC) (Kretser et al., 1989), which included

many small lakes and ponds (1 to 4 ha) having relatively high DOC, revealed that about 38% of the lakes

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35 had pH < 5 due to the presence of organic acids, and that organic acids generally depressed the pH of

Adirondack lakes by 0.5 to 2.5 pH units in the ANC range of 0 to 50 μeq/L (Baker et al., 1990b).

Specification of the acid-base character of water high in DOC is somewhat uncertain. Attempts

have been made to describe the acid-base behavior of organic acids using a single H + dissociation

constant (pK

a ), despite the fact that organic acids in natural waters are made up of a complex mixture of

acidic functional groups. A portion (perhaps one-third) of the acidity in organic acids is quite strong, with

some ionization occurring at pH values well below 4.0 (Driscoll et al., 1994; Hemond, 1994). A number

of modeling approaches have been used to estimate the acidity of organic acids in fresh waters, often as

simple organic acid analogs having different pK

a values (Oliver et al., 1983; Perdue et al., 1984; Driscoll

et al., 1994).

The importance of naturally occurring organic acids as agents of surface water acidification was

reinforced by a modeling study (Sullivan et al., 1996a) that showed that inclusion of organic acids in the

Model of Acidification of Groundwater in Catchments (MAGIC) had a substantial effect on model

predictions of surface water pH, even in waters where DOC concentrations were only moderate. MAGIC

hindcasts of pre-industrial lakewater pH of Adirondack lakes showed poor agreement with diatom

inferences of pre-industrial pH when organic acids were not considered in the MAGIC model (Sullivan

et al., 1996a). Revised MAGIC hindcasts of pre-industrial lakewater pH that included an organic acid

representation (Driscoll et al., 1994) showed considerably closer agreement with diatom inferences

(Figure B-1). The mean difference between MAGIC and diatom estimates of pre-industrial pH was

reduced from 0.6 pH units to 0.2 pH units when organic acids were included in the model, and the

agreement for individual lakes improved by up to a full pH unit (Sullivan et al., 1996a).

Rosenqvist (1978) and Krug et al. (Krug et al., 1985) hypothesized that a significant component of

the mobile acid anions contributed from atmospheric deposition (e.g., SO 4 2− , NO 3 − ) replace organic

anions that were previously present in solution. Under this anion substitution hypothesis, the net result of

acidic deposition is not so much an increase in cations (including potentially toxic H + and Al n+ ) as much

as an exchange of SO 4 2− and NO 3 − anions for organic anions, with little or no change in ANC and pH.

This hypothesis has received some support from paleolimnological studies, which suggested historic

decreases in DOC concentrations during the period of lakewater acidification in the 1900s (Davis et al.,

1985a,b; Kingston and Birks, 1990; Dixit et al., 2001). Other studies have found a decrease in organic

acidity which was at least partly attributable to the extent of organic acid protonation. David et al. (1999)

measured a decrease in organic anion concentrations in stream water in response to the experimental

whole-watershed acidification experiment at the Bear Brook Watershed in Maine. Wright et al. (1993)

concluded that ANC increases in a small watershed in Norway, where rates of acidic deposition were

experimentally reduced, were limited by the increasing role of organic acids that accompanied decreasing

acid deposition load.

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28 Complexation of organic acids by metals (Almer et al., 1974; Lind and Hem, 1975; Dickson, 1978;

Cronan and Aiken, 1985) and pH-dependent changes in dissociation of organic acids (Oliver et al., 1983;

Wright et al., 1988a) are probably important components of the organic acidity response. Loss of DOC in

response to acidic deposition can also cause a shift in Al species composition towards lesser complexation

with organic ligands. Such a shift from AL

o to Al i increases toxicity of the Al to aquatic biota (Baker and

Schofield, 1982). Changes in pH can alter the charge density of organic solutes and thus influence organic

contributions to acidity (e.g., Wright et al., 1988a,b). David et al. (1999) found that the charge density of

organic acids decreased by about 1 μeq/L/mg C at West Bear Brook in Maine, in response to 6 years of

experimental acidification, probably due to greater protonation of organic acid anions at the lower pH.

Similar results were reported by Lydersen et al. (1996) at Lake Skjervatjern in Norway. Values of the

organic acid charge density in the ALSC lakes in the Adirondack Mountains increased with increasing pH

between pH values of 5.0 to 7.0 due to the presence of weakly acidic functional groups (Driscoll et al.,

1994).

Hedin et al. (1990) artificially acidified a small, moderately high-DOC (725 μM C) stream with

H 2 SO 4 at HBEF in New Hampshire. Streamwater pH (4.4) was near the range of reported average pK

a

values for organic acids, suggesting that the capacity of organic acids to buffer mineral acidity should be

high. The acid loading rate was adjusted to achieve an increased streamwater SO 4 2− concentrations of

150 μeq/L at the downstream sampling point 108 m below the point of acid addition. Adjustments were

made for dilution by soil water or inflow from small tributaries. Although streamwater DOC did not

change significantly, the concentration of organic anions (as calculated from the charge balance)

decreased by 17 μeq/L. Thus, the overall capacity of organic anions to neutralize mineral acid inputs

offset about 11% of the added acid (Hedin et al., 1990). This experiment only considered interactions

between mineral acid and organic matter within the stream. Any additional buffering that may have been

provided within the terrestrial catchment was not represented in the experimental design. Also, any

possible catchment-mediated influences of the experimental acidification on organic acid properties or

terrestrial DOC mobilization were excluded from the experiment because the acid was not applied to the

catchment soils.

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13 Source: Sullivan et al. (1996).



### Figure B­1. MAGIC model hindcast

estimates of pre­industrial pH

versus diatom­inferred pH for 33

statistically selected Adirondack

lakes: a) without including organic

acid representation in the MAGIC

simulations, and b) including a

triprotic organic acid analog model

in the MAGIC simulations.

Results of a resurvey of 485 Norwegian lakes sampled in both 1986 and 1995 provided evidence in

support of an increase in organic acid anion concentrations in association with decreased lakewater SO 2−

4

concentration (Skjelkvåle et al., 1998). The organic acid anion concentration increased by an amount

equal to between 9% and 15% of the decrease in SO 2−

4 concentration in the four regions of the country

most heavily affected by the decrease in S d eposition during the intervening 10 year period. Lakewater

SO 2−

4 concentrations decreased by 9 μeq/L (western and northern Norway) to 20 to 21 μeq/L (eastern and

southern Norway). Only in mid-Norway, where average SO 2−

4 concentration decreased by only 6 μeq/L,

did the organic acid anion concentration remain unchanged between 1986 and 1995 (Skjelkvåle et al.,

1998).

Recent monitoring data have shown that DOC and organic acid anion concentrations in many lakes

and streams in the U.S. have increased in association with decreased S d eposition. It is likely that a high

percentage of this DOC originates from wetland soils within the monitored watersheds. This result

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10 appears to be partly responsible for the limited lakewater ANC and pH recovery that has occurred at many

locations. The response of surface waters to changes in acidic deposition has included a general increase

in surface water DOC (Figure B-2). All regions of the eastern U.S. analyzed by Stoddard et al. (2003) that

had sufficient DOC data for analysis exhibited increases in DOC concentrations during the 1990s. All

regional trends were significant with the exception of the Northern Appalachian Plateau, the region with

the lowest median DOC concentration. The median increase in DOC of 0.05 mg/L/yr reported by

Stoddard et al. (2003) corresponds to an overall increase of about 10% across study regions, similar to

trends reported elsewhere in the northern hemisphere (Evans and Monteith, 2001; Skjelkvåle et al., 2001).

This suggests a common cause. Both climate warming and decreasing acidic deposition are possible

causal agents.

August 2008 B­21 DRAFT—DO NOT QUOTE OR CITESource: Stoddard et al. (2003).



### Figure B­2. Cumlative frequency diagram (distribution) of slopes for SO 2−

4 

### , NO −

3 

### , Gran ANC,

hydrogen ion, [Ca 2+ 

### + Mg 2+ 

### ], and DOC concentrations in LTM surface water monitoring sites, by

region, for the period 1990–2000. The Ridge/Blue Ridge Province did not have sufficient DOC data

to allow trend analysis.

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# B.2. Factors That Determine Ecosystem Sensitivity

B.2.1. Transitional Ecosystems

B.2.1.1. Wetlands and Peatlands

Wetlands and peatlands often contain highly acidic soils. Their acidity is mainly attributable to the

presence of large quantities of naturally occurring organic materials. Fulvic and humic acids, formed

during the breakdown of organic matter, contribute substantial organic acidity to soil and surface waters

in wetland and peatland environments. In the case of ombotrophic bogs and poor fens, there is also a

scarcity of base cations, which would serve to buffer both organic and mineral acidity.

Because wetland and peatland vegetative communities are adapted to high levels of natural organic

acidity, it is unlikely that S or N deposition would cause any acidification-related effects at levels of

acidic deposition commonly found in the U.S. Nevertheless, wetlands are closely tied to a number of

important biogeochemical processes that regulate watershed response to acidic deposition. The major

interactions are described below.

High concentrations of DOC in brownwater lakes and streams are often due to the influence of

wetlands on hydrography within the watershed. This presence of high concentrations (higher than about

500 μM) of DOC can substantially reduce the pH and ANC of surface waters, buffer those waters against

pH changes in response to added mineral acidity, and form stable complexes with dissolved Al, thereby

reducing its toxicity to aquatic life. Therefore, the response of surface waters to acidic deposition is

strongly influenced by the extent of upstream and shoreline wetland development.

Wetlands also serve as a (sometimes-temporary) sink for atmospheric S a nd N. Chemical reduction

reactions and biological uptake contribute to S a nd N storage in wetland soils. Oxidation during drought

periods, when water levels recede, followed by flushing from wetland to downstream surface water

during subsequent storm flow, can cause substantial pulses of mineral acidity in downstream receiving

waters. On a chronic basis, the concentration of SO 2−

4 (and associated acidity) in surface water can be

substantially lower as a consequence of dissimulatory S r eduction in upslope wetlands. On an episodic

basis, wetlands can contribute to wide fluctuations in downstream surface water acid-base chemistry.

Such fluctuations can include pulses of acidity that may be toxic to aquatic biota.

Wetlands provide anaerobic substrate for S-reducing bacteria. These bacteria are also partly

responsible for the increased rate of mercury (Hg) methylation that is known to occur in wetlands. As a

consequence, fish in lakes drained by wetlands often have much higher concentrations of tissue methyl

Hg, as compared with fish in lakes that lack watershed wetlands (Driscoll et al., 2007).

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## B.2.1.2. Ponds

The factors that determine the sensitivity of ponds to acidification from acidic deposition are

generally similar to those that determine the sensitivity of lakes (discussed in the following section). In

general, however, ponds and small lakes tend to exhibit low ANC and pH at a greater frequency than do

larger lakes (Sullivan et al., 1990). This pattern is mainly a consequence of the higher concentrations of

DOC frequently found in ponds as compared with larger lakes. In addition, because larger bodies of water

tend to have larger watersheds, there is a greater likelihood that they will integrate conditions across a

broader landscape, increasing the possibility of receiving at least a moderate level of base cation supply

(Sullivan et al., 1990). Thus, where lakes are acid-sensitive, it is likely that ponds are also acid-sensitive.

However, synoptic databases of pond acid-base chemistry are generally not available.



# B.2.2. Streams and Lakes

Acidic deposition that falls as precipitation directly on the lake surface may eventually be

neutralized by in-lake reduction processes which are controlled in part by hydraulic residence time (Baker

and Brezonik, 1988). Natural hydrologic events also alter acidification and neutralization processes

during snowmelt and change flowpaths during extended droughts (Webster et al., 1990).

Leaching of base cations by acidic deposition can deplete the soil of exchangeable bases. The

importance of this response has recently been widely recognized because most watersheds are not

exhibiting much ANC and pH recovery of drainage water in response to recent large decreases in S

deposition. This limited recovery can be at least partially attributed to decreased base cation

concentrations in surface water. This understanding of the base cation response has developed slowly.

During the 1980s, the generally accepted paradigm of watershed response to acidic deposition was

analogous to a large-scale titration of ANC (Henriksen, 1984). Atmospheric inputs of acidic anions were

believed to result in movement of those anions through soils into drainage waters with near proportional

loss of surface water ANC. This view was modified by Henriksen (1984), who suggested that a modest

component of the added SO 2−

4 (up to a maximum of about 40%) could be charge-balanced by increased

mobilization of base cations from soils, and the remaining 60% to 100% of the added SO 2−

4 resulted in

loss of ANC in surface waters. During the latter part of the 1980s, it became increasingly clear that a

larger component (> 40%) of the added SO 2−

4 was in fact neutralized by base cation release in most cases

and the ANC (and therefore also pH) of surface waters typically did not change as much as was earlier

believed. This understanding developed in large part from paleoecological studies (e.g., Charles et al.,

1990; (Sullivan et al., 1990), which indicated that past changes in lakewater pH and ANC had been small

relative to estimated increases in lakewater SO 2−

4 concentrations since pre-industrial times (Sullivan,

2000). The belief that changes in acidic deposition were accompanied mainly by changes in ANC and pH

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26 has been replaced by the realization that changes in SO 2−

4 were accompanied mainly by changes in base

cations. Thus, surface waters have not been acidified as much by historical deposition as was earlier

believed. Furthermore, surface water ANC and pH should not be expected to show substantial chemical

recovery upon reduced emissions and deposition of S and N. The magnitude of the base cation response

has clearly limited the extent of surface water acidification caused by acidic deposition. However, this

same response has contributed to base cation deficiencies in some soils, with associated adverse terrestrial

effects.



# B.2.3. Other Types of Ecosystems

There has been little work on the rates of atmospheric deposition to urban ecosystems despite

extensive data on concentrations and chemical reactions of air pollutants in cities (U.S. Environmental

Protection Agency, 2004). Nevertheless, urban ecosystems are often subjected to large rates of deposition

of anthropogenic pollutants (Lovett et al., 2000a). Decades of research on urban air quality indicate that

cities are often important sources of emissions of NO X , SO X , and dust. Urban N deposition may affect

nutrient cycles and soil acid-base chemistry in vegetated areas in and around cities, but such possible

effects have not been studied sufficiently to draw conclusions about sensitivities or effects.

To determine the patterns of atmospheric deposition and throughfall in the vicinity of a large city,

Lovett et al. (2000a) measured bulk deposition, oak forest throughfall, and particulate dust at sites along a

transect within and to the north of New York City. They found that throughfall N was twice as high in the

urban areas compared with suburban and rural areas. Most of the urban dry deposition of NO − 3 was from

gaseous NO X

. Because there is limited biological uptake of throughfall N in an urban setting, it is

believed that a relatively high (but unknown) percentage of N deposited to the urban landscape leaches to

surface waters. Aquatic effects associated with N leaching from urban environments would be expected to

be most pronounced near coastal cities. This is because atmospheric deposition to near-coastal urban

environments can provide a substantial N load to estuaries and near shore oceanic environments, which

tend to be N-limited. See further discussion in ISA Section 3.3.2.4.



# B.3. Distribution and Extent of Ecosystem Effects

B.3.1. Terrestrial Ecosystems

Coniferous forests, with soils that are naturally more acidic, generally have lower pH and base

saturation than soils in deciduous forests (Fernandez et al., 2003). In a paired watershed study at Bear

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35 Brook Watershed in Maine, one watershed with mixed coniferous and deciduous species received

(NH 4 ) 2 SO 4 corresponding to about 25 kg N/ha/yr and 29 kg S/ha/yr. After a decade of experimental

acidification, the treated watershed had 66 kg/ha/yr less exchangeable Ca 2+ and 27 kg/ha/yr less

exchangeable Mg 2+ than the untreated watershed (Fernandez et al., 2003). Soils under conifers (red

spruce, balsam fir [ Abies balsamea ], hemlock [ Tsuga canadensis ]) appeared to be more sensitive to

acidification than those under hardwoods (American beech [ Fagus grandifolia ], yellow birch [ Betula

alleghaniensis ], sugar, and red maples [ Acer rubrum ]). The hardwoods demonstrated no significant

effects from (NH 4 ) 2 SO 4 addition. Differences in response to acid treatment among vegetation covers were

most pronounced in upper soil (O horizon and upper 5 cm of the B horizon). The study did not distinguish

between effects from NH 4 + versus SO 4 2− additions.

Kozlowski (1985) suggested that plants and soils act as sinks for SO 2 deposition at low exposures

of 1 to 4 μg/m 3 , with no discernible effects on ecosystem structure at those levels. Shugart and

McLaughlin (1985) cautioned that forest responses to SO 2 and other stressors are strongly controlled by

the successional dynamics of impacted forests. Thus, efforts to better understand and quantify forest

dynamics and development will be paramount to predicting chronic pollution effects.

Results of N fertilization studies have been used to infer the response of forests to atmospheric N

deposition. Such studies were reviewed by Johnson (1991) and EPA (1993), illustrating that forests can

respond differently to periodic large pulsed fertilizer inputs, as compared with steady, low-level inputs

from atmospheric deposition. For example, multiple or continuous inputs of N may stimulate populations

of nitrifying bacteria (U.S. Environmental Protection Agency, 1993). This might be expected to modify

the competitive interactions between trees and microbes and affect both the forest growth response and

the extent of NO 3 − leaching and associated acidification.

In the southern Appalachian Mountains, acidification sensitivity has been evaluated for two

common tree species: red spruce (sensitive) and loblolly pine ( Pinus taeda ; insensitive). Dendrochronological analyses of tree cores collected for permanent plots in the Great Smoky Mountains National

Park (37 trees cores from low elevation [~1500 m]; 35 tree cores from high elevation sites [~2000 m]),

demonstrated a positive correlation between temporal and spatial trends in red spruce growth and acidic

deposition, with a greater response in trees on ridges than in draws. Ridges are naturally more acidified,

receive higher levels of acidic deposition, and have shallower soils with lower base saturation (Webster

et al., 2004).

Loblolly pine seems to have low susceptibility to adverse effects from acidic deposition. A

simulated acid addition experiment showed no significant effect of acidification on foliar nutrition in

loblolly pine seedlings, at application levels of 21 to 26 kg/ha SO 4 -S and 8 to 10 kg/ha NO 3 -N (Baker

et al., 1994). Loblolly pines grown on old agricultural fields showed signs of N deficiency over 25 years

of growth despite atmospheric deposition of 5 to 10 kg N/ha/yr (Richter et al., 2000).

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35 In the northeastern U.S., two species of coniferous tree (red spruce and red pine) have been shown

to be sensitive to acidification. Aber et al. (2003) reported a decrease in C:N ratio from about 35 to about

25 along an increasing N deposition gradient of 3 to12 kg N/ha/yr across the Northeast. At the Harvard

Forest LTER site, at chronic experimental N addition levels of 50 and 150 kg N/ha/yr, Magill et al. (2004)

found 31% and 54% decreases, respectively, in red pine growth after 15 years of chronic N application.

No additive effect of S was seen after 11 years of a combined N and S treatment, with an application of

74 kg S/ha/yr and 50 kg N/ha/yr. There were no significant differences in baseline measurements between

the low N and combined N and S treatments.

Recent evidence indicates that mortality in red spruce in the southern Appalachian Mountains is not

abnormal when compared to historical rates, and that Fraser fir stands killed by the balsam woolly adelgid

( Adelges piceae ) are largely being replaced by vigorous re-growth of young stands of that species (Van

Miegroet et al., 2007). To what extent spruce or fir mortality in the southern Appalachian Mountains will

be replaced with a species mix similar to that existing prior to the mortality remains to be seen.

At the Fernow Experimental Forest in West Virginia, (NH 4 ) 2 SO 4 inputs of 54 kg N/ha/yr and 61 kg

S/ha/yr (application plus ambient atmospheric deposition), each about three times the ambient deposition

level, were applied to one watershed for 4 years. Few differences in soil and forest floor chemistry were

found in response to the N addition, although pH was significantly lower in the treatment watershed,

corresponding to increased base-flow concentrations of NO 3 − and Ca 2+ (Gilliam et al., 1996).

Deciduous forests show variable responses to acidification depending on the tree species present.

Along an increasing N deposition gradient in the northeastern U.S., from 4.2 to 11.1 kg N/ha/yr, Lovett

and Rueth (1999) found a twofold increase in mineralization in soils of sugar maple stands, but no

significant relationship between increased deposition and mineralization in American beech stands. This

difference might be attributable to the lower litter quality in beech stands. Thus, sugar maple appears to be

more susceptible to effects of increasing deposition and concomitant soil acidification from either direct

leaching of NO 3 − or enhanced nitrification. For northeastern hardwoods, Aber et al. (Aber et al., 2003)

found a decrease in C:N ratio from 24 to 17 over a deposition gradient of 3 to 12 kg N/ha/yr. This

decrease was similar but less steep than the decrease seen in conifers.

Across an 800 km pollution gradient (3 to 11 kg SO 4 -S/ha/yr; 2 to 4 kg NO 3 -N/ha/yr) in northern

hardwood forests, with maples dominant, Pregitzer et al. (1992) found a 200 to 300 μg/g increase in foliar

S, and litter fall S content ranged from 872 to 1356 μg/g. While foliar N did not change across the

gradient, litterfall N was correlated with changing deposition. Pregitzer and Burton assert that their data

did not suggest a causal link between acid deposition and forest decline. Decline would be impossible to

document in the short 5–year time frame of their study. They did, however, assert that their results

supported the plausibility of altered tree nutrition across large geographic regions due to atmospheric

deposition.

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# B.3.2. Transitional Ecosystems

Wetlands are common in many areas that contain acid-sensitive surface waters. For example,

wetlands constitute about 14% of the land surface in the Oswegatchie/Black River watershed in the

southwestern Adirondack Mountains (Ito et al., 2005), one of the regions of the U.S. most affected by

surface water acidification from acidic deposition. There are no studies, however, that have documented

the extent or magnitude of acidification effects of S and N deposition on wetland ecosystems in the U.S.

The topic of acidification effects on wetlands is not well represented in the literature, and therefore

the distribution of ecosystem effects for these systems is not presented. Because levels of natural organic

acidity tend to be high in wetland soils and water, it is not likely that such ecosystems are affected by the

levels of acidic deposition commonly encountered in the U.S. It is more likely that atmospheric

deposition affects wetlands via nutrient N enrichment pathways. (See Discussion in ISA Sections 3.3.2.2

and 3.3.5.2) Gorham et al. (1987) hypothesized that acidic deposition to mineral-poor fens might cause

depletion of exchangeable base cations and decreased pH of soil water. This mechanism was suggested as

a possible cause of transition from mineral-poor fen to Sphagnum bog. Such an effect has not been

observed in response to acidic deposition at levels found in the U.S.

Synoptic surveys of ponded waters generally are restricted to lakes larger than 1 ha, 4 ha, or 10 ha.

Reasons for this limitation are varied and can include the perception that larger lakes are more important,

the failure of regional topographic map coverages to include the smaller lakes and ponds, and the fact that

smaller lakes tend to be much more numerous than larger lakes within the major lake districts of the U.S.

In general, if the larger lakes in a given region are sensitive to acidification, the smaller ponds would also

be expected to be sensitive. In most cases, data to demonstrate this are not available.

Ponds that have been observed to be sensitive to acidic deposition have been found in the

Adirondack Mountains in New York (Kretser et al., 1989) and the Mount Zirkel Wilderness Area located

in the Colorado Rockies (Campbell et al., 2004). Acid-sensitive ponds are likely to be found elsewhere as

well.



# B.3.3. Aquatic Ecosystems

In most regions of the U.S., the majority of lakes and streams are not highly sensitive to existing

levels of ambient air pollution. In addition, air pollution levels are generally decreasing in many parts of

the country, especially in the eastern U.S., in response to federal and state air pollution control

regulations. Therefore, the highly sensitive, and acidified, systems tend to be restricted to a relatively

small percentage of the overall aquatic resource base. There are exceptions to this generalization, such as

for example in Monongahela National Forest, WV, where a high percentage of the streams are acidsensitive and highly acid-affected (cf. Sullivan et al., 2002). Similarly, a high percentage of Adirondack

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26 � results of watershed simulation models that projected past or future chemical changes in

response to changes in S deposition

� empirical biological dose/response models

� improved relationships between surface water chemistry and ambient acidic deposition

� trend analyses of long-term monitoring chemical data in regions that have experienced large

recent changes in acidic deposition levels

� paleolimnological reconstructions of past water chemistry using fossil remains of algae

deposited in lake sediments

� results from whole-watershed or whole-lake acidification or deacidification field experiments l akes (and p resumably a lso streams) are acid-sensitive and have been acidified by atmospheric deposition

of S a nd N (Driscoll et al., 1991).

Studies to assess relationships between atmospheric deposition loading (N and S) and the estimated

or expected extent, magnitude, and timing of aquatic acidification effects (U.S. Environmental Protection

Agency, 1995; cf. Van Sickle and Church, 1995; NAPAP, 1998) often employ a “weight of evidence”

evaluation of the relationships between deposition and effects, as followed by NAPAP in the Integrated

Assessment (IA) (NAPAP, 1991).

Several kinds of evidence were used in the IA to assess the extent and magnitude of acidification in

sensitive regions of the U.S. These included:

Evidence from e a ch type of study contributes to understanding of the quantitative importance of

acidification and neutralization processes and their effects on the chemistry and biology of affected

ecosystems.



## B.3.3.1. Status of Surface Waters

– Regional Overview

In the NSS, DOC concentrations were much higher in lowland coastal streams, compared with

inland streams. National Stream Survey data also supported the hypothesis that atmospheric sources and

watershed retention of S control regional patterns in streamwater SO 2−

4 concentrations. Most NSS

watersheds retained the vast majority of the total N loading from wet deposition. The 1986 data

suggested, however, that some atmospherically deposited N may have been reaching streams in the

northern Appalachians (Kaufmann et al., 1991).

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19 � Southwestern Adirondacks

�

�

�

� New England Uplands

Low Si Eastern Upper Midwest

Forested Mid-Atlantic Highlands

Mid-Atlantic Coastal Plain

� Northern Florida Highlands I n the 1 990 NAPAP State of Science/Technology (SOS/T) summary, Baker et al. (1991a) identified

six high interest subpopulations that accounted for most of the U.S. surface waters that were acidic with

acidic deposition having been identified as the likely source (Figure B-3; Table B-2):

Stream data fo r t he NSS w as unavailable for three of these high-interest areas: the Adirondacks,

New England, and Upper Midwest. The national WSA data indicated that acidic streams in the Upper

Midwest are likely to be rare but there are acidic streams in the Adirondacks/New England region.

Specific areas of interest within the other three high-interest regions are described below.

In addition to the large water chemistry databases developed by the EPA, there are also some

important supplemental databases in some regions. For example, based on results of lake surveys

conducted during the 1980s, about 70% of the known acidic lakes in Maine were either seepage type or

high elevation (Kahl et al., 1991). The Maine seepage lake dataset includes 120 of the estimated 150 lakes

in Maine that meet the following criteria: (1) located in sand and gravel mapped by the USGS o r Maine

Geological Surveys; (2) depth at least 1 m; and (3) area at least 0.4 ha (1 ac). Sampling was conducted in

1986–87 and 1998–2000, and included at least one fall index sample for each lake. There were 87 lakes

with Gran ANC less than 100 μeq/L.

The Maine high elevation lake dataset includes 90 lakes above 600 m elevation. Sampling was

conducted during the periods 1986–88 and 1997–99. The study included the vast majority of Maine lakes

that are at least 1 m deep and at least 0.4 ha (1 ac) in area. There were 64 lakes with Gran ANC less than

100 μeq/L.

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12 Source: Baker et al. (1990b).



### Figure B­3. Location and percentage of acidic surface waters in U.S. high­interest subpopulations

with respect to acidic deposition effects. Estimates are for the upstream reach ends in the NSS

data. Population estimates in the Mid­Atlantic Highlands apply only to the forested watersheds,

while estimates in the eastern Upper Midwest apply only to low­silica (≥ 1 mg/L) lakes. Figure taken

from Figure 9–106 in the 1990 NAPAP SOS/T report.



## B.3.3.2. Recent Changes in Surface Water Chemistry

Surface water acid-base chemistry monitoring throughout the eastern U.S. occurs primarily in two

EPA programs: the Temporally Integrated Monitoring of Ecosystems (TIME) project (Stoddard, 1990)

and the Long-Term Monitoring (LTM) project (Ford et al., 1993; Stoddard et al., 1998). Both projects are

operated in cooperation with numerous state agencies, academic institutions and other federal agencies.

Each is described below.

The regions represented by the LTM and TIME monitoring programs (Annex A) are estimated to

contain 95% of the lakes and 84% of the streams in the U.S. that have been anthropogenically acidified by

acidic deposition. The Adirondacks had a large proportion of acidic surface waters (14%) in the NSWS;

from 1984 to 1987, the ALSC sampled 1,469 Adirondack lakes greater than 0.5 ha in size and estimated

that many more (26%) were acidic (Driscoll et al., 1991). The higher percentage of acidic lakes in the

ALSC sample was due to inclusion of smaller lakes and ponds (1 to 4 ha in area), many of which were

acidic as a consequence of naturally occurring organic acids (Sullivan et al., 1990). The proportions of

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35 lakes estimated by NSWS to be acidic were smaller in New England and the Upper Midwest (5% and 3%,

respectively), but because of the large numbers of lakes in these regions, there were several hundred

acidic waters in each of these two regions.

The Valley and Ridge Province and Northern Appalachian Plateau had 5% and 6% acidic sites,

respectively. The only potentially acid-sensitive region in the eastern U.S. not assessed in the Stoddard

et al. (2003) report was Florida, where the high proportion of naturally acidic lakes, and a lack of longterm monitoring data, make assessment of the effects of acidic deposition problematic (Stoddard et al.,

2003).

The TIME project is structured as a probability sampling. Each site is chosen statistically to be

representative of a target population. In the Northeast (New England and Adirondacks), this target

population consists of lakes with Gran ANC < 100 μeq/L, which are those likely to be most responsive to

changes in acidic deposition. In the Mid-Atlantic, the target population is upland streams with ANC <

100 μeq/L. Each lake or stream is sampled annually, and results are extrapolated to the target populations

(Larsen and Urquhart, 1993; Larsen et al., 1994; Stoddard et al., 1996; Urquhart et al., 1998). The TIME

project began sampling northeastern lakes in 1991. Data from 43 Adirondack lakes can be extrapolated to

the target population of about 1,000 lakes having ANC < 100 μeq/L, out of a total population of 1,830

lakes with surface area > 1 ha. Data from 30 lakes representing about 1,500 lakes having ANC <

100 μeq/L, out of a total population of 6,800 lakes, are included in the TIME program in New England.

As a compliment to lake and stream sampling in the statistical populations of lakes in TIME, the

LTM project samples a subset of sensitive lakes and streams with long-term data, many dating back to the

early 1980s. Each LTM site is sampled 3 to 15 times per year, and the resulting data are used to

characterize the response of the most sensitive aquatic systems in each region to changing levels of acidic

deposition. In most regions, a small number of higher ANC (e.g., Gran ANC > 100 μeq/L) sites are also

sampled. Because of the long-term records at most LTM sites, their trends can also be placed in a better

historical context than those of TIME sites, where data are only available from the 1990s. Monitoring

results from the LTM project have been widely published (Kahl et al., 1991, 1993b; Driscoll and Van

Dreason, 1993; Murdoch and Stoddard, 1993; Stoddard and Kellogg, 1993; Webster et al., 1993; DeWalle

and Swistock, 1994; Driscoll et al., 1995; Stoddard et al., 1998). Overall results were summarized by

Stoddard and Kellog (1993).

Monitoring data from the LTM and TIME projects were used to evaluate recent changes in lake and

stream chemistry from 1990 to 2000 in many of the sensitive areas of the eastern U.S. including New

England, the Adirondack Mountains, the Northern Appalachian Plateau, the Ridge and Blue Ridge

provinces of Virginia, and the Upper Midwest (Stoddard et al., 2003). There are also substantial numbers

of acid-sensitive streams in the Blue Ridge Province in North Carolina and portions of South Carolina

and Tennessee that have been affected by acidic deposition but that were not included in this analysis. In

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29 general, the results of the TIME/LTM data analysis suggest that about one-quarter to one-third of the

lakes and streams that were chronically acidic in the 1980s were no longer chronically acidic in the year

2000. However many still had low ANC and were potentially susceptible to episodic acidification

(Stoddard et al., 2003).

Stoddard et al. (2003) found little evidence of regional change in the acidity status of lakes in New

England or streams in the Ridge/Blue Ridge regions. Furthermore, none of the study regions showed an

increase in the number of chronically acidic waters, even though there was a decline in base cation

concentrations and a likely increase in natural organic acidity (Stoddard et al., 2003). An important caveat

in this analysis is that changes in Gran ANC used in the analysis were based on the median change of all

sites in a region (Table B-4). However, as shown in (Table B-5), the rates of ANC increase were generally

more rapid in chronically acidic lakes with ANC less than 0 μeq/L and streams with ANC between 0 and

25 μeq/L. If acidic sites are recovering more rapidly than the population of sites as a whole, then the

estimates of change in the number of acidic lakes and streams presented would be conservative.

While general estimates for large regions are useful in providing a broad picture of the extent and

status of surface water acidity, specific results from studies within those regions can help isolate trends

and determine the specific mechanisms that contribute to change. The following sections report on the

current status, past acidification, and potential future conditions for lakes and streams in acid sensitive

areas of the Northeast, Southeast, Upper Midwest, and Western U.S.



# B.3.4. Regional Assessments

B.3.4.1. Northeastern Surface Waters

Current Status

The Adirondacks and New England are two of the most acid sensitive and intensively studied

regions in the Northeast. The glaciated soils and location downwind from emissions sources have made

these areas the subject of intense scientific study over the past four decades. Most of this research has

focused on lake ecosystems, though important stream studies have been undertaken at specific research

sites and more regional stream survey work is being conducted. As discussed below, the surface water

chemistry in these areas integrates atmospheric deposition, local geology, and upland watershed

characteristics.

Available surface water datasets for Adirondack lakes include TIME, the Environmental

Monitoring and Assessment Program (EMAP), and ALSC, each of which is useful for documenting

chemical status and recent chemical changes. Population estimates from the TIME dataset suggest that

13.0% of Adirondack lakes (238 lakes) were chronically acidic in the early 1990s during baseflow

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35 conditions in the summer. By applying an approximate rate of change in Gran ANC of +0.8 μeq/L/yr to

these estimates (based on trend slopes for TIME and LTM data, (Table B-6), Stoddard et al. (2003)

projected that approximately 8.1% of the population (149 Adirondack lakes) remained chronically acidic

in 2000. This finding suggests that roughly 38% of lakes in the Adirondacks that were chronically acidic

in the early 1990s were not chronically acidic a decade later. Certain caveats need to be included with the

results of this analysis, however. Summertime baseflow sampling reflects the least acidic conditions

experienced throughout the year. In addition, LTM trends, which are based on year-round sampling, may

not be representative of trends in the summer-only sampling of the TIME program, and the rate of change

determined through the TIME program was not controlled for possible differences in flow conditions

between the two sample periods. Lastly, the ANC value of 0 μeq/L/year used to define acidic waters has

been shown to be below the level needed to protect aquatic ecosystems in the Adirondack region (Baldigo

et al., 2007; Lawrence et al., 2007).

A study by Driscoll et al. (2001b) used EMAP data from 1991 to 1994 to evaluate the extent of

acidic lakes in the Adirondacks for that period. The EMAP survey is a probability based survey

representative of lakes with surface area greater than 1 ha (1,812 lakes). The survey was conducted during

low-flow summer conditions, and the results therefore likely reflect the highest ANC values for the year.

Results from the survey indicate that 10% of the population of Adirondack lakes were chronically acidic

(ANC values of less than 0) and 31% were sensitive to episodic acidification (ANC values between 0 and

50) during the study period (Driscoll et al., 2001b).

The ALSC conducted a comprehensive survey of Adirondack lakes greater than 0.2 ha in surface

area between 1984 and 1987 (Kretser et al., 1989). Of the 1,489 lakes surveyed, 24% had summer pH

values below 5.0, 27% were chronically acidic (ANC < 0), and an additional 21% were probably

susceptible to episodic acidification (ANC between 0 and 50) (Driscoll et al., 2007).

For the New England region, the TIME population data indicates that 5.6% of the population (386

lakes) in New England exhibited Gran ANC < 0 μeq/L during the period of 1991 to 1994. This result is

similar to the EMAP findings which indicate that 5% of lakes in New England and in the eastern Catskill

region of New York had ANC values less than 0 μeq/L. The EMAP analysis also estimated that an

additional 10% of the population had low ANC values, between 0 and 50 μeq/L, and were probably

sensitive to episodic acidification (Driscoll et al., 2001b).

Both TIME and LTM data from the New England region indicate that only a small increase in Gran

ANC had occurred during the reported monitoring period (+0.3 μeq/L/yr). As a result, it is estimated that

the proportion of chronically acidic lakes decreased only 0.1% from 5.6% to 5.5% over the previous 10

years (Table B-4) (Stoddard et al., 2003).

State surveys within New England provide additional information on the variation in lakewater

chemistry across the region. In Maine, approximately 100 clearwater lakes in that state have been

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29 classified as acidic, based on surveys conducted by EPA and the University of Maine (Kahl et al., 1999).

An estimated 13% of the high-elevation lakes in Maine are acidic, compared to less than 1% of Maine

lakes (>4 ha) represented in EPA’s Eastern Lakes Survey (ELS) (Linthurst et al., 1986a; Kahl et al., 1991).

Most acidic lakes in Maine are either seepage lakes located in sand and gravel deposits, or high-elevation

lakes located above 600 m elevation. Roughly 60% of the acidic lakes are seepage lakes. The acidsensitive seepage lakes are located in mapped sand and gravel deposits, are at least 1 m deep, and are at

least 0.4 ha (1 acre) in surface area. About 45 of the 150 lakes of this type in Maine are acidic (Kahl et al.,

1999).

Whereas lakes in the Adirondacks and New England have been intensively studied, there are no

published data which describe the status of streamwater acid-base chemistry at a regional scale, except for

the Catskill Mountains.

In the absence of regional streamwater studies, insights can be gained from site-specific long-term

studies in the region. The HBEF h as one of the longest continuous records of precipitation and

streamwater chemistry in the U.S. Compared to model hindcast approximations, current conditions at

HBEF i ndicate that soil percent base saturation has decreased in response to acidic deposition and

because of accumulation of nutrient cations by forest vegetation. Further, acidic deposition has

contributed to a nearly fourfold increase in stream SO 2−

4 concentration; a decrease in ANC from positive

to negative values; a decrease in stream pH to 5.0; and an increase in stream Al, largely occurring as the

inorganic form which has been shown by Lawrence at al. (2007) to be an unequivocal indication of the

effects of acidic deposition. Driscoll et al. (2001b) estimated that roughly 6% of lakes and streams in the

Northeast are considered more sensitive to acidic deposition than the stream monitored at HBEF ( Driscoll

et al., 2001b).



### Past Acidification

There are limited surface water data that directly document historic conditions and the response to

atmospheric deposition since the time of the Industrial Revolution ((Charles, 1991). To address this gap,

scientists use sediment cores from lakes and detailed computer models to try to reconstruct past

conditions as well as understand the mechanisms that contribute to changing conditions.



### Paleolimnological Studies

Paleolimnological studies use the remains of diatoms and other algae embedded in lake sediments

to reconstruct historical water chemistry. In the Adirondack Mountains and northern New England, both

diatom and chrysophyte algal remains in sediment cores have been used to evaluate patterns of past

acidification in a large number of lakes.

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35 Major findings of the Paleoecological Investigation of Recent Lakewater Acidification (PIRLA)-I

and PIRLA-II research programs in the Adirondack Mountains suggested that: (1) Adirondack lakes had

not acidified as much since pre-industrial times as had been widely believed prior to 1990; (2) many

Adirondack lakes with ambient pH greater than 6.0 had not acidified historically, whereas many of the

lakes having pH less than about 6.0 had acidified; (3) many of the lakes having high pH and ANC had

increased in pH and ANC since the previous century; and (4) the average F-factor for acid-sensitive

Adirondack lakes was near 0.8 (Charles et al., 1990; (Sullivan et al., 1990). The results of these studies

had major effects on scientific understanding of the extent to which lakes had acidified in response to

acidic deposition. The view of surface water acidification as a large-scale titration of ANC (Henriksen,

1980, 1984) was replaced by the realization that base cation concentrations typically changed more than

ANC in response to acidic deposition. This realization also modified expectations for chemical recovery

of surface waters as acidic deposition levels have decreased (Sullivan, 2000).

Diatom and chrysophyte reconstructions of pH and ANC for a statistically selected group of

Adirondack lakes suggested that about 25% to 35% of the Adirondack lakes that are larger than 4 ha had

acidified since preindustrial time (Cumming et al., 1992). Low-ANC lakes of the southwestern

Adirondacks acidified the most, probably due to low initial buffering capacity and high rainfall and

deposition of S and N in that area. Cumming et al. (1992) estimated that 80% of the Adirondack lakes that

had ambient pH ≥ 5.2 had experienced large declines in pH and ANC since the previous century, and that

30% to 45% of the lakes with pH between 5.2 and 6.0 had also acidified.

Cumming et al. (1994) reported the results of chrysophyte inferences of pH in recently deposited

lake sediments to assess acidification timing for 20 low-ANC Adirondack lakes. Lakes that were inferred

to have acidified since about 1900 tended to be small, high-elevation lakes with lower inferred preindustrial pH than the group of study lakes as a whole. These were probably the most acid-sensitive and

were the first to acidify with increasing acidic deposition. Husar and Sullivan (1991) estimated that S

deposition was about 4 kg S/ha/yr at that time. These lakes are located in the high peaks area and in the

southwestern portion of Adirondack Park. A second category of acidification response included highelevation lakes that were historically low in pH (<5.5) but that acidified further beginning about 1900.

The third identified type of response included lakes with pre-industrial pH in the range of about 5.7 to 6.3

that started to acidify around 1900 but showed their greatest pH change around 1930 to 1950. The final

category included lakes that were not inferred to have acidified. They had pre-industrial pH around 6.0

and are located at lower elevation.

Davis et al. (1994) conducted paleolimnological studies of 12 lakes in northern New England that

were low in pH and ANC. Past logging, forest fire, and vegetation composition in the watersheds were

estimated from oral and written historical information, aerial photographs, and tree ring analyses. Lake

sediment cores were collected and analyzed for pollen, diatoms, and chemistry to reconstruct past lake

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33 conditions for several hundred years. All 12 lakes were historically low in pH and ANC, with diatominferred pre-industrial ANC of –12 to 31 μeq/L. The inferred pH and ANC values of the lakes were

relatively stable throughout the one to three centuries of sediment record prior to watershed disturbance

by Euro-Americans. From the early 19th into the 20th century, however, all of the lakes showed increased

diatom-inferred pH changes of about 0.05 to 0.6 pH units and increased diatom-inferred ANC of about 5

to 40 μeq/L. Most of these inferred increases in pH and ANC coincided with watershed logging. For all

study lakes, recovery to pre-logging acid-base lake chemistry conditions was followed by continued

decline in pH by 0.05 to 0.44 pH units and ANC by up to 26 μeq/L, probably because of acidic

deposition. The 12–lake mean inferred decreases in pH and ANC in response to acidic deposition were

0.24 pH units and 14 μeq/L, respectively (Davis et al., 1994).



### Modeling Studies

The most extensive regional modeling study that provides estimates of past acidification of

Adirondack lakes is that of Sullivan et al. (2006a). They modeled past changes in the acid-base chemistry

of 70 Adirondack lake watersheds, including 44 that were statistically selected to be representative of the

approximately 1,320 lake watersheds in the Adirondacks that have lakes larger than 1 ha and deeper than

1 m and that have ANC ≥ 200 μeq/L. Model hindcasts were constructed using both the MAGIC and

PnET-BGC models.

Based on MAGIC model outputs extrapolated to the regional Adirondack lake population,

maximum past acidification occurred by about 1980 or 1990, with median ANC of the population under

investigation of about 61 μeq/L (reduced from a median of 92 μeq/L estimated for the pre-industrial

period). By 1990, 10% of the population target lakes had decreased in ANC to below −16 μeq/L and 25%

had ANC < 28 μeq/L. Percentile values in 2000 illustrated limited chemical recovery (3 to 5 μeq/L)

compared with simulated values in 1980 and 1990.

The MAGIC model simulations suggest that none of the target lakes were chronically acidic (had

ANC < 0 μeq/L) under pre-industrial conditions, but that by 1980 there were about 204 acidic Adirondack

lakes. That number decreased by an estimated 14% between 1980 and 2000. Similarly, the MAGIC model

simulations suggested that there were no Adirondack lakes having ANC < 20 μeq/L in 1850, but by 1990

there were 263 such lakes. Many lakes (N = 191) were estimated to have had pre-industrial ANC below

50 μeq/L, and this estimate increased threefold by 1990, followed by a decrease to 399 lakes in 2000.

PnET-BGC model simulations generated output generally similar to results provided by MAGIC

model simulations. PnET-BGC simulations suggested that lakewater SO 2−

, NO

− 4 3 , and base cation

concentrations under pre-industrial conditions were much lower than current values. In 1850, simulated

SO 2−

4 concentrations in all study lakes were less than 25 μeq/L, and the median value was about

15 μeq/L. By 1980, the median simulated SO 2−

4 concentration had increased more than sixfold to about

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33 100 μeq/L. Simulated lake NO − 3 concentrations also increased markedly during that time, with the

median value increasing from about 4 μeq/L in 1850 to 12 μeq/L in 1980. Simulated increases in the sum

of divalent base cation concentrations were less than for SO 2−

4 concentrations, with the median value

increasing from 93 μeq/L in 1850 to 140 μeq/L in 1980. This large change in SO 2− −

4 + NO 3 relative to the

change in the sum of base cation concentrations was the major mechanism driving the decreases in ANC

and pH associated with historical increases in acidic deposition.

Simulated lakewater ANC and pH and soil base saturation decreased from pre-industrial conditions

to recent times. Results from PnET-BGC suggested that the median Adirondack lake, from among the

estimated 1,320 lakes in the population larger than 1 ha that had measured recent ANC < 200 μeq/L, had

pre-industrial ANC near 80 μeq/L; an estimated 10% of the lake population had pre-industrial ANC <

41 μeq/L; and one-fourth had pre-industrial ANC < 64 μeq/L. Percentiles for the year 2000 suggest

decreases in SO 2−

4 , NO

− 3 , and sum of base cations, and small increases in ANC since 1990 for lowerANC lakes.

Results from PnET-BGC suggest that none of the lakes in the Adirondack population had preindustrial ANC below 20 μeq/L. By 1990, there were 289 lakes having ANC < 20 μeq/L and 217

chronically acidic (ANC ≥ 0 μeq/L) lakes according to PnET-BGC simulations. There were 202 lakes in

the population simulated to have had pre-industrial ANC below 50 μeq/L, and this number increased 2.8

times by 1980 under the PnET-BGC simulations.

PnET-BGC has also been used to characterize past conditions at streams within the HBEF.

Gbondo-Tugbawa et al. (Gbondo-Tugbawa et al., 2002) used relationships between current emissions and

deposition, and estimates of past emissions to reconstruct historical deposition conditions. Their analysis

also considered land disturbances such as logging in 1918–1920 and hurricane damage in 1938. Using

this approach, they estimated that past soil base saturation in the mineral soil (circa 1850) was

approximately 20%, stream SO 2−

4 concentration was approximately 10 μeq/L, stream ANC was about

40 μeq/L, stream pH was approximately 6.3, and stream Al i concentration was below 1 μmol/L (Driscoll

et al., 2001b).



### Recent Trends

Sulfur deposition has contributed to chronic soil and surface water acidification in the eastern U.S.

to a much greater extent than has N deposition. Nitrate concentrations in acid-sensitive drainage waters in

the eastern U.S. are generally much lower than SO 2−

4 concentrations.

The concentration of SO 2−

4 in precipitation has declined for about the past three decades

throughout the northeastern U.S., in response to decreased atmospheric emissions and deposition. EPA’s

LTM Program has been collecting monitoring data since the early 1980s for many lakes and streams in

acid-sensitive areas of the U.S., including the Northeast. These data allow evaluation of trends and

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35 variability in key components of lake and streamwater chemistry prior to, during, and subsequent to Title

IV implementation. Throughout the northeastern U.S., the concentration of SO 2−

4 in surface waters has

decreased substantially (Figure B-4) in response to decreased emissions and atmospheric deposition of S.

Decreased concentrations of SO 2−

4 in lakes and streams of a third, or more, have been commonly

observed.

Lakewater SO 2−

4 concentrations have decreased steadily in Adirondack lakes, at least since 1978

(Driscoll et al., 1995; Stoddard et al., 2003). Initially, there was not a systematic increase in lakewater pH

or ANC in response to the decreased SO 2−

4 concentrations. Rather, the decline in lakewater SO 2−

4 during

the 1980s was charge-balanced by a nearly equivalent decrease in concentrations of base cations in many

of the low-ANC lakes (Driscoll et al., 1995). Similar findings were reported by Stoddard and Kellog

(1993) for lakes in Vermont. F-factors for the nine ALTM lakes that showed significant declines in both

the sum of base cations (SBC) and (SO 2−

4 + NO

− 3 ) concentration ranged from 0.55 to greater than 1.0,

with a mean of 0.93 (Driscoll et al., 1995). These high F-factor values for chemical recovery from

acidification were similar to results of historical acidification obtained by Sullivan et al. (Sullivan et al.,

1990), based on diatom reconstructions of historical change for 33 Adirondack lakes.

Trend analysis results for the period 1982 to 1994 were reported by Stoddard et al. (1998) for 36

lakes in the northeastern U.S. having ANC ≥ 100 μeq/L. Trend statistics among sites were combined

through a meta-analytical technique to determine whether the combined results from multiple sites had

more significance than the individual Seasonal Kendall Test statistics. All lakes showed significant

declining trends in SO 2− 2

4 concentration ( Δ SO − 4 = −1.7 μeq/L/yr; p ≥ 0.001). Lakewater ANC responses

were regionally variable. Lakes in New England showed evidence of ANC recovery ( Δ ANC =

0.8 μeq/L/yr; p ≥ 0.001), whereas Adirondack lakes exhibited either no trend or further acidification,

largely because of declines in base cation concentrations.

The observed changes in the concentration of NO − 3 in some surface waters have likely been due to

a variety of factors, including climate. During the 1980s, NO

− 3 concentration increased in many surface

waters in the Adirondack and Catskill Mountains in New York (Driscoll and Van Dreason, 1993; Murdoch

and Stoddard, 1993). There was concern that northeastern forests were becoming N-saturated, leading to

increased NO − 3 leaching from forest soils throughout the region. Such a response could negate the

benefits of decreased SO 2−

4 concentrations in lake and stream waters. However, this trend was reversed in

about 1990, and the reversal could not be attributed to a change in N deposition. Nitrate leaching through

soils to drainage waters is the result of a complex set of biological and hydrological processes. Key

components include N uptake by plants and microbes, transformations between the various forms of

inorganic and organic N, and local precipitation patterns. Most of the major processes are influenced by

climatic factors, including temperature, moisture, and snowpack development. Therefore, NO − 3

concentrations in surface waters respond to many factors in addition to N deposition and can be difficult

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24 to predict. It is likely that monitoring programs of several decades or longer will be needed to separate

trends in NO 3 − leaching from climatic variability in forested watersheds (Driscoll et al., 1995).

Monitoring data collected during the 1990s in the LTM and TIME projects illustrated that most

regions included in the monitoring efforts showed large declines in SO 4 2− concentrations in surface waters

over the 10 years of monitoring, with rates of change ranging from 1.5 to 3 μeq/L/yr (Figure B-4). These

declines in lake and stream SO 4 2− concentration were considered consistent with observed declines in S

wet deposition. Surface water NO 3 − concentrations also decreased, but only in the two regions that had

the highest ambient surface water NO 3 − concentrations (Adirondacks and Northern Appalachian Plateau),

but were relatively unchanged in regions with lower concentrations. DOC increased in each region over

time. This finding suggests an increase in the contribution of natural organic acidity, which would

partially offset the expected chemical recovery from decreased acidic deposition.

ANC increased in the Adirondacks at a rate of about +1 μeq/L/yr, despite a decline in surface water

base cation concentrations (Ca 2+ + Mg 2+ ; Figure B-4). The decline in base cations offset some of the

decline in SO 4 2− , and thus limited the increase in ANC or pH that occurred in response to lower SO 4 2−

concentrations. Surface water ANC and pH increased significantly in the 1990s; Al i concentrations

declined slightly. Regional surface water ANC did not change significantly in New England (Stoddard

et al., 2003).

Moderate increases in surface water ANC during the 1990s reduced the estimated number of acidic

lakes and stream segments in the northeastern U.S. Stoddard et al. (2003) estimated that there were 150

Adirondack lakes in the year 2000 that had ANC less than 0 (8.1% of the lake population), compared to

13% (240 lakes) in the early 1990s.

Lakewater SO 4 2− concentrations in the most acid-sensitive Maine lakes declined by about 12% to

22% during the period 1982 to 1998 (Kahl, 1999). Only in the seepage lakes, however, was there

evidence of a small decline in lakewater acidity during that period (Table B-7).

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### 1 Figure B­4. Measured concentration of SO 2−

4 

### in selected representative lakes and streams in six

regions of the U.S. during the past approximately 15 years. Data were taken from EPA’s Long­Term

Monitoring (LTM) program.

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### Figure B­5. Summary of regional trends in surface water chemistry from 1990 through 2000 in

regions covered by the Stoddard et al. (2003) report.

However, evidence for reductions in lakewater ANC in seepage lakes from the mid-1980s to 1998

were based on a comparison of only two sampling points, which may have been influenced by climatic

variation. Therefore, the conclusion of decreasing acidity of seepage lakes is considered preliminary. The

seepage lakes are generally hydrologically isolated from their surrounding soil environment. They

therefore did not show a clear decreasing trend in base cation concentrations, as has been found in

drainage lakes throughout the Northeast. The high-elevation lakes, in contrast, showed small declines in

lakewater acidity during the 1980s, but that trend slowed or reversed in the 1990s (Kahl, 1999). Both the

seepage and high elevation lakes showed increased DOC concentrations of 10% to 20%, generally by

about 0.5 to 1.0 mg/L. The increase in dissolved organic matter would be expected to limit the extent of

ANC and pH recovery that would otherwise accompany the observed decreases in SO 2−

4 concentration.

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33 Whereas NO − 3 concentrations decreased during the 1990s in many lake chemistry datasets (cf. Stoddard

et al., 2003), the high-elevation lakes in Maine continued to show high concentrations.

The reference stream of the Bear Brook Watershed Study (East Bear Brook) has the longest

continuous, high-frequency data record of stream chemistry in Maine. Sulfate and NO − 3 concentration

have both declined substantially since 1987. Base cations declined by an almost equivalent amount, and

the increase in ANC has been limited (Kahl, 1999).

Long-term stream water data from the HBEF reveal a number of changes that are consistent with

trends in lakes and streams across Europe and eastern North America (Stoddard et al., 1999, 2003; Evans

and Monteith, 2001). Stream water draining the HBEF reference watershed (Watershed 6) had a 32%

decline in annual volume-weighted concentrations of SO 2−

4 (−1.1 μeq/L) between 1963 and 2000

(Driscoll et al., 2007). This decrease in stream SO 2−

4 concentration corresponds to both decreases in

atmospheric emissions of SO 2 and to bulk precipitation concentrations of SO 2−

4 (Likens et al., 2001). In

addition, there has been a long-term decrease in stream concentrations of NO − 3 that is not correlated with

a commensurate change in emissions of NO X or in bulk deposition of NO

− 3

. The long-term declines in

stream concentrations of strong acids (SO 2

4 + NO

− 3 ; −1.9 μeq/L/yr) have resulted in small but significant

increases in pH, from 4.8 to 5.0 (Driscoll et al., 2007). Streams at HBEF remain acidic compared to

background conditions, when stream pH was estimated to be approximately 6.0 (Driscoll et al., 2007).

The increase in stream pH has been limited because of marked concurrent decreases in the sum of base

cations (−1.6 μeq/L/yr; Driscoll et al., 2001b).



### Future Projections

MAGIC model simulations were conducted for the NAPAP IA to forecast the response of lakes and

streams in the eastern U.S. to S d eposition. Results were reported by NAPAP (1991), Sullivan et al.

(1992), and Turner et al. (1992). The projected median change in lakewater or streamwater ANC during

50–year simulations was similar among regions, except in the Southern Blue Ridge and Mid-Atlantic

Highlands, where acidification was delayed due to S a dsorption on watershed soils. MAGIC projected

relatively small future loss of ANC in most northeastern watersheds under continued constant deposition.

These modeled changes were due to a simulated slight depletion of the supply of base cations from soils

(Turner et al., 1992).

On average, each kg/ha/yr change in S deposition was projected by MAGIC to cause a 3 to 4 μeq/L

median change in surface water ANC. Such projected changes in ANC, while considerably smaller than

was generally thought to occur in the 1980s, nevertheless suggested widespread sensitivity of surface

water ANC to changes in S deposition throughout the northeastern U.S. (Sullivan, 2000).

Since 1990, adjustments have been made to the MAGIC model and its application method in

response to model testing using paleolimnological data (Sullivan et al., 1992, 1996a) and the results of

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27 acidification and deacidification experiments (Norton et al., 1992; Cosby et al., 1995, 1996) and empirical

studies (Sullivan and Cosby, 1998). The net effect has been that the model projects somewhat less

sensitivity of Adirondack lakes to change in S deposition as compared with the version of MAGIC

applied in 1990 (Sullivan and Cosby, 1998).

Model projections of future acid-base chemistry under three scenarios of future atmospheric

emissions controls were presented by Sullivan et al. (2006b) and Zhai et al. (2008) for lakes in the

Adirondack Mountains to evaluate the extent to which lakes might be expected to continue to increase in

ANC in the future. Estimated levels of S deposition at one representative watershed are shown in figure

B-6 for the hindcast period and in the future under the three emissions control scenarios. Model

simulations for 44 statistically selected Adirondack lakes using the MAGIC and PnET-BGC models were

extrapolated to the regional lake population. Cumulative distribution frequencies of ANC response

projected by MAGIC are shown in figure B-7 for the past (1850), peak acidification period (1990), and

future (2100). Results for the future are given for each of the scenarios.

Results suggested that the ongoing trend of increasing lakewater ANC for the most acid-sensitive

lakes would not continue under future emissions and deposition levels anticipated as of 2003 (Base Case

Scenario). The numbers of Adirondack lakes having ANC below 20 and below 50 μeq/L were projected to

increase between 2000 and 2100 under that scenario, and the number of chronically acidic Adirondack

lakes (i.e., ANC less than 0) was projected to stabilize at the level reached in 2000. This projected reversal

of chemical recovery of acid-sensitive lakes was due to a continuing decline in the simulated pool of

exchangeable base cations in watershed soils.

Simulations suggested that re-acidification might be prevented with further reductions in emissions

and deposition.

Chen and Driscoll (2005) applied the PnET-BGC model to 44 EMAP lake watersheds in the

Adirondacks. PnET-BGC was used to predict the acid-base chemistry of soils and surface waters, and to

assess the fisheries status during pre-industrial conditions (~1850) and under three future acidic

deposition scenarios.

August 2008 B­44 DRAFT—DO NOT QUOTE OR CITESource: Sullivan et al. (Sullivan, 2003).



### Figure B­6. Estimated time series of S deposition at one example watershed in the SW Adirondack

Mountains used by Sullivan et al. (2006) as input to the MAGIC model for projecting past and future

changes in lakewater chemistry attributable to acidic deposition. Future deposition estimates were

based on three emissions control scenarios (Base Case, Moderate Additional Controls, Aggressive

Additional Controls).

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8 Source: Sullivan et al. (2006b).



### Figure B­7. Simulated cumulative frequency distributions of lakewater ANC at three points in time

for the population of Adirondack lakes.

Model hindcasts using PnET-BGC indicated that acidic deposition has greatly altered surface

waters and soils in the Adirondacks over the past 150 years, and that some ecosystems are continuing to

acidify despite decreases in S d eposition. The model was applied to three future emissions scenarios: base

case, moderate emissions reductions, and aggressive emissions reductions. A case study for Indian Lake

in the Adirondacks illustrated that larger reductions in deposition caused greater decreases in SO 2−

4 and

base cation concentrations in stream water and greater recovery in pH and ANC. Within the full

population of lake-watersheds, some showed decreasing ANC and pH values from 1990 to 2050 even

under the moderate and aggressive reduction scenarios. By 2050 to 2100, however, nearly all lakes

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29 experienced increasing ANC and pH. The rate of soil base saturation regeneration increased very slowly

over the modeled time period, compared to changes in surface water chemistry. For 95% of the lakewatersheds studied, simulated soil base saturation remained below 20% in 2100 under all emissions

scenarios.

There are few streams in the northeastern U.S. for which future acid-base chemistry status has been

modeled. One notable exception is the modeling conducted for streams at the HBEF a nd in the Catskill

Mountains. Calculations performed by Driscoll et al. (2003c) using the PnET-BGC model suggested that

“aggressive reductions in N emissions alone will not result in marked improvements in the acid-base

status of forest streams.” For example, in response to an aggressive utility emissions control scenario

(hypothesized 75% reduction in utility N emissions beyond the CAAA), the ANC values of Watershed 6

at HBEF i n New Hampshire and Biscuit Brook in the Catskill Mountains in New York were only

projected to increase by 1 and 2 μeq/L, respectively, by the year 2050 (Driscoll et al., 2003c). Projected

changes in water chemistry in response to differing levels of N deposition were small in comparison with

model projections of variations resulting from climatic factors (Aber and Driscoll, 1997; Driscoll et al.,

2003c).



## B.3.4.2. Southeastern Surface Waters

The two regions in the Southeast that were identified by Charles (Charles, 1991) as containing lowANC surface waters are the Appalachian Mountains and Northern Florida. The Appalachian Mountain

region contains many streams that have low ANC, and it receives one of the highest rates of acidic

deposition in the U.S. (Herlihy et al., 1993). Streamwater acid-base chemistry has been extensively

studied in this region (e.g., Church et al., 1992; Herlihy et al., 1993; Van Sickle and Church, 1995;

Sullivan et al., 2002, 2003).

Northern Florida contains the highest percentage of acidic lakes of any lake population in the U.S.

(Linthurst et al., 1986a,b). Most lakes in Florida are located in marine sands overlying carbonate bedrock

and the Floridan aquifer, a series of limestone and dolomite formations that underlies most of Florida.

Most of the acidic and low-ANC lakes are located in the Panhandle and north central lake districts.

The current status, past acidification and recent trends in surface waters chemistry for both the

Appalachian Mountains and northern Florida are discussed below.



### Appalachian Mountains

Current Status

One of the most important processes affecting watershed acid-neutralization throughout much of

the Southeast is S-adsorption on soil. If S a dsorption on soil is high, relatively high levels of S d eposition

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35 have little or no effect on stream acid-base chemistry, at least in the short-term. However, this

S-adsorption capacity can become depleted over time under continued S deposition, and this causes a

delayed acidification response.

Sulfur-adsorption varies by physiographic province. It is highest in the soils of the Southern Blue

Ridge, where typically about half of the incoming S is retained. Adsorption is lower in the Valley and

Ridge watersheds and especially in the Appalachian Plateau (Herlihy et al., 1993). In general, S

adsorption is higher in the southern portions of the Appalachian Mountain region.

The Mid-Atlantic Highlands consists of the portions of the Blue Ridge Mountains, Ridge and

Valley, and Appalachian Plateau ecoregions between the Virginia-North Carolina border and the Catskill

Mountains in southeastern New York. Acid mine drainage (AMD) is a major source of acidity to streams

in the Mid-Atlantic Highlands but in many cases is easy to identify due to the high concentrations of

SO 4 2− in the streams that are influenced by AMD (Herlihy et al., 1991). Acidic and low-ANC streams

affected by AMD were removed before analyses of acid-base chemistry population statistics.

Streams in the Appalachian Mountain portion of the mid-Atlantic region receive some of the largest

acidic deposition loadings of any region of the U.S. A compilation of survey data from the midAppalachians yields a consistent picture of the acid-base status of streams. Acidic streams, and streams

with very low ANC, are almost all located in small (watershed area < 20 km 2 ), upland, forested

catchments in areas of base-poor bedrock. Acidic surface waters in this region are nearly always found in

forested watersheds because the thin soils and steep slopes that make these watersheds unsuitable for

agriculture and other development also contribute to their sensitivity to acidic deposition (Baker et al.,

1991a).

In the subpopulation of upland forested streams, which comprises about half of the total stream

population in the mid-Appalachian area, data from various local surveys showed that 5% to 20% of the

streams were acidic, and about 25 to 50% had ANC < 50 μeq/L (Herlihy et al., 1993). NSS estimates for

the whole region showed that there were 2330 km of acidic streams and 7500 km of streams with ANC <

50 μeq/L. In these forested reaches, 12% of the upstream reach ends were acidic and 17% had pH ≥ 5.5

(Table B-4). Sulfate from atmospheric deposition was the dominant source of acid anions in acidic midAppalachian streams. In these acidic streams, the low pH (median 4.9) and high levels of Al i (median

129 μg/L) leached through soils by acidic deposition were considered to be sufficiently high to cause

damage to aquatic biota. Acidic streams in this subpopulation typically had low DOC (mean 1.5 mg/L).

Localized studies have clearly shown that streamwater ANC is closely related to bedrock

mineralogy (Herlihy et al., 1993). Sullivan et al. (2007) delimited a high-interest area for streamwater

acidification sensitivity within the Southern Appalachian Mountain region (Virginia/West Virginia to

Georgia) based on geological classification and elevation. It covered only 28% of the region, and yet

included almost all known acidic and low ANC (<20 μeq/L) streams, based on evaluation of about 1,000

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34 streams for which water chemistry data were available. They found that the vast majority of low ANC

sample streams were underlain by the siliceous geologic sensitivity class, which is represented by such

lithologies as sandstone and quartzite. Low ANC streamwater throughout the region was also found to be

associated with a number of watershed features in addition to lithology and elevation, including

ecoregion, physiographic province, soil type, forest type and watershed area.

Sulfate mass balance analyses indicated that, because of watershed SO 4 2− retention, soils and

surface waters of the region have not yet realized the full effects of elevated S deposition. On average,

based on NSS data, sites in the Blue Ridge Mountains retain 35% of incoming SO 4 2− from atmospheric

deposition. The amount of SO 4 2− retention was strongly related to ecoregion in the order, Piedmont > Blue

Ridge and Ridge & Valley > Appalachian Plateau.

Herlihy et al. (1993) believed that the observed differences were due to the effects of cumulative

loadings from atmospheric S deposition and not due to inherent ecoregional differences in the soils. They

also concluded that S retention will likely continue to decrease in the future, resulting in further losses of

stream ANC.

Both mineral acids and organic acids play important roles in the acid-base status of streams in the

Mid-Atlantic Coastal Plain (Baker et al., 1991a). Acidic streams in the New Jersey Pine Barrens (Table B2) are largely inorganically dominated, but most likely they were naturally organically acidic in the past.

It is uncertain what effect the addition of inorganic acids from acidic deposition has had on these low

ionic strength colored systems. Over half the streams in the Pine Barrens included in the NSS were acidic,

and virtually all (96%) had ANC less than 50 μeq/L. Human disturbances in the Pine Barrens often result

in the alkalinization of streams (increases in pH and ANC) that alter the natural Pine Barrens aquatic

biota. Outside of the Pine Barrens in the NSS, the remainder of the acidic streams in the Coastal Plain

were all high DOC organically dominated systems. Low DOC (<4 mg/L), acidic streams have been

observed, however, in other Mid-Atlantic Coastal Plain surveys.

The Virginia Trout Stream Sensitivity Study (VTSSS) surveyed streamwater chemistry for 344

(~80%) of the native brook trout ( Salvelinus fontinalis ) streams in western Virginia. About half of the

streams included in the VTSSS had ANC < 50 μeq/L. In contrast, the NSS (Kaufmann et al., 1988) data

for western Virginia suggested that only about 15% of the streams in the NSS target population had ANC

< 50 μeq/L. These differences may reflect the smaller watershed size, more mountainous topography, and

generally more inert bedrock of the VTSSS watersheds, as compared with the overall NSS stream

population.

In the Appalachian Plateau of West Virginia there are two wilderness areas located in close

proximity in an area of base-poor bedrock -- Dolly Sods and Otter Creek Wilderness Areas. Most streams

draining these wilderness areas are acidic or low in ANC.

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29 In the Great Smoky Mountains National Park in North Carolina and Tennessee, Cook et al. (1994)

reported high NO − 3 concentrations (~100 μeq/L) in upland streams which were correlated with elevation

and forest stand age. The old growth sites at higher elevation showed the highest NO − 3 concentrations.

This pattern could have been due to the higher rates of N deposition and flashier hydrology at high

elevation, and also decreased N uptake by trees in older forest stands. High N deposition at these sites has

likely contributed to both chronic and episodic acidification of streamwater (Flum and Nodvin, 1995;

Nodvin et al., 1995).



### Recent Trends

Population estimates from TIME surveys of streams in the Northern Appalachian Plateau region

suggested that 5014 km of streams (11.8% of the stream length) were acidic in 1993−94, but that only

3600 km of streams (8.5% of the stream population) remained acidic in this region in 2000. The

approximate rate of estimated change in Gran ANC in the region (Table B-5) was +0.79 μeq/L/yr. On this

basis, Stoddard et al. (2003) estimated that roughly 3600 km of stream (8.5%) remained acidic 10 years

later. This represents about a 28% decrease in acidic stream length over the preceding decade.



### Future Projections

Model projections of future changes in acid-base chemistry of streams in the southeastern U.S.

were presented by Sullivan et al. (2002, 2003, 2005). In the eight-state Southern Appalachian Mountains

region, Sullivan et al. (2005) modeled future effects of atmospheric S a nd N deposition on aquatic

resources. Modeling was conducted with the MAGIC model for 40 to 50 sites within each of three

physiographic provinces, stratified by stream water ANC class. Simulations were based on assumed

constant future atmospheric deposition at 1995 levels and on three regional strategies of emissions

controls provided by the Southern Appalachian Mountains Initiative (SAMI), based on the Urban to

Regional Multiscale One-Atmosphere model (Odman et al., 2002).

The NSS s tatistical frame (Kaufmann et al., 1991) was used to estimate the number and percentage

of stream reaches in the region that were projected to change their chemistry in response to the emissions

control strategies. There was a small decline in the estimated length of projected acidic (ANC ≥ 0)

streams in 2040 from the least to the most restrictive emissions control strategy, but there was little

difference in projected stream length in the other ANC classes as a consequence of adopting one or

another strategy. However, projections of continued future acidification were substantially larger under a

scenario in which S a nd N deposition were held constant into the future at 1995 levels. Turner et al.

(1992) also reported MAGIC model simulation results that suggested substantial acidification

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28 (~20 μeq/L) of aquatic systems would occur in the southeastern U.S. if deposition remained constant at

1985 levels. Those model analyses were conducted as part of the NAPAP (1991) IA.

The SAMI emissions control strategies used in the modeling represented air regulatory

requirements being implemented at the time of SAMI’s formation, expected reductions under recent

federal regulatory actions, and additional emissions controls applied to all emissions sectors in the eight

SAMI states. The spatial variability of these emissions controls resulted in varying estimated future

changes in S and N deposition at different locations within the SAMI region. The SAMI strategies were

designated A2, B1, and B3. A2 is the reference strategy that represented SAMI’s best estimates for air

emission controls under regulations for which implementation strategies were relatively certain at the

time of the study (about the year 2000). Emissions reductions under the A2 strategy included the acid rain

controls under Title IV of the 1990 Amendments to the CAAA, the 1–h O 3 standard, NO X reductions

required under EPA’s call for revised State Implementation Plans (Sips), and several highway vehicle and

fuel reductions. The A2 strategy was applied for all eastern states and focused on the utility and highway

vehicle sectors. The B1 and B3 strategies assumed progressively larger emissions reductions, targeted

only to the eight SAMI states but covering all emissions sectors.

Streams exhibited a broad range of response to the cumulative S deposition loadings received to

date and the large simulated decreases in S deposition in the future under the emissions control strategies

(Table B-8). Some streams showed modeled stream water SO 4 2− concentrations increasing in the future,

even while S deposition was reduced by more than two-thirds. These were mostly sites that had relatively

low SO 4 2− concentrations in 1995 ( ≥ about 50 μeq/L) because of S adsorption on soils. They generally

showed simulated future acidification, which was most pronounced for the A2 strategy. Other streams

were simulated to show relatively large decreases in future stream water SO 4 2− concentrations and

concurrent increases in ANC in response to the strategies, with progressively larger changes from the A2

to the B3 strategy. These tended to be streams that had relatively high concentrations of SO 4 2−

(>50 μeq/L) in 1995, suggesting that they were closer to steady state with respect to S inputs and outputs.

Some streams were projected to exhibit future decreases in both SO 4 2− and NO 3 − concentrations but

nevertheless to continue to acidify. This response was attributed by Sullivan et al. (2004) to large

simulated decreases in base cation concentrations at these sites due to soil base cation depletion.

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4 Source: Sullivan et al. (2004).



### Figure B­8. Map showing simulated changes in streamwater ANC from 1995 to 2040 in response to

the SAMI A2 emmissions control strategy, representing existing emissions control regulations.

Most simulated changes in stream water ANC from 1995 to 2040 were rather modest (Table B-8),

given the very large estimates of decreased S d eposition. Few modeled streams showed projected change

in ANC of more than about 20 μeq/L. Some of the largest changes were simulated for some of the streams

that were most acidic in 1995. For such streams, however, even relatively large increases in ANC would

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32 still result in negative ANC stream water, and therefore little biological benefit would be expected from

the simulated improvement in chemistry. The model results suggested, however, that benefits would

continue to accrue well beyond 2040 for all strategies, even if deposition was held constant at 2040 levels

into the future.



### Florida Surface Waters

Current Status

According to the ELS s urvey conducted in 1984, 75% of the Florida Panhandle lakes were acidic at

that time, as were 26% of the lakes in the northern peninsula. Most of the acidic lakes were clearwater

(DOC < 400 μmol) seepage lakes in which the dominant acid anions were Cl − and SO 2−

4

. Most of the

acidic and low-ANC lakes are located in the Panhandle and north central lake districts. In these areas, the

Floridan aquifer is separated from overlying sand deposits by a confining layer called the Hawthorne

formation. The major lake districts are located in karst terrain, where lakes formed through dissolution of

the underlying limestone followed by movement of surficial deposits into solution cavities (cf. Arrington

et al., 1987).

Flow of water from most of the lakes is downward, recharging the Floridan aquifer. Lake stage

varies in response to long-term trends in precipitation, and perhaps in response to groundwater

withdrawals. ANC generation in most lakes that have been studied appears to be due primarily to in-lake

SO 2−

4 and NO − 3 reduction (Baker et al., 1988; Pollman and Canfield, 1991). Retention of SO 2−

4 by

watershed soils may also be important. Lakes can be highly alkaline where groundwater interacts with the

deeper aquifer. Lakes with hydrologic contributions from shallow aquifers in highly weathered sands can

be quite acidic and may be sensitive to acidic deposition.

DOC concentrations are high in many Florida lakes, but organic anions are less important than

SO 2−

4 in most low-ANC and acidic lakes (Pollman and Canfield, 1991). Aluminum concentrations tend to

be very low in Florida lakes despite the high lakewater acidity because most of the Al n+ is removed from

soil solution by precipitation and ion exchange reactions within 75 cm depths (Grates et al., 1985), and

relatively little Al n+ is transported in groundwater to lake waters.

Baker et al. (1988) reported that retention of inorganic N is nearly 100% in most Florida lakes.

ANC generation from SO 2−

4 retention may approach 100 μeq/L in some Florida lakes (Pollman and

Canfield, 1991). These in-lake processes are important for generating ANC. Base cation deposition and

NH + 4 assimilation can also influence the acid-base status of lakes in Florida.

The Northern Florida Highlands high interest area identified by Baker et al. (1991a) consists of the

northern portion (north of 29°N latitude) of the Central Lake District and the Florida Panhandle (Figure

B-3). Acidic streams were located in the Florida Panhandle and were mildly acidic (mean pH 5.0) and

extremely dilute, with very low sea salt-corrected SBC (mean 21 μeq/L) and sea salt corrected SO 2−

4

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33 concentrations (mean 16 μeq/L). One-fourth of these acidic Panhandle streams were organic-dominated

but the remaining sites all had DOC < 2 mg/L and were inorganically dominated. Inorganic monomeric Al

concentrations in these acidic streams were very low (mean 11 μg/L). In these low DOC, low ANC

Panhandle streams, it was suggested that the degree of SO 2−

4 and NO − 3 retention was an important control

on streamwater ANC (Baker et al., 1991a).



### Past Acidification

Considerable research has been conducted on past acidification in Florida lakes. Historical analyses

of lake chemistry (Battoe and Lowe, 1992), inferred historical deposition (Hendry and Brezonik, 1984;

Husar and Sullivan, 1991), and paleolimnological reconstructions of lake pH (Sweets et al., 1990; Sweets,

1992) suggest evidence that some Florida lakes have acidified in response to acidic deposition. However,

the role of acid deposition in lakewater acidification is not entirely clear (cf. Pollman and Canfield, 1991),

and the interpretation is complicated by regional and local changes in land use and hydrology (Sullivan,

2000).

An alternative explanation for the apparent acidification of some lakes is the regional decline in the

potentiometric surface of the groundwater (Sweets et al., 1990). Large groundwater withdrawals of the

Floridan aquifer for residential and agricultural purposes may have reduced groundwater inflow of base

cations into seepage lakes, and therefore caused less buffering of acidity. Other land use changes may

have increased lake pH by providing inputs of fertilizer, which would increase lake productivity.

Paleolimnological evidence of this effect was provided by Brenner and Binford (1988) and Deevey et al.

(1986).

It is likely that lake chemistry in Florida has been heavily effected by land use changes. More than

half of the Florida lakes included in the ELS showed evidence of disturbance based on deviations from

expected geochemistry (Pollman and Canfield, 1991). Such effects complicate efforts to determine the

role of acidic deposition in controlling lakewater acid-base chemistry.

Diatom-inferred pH reconstructions of lakewater chemistry of six seepage lakes in Florida were

calculated as part of the PIRLA-I project and reported by Sweets et al. (1990). An additional 10 seepage

lakes were cored as part of PIRLA-II, and results of those analyses were reported by Sweets (1992).

Paleolimnological study lakes are located in the Panhandle, the Trail Ridge Lake District, and Ocala

National Forest, generally in terraces of highly weathered loose sand that were deposited on top of the

clay-confining layer.

Of the six lakes analyzed in PIRLA-I, two (Barco and Suggs) were inferred to have acidified since

1950 (Sweets et al., 1990). The timing of the onset of inferred acidification correlated with increases in

SO 2 emissions and S d eposition, which increased consistently between about 1945 and 1985 (Husar and

Sullivan, 1991).

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31 Of the 16 Florida seepage lakes studied in the PIRLA-II projects, 5 were located in or near the Trail

Ridge Lake District, and all showed diatom-inferred acidification of at least 0.2 pH units (Sweets, 1992).

Lakes located in the Panhandle region and Ocala National Forest generally did not show evidence of

recent acidification. The exception was Lake Five-O, which was inferred to have decreased by 2 pH units.

However, the diatom data suggested that this pH decline was associated with a sudden change in

chemistry, probably caused by a catastrophic disturbance such as sinkhole development, rather than by

acidification from atmospheric deposition (Pollman and Sweets, 1990; Sweets, 1992).



## B.3.4.3. Upper Midwest

The Upper Midwest contains numerous lakes created by glaciation. The region has little

topographic relief and with its deep glacial overburden, it also has little or no exposed bedrock. Acidsensitive surface waters in the Upper Midwest are mainly seepage lakes (Eilers et al., 1983). Most

drainage lakes and some of the seepage lakes in the Upper Midwest region receive substantial inflow

from groundwater, which is generally high in base cation concentrations from dissolution of carbonate

and silicate minerals. Relatively high concentrations of base cations in these lakes make them insensitive

to acidification from acidic deposition. The seepage lakes that have low base cation concentrations, and

that are therefore more acid-sensitive, generally receive most of their water input from precipitation

directly on the lake surface (Baker et al., 1991b).



### Current Status

Based on the ELS s urvey, the Upper Midwest has a large population of low ANC lakes, but

relatively few chronically acidic (ANC ≥ 0) lakes (Linthurst et al., 1986a,b). Acidic lakes in the Upper

Midwest are primarily small, shallow, seepage lakes that have low concentrations of base cations and Al

and moderate SO 2−

4 concentrations. Organic anions, estimated by both the Oliver et al. (1983) method

and the anion deficit, tend to be less than half the measured SO 2−

4 concentrations in the acidic lakes

(Eilers et al., 1988), but much higher in many of the drainage lakes that are less sensitive to acidification

from acidic deposition.

Groundwater flow-through lakes in the Upper Midwest can be identified on the basis of having Si

concentration greater than about 1 mg/L (Baker et al., 1991b). They generally have high pH and ANC,

due to groundwater inputs of base cations (e.g., Baker et al., 1991b). Based on results from the ELS

survey, only 6% of these lakes had ANC ≥ 50 μeq/L and none were acidic. Groundwater recharge lakes

(those having Si concentration less than 1 mg/L) constituted 71% of the seepage lakes in the Upper

Midwest, and were more frequently low pH and ANC. Five percent were acidic and 9% had pH ≥ 5.5.

Nearly 90% of Upper Midwestern lakes that had ANC ≥ 50 μeq/L were in this category (Baker et al.,

1991b). Such lakes tend to be susceptible to acidification from acidic deposition.

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33 Sullivan (2000) summarized patterns in lakewater chemistry across the Upper Midwest from the

ELS s urvey. Lakewater pH, ANC, base cations, and DOC all decreased from west to east across the

region. Lakewater SO 2−

4 concentrations did not show a comparable change, despite a substantial increase

in wet SO 4 deposition from Wisconsin to Michigan. Cook and Jager (1991) attributed the absence of a

more pronounced gradient in lakewater SO 2−

4 concentration across the region to watershed sources of S i n

Minnesota and high anion retention in seepage lakes, which predominate in the eastern portion of the

region. The retention of SO 2−

4 by dissimilatory reduction is generally high for seepage lakes because of

their long hydraulic retention times ( τ w). For example, an Upper Midwestern seepage lake with mean

depth of 3 m and hydraulic retention time of 7.5 years would be expected to lose about 50 μeq/L of SO 2−

4

from the water column by this process (Cook and Jager, 1991).

Lakewater concentrations of inorganic N reported by the ELS w ere low throughout the Upper

Midwest. In addition, snowmelt would not be expected to provide any significant NO − 3 influx to lakes in

the Upper Midwest because most snowmelt infiltrates the soil before reaching the drainage lakes, and

because snowmelt input of N into seepage lakes would be limited mainly to the snow on the lake surface

and immediate near-shore environment. Aluminum concentrations are far lower in the Upper Midwest

than in lakes of similar pH in the Northeast.

Wetlands are common throughout the Upper Midwest. They contribute to high production of

organic matter which is reflected in high DOC concentrations in many lakes. Despite the abundant

wetlands, SO 2−

4 is the dominant anion in the low-ANC ( ≥ 50 μeq/L) groundwater recharge seepage lakes.

Base cation production is the dominant ion-enrichment process in most Upper Midwestern lakes.

Even in low-ANC groundwater-recharge seepage lakes, base cation production accounts for 72% to 86%

of total ANC production (Cook and Jager, 1991).



### Past Acidification

Space-for-time substitution analysis was used to infer the general levels of past change in lake

water acid-base chemistry in the Upper Midwest. Such an analysis assumes that study lakes were

generally similar in acid-base chemistry prior to the onset of acidic deposition and that the only

substantial driver of recent change in acid-base chemistry has been the level of acidic deposition. Across

an increasing S d epositional gradient from eastern Minnesota eastward to eastern Michigan, ANC

expressed as (HCO − 3 - H + ) decreased and the ratio SO 2−

4 to SBC increased in the groundwater recharge

seepage lakes. In Michigan and Wisconsin, many lakes had SO 2−

4 > SBC, indicating that the acidity was

due to high SO 2−

4 relative to SBC concentration. There were also many lakes that had high concentrations

of DOC, and organic acidity probably accounted for many of these lakes having ANC < 0. The spatial

pattern in (HCO − 3 - H + ) could not be attributed to DOC, which generally showed a decreasing trend with

increasing acidic deposition.

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33 1. Sensitivity to mineral and organic acidity increased from west to east because of decreasing

lakewater base cation concentrations, and this may have been due, in part, to changes in base

cation deposition and precipitation volume along this gradient.

2. High concentrations of DOC were responsible for the acidic conditions in some of the lakes,

and DOC may have decreased in response to acidic deposition.

3. Many of the lakes in eastern Michigan, and some in Wisconsin, were acidic because of high

2− SO 4 relative to base cation concentration, and had probably been acidified by acidic

deposition. The c oncentration of lakewater (Ca 2+ + Mg 2+ ) also decreased with increasing acidic deposition,

probably due to lower levels of base cation deposition and greater amounts of precipitation in the eastern

portion of the region. Atmospheric deposition is an important source of base cations for groundwater

recharge seepage lakes because of minimal groundwater inputs. In the eastern portion of the region, such

lakes are more sensitive to pH and ANC depression in response to either elevated SO 2−

4 or DOC. The

spatial patterns for low ANC groundwater recharge lakes in the Upper Midwest are consistent with the

following hypotheses (Sullivan et al., 1990)(Sullivan, 1990, 2000):

Diatom-inferred pH reconstructions were completed for 15 lakes in the Upper Midwest region, and

summarized by Kingston et al. (1990) and Cook and Jager (1991). Four lakes, all of which had measured

pH < 5.7, showed a diatom-inferred pH decline of 0.2 to 0.5 pH units during the preceding 50 to 100

years. Diatom-inferred pH increased in one lake by 0.2 pH units. No change was inferred for the other 10

lakes, including 4 lakes with pH > 6.0. No major, recent, regional acidification was indicated by the

diatom-inferred pH reconstructions. Inferred changes in most lakes were small, and were no greater

during the industrial period than during the pre-industrial period (Sullivan, 2000).

Although diatom data suggested that some Upper Midwestern lakes may have acidified since preindustrial times, there is little paleolimnological evidence indicating substantial widespread acidification

in this region (Cook et al., 1990; Kingston et al., 1990). Land use changes and other human disturbances

of Upper Midwestern lakes and their watersheds have probably exerted more influence on the acid-base

chemistry of lakes than has acidic deposition (Kingston et al., 1990; (Sullivan et al., 1990)Sullivan, 1990,

2000). The portion of the region most likely to have experienced acidification from acidic deposition is

the Upper Peninsula of Michigan, where acidic seepage lakes are particularly numerous (Baker et al.,

1991a); acidic deposition is highest for the region, and the [SO 2−

4 ]/[SBC] ratio is commonly >1.0. The

percentage of acidic lakes in the eastern portion of the Upper Peninsula of Michigan (east of longitude

87°) was estimated to be 18% to 19% in 1984 (Schnoor et al., 1986; Eilers et al., 1988).

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23 Recent Trends

Regional trend values for long-term monitoring lakes during the period 1990 to 2000 suggested

that SO 2−

4 declined in lakewater by 3.63 μeq/L/yr, whereas lakewater NO − 3 concentrations were relatively

constant. The large decrease in SO 2−

4 concentration was mainly balanced by a large decrease in base

cation concentrations (−1.42 μeq/L/yr) and an increase in ANC (+1.07 μeq/L/yr). All of these trends were

significant at p < 0.01 (Stoddard et al., 2003). In the Upper Midwest, an estimated 80 of 251 lakes that

were acidic in the mid-1980s were no longer acidic in 2000. This change may be due to reduced levels of

S d eposition (Stoddard et al., 2003).



## B.3.4.4. West

Portions of the mountainous West contain large areas of exposed bedrock, with little soil or

vegetative cover to neutralize acidic inputs. This is particularly true of alpine regions of the Sierra

Nevada, northern Washington Cascades, the Idaho batholith, and portions of the Rocky Mountains in

Wyoming and Colorado. However, the percentage of exposed bedrock in a watershed does not always

indicate acid-sensitivity. If the bedrock contains even small deposits of calcareous minerals or if physical

weathering such as that caused by glaciers causes a high production of base cations within the watershed

(Drever and Hurcomb, 1986), surface waters may be alkaline, and are not sensitive to acidification from

acidic deposition.

The areas that are sensitive to adverse effects from acidic deposition in the western U.S. form two

nearly continuous ranges, the Sierra Nevada, which extends through most of the length of California, and

the Cascade Mountains, which extend from northern California to northern Washington (Source: Landers

et al. (1987).

Figure B-9). The sensitivity of the Rocky Mountains varies widely because the ranges are

discontinuous with highly variable geological composition. For that reason, assessments of the sensitivity

of Rocky Mountain aquatic resources to acidification should be specific to individual ranges (Turk and

Spahr, 1991).

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11 Source: Landers et al. (1987).



### Figure B­9. Major geomorphic units and locations of lakes sampled in the Western Lake Survey.

Those areas known to contain sensitive lake resources are shaded with cross­hatching.

The NAPAP SOS/T Reports and the IA (NAPAP, 1991) provided only a cursory treatment of

aquatic effects issues in the West, largely because it was well known that atmospheric deposition of S a nd

N were generally low compared to highly affected areas in the East (Sullivan, 2000) and because results

from the WLS (Landers et al., 1987) indicated that there were virtually no acidic (ANC ≥ 0) lakes in the

West. NAPAP (1991) indicated, however, that high-elevation areas of the West contained many of the

watersheds most sensitive to the potential effects of acidic deposition.

Because of the proximity of western urban and industrial pollution sources to individual mountain

ranges, it is important to consider emissions in the immediate vicinity of sensitive resources as well as

regional emissions. Atmospheric deposition in the far western ranges (i.e., Sierra Nevada, Cascade

Mountains) is largely influenced by local emissions, particularly emission sources to the west (upwind) of

sensitive resources. In the Rocky Mountains, deposition chemistry is often influenced by a more complex

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26 collection of emission sources. For example, in the Mt. Zirkel Wilderness of northwestern Colorado,

SO 2− and NO − 4 3 in the snow appeared to originate largely from sources in the Yampa Valley, about 75 km

to the west (Turk et al., 1992). Rocky Mountain National Park is affected by emissions from the Front

Range to the southeast.

The acid base chemistry of lake and stream waters in Rocky Mountain National Park appears to be

primarily a function of the interactions among several key parameters and associated processes:

atmospheric deposition, bedrock geology, the depth and composition of surficial deposits and associated

hydrologic flowpaths, and the occurrence of soils, tundra, and forest vegetation (Sullivan, 2000). Potential

biological effects of acidic deposition on lakes in the Rocky Mountains are primarily attributable to

acidification from high NO − 3 concentration. In general, such effects tend to be episodic, rather than

chronic. Highest NO − 3 concentrations in both precipitation and surface waters are found above timberline

in Colorado, where biological activity, and therefore NO − 3 uptake, by terrestrial and aquatic biota is

lowest.



### Current Status

The available information on acid-base chemistry of surface waters in the West is based mostly on

synoptic data from the WLS ( Landers et al., 1987) and some more localized studies. Acid anion

concentrations in most western lakes are low during fall, but can be higher during snowmelt (Williams

and Melack, 1991). Available data from intensive study sites in the West (e.g., Loch Vale, CO, Emerald

Lake Basin, CA, and the Glacier Lakes Watershed, WY) suggest that episodic depression of stream pH

may be more pronounced than for lakes. However, there are no available systematic regional stream

chemistry data with which to assess regional sensitivity of streams to acidic deposition.

In most western lakes concentrations of SO 2−

4 are low, although watershed sources of S are

substantial in some cases (Table B-9). Turk and Spahr (1991) presented a conceptual model for expected

SO 2−

4 distributions in lakewaters in the West that can be used as an aid in identifying the proportion of

watersheds with significant watershed sources of S. Considering that atmospheric sources can account for

generally < 30 μeq/L of SO 4 in the West, it appears that many lakes, particularly in Colorado, receive

variable amounts of watershed S ( Sullivan, 2000; Figure B-10).

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14 Figure B­10. Estimated percent changes in the total deposition of sulfur, reduced N, and nitrate­N at

MAGIC modeling sites from 1995 to 2040 under each of the emissions control strategies.

Source: Sullivan et al. (2004).

Nitrate concentrations in most WLS lakes were near 0 during fall (Landers et al., 1987), although

fall NO − 3 concentrations were high in some cases (Table B-10). For example, nearly one fourth of the

lakes in northwest Wyoming had NO − 3 > 5 μeq/L and almost 10% had NO − 3 > 10 μeq/L (Table B-10). In

both the Sierra Nevada and Colorado Rockies subregions, about 10% of the lakes had fall NO − 3

concentrations above 5 μeq/L (Table B-10).

It is important to note that even low to moderate concentrations of NO − 3 in western lakes might be

significant in view of: (1) the low base cation concentrations in many lakes; (2) potential for continuing N

deposition to eventually exhaust natural assimilative capabilities; and (3) the fact that these distributions

are based on fall data. Time-intensive discharge and chemical data for two alpine streams in Loch Vale

watershed identified strong seasonal control on streamwater NO − 3 concentrations (Campbell et al., 1995).

In spite of the paucity of soil cover, the chemical composition of streams is regulated much as in typical

forested watersheds. Soils and other shallow groundwater matrices such as boulder fields are more

important in controlling surface water chemistry than their abundance would indicate. Spring streamwater

NO − 3 concentrations ranged to 40 μeq/L, compared with summer minimum values near 10 μeq/L. Elution

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34 of acidic waters from snowpack along with dilution of base cations originating in shallow groundwater

caused episodes of decreased ANC in alpine streams (Campbell et al., 1995). A subalpine stream in the

same watershed similarly displayed decreased ANC and elevated monomeric Al concentrations during

snowmelt over 2 years of intensive sampling, related to elevated concentrations of SO 4 2− , NO 3 − , and DOC

(Denning et al., 1991). Limited data collected during snowmelt suggest that spring concentrations could

be several times higher than samples collected during the fall (e.g., Reuss et al., 1995).

The surface water chemistry data for the West indicate that the Sierra Nevada and Cascade

Mountains constitute the mountain ranges with the greatest number of sensitive resources (Table B-9 and

Table B-10). Lakes in the Sierra Nevada are especially sensitive to effects from acidic deposition because

of the predominance of granitic bedrock, thin acidic soils, large amounts of precipitation, coniferous

vegetation, and dilute nature of the lakes (Melack et al., 1985; Melack and Stoddard, 1991). Surface

waters in this region are among the poorly buffered surface waters in the U.S. (Landers et al., 1987;

Melack and Stoddard, 1991). The hydrologic cycle is dominated by the annual accumulation and melting

of a dilute, mildly acidic (pH about 5.5) snowpack.

During the 1980s, an Integrated Watershed Study (IWS) was conducted at seven lakes in the Sierra

Nevada, including Emerald Lake and surrounding watersheds (~3,000 m elevation) to determine the

effects of acidification on surface waters (Tonnessen, 1991). Three lakes (Lost, Pear, and Emerald) had

volume-weighted mean ANC in the range of 15 to 30 μeq/L. Moderate ANC waters (Topaz, Spuller, and

Marble Fork) exhibited mean ANC in the range of 30 to 50 μeq/L. Crystal and Ruby Lakes had mean

annual ANC > 50 μeq/L.

Many Cascade and Rocky Mountain lakes are also highly sensitive to potential acidic deposition

effects (Nelson, 1991; Turk and Spahr, 1991). It does not appear that chronic acidification has occurred to

any significant degree, although episodic acidification has been reported for lakes in the Colorado Front

Range (Williams and Tonnessen, 2000). The data that would be needed for determining the extent and

magnitude of episodic acidification have not been collected to a sufficient degree in acid-sensitive areas

of the West to support regional assessment of episodic acidification (Sullivan, 2000).

Along the eastern edge of the Continental Divide in Colorado and southeastern Wyoming,

Musselman et al. (1996) conducted a synoptic survey of surface water chemistry in the mountainous areas

that are exposed to relatively high (by western standards) deposition of N. A total of 267 high-elevation

lakes situated in watersheds having a high percentage of exposed bedrock or glaciated landscape were

selected for sampling. None of the lakes were chronically acidic (ANC < 0), although several had ANC <

10 μeq/L, and more than 10% of the lakes had ANC < 50 μeq/L. The WLS data for lakes in Colorado and

Wyoming demonstrate that surface waters in this area had fall concentrations of NO 3 − in the range of 10

to 30 μeq/L, and likely had substantially higher NO 3 − concentrations during spring.

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33 The weight of evidence suggests many high-elevation lakes in the West receive N deposition

sufficiently high to cause chronic NO − 3 leaching, and likely some degree of associated chronic and

episodic acidification. However, existing data are insufficient to make a conclusive determination

(Sullivan, 2000).



### Past Acidification

The limited paleolimnological data available for lakes in the western U.S. suggest that widespread

chronic acidification probably has not occurred. Some lakes may have experienced recent pH declines,

but the magnitude of such changes has likely been small (Sullivan, 2000).

In the Sierra Nevada, paleolimnological reconstructions of lakewater pH and ANC were calculated

by Holmes et al. (1989) at 24 depth intervals at Emerald Lake, for the period 1825 to the present.

Significant trends were not found for either pH or ANC, and the authors concluded that Emerald Lake had

not been acidified by acidic deposition. Whiting et al. (1989) completed paleolimnological analyses of

three additional lakes in the Sierra Nevada. Eastern Book Lake (pH = 7.06) showed evidence of both

long-term alkalization (~0.3 pH units over the past 200 years) and pH fluctuations since 1970. Lake 45

(pH = 5.16) may have acidified slightly (~0.2 pH units) over the last 60 years. Lake Harriet (pH = 6.52)

showed no significant change.

In Rocky Mountain National Park, Colorado, Baron et al. (1986) investigated metal stratigraphy,

diatom stratigraphy, and inferred pH profiles of four subalpine lakes. They found no evidence of historical

influence on pH attributable to atmospheric deposition. Other paleolimnological studies of Rocky

Mountain lakes report similar results: metals (primarily lead) exhibit temporal dynamics related to the

increase and decline of precious metal mining in the region, but these are asynchronous with other metal

or biological indicators of acidification (Wolfe et al., 2003). Both the study by Wolfe et al. (2003) and a

study by Saros et al. (2003) showed no evidence of acidification of lake waters over time, but increasing

evidence of eutrophication from atmospheric N deposition (see Annex C).

DayCent-Chem, a model that simulates the daily dynamics of plant production, soil organic matter,

cation exchange, mineral weathering, elution, stream discharge, and stream solute concentrations, was

able to recreate daily stream chemistry dynamics over 13 years for an alpine watershed in the Colorado

Front Range (Hartman et al., 2007). Using the model to hindcast stream chemical dynamics back to 1900

revealed changes in simulated pH coincident with maximum SO 2 emissions in the late 1960s and early

1970s. Model simulations suggested annual mean pH values decreased to 5.6 to 5.8 during the years of

maximum regional SO 2 emissions, and have since recovered to circumneutral values. Simulated ANC

responded to both SO 2 and NO X emissions, decreasing to annual values of 20 to 25 μeq/L during years of

highest SO 2 or NO X emissions compared with current mean annual ANC values near 50 μeq/L (Hartman

et al., 2007).

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28 Recent Trends

Limited monitoring data are available on recent trends in surface water chemistry in the western

regions and are mostly limited to the recent past and a number of reconnaissance studies (Melack and

Stoddard, 1991; Nelson, 1991; Turk and Spahr, 1991). Existing information on recent trends in surface

water chemistry since the 1980s suggests that conditions vary widely across the West. Parts of Colorado,

Wyoming and the western Cascades showed decreased ANC, while Emerald Lake experienced reduced

NO − 3 concentrations.

Turk et al. (1993) reported the results of 5 years of monitoring for ten lakes in the Mt. Zirkel and

Weminuche Wilderness areas in Colorado. Based on lake concentrations of SO 2− l − 4 and C and on wet

deposition concentrations of SO 2−

4 , NO

− 3 , and H +

, Turk and Spahr (1991) concluded that low-ANC lakes

had lost no more than 5 μeq/L ANC in the Bitterroot Range of the Northern Rocky Mountains, 12 μeq/L

ANC in the Wind River Range of Wyoming, and 10 μeq/L ANC in the Front Range of Colorado. It is

likely that the actual ANC losses had been much less than these estimates (Sullivan, 2000).



### Future Projections

The DayCent-Chem model was used to project a timeline to acidification for an alpine watershed

of Rocky Mountain National Park (Hartman et al., 2007). At current levels of N deposition of 4 to 6 kg

N/ha/yr, acidification does not occur over 48 years of simulation, but increasing deposition amounts lead

to first episodic acidification over time at deposition of 7.0 to 7.5 kg N/ha/yr. MAGIC model simulation

results suggested that a sustained N deposition load of 12.2 kg N/ha/yr would be required over a period of

50 years to cause chronic acidification of the Andrews Creek watershed in Rocky Mountain National Park

(Sullivan et al., 2005).



## B.3.4.5. Temporal Variability in Water Chemistry

Water chemistry changes on both intra-annual and inter-annual time scales in response to changes

in environmental conditions. Because of this variability, many years of data are required to establish the

existence of trends in surface water chemistry. Assignment of causality to changes that are found to occur

is even more difficult.

Temporal variability in surface water and soil solution chemistry, and patterns in nutrient uptake by

terrestrial and aquatic biota, influence acidification processes and pathways. Thus, conditions are

constantly changing in response to episodic, seasonal, and inter-annual cycles and processes. In particular,

climatic fluctuations that govern the amount and timing of precipitation inputs, snowmelt, vegetative

growth, depth to groundwater tables, and evapoconcentration of solutes influence soil and surface water

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35 chemistry and the interactions between pollution stress and sensitive aquatic and terrestrial biological

receptors.

Decreases in pH with increases in flow are nearly ubiquitous in drainage waters throughout the

U.S. (Wigington et al., 1991). Chemical changes during episodes are controlled in part by acidic

deposition and in part by natural processes, including dilution of base cation concentrations, nitrification,

flushing of organic acids from terrestrial to aquatic systems, and the neutral salt effect. Episodic

acidification pulses may last for hours to weeks, and sometimes result in depletion of ANC in acidsensitive streams and lakes to negative values and concomitant increases in Al i in solution to toxic levels.

During episodes, which are driven by rainstorms and/or snowmelt events, both discharge

(streamflow volume per unit time) and water chemistry change, sometimes dramatically. This is important

because streams may in some cases exhibit chronic chemistry that is still suitable for aquatic biota, but

nevertheless experience occasional episodic acidification with lethal consequences (cf. Wigington et al.,

1993).

The most important factor governing watershed sensitivity to episodic acidification is the pathways

followed by snowmelt water and stormflow water through the watershed. These pathways determine the

extent of acid neutralization provided by the soils and bedrock in that watershed. High-elevation

watersheds with steep topography, extensive areas of exposed bedrock, deep snowpack accumulation, and

shallow, base-poor soils tend to be most sensitive to episodic acidification.

Rainfall and snowmelt typically pass through the soil profile prior to reach a stream channel. The

typical soil profile in acid-sensitive watersheds has lowest pH in upper organic soil horizons, increasing

down the profile to higher pH at depth. Drainage water chemistry during baseflow conditions is generally

reflective of conditions in the lower soil horizons and the subsoil. During high flows during snowmelt or

rainfall events, however, flow-routing favors water flowpaths through upper horizons. During such

events, drainage water chemistry, therefore, typically reflects the lower pH, higher organic content, and

lower ANC of these upper soil horizons (Sullivan, 2000). As such, storm flow and snowmelt are often

associated with episodes of extreme surface water acidity due to an increase in the proportion of flow

derived from water that has moved laterally through the surface soil without infiltration to deeper

soil horizons (Wigington et al., 1991).

The routing of water as it flows through a watershed determines the degree of contact with

acidifying or neutralizing materials and therefore influences (along with soils and bedrock characteristics)

the amount of episodic acidification that occurs. In any given watershed, surface water ANC may vary in

time depending upon the proportion of the flow that has contact with deep versus shallow soil horizons;

the more subsurface contact, the higher the surface water ANC (Turner et al., 1991). This can be

attributed in part to higher base saturation and (in some watersheds) greater SO 2−

4 adsorption capacity in

subsurface soils. It may also relate to the accumulation in the upper soil horizons of acidic material

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34 derived from atmospheric deposition and decay processes (Lynch and Corbett, 1989; Turner et al., 1991).

Episodic acidification is often the limiting condition for aquatic organisms in streams that can be suitable

for aquatic life under baseflow conditions.

Episodes are generally accompanied by changes in at least two or more of the following chemical

parameters: ANC, pH, base cations, SO 4 2− , NO 3 − , Al n+ , organic acid anions, and DOC (Sullivan, 2000).

The EPA’s Episodic Response Project (ERP) confirmed the chemical and biological effects of episodic pH

depressions in lakes and streams in parts of the U.S. (Wigington et al., 1993). The ERP illustrated that

episodic processes are mostly natural, that SO 4 2− and especially NO 3 − attributable to atmospheric

deposition play important roles in the episodic acidification of some surface waters, and that the chemical

response that has the greatest effect on biota is increased Al i concentration. Similar findings had been

reported elsewhere, especially in Europe, but the ERP helped to clarify the extent, causes, and magnitude

of episodic acidification in portions of the U.S. (Sullivan, 2000).

Water chemistry trends documented by long-term monitoring programs and reported here represent

recovery from chronic acidification. Most surface waters exhibit seasonally lower ANC and pH values

than would be captured by trend analysis that considers only chronic chemistry data. In many cases, sites

that are relatively low in ANC, but not chronically acidic, undergo short-term episodic acidification to

negative ANC values during spring snowmelt, or during intense rain events. Lawrence (2002) found that

16% of total stream reaches in the West Branch Neversink River, in the Catskill Mountains of New York,

were chronically acidic, whereas 66% of total stream reaches has a high likelihood of becoming acidic

during high flows.

Most research on episodic processes has been conducted on stream systems, which tend to be more

susceptible to such effects than lakes. Spatial variability can be considerable in lakes, and this complicates

efforts to quantify the magnitude of episodic effects (Gubala et al., 1991). Moreover, synoptic lake

surveys are typically conducted during the autumn “index period,” during which time lakewater chemistry

exhibits low temporal variability. Although autumn is an ideal time for surveying lakewater chemistry in

terms of minimizing variability, lakewater samples collected during autumn provide little relevant data on

episodic processes, and in particular on the dynamics or importance of N as an agent of acidification.

Nitrate concentrations in lakewater are elevated during the autumn season only in lakes having

watersheds that exhibit fairly advanced symptoms of N saturation (Stoddard, 1994).

Mixing zones have received little attention despite the fact that they can be acutely toxic to aquatic

biota. Whether an area of acidic water that comes in contact with non-acidic water is a safe haven or a

toxic zone depends on many parameters, one of the most important of which is the amount and form of Al

species produced at the boundaries. For example, Al hydroxide (Al(OH 3 )) can precipitate out of solution

if pH is suddenly increased within a mixing zone. This form of Al is acutely toxic to fish.

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34 The mechanisms that produce acidic episodes can include dilution of base cations and flushing of

NO 3 − , SO 4 2− and/or organic acids from forest soils to drainage water (Kahl et al., 1992; Wigington et al.,

1996; Wigington, 1999; Lawrence, 2002). Acidic deposition can contribute to episodic acidification of

surface water both by supplying N which can produce pulses of NO 3 − during high flow periods,

contributing hydrologically mobile SO 4 2− through dry deposition, and by lowering baseline pH and ANC,

so that episodes are sufficient to produce biologically harmful conditions (Stoddard et al., 2003).

Episodic acidification due to atmospheric deposition is most commonly associated with N

deposition, and effects tend to be most pronounced during snowmelt. However, snowmelt can flush into

surface waters N that was deposited from the atmosphere to the snowpack and also N that was

mineralized within the soil under the snowpack during winter. A substantial component of the NO 3 − flux

may have been derived from mineralization of organic N (Ley et al., 2004). Much of the N released from

the snowpack during the melting period is retained in underlying soils and only a component of that is

flushed to surface waters. Where soils are sparse, as in alpine regions of the western U.S., most snowpack

N is flushed to surface waters, and even though there is evidence through use of isotopic tracers that much

of the N was cycled microbially, snowpack N has been reported to caused temporary acidification of

alpine streams (Williams and Tonnessen, 2000; Campbell et al., 2002).

Episodic pH and ANC depressions during snowmelt are largely driven by base cation dilution and

NO 3 − enrichment in most areas (cf. Wigington et al., 1991, 1993; Campbell et al., 1995; Stoddard, 1995),

although Denning et al. (1991) found a significant decline of both pH and ANC associated with DOC

flushing from forest soils. Pulses of increased SO 4 2− during hydrological episodes are usually attributable

to S storage and release in soils (for example, in the southeastern U.S.) or wetlands. More commonly, lake

and streamwater concentrations of SO 4 2− decrease or remain stable during snowmelt. This is probably

because most stream flow during episodes is derived from water previously stored in watershed soils that

is then forced into streams and lakes by the piston effect.

In the Northeast, the most severe acidification of surface waters generally occurs during spring

snowmelt (Charles, 1991). Stoddard et al. (2003) found that on average, spring ANC values in New

England, the Adirondacks, and the Northern Appalachian Plateau were about 30 μeq/L lower than

summer values during the period 1990 to 2000 (Figure B-11). This implies that lakes and streams in these

regions would need to recover to chronic Gran ANC values above about 30 μeq/L before they could be

expected to not experience acidic episodes (Stoddard et al., 2003). However, the estimate of 30 μeq/L is

certain to be low because the comparison was made with non-episodic sampling in spring.

In the West, episodic acidification is an especially important issue for surface waters throughout

high-elevation areas. A number of factors pre-dispose western systems to potential episodic effects

(Peterson et al., 1998; (Sullivan, 2000), including:

August 2008 B­67 DRAFT—DO NOT QUOTE OR CITE� the abundance of dilute to ultradilute lakes which exhibit very low concentrations of base

cations, and therefore ANC, throughout the year;

� large snowpack accumulations at the high-elevation sites, thus causing substantial episodic

acidification via the natural process of base cation dilution; and

� short hydraulic retention times for many of the high-elevation drainage lakes, thus enabling

snowmelt to rapidly flush lake basins with highly dilute meltwater. 

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16 Based on measurements of microbial biomass, CO 2 flux through the snowpack, and soil N pools,

Williams et al. (1996b) concluded N cycling under the snowpack in Colorado during the winter and

spring was sufficient to supply the NO − 3 measured in stream waters. Brooks et al. (1996) investigated soil

N dynamics throughout the snow-covered season on Niwot Ridge, CO. Sites with consistent snow cover

had a 3 to 8 cm layer of thawed soil under the snowpack for several months before snowmelt began.

Nitrogen mineralization in this thawed layer contributed to N

r pools that were significantly larger than the

pool of N stored in the snowpack. As snowmelt began, soil inorganic N pools decreased sharply,

concurrent with a large increase in microbial biomass N. As snowmelt continued, both microbial N and

soil inorganic N decreased, presumably due to increased demand by growing vegetation (Brooks et al.,

1996).

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10 Figure B­11. Relationship between mean summer and spring ANC values at LTM sites in New

England, the Adirondacks, and the Northern Appalachian Plateau.

Source: Stoddard et al. (2003).

In the Sierra Nevada, the hydrology of alpine and subalpine ecosystems is dominated by snowfall

and snowmelt, with over 90% of the annual precipitation falling as snow. The relatively small loads of

acidic deposition can supply relatively high concentrations of SO 2− and NO − 4 3 to lakes and streams during

the early phase of snowmelt (Stoddard, 1995) through the process of preferential elution (Johannessen

and Henriksen, 1978).

Lakewater pH and ANC in the Sierra Nevada generally decrease with increasing runoff, reaching

minima near peak snowmelt discharge. Most other solutes exhibit temporal patterns that indicate dilution

or a pulse of increased concentration followed by either dilution or biological uptake. Williams and

Melack (1991) and Williams et al. (1995) documented ionic pulses (2 to 10 days in duration) in meltwater

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35 concentrations in the Emerald Lake watershed twofold to twelvefold greater than the snowpack average.

Sulfate and NO − 3 concentrations in meltwater decreased to below the initial bulk concentrations after

about 30% of the snowpack had melted. The initial meltwater draining from the snowpack had

concentrations of NO − 3 and NH + 4 as high as 28 μeq/L, compared to bulk snowpack concentrations <

5 μeq/L (Williams et al., 1995). Streamwater NO − 3 concentrations peaked during the early snowmelt

period, with maximum streamwater concentrations of 18 μeq/L. During summer, streamwater NO − 3

concentrations were always near or below detection limit.

Stoddard (1995) reported results for two lakes in the Sierra Episodes Study, one of which (Treasure

Lake) typified the response of the majority of high elevation lakes in the study and one whose response

was most extreme (High Lake). At Treasure Lake, ANC began to decline at the onset of snowmelt and

reached a minimum at peak runoff, corresponding with minimum base cation, NO

− 2

3 , and SO − 4

concentrations. The lakewater did not become acidic. High Lake watershed contained a deeper snowpack,

and began melting later in the season. ANC fell to 0 and below twice during the first 10 days of

snowmelt. The ANC minimum corresponded with maximum concentrations of base cations, NO − 3 and Al.

Nitrate concentrations increased to values greater than 40 μeq/L, exceeding concurrent increases in base

cations and causing the lake to become acidic for brief periods. Stoddard (1995) concluded that High

Lake appeared to be representative of the most extreme conditions of episodic acid-sensitivity in the

Sierra Nevada.

Data regarding episodic variability in streamwater ANC for six intensively studied sites within

Shenandoah National Park for the period 1993 to 1999 are presented in Sullivan et al (2003); (Figure B12. The minimum measured ANC each year at each site (which generally is recorded during a large rain

or snowmelt episode) is plotted against the median spring ANC for that year at that site. Sites that

exhibited median spring ANC below about 20 μeq/L (Paine Run, White Oak Run, Deep Run) generally

had minimum measured ANC about 10 μeq/L lower than median spring ANC.

In contrast, at the high-ANC Piney River site (median spring ANC > 150 μeq/L), the minimum

measured ANC was generally more than about 40 μeq/L lower than the respective median spring ANC. At

sites having intermediate ANC values, with median spring ANC in the range of about 30 to 90 μeq/L, the

minimum ANC measured each year was generally about 20 to 30 μeq/L lower than the respective median

spring ANC. Thus, there is a rather clear pattern of larger episodic ANC depressions in streams having

higher median ANC and smaller episodic ANC depressions in streams having lower median ANC. The

two sites that had median spring ANC between about 0 and 10 μeq/L consistently showed minimum

measured values below 0. Streams having low chronic ANC can be expected to experience relatively

small episodic ANC depressions. However, those depressions can result in minimum ANC values that are

associated with toxicity to aquatic biota.

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10 Source: Sullivan et al. (Sullivan, 2003).



### Figure B­12. Minimum streamwater ANC sampled at each site during each year versus median

spring ANC for all samples collected at that site during that spring season. Data are provided for all

intensively studied streams within Shenandoah National Park during the period 1993−1999. A 1:1

line is provided for reference. The vertical distance from each sample point upwards to the 1:1 line

indicates the ANC difference between the median spring value and the lowest sample value for each

site and year.

A recent study by Deviney et al. (2006) used hourly ANC predictions over short time periods to

compute recurrence intervals of annual water-year minimum ANC values for periods of 6, 24, 72, and 168

h. They extrapolated the results to the rest of the Shenandoah National Park catchments using catchment

geology and topography. On the basis of the models, they conclude that large number of Shenandoah

National Park streams have 6– to 168–h periods of low ANC values, which may stress resident fish

populations (Deviney et al., 2006). Specifically, on the basis of a 4–year recurrence interval,

approximately 23% of the land area (44% of the catchments) can be expected to have conditions that are

indeterminate (ANC 20 to 50), episodically acidic (ANC 0 to 20) or chronically acidic (ANC less than 0)

for 72 continuous hours. Many catchments are predicted to have successive years of low-ANC values

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34 potentially sufficient to extirpate some species (Deviney et al., 2006). The authors of the study reported

that smaller catchments are more vulnerable to episodic acidification than large catchments underlain by

the same bedrock. Catchments with similar topography and size are more vulnerable if underlain by less

basaltic and carbonate bedrock.

There are several different mechanisms of episodic acidification in operation in the streams in

Shenandoah National Park, depending at least in part on the bedrock geology of the stream. The most

acidic conditions in Shenandoah National Park streams occur during high-flow periods, in conjunction

with storm or snowmelt runoff. The general relationship between flow level and ANC is evident in

Source: Sullivan et al. (Sullivan, 2003).

, which plots ANC measurements against flow for three intensively studied streams representing

the major bedrock types in the park. The response of all three streams is similar in that most of the lower

ANC values occur in the upper range of flow levels.

Consistent with observations by Eshleman (1988), the minimum ANC values that occur in response

to high flow are related to baseflow ANC values. Paine Run (siliciclastic bedrock) had a mean weekly

ANC value of about 6 μeq/L and often had high-flow ANC values that were less than 0 μeq/L. Staunton

River (granitic bedrock) had a mean weekly ANC value of about 82 μeq/L and had only a few high-flow

ANC values less than 50 μeq/L. Piney River (basaltic bedrock) had a mean weekly ANC value of

217 μeq/L and no values as low as 50 μeq/L.

Eshleman and Hyer (2000) estimated the contribution of each major ion to observed episodic ANC

depressions in Paine Run, Staunton River, and Piney River during a 3−year period. During the study, 33

discrete storm events were sampled and water chemistry values were compared between antecedent

baseflow and the point of minimum measured ANC (near peak discharge). The relative contribution of

each ion to the ANC depressions was estimated using the method of Molot et al. (1989), which

normalized the change in ion concentration by the overall change in ANC during the episode. At the lowANC (~0) Paine Run site on siliciclastic bedrock, increases in NO 3 − and SO 4 2− , and to a lesser extent

organic acid anions, were the primary causes of episodic acidification. Base cations tended to compensate

for most of the increases in acid anion concentration. ANC declined by 3 to 21 μeq/L (median 7 μeq/L)

during the episodes studied.

At the intermediate-ANC (~60 to 120 μeq/L) Staunton River site on granitic bedrock, increases in

SO 4 2− and organic acid anions, and to a lesser extent NO 3 − , were the primary causes of episodic

acidification. Base cation increases compensated these changes to a large degree, and ANC declined by 2

to 68 μeq/L during the episodes (median decrease in ANC was 21 μeq/L).

August 2008 B­72 DRAFT—DO NOT QUOTE OR CITESource: Sullivan et al. (Sullivan, 2003).



### Figure B­13. Relationship between ANC and runoff for streamwater samples collected at intensively

studied sites in Shenandoah National Park. The data represent samples collected during the 1992–

1997 period.

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Figure B­14. Decrease in ANC and pH and increase in dissolved aluminum in response to a sharp

increase in streamflow in three watersheds within Shenandoah National Park during a hydrological

episode in 1995. The watersheds were selected to be representative of the three geologic

sensitivity classes within the park. Data are shown for the month of January 1995. At the high-ANC (~150 to 200 μeq/L) Piney River site on basaltic (69%) and granitic (31%)

bedrock, base cation concentrations declined during episodes (in contrast with the other two sites where

base cation concentrations increased). Sulfate and NO − 3 usually increased. The change in ANC during the

episodes studied ranged from 9 to 163 μeq/L (median 57 μeq/L; Eshleman and Hyer, 2000).

Previous studies have shown that mobilization of dissolved Al during episodic acidification is a

primary cause of fish mortality in streams that have low ANC under baseflow conditions (Wigington

et al., 1993). Streams with higher ANC during baseflow are less likely to become sufficiently acidic

during episodes to bring much Al into solution.

Figure B-14 provides an example of changes in ANC, pH, and total monomeric Al that occurred in

Paine Run, Staunton River, and Piney River during a high-flow episode in January 1995. Under baseflow

conditions, ANC at the Paine Run site was above 0 μeq/L, pH was above 5.5, and Al concentration was

less than about 1 μM. Discharge levels increased dramatically during the episode, resulting in depression

of ANC to less than 0 μeq/L, pH values less than 5.5, and an increase in Al concentration to near 3 μM,

above the threshold for adverse effects on some species of aquatic biota.

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31 ANC min = 0.79 ANC 2

chronic − 5.88 (r = 0.97; se of slope = 0.02, p = 0.001) Source: Sullivan et al. (Sullivan, 2003).

The same episode also resulted in substantial declines in ANC in the granitic (Staunton River) and

basaltic (Piney River) watersheds. However, ANC values at these two sites were relatively high prior to

the episode (about 75 and 175 μeq/L, respectively) and did not decline to below about 50 μeq/L during

the episode at either site, and pH values remained above 6.0 and 6.5, respectively (Figure B-14).

In general, pre-episode ANC is a good predictor of minimum episodic ANC and also a reasonable

predictor of episodic Δ ANC. Higher values of pre-episode ANC lead to larger Δ ANC values, but

minimum ANC values of such streams are generally not especially low. Lowest minimum ANC values are

reached in streams that have low pre-episode ANC, but the Δ ANC values for such streams are generally

small.

Webb et al. (1994) developed an approach to calibration of an episodic acidification model for

VTSSS l ong-term monitoring streams in western Virginia that was based on the regression method

described by Eshleman (1988). Median, spring quarter ANC concentrations for the period 1988 to 1993

were used to represent chronic ANC, from which episodic ANC was predicted. Regression results were

very similar for the four lowest ANC watershed classes, and they were therefore combined to yield a

single regression model to predict the minimum measured ANC from the chronic ANC. Extreme ANC

values were about 20% lower than chronic values, based on the regression equation:

Because t he m odel was based on estimation of the minimum ANC measured in the quarterly

sampling program, it is probable that the true minimum ANC values were actually somewhat lower than

20% below the measured chronic ANC. Nevertheless, regression approaches for estimation of the

minimum episodic ANC of surface waters, such as was employed by Webb et al. (1994) for western

Virginia, provide a basis for predicting future episodic acidification. It must be recognized, however, that

future episodic behavior might vary from current behavior if chronic conditions change dramatically.

The relative importance of the major processes that contribute to episodic acidification varies

among the streams within Shenandoah National Park, in part as a function of bedrock geology and

baseflow streamwater ANC. Sulfur-driven acidification was an important contributor to episodic loss of

ANC at all three study sites, probably because S adsorption by soils occurs to a lesser extent during highflow periods. This is due, at least in part, to diminished contact between drainage water and potentially

adsorbing soils surfaces. Dilution of base cation concentrations was most important at the high-ANC site.

The documented importance of NO − 3 to episodic acidification was a relatively recent development,

attributed to the effects of gypsy moth ( Lymantria dispar ) infestation in many watersheds within

Shenandoah National Park (Webb et al., 1995). Consumption of foliage by the moth larvae converted

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30 foliar N, which is normally tied up in long-term N cycling processes, into more labile N forms on the

forest floor.

Thus, episodic acidification of streams in Shenandoah National Park can be attributed to a number

of causes, including dilution of base cations and increased concentrations of sulfuric, nitric, and organic

acids (Eshleman et al., 1995; Hyer et al., 1995). For streams having low pre-episodic ANC, episodic

decreases in pH and ANC and increases in toxic Al concentrations can have adverse effects on fish

populations. Not all of the causes of episodic acidification are related to acidic deposition. Base-cation

dilution and increase in organic acid anions during high-flow conditions are natural processes. The

contribution of nitric acid, indicated by increased NO − 3 concentrations, has evidently been (at least for

streams in the park) related to forest defoliation by the gypsy moth (Webb et al., 1995; Eshleman et al.,

1998). However, significant contributions of sulfuric acid, indicated by increased SO 2−

4 concentrations

during episodes in some streams, is an effect of atmospheric deposition and the dynamics of S adsorption

on soils (Eshleman and Hyer, 2000).



# B.4. Effects on Biota

Soil and surface water acidification involve changes in a number of chemical parameters, each of

which has the potential to influence the health and vigor of biological communities and the species that

comprise them. I n m ost cases where biological effects of acidification have been documented, the most

important chemical parameters involved in those effects have been pH, Al i , and Ca 2+

. Less commonly, one

or more base cations other than Ca 2+ (e.g., Mg 2+

, K + ) or C are also involved. This is true for both aquatic

and terrestrial effects.

A number of authors have examined the complex interactions between pH, Al, and Ca 2+ that must

be considered when attempting to determine the effects of acidification on both aquatic and terrestrial

biota (e.g., Mount et al., 1988; Ingersoll et al., 1990b; Wood et al., 1990). Calcium concentration

significantly affects the distribution of species and their ability to survive in acidified environments.

Aluminum, leached by acid precipitation from soils in the watershed, complicates the response

considerably because some forms of Al are highly toxic to both aquatic and terrestrial species. Aluminum

and hydrogen ions interact both synergistically and antagonistically depending on conditions (Havas,

1985; Rosseland and Staurnes, 1994). In the presence of naturally occurring organic acids, Al toxicity can

be reduced or eliminated. A number of authors have examined the complex interactions between pH, Al,

and Ca2+ that must be considered when attempting to determine the effects of acidification on both

aquatic and terrestrial biota (e.g., Mount et al., 1988; Ingersoll et al., 1990a; Wood et al., 1990).

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# B.4.1. Types of Effects of Acidification on Biota

Ecological effects occur at four levels of biological organization: (1) the individual, (2) the

population, comprised of many individuals, (3) the biological community, composed of many species,

(Billings, 1978), and (4) the ecosystem. Several metrics have been developed to describe the effects of

acidification at each of these levels of organization. For the individual, effects are assessed in terms of

sublethal effects on condition. At the population level, effects are measured by changes in the population

of a certain species. At the community level, species richness and community structure can be used to

evaluate effects, and at the ecosystem level, changes in nutrient cycling and ecosystem processes are

assessed. Most of these indices have been applied primarily to aquatic ecosystems. Each is discussed

below.

Baker et al. (1990a) conducted a rigorous review of the effects of acidification on aquatic biota for

the 1990 NAPAP State of Science/Technology reports. They evaluated hundreds of laboratory, in situ

bioassay, field surveys, whole-system field experiments, and smaller mesocosm studies on the effects of

acidification on aquatic biota. Their 381–page report is the most exhaustive source summarizing the

aquatic biological effects of acidification from acidic deposition. The summaries provided here in sections

B.4 and B.6 rely heavily on this source.

In Shenandoah National Park, a statistically robust relationship between acid-base status of streams

and fish species richness was documented. The three-year Fish in Sensitive Habitats (FISH) study of

stream acidification in Shenandoah National Park demonstrated negative effects on fish from both chronic

and episodic acidification (Bulger et al., 1999). Biological differences in low- versus high-ANC streams

included species richness, population density, condition factor, age, size, and field bioassay survival. Of

particular note was that both episodic and chronic mortality occurred in young brook trout exposed in a

low-ANC stream, but not in a high-ANC stream (MacAvoy and Bulger, 1995), and that blacknose dace

( Rhinichthys atratulus ) in low-ANC streams were in poor condition relative to blacknose dace in higherANC streams (Dennis et al., 1995; Dennis and Bulger, 1995).



## B.4.1.1. Individual Condition Factor

Relatively little is known about changes in the condition of fish or other aquatic biota resulting

from acidification. It is expected that sublethal effects will occur in acid-sensitive species well before the

species is eliminated from a particular lake, stream, or terrestrial habitat. For that reason, loss of an acidsensitive species is not necessarily an ideal indicator of acid stress. Clearly, stress begins to occur prior to

species elimination. Sublethal effects are more difficult to quantify, but are nevertheless important.

Condition factor is one measure of sublethal effect that has been used to quantify effects of

acidification on fish. Condition factor is an index to describe the relationship between fish weight and

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34 length. Expressed as fish weight/length 3 , multiplied by a scaling constant, this index reflects potential

depletion of stored energy reserves (Everhart and Youngs, 1981; Goede and Barton, 1990; Dennis and

Bulger, 1995). Condition factor is interpreted as depletion of energy resources such as stored liver

glycogen and body fat (Goede and Barton, 1990). Fish with higher condition factor are more robust than

fish having low condition factor.

Field studies have shown lower condition factor in fish found in more acidic streams (Dennis and

Bulger, 1995). Condition factor has been developed and applied mainly for blacknose dace. This species

is widely distributed in Appalachian Mountain streams and is moderately tolerant of low pH and ANC,

relative to other fish species in the region. However, the concept is probably applicable to other species as

well. Condition factor may be a useful metric for many species in aquatic ecosystems that are only

marginally affected by acidification.

Bulger et al. (1999) observed a positive relationship between condition factor and pH in streams in

Shenandoah National Park (Figure B-15). Dennis and Bulger (1995) found a reduction in the condition

factor for blacknose dace in waters near pH 6.0. The four populations shown in Figure B-15 with the

lowest condition factor have mean habitat pH values within or below the range of critical pH values at

which Baker and Christensen (1991) estimated that negative population effects for blacknose dace are

likely for the species. The mean length-adjusted condition factor of fish from the study stream with the

lowest ANC was about 20% lower than that of the fish in best condition. Comparisons with the work of

Schofield and Driscoll (Schofield and Driscoll, 1987) and Kretser et al. (1989) suggest that pH in the lowpH Shenandoah National Park streams is near or below the limit of occurrence for blacknose dace

populations in the Adirondack region of New York (Sullivan et al., 2003).

Chronic sublethal stress caused by pH below about 6.0 may have serious effects on a variety of

wild fish populations. There is an energy cost in maintaining physiological homeostasis; the calories used

to respond to stress are a part of the fish’s total energy budget and are unavailable for other functions,

such as growth and reproduction (Schreck, 1981, 1982; Wedemeyer et al., 1990).

Observed differences in condition factor may occur because maintenance of internal chemistry in

the more acidic streams would require energy that otherwise would be available for growth and weight

gain (Dennis and Bulger, 1999; Sullivan et al., 2003). The energy costs to fish for active ionoosmoregulation can be substantial (Farmer and Beamish, 1969; Bulger, 1986). Because of the steep

gradient in Na + and Cl − concentrations between fish blood and freshwater, there is constant diffusional

loss of these ions, that must be replaced by energy-requiring active transport. Low pH increases the rate

of passive loss of blood electrolytes (especially Na + and Cl

− ), and Al elevates losses of Na + and Cl − above

the levels that occur due to acid stress alone (Wood, 1989).

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7 Figure B­15. Length­adjusted condition factor (K), a measure of body size in blacknose dace

( Rhinichthys atratulus ) compared with mean stream pH among 11 populations (n = 442) in

Shenandoah National Park. Values of pH are means based on quarterly measurements, 1991–94; K

was measured in 1994. The regression analysis showed a highly significant relationship (p < 0.001)

between mean stream pH and body size, such that fish from acidified streams were less robust than

fish from circumneutral streams.

Source: Bulger et al. (1999).

It is also possible that the loss of sensitive individuals or early life stages within species may reduce

competition for food among the survivors, resulting in better growth rates, survival, or condition.

Similarly, competitive release (increase in growth or abundance subsequent to removal of a competitor)

may result from the loss of a sensitive species, with positive effects on the density, growth, or survival of

competitor population(s) of other species (Baker et al., 1990b). However, in some cases where

acidification continued, transient positive effects on size of surviving fish were shortly followed by

extirpation (Bulger et al., 1993).

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29 Acid stress is at least partly responsible for the lower condition of blacknose dace populations in

Shenandoah National Park, though reduced access to food or lower food quality (Baker et al., 1990b),

either resulting from the nature of soft water streams or exacerbated by acidification, cannot be ruled out.

Primary productivity is low in headwater streams and lower still in soft water headwaters, which are more

likely to be acidified. Production of invertebrates is likely to be low in such streams as well (Wallace

et al., 1992). Thus, lower food availability cannot be discounted as a potential contributor to lowered

condition in Shenandoah National Park blacknose dace populations in low-pH streams. Nevertheless,

reduced growth rates have been attributed to acid stress in a number of other fish species, including

Atlantic salmon ( Salmo salar ), Chinook salmon ( Oncorhynchus tshawytscha ), lake trout ( Salvelinus

namaycush ), rainbow trout ( Oncorhynchus mykiss ), brook trout, brown trout ( Salmo trutta ), and Arctic

char ( Salvelinus alpines ).



## B.4.1.2. Species Composition

Species composition refers to the mix of species that are represented in a particular ecosystem.

Acidification alters species composition in aquatic ecosystems. There are a number of species common to

many oligotrophic waters that are sensitive to acidic deposition and that 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, but

little or no change in total community abundance or biomass. The effects of acidification are continuous,

with more species being affected at higher degrees of acidification. Therefore, the degree of alteration of

surface water biological community composition increases as surface waters become more acidic. There

is a consistent pattern of lower community diversity with increased acidification.



## B.4.1.3. Taxonomic Richness

Taxonomic richness is a metric that is commonly used to quantify the effects of an environmental

stress such as acidification or eutrophication. The richness metric can be applied at various taxonomic

levels. For example, the number of fish species can be used as an index of acidification (cf. Bulger et al.,

1999). Similarly, acidification effects on aquatic insects can be evaluated on the basis of the number of

families or genera of mayflies (order Ephemeroptera) (Sullivan et al., 2003).

Acidification results in the loss of acid-sensitive species, with more species lost with higher

degrees of acidification. A direct outcome of population loss caused by acidification is a decline in species

richness (the total number of species in a stream or lake). This is a highly predictable outcome of regional

acidification, although the pattern and rate of species loss varies from region to region.

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34 Decreases in ANC and pH and increases in Al i concentration contribute to declines in species

richness and abundance of zooplankton, macroinvertebrates, and fish (Schindler et al., 1985; Keller and

Gunn, 1995). Species richness is positively correlated with pH and ANC (Rago and Wiener, 1986; Kretser

et al., 1989) because of the elimination of acid-sensitive species (Schindler et al., 1985). Knowledge of

the spatial distribution of pH and other water quality variables is necessary to explain the presence or

absence of species within heterogeneous environments. Organisms that are mobile and can sense the pH

of their environment can move to areas (called refugia) that have more favorable water chemistry.

Although some species are favored by increased acidity, species diversity generally decreases as surface

water acidity increases.

Decreases in species richness have been observed for all major trophic groups of aquatic organisms

(Baker et al., 1990a). Baker et al. (1990a) discussed 10 selected studies that documented this

phenomenon, with sample sizes ranging from 12 to nearly 3,000 lakes and streams analyzed per study.

Lake and stream size can be an important complicating factor in interpreting species richness data.

Larger lakes and streams in larger watersheds would generally be expected to contain more species than

smaller lakes or streams in smaller watersheds, irrespective of acid-base chemistry. Nevertheless, when

adjusted for lake size, lakes with pH less than approximately 6.0 contain significantly fewer species than

lakes with pH above 6.0 (Figure B-16) (Frenette et al., 1986; Rago and Wiener, 1986; (Schofield and

Driscoll, 1987); Matuszek and Beggs, 1988).

Studies in the Adirondack Mountains demonstrated the effect of acidification on species richness;

of the 53 fish species recorded in Adirondack lakes by the ALSC, about half (26 species) were absent

from lakes with pH below 6.0. Those 26 species included 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), plus ecologically

important minnows that serve as forage for sport fish. Fully 346 of 1,469 lakes surveyed by the ALSC

supported no fish at all at the time of the survey. These lakes were significantly lower in pH, dissolved

Ca 2+

, and ANC, and had higher concentrations of Al i than lakes hosting one or more species of fish

(Gallagher and Baker, 1990). Among lakes with fish, there was an unambiguous relationship between the

number of fish species and lake pH, ranging from about one species per lake for lakes having pH less than

4.5 to about six species per lake for lakes having pH > 6.5 (Kretser et al., 1989; Driscoll et al., 2001a).

Figure B-17 shows the mean number of fish species for pH classes from 4.0 to 8.0 in lakes in the

Adirondacks. It is important to note, however, that there are many possible causes of fish absence in

addition to acidification. These include lack of suitable habitat (especially for spawning), winter kill,

blocked access, etc.

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9 Source: Matuszek and Beggs (1988).



### Figure B­16. Mean residual number of species per lake for lakes in Ontario, by pH interval. The

residual number of species for a lake is the deviation of the observed number from the number

predicted by lake area.

Sullivan et al. (2006a) developed a relationship between fish species richness and ANC class for

Adirondack lakes. Fish species richness observations, as a function of ANC (μeq/L) class, were fit to a

logistic relationship by a non-linear regression analysis. Under chronically acidic conditions (summer

index or annual average ANC < 0 μeq/L), Adirondack lakes are generally fishless. There was a marked

increase in mean species richness with increases in ANC up to values of approximately 100 μeq/L. The

asymptote for the fish species equation was 5.7 species. This analysis suggests that there could be loss of

fish species with decreases in ANC below approximately 100 μeq/L. It does not account, however, for the

possibility that lakes having higher ANC are often larger, and therefore support more fish species because

of increased habitat diversity and complexity.

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5 Figure B­17. Number of fish species per lake or stream versus acidity statues, expressed either as

pH or ANC. (A) Adirondack lakes (Sullivan et al., 2006a).; (B) streams in Shenandoah National Park

(Bulger, 1999). The data for the Adirondacks are presented as mean and range of species richness

within 10 μeq/L ANC categories, based on data collected by the Adirondack Lakes Survey

Corporation.

As an element of the FISH project (Bulger, 1999), numbers of fish species were compared among

13 Shenandoah National Park streams spanning a range of pH and ANC conditions. There was a highly

significant (p < 0.0001) relationship between stream acid-base status (during the 7–year period of record)

and fish species richness among the 13 streams. The streams with the lowest ANC hosted the fewest

species (see figure B-18).

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11 Source: Redrawn from Bulger et al. (1999).



### Figure B­18. Number of fish species among 13 streams in Shenandoah National Park. Values of

ANC are means based on quarterly measurements, 1987–94. The regression analysis shoed a

highly significant relationship (p < 0.0001) between mean stream ANC and number of fish species.

Streams having ANC consistently <75 μeq/L had three or fewer species.

Median stream ANC values and watershed areas are shown in Table B-11 for the 14 streams used

by Bulger et al. (1999) to develop the relationship between ANC and fish species richness shown in

Figure B-18. Despite the overall similarities, these study streams vary in watershed area by a factor of 10.

The streams that have larger watershed areas generally have more fish species than the streams having

smaller watershed areas. All of the “rivers” have watersheds larger than 10 km 2 and ANC higher than

75 μeq/L. In contrast, the majority (but not all) of the “runs” have watershed area smaller than 10 km 2 and

ANC less than 20 μeq/L. All of the streams that have watershed areas smaller than 10 km 2 have three or

fewer known species of fish present. All of the streams having larger watersheds (>10 km 2 ) have three or

more known fish species; seven of nine have five or more species; and the average number of fish species

is six. There is no clear distinction between river and run, but it is clear that as small streams in

Shenandoah National Park combine and flow into larger streams and eventually to rivers, two things

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34 happen: acid-sensitivity generally declines, and habitat generally becomes suitable for additional fish

species (Sullivan et al., 2003).

South of Shenandoah National Park the effects of surface water acidification on fish species

richness have been studied in some detail in the St. Marys River in Virginia. Fish species richness was

closely associated with surface water acid-base chemistry. Bugas et al. (1999) conducted electrofishing in

the St. Marys River in 1976, and every 2 years from 1986 through 1998. Systemic stream acidification

occurred during the study period. Sampling occurred at six sites between the downstream end of the St.

Marys Wilderness and the headwaters over a distance of about 8 km. The number of fish species in the St.

Marys River within the wilderness declined from 12 in 1976 to 4 in 1998. Three of the four species

present in 1998 (brook trout, blacknose dace, fantail darter [ Etheostoma flabellare ]) are tolerant of low

pH and are typically the only fish species present in streams having similar levels of acidity in

Shenandoah National Park, which is also located in Virginia (Bulger et al., 1999). Bugas et al. (1999)

reported that successful brook trout reproduction in the St. Marys River occurred only 1 year out of 4

during the period 1995 through 1998. Eight of the fish species recorded in one or more early years have

not been observed in more recent years. Several, including blacknose dace, rainbow trout, and torrent

sucker ( Thoburnia rhothoeca ), showed a pattern of being progressively restricted over time to lower river

reaches, which generally have higher ANC. The number of fish species decreased with decreasing

minimum ANC, from nine species at ANC of about 160 μeq/L to one to three species at ANC near 0. The

best fit regression line suggested, on average, a loss of one species for every 21 μeq/L decline in annual

minimum recorded ANC value.

Dynamic water chemistry model projections have been combined with biological dose-response

relationships to estimate declines in fish species richness with acidification. A relationship derived from

the data in Figure B-18 was used by Sullivan et al. (2003) with stream ANC values predicted by the

MAGIC model to provide estimates of the expected number of fish species in each of the modeled

streams for the past, present, and future chemical conditions simulated for each stream. The coupled

geochemical and biological model predictions were evaluated by comparing the predicted species

richness in each of the 13 streams with the observed number of species that occur in each stream. The

agreement between predicted and observed species numbers was good, with a root mean squared error

(RMSE) in predicted number of species across the 13 streams of 1.2 species. The average error was 0.3

species, indicating that the coupled models were unbiased in their predictions. Model reconstructions of

past species richness in the streams suggested that historical loss of species had been greatest in the

streams located on the most sensitive geological class (siliciclastic). The average number of species lost

from streams on the three bedrock types examined were estimated as: 1.6 species on siliciclastic bedrock;

0.4 species on granitic bedrock; and 0.4 species on basaltic bedrock. In the case of the siliciclastic

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32 streams, the projected past changes were much larger than the average error and RMSE of the coupled

models, suggesting that the projections were reasonably robust.

It appears that fish species richness is controlled by multiple factors, of which both acidification

and watershed area can be important. Watershed area might be important in this context because smaller

watersheds may contain smaller streams having less diversity of habitat, more pronounced effects on fish

from high-flow periods, or lower food availability. Such issues interact with other stresses, including

acidification, to determine overall habitat suitability.

For Shenandoah National Park, Bulger et al. (1999) concluded that the most important cause of the

observed decline in species richness with decreasing ANC was acid stress associated with acidification.

However, an additional causal factor may have been the decrease in the number of available aquatic

niches when moving from downstream locations (which are seldom low in pH and ANC) to upstream

locations (which are often low in pH and ANC in this region; Sullivan et al. 2003). The relative

importance of this latter factor, compared with the importance of acid stress, in determining this

relationship is unknown.

In the Adirondack region, Driscoll et al. (2001b) concluded that high-elevation lakes are more

likely to be fishless than larger lakes at low elevation (Gallagher and Baker, 1990) because they have poor

access for fish immigration, poor fish spawning substrate, or low pH, or they may be susceptible to

periodic winter kills. Nevertheless, small, high-elevation Adirondack lakes with fish also had significantly

higher pH compared with fishless lakes; acidity is likely to play an important role in the absences of fish

from such lakes (Driscoll et al., 2001b).



## B.4.1.4. Community Structure

Ecosystem response to pollutant deposition is a direct function of the ecosystem’s ability to

ameliorate resulting changes in individual species (Strickland et al., 1993). In order to determine

ecosystem response and the possible effects on community structure, species responses must be scaled in

both time and space and be propagated from the individual to the more complex levels of community

interaction within an ecosystem.

Individuals within a population vary in their ability to withstand a stress. The response of each

individual is based on its genetic constitution (genotype), its stage of growth at time of exposure to the

stress, and the microhabitat in which it lives (Levlin, 1998). The range within which individuals in the

population can exist and function determines the ability of the population to survive when exposed to a

chronic stress. Those individuals that are able to cope with the stress survive and reproduce. The same

kinds of pressures act on populations of different species. Competition among species results in

community change over time and eventually produces ecosystems composed of populations of species

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28 that have the capability to tolerate the stress (Guderian et al., 1985; Rapport and Whitford, 1999; U.S.

Environmental Protection Agency, 2004).

Work conducted on the biological effects of acidification has largely been focused on the response

of fish, especially salmonids (trout and salmon). This focus tends to be driven by the value people place

on fish and fishing, rather than any ecological consideration. Other vertebrate, invertebrate, plant, and

algal communities are also sensitive to acidification. In general, higher order trophic groups are more

susceptible to acidification. Thus, in terms of changes in community structure in response to aquatic

acidification, the general progression of sensitivity is as follows: fish > invertebrates (benthic and

zooplankton) > algae > microbes (Baker et al., 1990a). Population-level fish response to acidification is

primarily through recruitment failure, a result of increased mortality of early life stages or indirect effects

through the food chain (loss of prey species). Al 2+

i , pH, and Ca have been identified as the variables most

likely to have the greatest influence on fish community structure.



## B.4.1.5. Indices of Ecological Effects

The most widely used index of acidification effect is the Acid Stress Index (ASI) developed by

Baker et al. (1990a). This index uses fish bioassay survival data fitted to a maximum likelihood logistic

regression model as a function of exposure to pH, Al, and Ca 2+ to predict the probability of fish survival

expressed as a percent mortality. This approach can aid in determination of effects on species composition

by predicting the probability of occurrence of species of varying acid sensitivity. Separate ASI models

were developed for tolerant, intermediate, and sensitive fish species. Approximate ASI reference levels

were established for various species based on logistic regression of fish presence as a function of the

sensitive, intermediate, and tolerant ASI values for brown bullhead ( Ameiurus nebulosus ), brook trout,

lake trout, and common shiner ( Luxilus cornutus ). They are presented in Table B-12.

The ASI was deemed a useful index of stress by Baker et al. (1990a), even though the relationships

between ASIs and fish population status could not be quantified precisely because of confounding factors.

Such factors included the abundance and types of food species, competitors and predators present,

variations in habitat quality, and density-dependent effects on fecundity.



# B.4.2. Timing of Effects

B.4.2.1. Life Stage Differences in Sensitivity

Episodic and chronic changes in the chemistry of surface waters can have different effects on

aquatic organisms and populations depending on species and the life history stages present. More is

known about the sensitivity to acidification of the life stages of fish than is known for other aquatic

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19 Source: Sullivan et al. (Sullivan, 2003).



### Figure B­19. Life stages of brook trout. organisms. In general, early life stages are more sensitive to acidic conditions than the young-of-the-year,

yearlings, and adults (Baker and Schofield, 1985; Johnson et al., 1987; Baker et al., 1990a). Also, small

fish, especially swim-up fry, are probably less mobile and less able to avoid exposure to adverse chemical

conditions than the relatively larger adults (Baker et al., 1996).

There are a number of issues of acidification timing that are important to determination of the

extent and magnitude of effects. One important issue concerns the timing of acidity exposure relative to

life stage. For example, adult fish are generally more tolerant of acidity than early life stages such as eggs,

fry and juveniles. There could be substantial differences in effect based on small differences in age or

timing of exposure to acidity. No definite pattern was observed by Baker et al. (1990a) across all studies

or species. This may reflect either differences in the test conditions or actual differences among species.

The presence of early life stages of brook trout, which are most sensitive to adverse effects from

acidification (Bulger et al., 2000), varies with season. For example, the most acid-sensitive stages of

brook trout development are present in Virginia streams throughout the cold season in general, and the

winter in particular (Source: Sullivan et al. (Sullivan, 2003).

Figure B-19).

The p rocesses of oogenesis and fertilization in fish and aquatic invertebrates are especially

sensitive to low pH (Muniz, 1991; Havas et al., 1995). In fish, this sensitivity is most likely due to

adverse effects on the female spawner. For instance, Beamish et al. (1976) reported that reduced serum

and plasma Ca 2+ in female fish in acidified Canadian lakes lead to a higher probability for failure in

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35 producing viable eggs. A depletion of Ca 2+ from bone and increased numbers of females with unshed eggs

have also been linked to sensitivity at this life stage (cf. Rosseland, 1986; Muniz, 1991).

After fertilization, the embryo seems to be susceptible to acidic waters throughout the whole period

of development. The periods shortly after fertilization and prior to hatching seem to be most critical

(Rosseland, 1986). The susceptibility of the embryo can be the result of direct exposure to elevated H +

concentrations and also to the toxic effects of Al i at intermediate pH-values. Low pH in the surrounding

water also results in pH-depression inside the egg, leading to either a prolongation of the hatching or to a

reduced hatching success (Rosseland, 1986). Eggs lying in gravel on stream and lake beds are to some

extent protected from exposure to rapid changes in pH (Gunn and Keller, 1984b; Lacroix, 1985b).

Nevertheless they can experience high mortality during periods of acid runoff, such as snowmelt (Gunn

and Keller, 1984a).

In fish, emergent alevins show susceptibility to the adverse effects of Al i and H + that increases with

age (Baker and Schofield, 1982; Wood and McDonald, 1982). Rosseland (1986) indicated that this

increasing sensitivity results from changes that take place in the respiratory system. Shortly after hatch,

alevins still respire through their skin but gradually gills become the primary organ of gas and ion

exchange. Gills are the locus for interference of H + and Al i with iono-regulatory exchange. Woodward

et al. (1989) exposed cutthroat trout ( Oncorhynchus clarki ) from the Snake River in Wyoming to pH

depressions from pH 4.5 to 6.5 in the laboratory. Fertilized egg, eyed embryo, alevin, and swim-up larval

stages were exposed to low pH for a period of seven days. Each life stage was monitored for mortality,

growth, and development for 40 days after hatching. Reductions in pH from 6.5 to 6.0 in low-Ca 2+ water

(70 μeq/L) did not affect survival, but reduced growth of swim-up larvae. The eggs, alevin, and swim-up

larval stages showed significantly higher mortality at pH 4.5 than at pH 6.5. Mortality was also higher at

pH 5.0 than at pH 6.5, but only statistically higher for eggs.

Woodward (1991) exposed greenback cutthroat trout ( Oncorhynchus clarki stomias ) in the

laboratory to 7–day pH depressions. Low-Ca 2+ (65 μeq/L) water at pH 6.5 was experimentally reduced to

pH values of 6.0, 5.5, 5.0, and 4.5. Four life stages were exposed: freshly fertilized egg, eyed embryo,

alevin, and swim-up larva. Alevin survival was reduced at pH 5.0, whereas survival of eggs, embryos, and

swim-up larvae was reduced at pH 4.5. Swim-up larvae showed feeding inhibitions at pH 4.5. The authors

concluded that the threshold for effects of acidity on greenback cutthroat trout in the absence of Al was

pH 5.0 (Woodward, 1991).

Yellowstone cutthroat trout ( O. c. bouveri ) were exposed to 7–day pH depressions by Farag et al.

(1993). Of the four life stages studied, eggs were most sensitive to low pH. Eggs exposed for seven days

to pH 5.0 test water showed a statistically significant reduction in survival compared with eggs exposed

for seven days to pH 6.5 water. Survival of alevin and swim-up larvae were significantly reduced from

near 100% at pH 6.5 to near 0% at pH 4.5. Intermediate pH values (6.0, 5.5) in all cases showed reduced

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32 survival compared with the control (6.5) but not by statistically significant amounts. Eyed embryos were

not sensitive to any of the exposures.

According to Bulger et al. (1999), adult brook trout in Shenandoah National Park streams are more

tolerant of acidity than are adult blacknose dace. For both species, the early life stages are more sensitive

than the adults, and brook trout young are actually more sensitive than blacknose dace adults (Bulger

et al., 1999). Blacknose dace spawn during summer and the eggs and very young fry are therefore

somewhat protected from the most acidic episodes, which typically occur during cold-season, high-flow

conditions.



## B.4.2.2. Biological Effects of Episodes

Episodic decreases in pH and ANC can produce chemical conditions in lakes, and especially in

streams, that are as harmful to biota as chronic acidification (Baker et al., 1996). Adverse effects on biota

occur particularly when changes involve pH, Al i , or Ca 2+ (Baker et al., 1990a). Aquatic biota vary greatly

in their sensitivity to episodic decreases in pH and increases in Al in waters having low Ca 2+

i

concentration. However, Baker et al. (1990a) concluded that episodes are most likely to affect biota if the

episode occurs in waters with pre-episode pH above 5.5 and minimum pH during the episode of less than

5.0.

Results from the ERP demonstrated that episodic acidification can have long-term adverse effects

on fish populations. Streams with suitable chemistry during low flow, but low pH and high Al i levels

during high flow, had substantially lower numbers and biomass of brook trout than in non-acidic streams

(Wigington et al., 1996). Streams having acidic episodes showed significant mortality of fish.

Some brook trout avoided exposure to stressful chemical conditions during episodes by moving

downstream or into areas with higher pH and lower Al i

. This movement of brook trout only partially

mitigated the adverse effects of episodic acidification, however, and was not sufficient to sustain fish

biomass or species composition at levels that would be expected in the absence of acidic episodes. Just as

spatially heterogeneous environments or refugia enable some species to survive in otherwise unfavorable

conditions, temporal heterogeneity often has the opposite effect. These findings suggest that stream

assessments based solely on chemical measurements during low-flow conditions will not accurately

predict the status of fish populations and communities in small mountain streams unless some adjustment

is made for episodic processes (Baker et al., 1990a, 1996; Wigington et al., 1996; (Sullivan, 2000).

In Shenandoah National Park, MacAvoy and Bulger (1995) used multiple bioassays over 3 years in

one of the low-ANC streams as part of the FISH project to determine the effect of stream baseflow and

acid episode stream chemistry on the survival of brook trout eggs and fry. Simultaneous bioassays took

place in mid- and higher-ANC reference streams. Acid episodes (with associated low pH and elevated Al i

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35 concentrations, and high streamwater discharge) induced rapid mortality in the low-ANC stream, while

the test fish in the higher-ANC stream survived (Bulger et al., 1999).

In the West, it has also been shown that native trout are sensitive to short-term increases in acidity.

For example, Woodward et al. (1989) exposed native western cutthroat trout to pH depressions (pH 4.5 to

6.5) in the laboratory. Reductions in pH from 6.5 to 6.0 in low-Ca 2+ water (70 μeq/L) did not affect

survival, but did reduce growth of swim-up larvae. Eggs, alevins, and swim-up larvae showed

significantly higher mortality at pH 4.5 as compared to pH 6.5. Mortality was also somewhat higher at pH

5.0, but only statistically higher for eggs. Some species of aquatic biota in western aquatic ecosystems

have been shown to be somewhat more sensitive to pH and ANC change than are cutthroat trout (Baker

et al., 1990a).

Multiple logistic regression models were used by Van Sickle et al. (1996) to relate fish bioassay

mortality rates to summary statistics of time-varying stream chemistry over the 20–day bioassay periods.

Higher mortality of all three test fish species (brook trout, dace, sculpin [ Cottus spp.]) during the in situ

bioassays was clearly associated with increased Al i . In addition, individual bioassays conducted during

chronically or episodically acidified conditions had higher median mortality than did those during non

acidic conditions, but no mortality differences were detected between chronically acidic and episodically

acidic conditions. Time-weighted median Al i was the best single predictor of 20–day mortality for both

brook trout and sculpin, whereas the number of days with Al i > 200 μg/L provided the best prediction of

blacknose dace mortality.

In the Northeast, Baker et al. (1996) studied the effects of episodic acidification on fish in 13 small

streams in the Adirondack and Catskill Mountains of New York and the Northern Appalachian Plateau in

Pennsylvania. They conducted in situ bioassays with brook trout and blacknose dace, mottled sculpin

( Cottus bairdi ) or slimy sculpin ( Cottus cognatus ) depending on the region, to measure direct toxicity.

Movements of brook trout individuals in relation to stream chemistry were tracked using radiotelemetry.

Electrofishing surveys assessed fish community status and the abundance and biomass of brook trout in

each stream. Streams with suitable conditions during low flow, but moderate-to-severe episodic

acidification during high flow, had higher fish mortality in bioassays, higher net downstream movement

of brook trout during events, and lower brook abundance and biomass compared to nonacidic streams.

These streams lacked the more acid-sensitive fish species (blacknose dace and sculpin). Movement of

trout into refugia (areas with higher pH and lower Al) during episodes partially mitigated the adverse

effects of episodes.

Chemical measurements by ERP during high flow correlated with fish community status. In

general, reduced trout abundance occurred in ERP streams with median high flow pH < 5.0 and Al i > 100

to 200 μg/L. Acid -sensitive fish species were absent from streams with median high flow pH < 5.2 and

Al i > 100 μg/L. More recently, Baldigo et al. (2007) found that mortality of brook trout young of the year

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29 occurred at concentrations as low as 54 μg/L .Al i was the single best predictor of fish mortality in ERP

bioassays (Van Sickle et al., 1996) and has been identified as an important toxic factor in other bioassays

and field studies (Mount et al., 1988; Ingersoll et al., 1990b; Rosseland et al., 1990). The relationships

between pH and Al i or ANC and Al i vary among streams (Wigington et al., 1996), and therefore

predictions of potential effects on fish based solely on pH or ANC may be misleading. High Al i

concentrations during episodes are probably the dominant cause of adverse effects on fish during episodic

acidity events.



### Biological Effects of Chronic Acidification

Changes in surface water acid-base chemistry, including pH, ANC, Al , and Ca 2+

i , can affect instream and in-lake biota. Adverse biological effects may be seen at pH less than about 6.0 to 6.5 and Al i

greater than about 30 to 50 μg/L (1 to 2 μM). It tends to increase with decreasing pH, and reaches

potentially toxic concentrations (> ~2 μM) in surface drainage waters having pH less than about 5.5.

Effects vary substantially by organism, life stage, and the concentration of DOC. Inorganic Al in solution

is also toxic to plants.

Calcium can ameliorate the toxic effects of acidity and Al on biota. Most organisms can tolerate

lower pH and higher Al i at higher Ca 2+ concentrations, but in natural environments, elevated

concentrations of Al i are only found in Ca 2+ -depleted systems. This effect is most important at low Ca 2+

levels. Overall biological effects noted with decreasing pH are described in Table B-13 (Baker et al.,

1990a). The organisms most likely to respond to such changes in water chemistry include fish, aquatic

insects, zooplankton, and diatoms. In some cases, amphibians are also important sensitive biological

receptors. Most available data are for fish response.

In most stream or lake survey areas, direct quantification of biological responses to surface water

acidification is not possible, given the scarcity or absence of biological long-term monitoring and doseresponse data. Few biological long-term monitoring studies have been conducted. Much of the available

in situ dose-response data have been generated from studies of streams in Virginia and Pennsylvania and

lakes in New York. Data with which to evaluate acidification relationships have been scarce in most other

regions.



### Lakes

Fish status assessments for the eastern and upper midwestern U.S. were conducted by Baker et al.

(1990a), by region, using a variety of assessment methods. For the northeast region, two water chemistry

models were linked to fish response models: the Integrated Lake-Watershed Acidification Study (ILWAS)

model and MAGIC. For the Adirondack subregion, three process models were used: ILWAS, MAGIC,

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33 and Regional MAGIC. For other areas in the eastern U.S. and for the Upper Midwest, analysis of fish

status was limited to application of the sensitive, intermediate, and tolerant toxicity models.

Assessment results reported by Baker et al. (1991a) for the Adirondack region are presented in

Table B-14 showing results based on the ASI. Table B-15 shows the estimated percentage of Adirondack

lakes with acid-base chemistry unsuitable for fish population survival according to various assessment

models based on responses for brook trout, lake trout, and common shiner. Assessment results for the

Northeast region are presented in Table 16 and Table 17.

In acid-sensitive lakes in the western U.S., the focus is often mainly on native cutthroat trout. It is

important to note, however, that many high-elevation western lakes and streams were historically fishless.

The top predators in such aquatic ecosystems were often amphibians or crustaceans. Thus, even though

cutthroat trout might be considered native to the region, they are not necessarily native to a particular lake

or stream.



### Streams

In streams, the major organisms of concern with respect to water acidification are fish, amphibians,

benthic macroinvertebrates, and periphyton (attached algae). All of these groups have shown adverse

effects in response to acidification (see Annex B-6). Most available data are for fish and aquatic insects,

mainly in the southeastern U.S. Streams affected by acidic deposition tend to occur at high elevation.

They are often high-gradient and flow through base-poor geology.

Baker et al. (1991a) presented assessment results for the mid-Appalachian region as the distribution

(percent) of NSS l ower node, upper node, and total streams classified in various ASI values (Table B-18)

(Baker et al., 1991a). Most of the streams were classified in the lowest ASI category (Table B-18).

Assessment results for the interior Southeast region were similar (Table B-19).

Some fish response research has also been conducted for streams in the Catskill Mountains. Baker

and Christensen (1991) estimated that the fish species found in the Neversink River Basin in the Catskill

Mountains are typically lost when pH decreases to the range of 4.7 to 5.2 (brook trout), 5.5 to 5.9 (slimy

sculpin), 4.7 to 5.7 (brown trout), 5.6 to 6.2 (blacknose dace), and 4.9 to 5.3 (Atlantic salmon).

The Shenandoah National Park FISH Project evaluated the effects of streamwater acidification on

fish populations and communities in streams in Shenandoah National Park. Fish species richness,

population density, condition factor, age distribution, size, and bioassay survival were all lower in streams

having low-ANC compared to intermediate-ANC and high-ANC streams (Bulger et al., 1995; Dennis

et al., 1995; Dennis and Bulger, 1995; MacAvoy and Bulger, 1995).

Bulger et al. (2000) developed model-based projections using the MAGIC model to evaluate the

potential effect of reductions in S d eposition of 40% and 70% from 1991 levels using data from VTSSS

and SWAS. Projections were based on four brook trout stream categories: Suitable, ANC > 50 μeq/L;

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35 Indeterminate, ANC 20 to 50 μeq/L; Marginal, ANC 0 to 20 μeq/L; and Unsuitable, ANC < 0 μeq/L.

Three scenarios of future acidic deposition were modeled: constant deposition at 1991 levels, 40%

reduction from 1991 deposition levels, and 70% reduction from 1991 deposition levels. Based on

observed 1991 ANC values, approximately 30% of all trout streams in Virginia were marginal or

unsuitable for brook trout because they were either episodically (24%) or chronically (6%) acidic. In

addition, another 20% of the streams were classified as indeterminate, and brook trout in these streams

may or may not have been affected. Based on the model simulations, 82% of these streams would not

have been acidic prior to the onset of acidic deposition and would likely have been suitable for brook

trout.

The model projections suggested that neither the 40% nor the 70% reductions in acidic deposition

would be expected to increase the number of streams that were suitable for brook trout above the ambient

50%. In fact, the results suggested that a 70% reduction in deposition would be needed in the long-term

just to maintain the number of streams that were considered suitable for brook trout. Because of the length

of time required to restore buffering capacity in watershed soils, most of the marginal or unsuitable

streams were expected to remain marginal or unsuitable for the foreseeable future.

To develop projections of probable past and future responses of aquatic biota to changing S

deposition in Shenandoah National Park, the MAGIC model was coupled by Sullivan et al. (2003) with

several empirical models that linked biological response to past and future model projections of water

quality. Unlike MAGIC, which is a geochemical, process-based model, the biological effects estimates

were based on observed empirical relationships rooted in correlation and expressed as linear relationships.

Correlation does not necessarily imply cause, but an observed pattern of co-variation between variables

does provide a context for analysis of a possible relationship. In this case, the projections did not require

extrapolation and are, therefore, statistically robust. To the extent that the observed empirical relationships

used in the coupled models do in fact reflect the effects of acid stress on aquatic biota, the projections

were also biologically robust.

The geochemical and biological response models also differ in that MAGIC is a dynamic model

and explicitly predicts the time course of changing water quality, whereas the empirical relationships used

for estimating biological response were static. These relationships reflected a point in time (when the

observations were made) and provided no information concerning the dynamics of biological response.

That is, the empirical models predicted a new biological status for a new water chemistry, but gave no

indication of the time required to achieve the biological status once the water quality change had

occurred.

There are thus two considerations that must be kept in mind when interpreting the biological

responses predicted using a combination of process-based and empirical modeling approaches: the

causality of the relationship between water quality and response, and the dynamics of biological response.

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33 With respect to the issue of causality, acidification is a disturbance and disturbance usually lowers species

richness. In turn, loss of species usually lowers ecosystem stability. Biodiversity loss is a predictable and

proven consequence of acidification, and there are abundant examples of this in North America and

Europe (cf. Bulger et al., 2000). With respect to the timing of biological response, it can be variable and

difficult to predict.



## B.4.2.3. Timing of Recovery from Acidification

Lakes and streams show spatial and temporal variability in response to a host of biotic and abiotic

factors. Against this background of variability, it is difficult to detect changes in biological communities

in response to changes in an individual environmental stressor without long-term biological data

(Schindler, 1990; Lancaster et al., 1996). Long-term data sets are rare, and there are few well-documented

instances of temporal changes in biological communities in response to changes in water chemistry.

Regardless, it is known that surface water acidification affects virtually all trophic levels (e.g., Flower and

Battarbee, 1983; Økland and Økland, 1986; Rundle and Hildrew, 1990; St. Louis et al., 1990; Ormerod

and Tyler, 1991; Siminon et al., 1993; Lancaster et al., 1996; (Sullivan, 2000).

Biological recovery can occur only if chemical recovery is sufficient to allow survival and

reproduction of acid-sensitive plants and animals. The time required for biological recovery is uncertain.

For terrestrial ecosystems, it may be decades after soil chemistry is restored because of the long life of

many plant species and the complex interactions of soil, roots, microbes, and soil biota. For aquatic

systems, research suggests that stream macroinvertebrate populations may recover relatively rapidly

(within approximately 3 years), whereas lake populations of zooplankton recover more slowly (Gunn and

Mills, 1998).

The timing of fish recovery is highly uncertain, and probably will depend heavily on dispersal

opportunities. Stocking could accelerate fish population recovery (Driscoll et al., 2001b). Fish populations

have recovered in acidified lakes when the pH and ANC have been raised through liming or reduction of

acidic deposition (Hultberg and Andersson, 1982; Beggs and Gunn, 1986; Dillon et al., 1986; Keller and

Pitblado, 1986; Raddum et al., 1986; Gunn et al., 1988; Kelso and Jeffries, 1988).

Studies in Canada have improved understanding of the feasibility and complexity of biological

recovery in response to chemical recovery from acidification. Biological recovery of previously acidified

lakes is expected to be a slower process than chemical recovery. Sometimes there are other environmental

stresses in addition to acidity, such as metal contamination (Gundersen and Rasmussen, 1995; Havas

et al., 1995; Jackson and Harvey, 1995; McNicol et al., 1995; Yan et al., 1996b). Barriers can be imposed

by water drainage patterns between lakes that hinder re-colonization by some fish species (Jackson and

Harvey, 1995). Predation by non-acid-sensitive fish species can affect the recovery of zooplankton and

macroinvertebrate communities (McNicol et al., 1995). Finally, tributary-spawned fish can be preyed

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34 upon when they move downstream into lakes inhabited by predatory fish and birds (Schofield and

Keleher, 1996).

Changes in surface water chemistry as a direct response to changes in S and N deposition are

difficult to predict. Both chemical and biological effects of changing deposition can lag as the ecosystem

comes into equilibrium with the modified deposition inputs. Soils or wetlands may continue to release S

at a high rate for many years subsequent to a decrease in S deposition. As a result, surface water SO 4 2−

concentrations may decrease in the future as a consequence of deposition changes that have already

occurred. If soil base cations have become depleted, base cation concentrations in some surface waters

could decrease in the future irrespective of any further changes in SO 4 2− concentrations. This would be

expected to contribute to additional acidification.

Studies in the U.S., Canada, and Europe have illustrated the feasibility and complexity of biological

recovery in response to decreased surface water acidity. There is currently no theoretical basis on which to

predict the paths of biological recovery. At some scale, each stream or river is unique. The null hypothesis

is that recovery will proceed in the same fashion as acidification, only backwards. Thus, for example, the

last species lost (the most acid-tolerant) would be the first to return. However, time lags are expected to

differ widely among species and among water bodies. Biological recovery of previously acidified lakes or

streams can lag behind chemical recovery because of such factors as (a) limits on dispersal and

recolonization; (b) barriers imposed by water drainage patterns (Jackson and Harvey, 1995); (c) the

influence of predation (McNicol et al., 1995); and (d) other environmental stresses (Gunn et al., 1995;

Havas et al., 1995; Jackson and Harvey, 1995; McNicol et al., 1995; Yan et al., 1996a,b).

Limitations on dispersal and recolonization can hamper biological recovery from chronic and

episodic acidification. If fish move into refugia areas during low pH and then return, behavioral

avoidance would reduce the overall effect of acidification on fish populations. However, if fish move out

of the stream system in response to sublethal episodes, as suggested by Baker et al. (1996), and do not

return or return in smaller numbers, then the population level effects of episodic acidification would be

greater than predicted based on mortality tests alone.

Stream macroinvertebrate communities are often dominated by immature life stages of flying

insects, such as mayflies, dragonflies, and stoneflies. Such species have rather rapid colonization times,

such that a functional stream macroinvertebrate community may return in only a few years in response to

improved chemistry. However, fish community recovery is expected to be quite variable, depending on

sources of colonists. In streams, fish could be introduced as soon as the water quality becomes suitable or

the macroinvertebrate community becomes established. In streams that had simple fish communities in

the past, a fish community might become rapidly established. It might take decades for complex

communities without species introductions.

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35 The Sudbury region of Ontario, Canada has been important for studying the chemical and

biological effects of S deposition. Mining and smelting of copper-nickel ore began in the 1880s. By the

1950s and 1960s, SO 2 emissions from the mining and smelting operations peaked at over 5,000 tons/day

and extensive acidification of nearby surface waters was documented (Beamish and Harvey, 1972).

Emissions of SO 2 then decreased during the 1970s to less than one-third of the peak values. This region

has been the focus of extensive chemical and biological effects work since the 1980s (Keller, 1992).

Sulfur emission reductions resulted in improved water quality in many lakes (Keller and Pitblado, 1986;

Keller et al., 1986), and some fisheries recovery was also documented (Gunn and Keller, 1990; Keller and

Yan, 1991). Griffiths and Keller (1992) found changes in the occurrence and abundance of benthic

invertebrates that were consistent with a direct effect of reduced lakewater acidity. A more recent

assessment of recovery of ecosystems in Canada provided further evidence of biological recovery, but

also showed that the spatial extent of recovery was limited to lakes that had been severely acidified by the

Sudbury smelter (Jeffries et al., 2003).

Whitepine Lake, located 90 km north of Sudbury, had low pH (5.4) and ANC (1 μeq/L) in 1980

and its fish populations displayed symptoms of acid stress. Acid-tolerant yellow perch ( Perca flavescens )

were abundant, but the more acid-sensitive species lake trout and white sucker ( Catostomus commersoni )

were rare and not reproducing. Fish populations were studied by Gunn and Keller (1990) from 1978

through 1987, and zooplankton were sampled at least monthly during the open-water periods of 1980

through 1988. During the period between 1980 and 1988, pH increased to 5.9 and ANC increased to

11 μeq/L. Young lake trout first reappeared in 1982 and became increasingly abundant throughout the

study. The number of benthic invertebrate taxa increased from 39 in 1982/83 to 72 in 1988, and the

relative abundance of many of the invertebrates found in 1982 changed along with the changes in water

chemistry (Gunn and Keller, 1990). Research at Sudbury clearly documented that chemical recovery of

lakes was possible upon reduced emissions and deposition of S, and also that biological recovery,

involving multiple trophic levels, would soon follow.

Baker et al. (1990a) used field-based models to test the potential for biological recovery. The

models were calibrated from the observed among-lake or among-stream associations between fish status

and the chemical and physical characteristics measured in the surface water. The models were generally

calibrated using chemistry data collected in conjunction with surveys of fish status. It was assumed that

the systems surveyed were at steady state and that the observed status of the fish community was

determined by the observed chemical and physical conditions in the lake or stream. For each species

considered, the current presence or absence of the species was analyzed as a function of the water quality

variables associated with acidification (e.g., pH, Al, Ca 2+ , ANC, and DOC) using maximum likelihood

logistic regression (Reckhow et al., 1987). Models developed from data from the ELS and the ALSC were

calibrated against data from Ontario lakes.

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Source: Baker et al. (1990a).



### Figure B­20. Example model application. Projected changes in the percentage of Adirondack lakes

(Direct/Delayed Response project target population) with acid­base chemistry unsuitable for the

survival of fish populations in the year 2034, versus current simulated conditions, based on

projected changes in water chemistry from the Model of Acidification of Groundwater in

Catchments (MAGIC) and using alternative models of fish response, given a 50% decrease in

deposition or a 30% increase in deposition. (a) brook trout, (b) lake trout, (c) common shiner. The results from the various models were compared to their prediction of the change in the number

of Adirondack lakes with unsuitable acid-base chemistry, given a 50% decrease or a 30% increase in S

deposition relative to the existing conditions. All the models provided similar results (Figure B-20) with

the exception of those that relied on the pCa/pH term to predict fish status. Those models seemed to

overestimate the effect of Ca 2+

, and thus underestimate predicted fish response to changes in acidic

deposition.

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30 An important consideration for measuring the success of S a nd N emissions controls is the

development of appropriate expectations for the magnitude of potential chemical recovery. Most lakes

inferred to have been measurably acidified by atmospheric deposition were already marginally acidic,

typically with pH less than about 6, before anthropogenic atmospheric pollution began prior to 1900.

Therefore, full recovery of currently acidic lakes would not be expected to yield neutral pH. Nevertheless,

increases in ANC may allow recovery of fish populations even if pH remains relatively low (Stoddard

et al., 2003).



# B.4.3. Effects by Ecosy s 

# tem Type

B.4.3.1. Terrestrial Ecosystems

Due to a strong dependency on atmospheric deposition and exposure to gaseous compounds as the

major sources of nutrients, lichens are affected by changes in these conditions. Vulnerability of lichens to

increased N input is generally greater than that of vascular plants (Fremstad et al., 2005). Even in the

Pacific Northwest, which receives uniformly low levels of N deposition, changes from acid-sensitive and

N-sensitive to pollution-tolerant and nitrophilic lichen taxa are occurring in some areas (Fenn et al.,

2003). In eastern North America and central Europe, areas experiencing relatively high levels of acidic

deposition have experienced noticeable reductions in cyanolichen abundance on both coniferous and

deciduous trees (Richardson and Cameron, 2004). Effects on lichen species biodiversity are also likely

(McCune, 1988; Van Haluwyn and van Herk, 2002).

Fenn et al. (2007) speculated that large, pollution-sensitive macrolichens, including epiphytic

cyanolichens, will be replaced by N-tolerant species in areas where development expands in western

Oregon and Washington into N-limited Coast Range forests. Currently, in the Pacific Northwest,

nitrophilic lichen species are common in and around Seattle, Portland, Spokane, the Tri-cities, Salem,

Oregon’s agricultural lands in the northeast and southwest, and the Willamette Valley (Fenn et al., 2007).

The USDA Forest Service website contains information about lichen species pollution tolerance,

diversity, and preferred habitat in relation to exposure to N (http://www.nacse.org/lichenair).

In London, epiphyte diversity, including a majority of the lichen taxa, declined in areas where NO 2

surpassed 40 μg/m 3 and NO X surpassed 70 μg/m 3

. Lichens remaining in areas affected by these levels of

exposure contained almost exclusively families Candelariaceae

, Physciaceae and Teloschistaceae

(Davies et al., 2007).

Progressive decline in ectomycorrhizal fungal (EMF) community structure and species richness

was observed at five Alaskan coniferous forest sites (white spruce [ Picea glauca ] dominant) along an

N deposition gradient (1 to 20 kg N/ha/yr) downwind from a large industrial complex on the Kenai

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33 Peninsula. The effects were attributed to both acidification and fertilization processes (Lilleskov et al.,

2002). EMF c ommunities are important in tree nutrition and C balance, and EMF trees tend to be

dominant in N-limited forest ecosystems. A shift in EMF c ommunity structure could result in changes in

tree species.

Westman et al. (1985) summarized the literature of negative effects of SO 2 on native plants,

including decreased pollen germination and tube elongation in both angiosperms and gymnosperms. It is

often difficult to separate the effects of SO 2 exposure on plants from the effects of S d eposition. This is

because areas that experience high SO 2 exposure generally also receive high S d eposition. Kozlowski

(1985) summarized relative susceptibility of different trees, lichens, and bryophytes to SO 2

.

Available information is not sufficient to draw conclusions regarding the increased likelihood of

future effects on the condition of hardwood forests in the Southern Appalachian Mountain region

(Sullivan et al., 2002). Certainly, such effects are less likely for hardwood forests than for spruce-fir

forests. Red oak seedlings grown in a greenhouse in deciduous forest soils exhibited no response to

acidified soil (pH 4.0 from 9:1 H 2 SO 4 :HNO 3 ) or to high or low SO 2−

4 inputs (12.8 to 24.8 mg/L). The

lack of response suggested that red oak seedlings are not sensitive indicators of acidification effects from

S d eposition (McClenahen, 1987).



### Grasslands and Alpine Tundra

Alpine communities are considered very sensitive to changes in N deposition, but documented

effects in the scientific literature have been attributed to nutrient enrichment, rather than acidification

(Seastedt et al., 2004; Bowman et al., 2006). Lower-elevation grasslands, especially those in semi-arid

environments, would be expected to be even less sensitive to acidification because of low water leaching

potential and the common presence of base-rich Mollisol and Aridisol soils. However, some effects of

acidification may be manifested in mesic grasslands.

In a review of SO

2 effects on grasses in the United Kingdom, Bell (1985) suggested that damage

can occur at levels as low as 150 μg/m 3

. However, he asserted that any ubiquitous critical load value must

be modified to include variations due to environmental conditions and combined effects with other

pollutants. He also suggested that many grass species exhibit a tolerance to SO

2 , resulting from more

intraspecific competition in agricultural grasslands. Westman et al. (1985) also provided evidence of the

evolution of a tolerant grass species, Bromus rubens, in southern California coastal sage scrub, influenced

by an average of 3.7 μmol/m 3 of SO

2 over 25 years.

Studies of SO

2 effects on timothy grass ( Phleum pratense ) showed diminished leaf production and

increased leaf senescence in seedlings exposed to 0.120 ppm SO

2 for 35 days (Mansfield and Jones,

1985). In another experiment, Mansfield and Jones (1985) reported that exposure to 0.120 ppm SO

2 in

seedlings over 40 days resulted in a 62% reduction in the dry weight of roots and 51% reduction in the

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28 dry weight of shoots, as well as a significant decline in leaf-area ratio (LAR) and specific leaf area (SLA)

by the end of the experiment. They suggested that decreased growth and shifts in LAR and SLA could

lead to decreased hardiness and increased susceptibility to water stress.

In a 5–year exposure of native mixed prairie grassland in Montana, Lauenroth and Milchunas

(1985) exposed grasses to a control (~20 μg/m 3 ) and three elevated levels of SO

2 (~60, 106, 184 μg/m 3 ).

Year-to-year S a ccumulation did not appear to occur over the 5–year course of the treatment, though

progressive increases in root and rhizome S c oncentrations were observed seasonally. No significant

negative effects on either above-ground net primary productivity or below-ground biomass dynamics in

grasses were observed, except a decrease in biomass for Bromus japonicus

. However, lichen cover

declined after 1–year of exposure at the low treatment level. Though no biomass or cover effects were

observed at the community level, there were minor population changes. These results are consistent with

the nature of semi-arid grasslands that typically adjust well to perturbations (Lauenroth and Milchunas,

1985).



### Arid Lands

At the time of the previous AQCD, it was believed that arid and semi-arid ecosystems were not as

susceptible to soil acidification and high NO −

3 leaching as are forested ecosystems. This is because of a

scarcity of water for NO −

3 leaching, except on an episodic basis, and because arid soils tend to be more

alkaline than soils in more humid environments. No new research has altered that conclusion. Arid lands

in the U.S. generally receive low levels of S d eposition. However, N deposition can be quite high,

especially in southern California in the vicinity of the Los Angeles Basin. Little work has been done on

the effects of acidification on arid land ecosystems. As reviewed by Fenn et al. (2003), acidification

effects have not been demonstrated at the Central Arizona-Phoenix LTER site, despite the almost 30 kg

N/ha/yr of deposition received. Nevertheless, N deposition has the potential to increase plant growth and

denitrification and alter community composition in arid environments (Egerton-Warburton and Allen,

2000; Allen et al., 2005). Such changes could alter key ecosystem processes and, as such, merit

consideration. There has been little research to examine these issues and, therefore, the state of knowledge

is similar to what it was in 1993.

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29 

## B.4.3.2. Aquatic Ecosystem

Chronic Effects

Sulfate

The study of Clow and Mast (1999) is unique in that SO 2−

4 trends were evaluated with both raw

data and data adjusted for existing trends in flow. From 1967 to 1983, Clow and Mast (1999) showed a

decreasing trend in SO 2−

4 concentrations in a Catskill river, no trend in three rivers in Maine,

Pennsylvania, and Virginia, and an increasing trend in a river in Ohio. The Maine river did show a

decreasing trend before flow adjustment of the data. From 1984 to 1996, however, Clow and Mast (1999)

found decreasing trends in SO 2−

4 concentrations in all five rivers, both with and without flow adjustment.

The rivers in Pennsylvania, Ohio, and Virginia were south of the maximum southern extent of glaciation,

and therefore were more likely to be subject to the effects of SO 2−

4 adsorption in soils of their watersheds.

In such streams, decreasing S-adsorption on soils would be expected to counteract the effects of

decreasing S d eposition in terms of effects on stream SO 2−

4 concentration.

Surface waters in other unglaciated regions exhibited decreasing trends in SO 2−

4 by the 1980s.

Concentrations of SO 2−

4 in 130 northeastern lakes in 1984 were compared to those in the same lakes in

2001 (Warby et al., 2005). Median concentrations in each subregion were lower in 2001 than 1984, and in

the region as a whole, the overall median decrease was 1.53 μeq/L/yr. A decrease in SO 2−

4 concentrations

that averaged 2.16 μeq/L/yr was also observed in 47 of 48 Adirondack lakes from 1992 to 2004, and a

similar decrease of 2.09 μeq/L/yr was observed in a subset of these lakes from 1982 to 2004 (Driscoll

et al., 2007).

The pattern of increasing concentrations of SO 2−

4 in surface waters before the year of peak S

emissions in 1973, followed by widespread decreasing trends in SO 2−

4 concentrations after the peak (with

the only exception being the Blue Ridge Mountain region in Virginia), provides convincing evidence of

the link between S emissions and SO 2−

4 concentrations in surface waters. A similar link has been shown in

Europe (Stoddard et al., 1999). On this basis, continued decreases in S e missions would be expected to

result in further decreases in SO 2−

4 concentrations in surface waters, although the rate of response is

uncertain due to an incomplete knowledge of S r etention mechanisms in terrestrial systems. Also, in a

detailed analysis of flow effects on SO 2− trends, Murdoch and Shanley (2006) found SO 2−

4 4 that higher

concentrations of SO 2−

4 occurred at corresponding high, medium and low flows in 2000 to 2002 than in

1997 to 1999 in two of the rivers studied by Clow and Mast (1999), and at high and medium flows in a

third river. Continued monitoring of surface waters will be needed to verify a future link between

emissions and SO 2−

4 concentrations in surface waters.

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34 Nitrate

Driscoll et al. (1985) found that NO −

3 concentrations in 20 lakes in the early 1980s in the

Adirondack region of New York averaged 12% of SO 2−

4 concentrations, whereas Lovett et al. (2000b)

found that baseflow NO −

3 concentrations in 1994–97 were an average of 37% of SO 2−

4 concentrations in

39 streams in the Catskill region of New York. Average concentrations of NO −

3 in most southeastern

streams also tend to be considerably less than SO 2−

4 concentrations (Webb et al., 2004).

High-frequency sampling in the study of Murdoch and Stoddard (1993) demonstrated the

importance of NO −

3 during high-flow conditions in Catskill streams in which concentrations periodically

equaled or exceeded SO 2−

4 concentrations. This study also reported increasing trends in NO −

3

concentrations during the period of 1970 to 1990 in all 16 Catskill streams for which data were available.

A similar increase in NO −

3 concentrations was reported for Adirondack lakes in the 1980s (Stoddard et al.,

1999). These increasing trends in NO −

3 concentrations were attributed to N saturation in response to

atmospheric deposition (Aber et al., 1998).

The relationship between N deposition and surface water NO −

3 concentrations up through the 1980s

suggested that continued N deposition would further the accumulation of N in terrestrial ecosystems and

drive continued increases in surface water NO −

3 concentrations. However, more recent information on

NO −

3 concentrations have been less consistent with the concept of N saturation. Goodale et al. (2003)

resampled New Hampshire streams in 1996–97 that had been previously sampled in 1973−74 and found

substantially lower NO −

3 concentrations in the more recent sampling, despite two decades of relatively

stable levels of deposition to otherwise undisturbed forests. The lower NO −

3 concentrations could not be

accounted for by differences in flow or forest succession, but interannual climate variation was proposed

as a possible cause. The long-term record of dissolved inorganic N (which is largely NO

−

3 ) concentrations

at the HBEF s howed a similar pattern; high concentrations in the late 1960s and 1970s, followed by

decreases to minimum values in the mid-1990s (Aber et al., 2002). These authors attributed this pattern to

a combination of environmental factors, but did not identify a single most important control variable. A

reversal from increasing trends in NO −

3 concentrations in the 1980s to decreasing trends in the 1990s was

also observed in Adirondack lakes (Driscoll et al., 2003a). A small decrease in atmospheric deposition of

N also occurred in this region through the 1990s, but was not considered sufficient by these authors to

explain the decreasing trend in lakewater NO −

3 concentrations. Rather, they proposed that increased

concentrations of atmospheric CO 2 may have resulted in a fertilization effect that increased

N assimilation (Driscoll et al., 2007).

In general, trends in surface water NO −

3 concentrations during the 1990s were much smaller than

trends in SO 2−

4 , with the only ecologically significant changes occurring in the two regions with the

highest ambient NO −

3 concentrations (Figure B-21). Lakes in the Adirondacks and streams in the

Northern Appalachian Plateau both exhibited small but significant downward trends in NO −

3 in the 1990s

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34 (Figure B-21). Both of these regions are central to the debate over whether N saturation is a legitimate

threat to the health of forests and surface waters (Stoddard, 1994; Aber et al., 1998). While declining NO

3 −

concentrations in these regions is a positive development for these ecosystems, we clearly do not know if

these trends will continue, especially because they do not appear to reflect changes in N emissions or

deposition. The presence of strong upward trends in NO

3 − in these same regions in the 1980s (Murdoch

and Stoddard, 1992; Stoddard, 1994) suggests that trends measured on the scale of a single decade may

reflect variability in long-term patterns of changing NO

3 − leakage from forested watersheds. Such patterns

are controlled by factors that may take many years of additional research to determine. While great

uncertainty exists and the time scales of N saturation may be longer than previously considered (e.g.,

centuries rather than decades), the long-term retention of N deposition in forested regions is unlikely to

continue indefinitely (Aber et al., 2003).

In New England and the Upper Midwest, where ambient NO

3 − concentrations are much lower than

in the Adirondacks and Northern Appalachian Plateau (Source: Stoddard et al. (2003).

Figure B-21), NO

3 − concentrations in surface waters were unchanged during the 1990s. The

Ridge/Blue Ridge province registered a small, but significant, decrease in NO

3 − during the 1990s, but

interpretation of trends for NO

3 − in this region is complicated by an outbreak of gypsy moths that also

occurred during this period. Forest defoliation by gypsy moths was the most likely cause of a pulse in

NO

3 − export from many streams in this region in the mid-1990s (Eshleman et al., 1998).

Some evidence of climate effects on long-term trends in NO

3 − concentrations in surface waters was

provided by studies of Mitchell et al. (1996) and Murdoch et al. (1998). A synchronous pattern in NO

3 −

concentrations was observed from 1983 to 1993 in four small watersheds in New York, New Hampshire,

and Maine, which included anomalously high concentrations during the snowmelt period of 1990. The

region-wide spike in NO

3 − concentrations followed an unusually cold December that may have disrupted

soil N cycling processes (Mitchell et al., 1996). Murdoch et al. (1998) also found that mean annual air

temperatures were strongly related to average annual NO

3 − concentrations in most years in a Catskill

watershed with elevated NO

3 − concentrations in stream water. Those relationships were explained by

microbial control of N release in watersheds that were considered to be N-saturated.

Efforts to explain the decreasing trends in NO

3 − concentrations under conditions of reasonably

stable atmospheric N deposition have focused on terrestrial N cycling and N-saturation theory. However,

processes within lakes may have also played a role in the trends in Adirondack lakes. In a study of 30 of

the 48 lakes studied by Driscoll et al. (2003a; 2007), Momen et al. (2006) found that concentrations of

NO

3 − were inversely correlated with concentrations of chlorophyll a in 11 lakes, and that chlorophyll a

was increasing in concentration in 9 lakes. The increase in pH observed in most of these lakes may have

stimulated productivity so that N assimilation by plankton increased (Momen et al., 2006).

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8 Thus, there is little or no apparent relationship between recent trends in N deposition and trends of

NO −

3 concentrations in surface waters, in sharp contrast to S deposition and SO 2−

4 concentrations. Rather

than disprove the concept of N-saturation; however, these studies more likely reflect the complexities of

N utilization within terrestrial and aquatic ecosystems. These complexities create considerable uncertainty

with regard to how future trends in NO −

3 concentrations in surface waters will respond to changing levels

of deposition.

August 2008 B­105 DRAFT—DO NOT QUOTE OR CITESource: Stoddard et al. (2003).



### Figure B­21. Time series data for SO 2−

4 

### , NO −

3 

### , base cations [Ca 2+ 

### + Mg 2+ 

### ], Gran ANC, pH, and DOC in

example Long Term Monitoring Lakes and streams that have relatively low ANC. The example

surface waters include Salmon Pond, Maine (New England region); Darts Lake, NY (Adirondack

region); East Branch Neversink River (Appalachian Plateau region); and Vandercook Lake,

Wisconsin (Upper Midwest region). Significant trends are indicated by trend lines. Shaded box

indicates time period of analyses reported by Stoddard et al. (2003).

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34 Base Cations

The earliest trends of base cation concentrations in acid-sensitive surface waters of the U.S. were

presented by Stoddard (1991) for 12 streams in the Catskill region. In 5 of 12 streams, concentrations of

(Ca 2+ + Mg 2+ ) increased from 1915–22 to 1945, but decreased from 1945–46 to 1990. In the remaining

seven streams, concentrations increased during both periods, but at a lower rate in the more recent period

in five of the seven streams. In streams that showed an increase in concentrations during both periods, the

average rate of increase from 1915 to 1922 was 2.8 μeq/L, whereas the average rate of increase from

1945 to 1990 was 1.2 μeq/L. Data on SO 2−

4 trends were not available for the early period, but the trends

in (Ca 2+ + Mg 2+ ) concentrations were consistent with the expected pattern of high rates of cation leaching

during the early stages of acidification from S d eposition.

Clow and Mast (1999) observed trends in (Ca 2+ + Mg 2+ ) concentrations that were generally

consistent with SO 2−

4 trends in five eastern rivers from 1968 to 1983. Decreasing trends in concentrations

of (Ca 2+ + Mg 2+ ) and SO 2−

4 concentrations were observed in a Maine river, and increasing trends in (Ca 2+

+ Mg 2+ ) and SO 2−

4 concentrations were observed in an Ohio river. None of the three other rivers showed a

decrease in concentrations of (Ca 2+ + Mg 2+ ), and only one showed a decreasing trend in SO 2−

4

concentrations. For the period 1984 to 1996, the trend in SO 2−

4 concentrations was negative in the Ohio

River and the concentrations of (Ca 2+ + Mg 2+ ) showed no trend. Also, a negative trend in (Ca 2+ + Mg 2+ )

concentrations in a Virginia river was coupled with a negative trend in SO 2−

4 concentrations. Relations for

the other three rivers were similar to the earlier period of 1984 to 1996.

The study of Likens et al. (1996) evaluated trends in base cations in relation to trends in (SO 2−

4 +

NO

−

3 ) in the long-term record for the HBEF. This record showed an approximately linear, increasing

relationship between concentrations of base cations and (SO 2− + NO

− 4 3 ) from 1964 to 1969, then a

reversal in 1970 to a decreasing trend up to 1994. The slope of the phase with increasing anion

concentrations was steeper than the slope for the phase with decreasing anion concentrations. This

indicates lower base cation leaching per equivalent of mobile anion, and therefore suggests depletion of

base cations stored in soil. The study of Lawrence et al. (1999b) showed decreased concentrations of base

cations at a rate that exceeded decreases in (SO 2−

4 + NO

−

3 ) in Catskill streams from 1984 to 1997. In

streams within western Virginia and in Shenandoah National Park, concentrations of base cations did not

exhibit significant trends from 1988 to 2001, perhaps due to the influence of S adsorption on streamwater

SO 2−

4 concentrations.

Regional declines in base cation concentrations were measured in the LTM Program from 1990 to

2000 in New England lakes, Adirondack lakes, Appalachian streams, and upper Midwest lakes (Stoddard

et al., 2003). These results were consistent with decreased Ca 2+ concentrations measured by Warby et al.

(2005) in 130 acid-sensitive lakes in the Northeast between 1984 and 2001. The rate of decrease

identified by Warby et al. (2005) for base cations (1.73 μeq/L) was somewhat less than the rate of

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33 decrease in SO 2−

4 concentrations (1.53 μeq/L). Driscoll et al. (2007), also documented decreasing trends

in base cation concentrations in 16 Adirondack Lakes from 1982 to 2004, and similar rates of decrease in

48 lakes (including the 16) from 1992 to 2004.

In summary, decreases in base cation concentrations over the past two to three decades are

ubiquitous and closely tied to trends in SO 2−

4 concentrations in acid-sensitive regions of the U.S. Reports

of increases in concentrations of base cations in acid-sensitive regions were not found in the literature. In

most regions, rates of decrease for base cations have been similar to those for SO 2−

4 and NO

−

3 , with the

exception of streams in Shenandoah National Park. Decreasing trends of base cation concentrations do

not necessarily indicate further acidification or recovery of surface waters, but do indicate lower leaching

rates in soils, a prerequisite for recovery of soil base saturation. However, decreased concentrations of

base cations, particularly Ca 2+

, would also be expected to lower productivity in oligotrophic surface

waters.



### Acid Cations

Measurements of pH (sometimes expressed as H + ) have been routinely collected in surface waters

in the U.S. where effects of acidic deposition have been monitored, but a long-standing reliance on

titrated ANC rather than pH as the primary chemical measurement has limited the amount of pH data

published. The longest continuous record of pH in surface waters dates back to 1963 at the HBEF

(Driscoll et al., 2001b). This record shows an overall increasing trend from 1963 to 1994, although most

of the increase occurred after 1980. In Adirondack lakes, 12 of 16 monitored from 1982 to 2004 showed

an increase in pH, but the rates of change among lakes were highly variable, and one lake showed a

decrease in pH (Driscoll et al., 2007). When expressed as H + concentration, the average increase for the

12 lakes was 0.18 μeq/L/yr. In this same region, pH also increased in 31 of 48 lakes (including the 16

lakes monitored from 1982) from 1992 to 2004. Two lakes showed increases in pH over the 12 years.

Comparison of pH measurements of 130 lakes in 1984 and in 2001, in the northeastern U.S.,

showed an overall average increase in pH of 0.002 units (Warby et al., 2005). However, in this

assessment, lakes in the Adirondack region did not show a significant increase, nor did lakes in central

New England, or Maine. The Catskill/Poconos region of New York and Pennsylvania showed an average

increase of 0.008 pH units per year, and southern New England showed an average increase of 0.002 pH

units per year. Through continuous monitoring from 1990 to 2000, Stoddard et al. (2003) found a decrease

in H + (0.19 μeq/L/yr) similar to that observed in the same Adirondack lakes by Driscoll et al. (2007) from

1992 to 2004 (0.18 μeq/L), and an increase in Appalachian streams (0.08 μeq/L/yr) and Midwest lakes

(0.01 μeq/L/yr). No trends were found in New England lakes or Blue Ridge streams in Virginia in this

study. Stream monitoring in the Adirondack region from 1991 to 2001 showed an increase in H + in one

stream, no trend in a second stream, and also an increase in a third stream near the outlet of a lake

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33 (Lawrence et al., 2004). In summary, decreasing trends in pH in surface waters are common through the

1990s up to 2004, but many exceptions occur, and overall, the rates of change have been small.

The discovery that Al i was toxic to aquatic life resulted in a considerable amount of data on Al

concentrations in surface waters in the 1980s, but most of this sampling was done either once or for a

limited period of time (Johnson et al., 1981; Driscoll and Newton, 1985; Driscoll et al., 1987; Lawrence

et al., 1987; Cronan et al., 1990). Monitoring of Al i concentrations was begun in 16 Adirondack lakes in

1982 and expanded to 48 lakes in 1990. From 1982 to 2004, 5 of the original 16 Adirondack monitoring

lakes showed decreasing trends in Al i concentrations at rates that ranged from 0.02 μM/yr to 0.18 μM/yr

(Driscoll et al., 2007). From 1992 to 2004, 24 of the 48 lakes showed decreasing trends in Al i

concentrations (Driscoll et al., 2007). The analysis of Stoddard et al. (2003) also observed an average

decrease in Al i concentrations from 1990 to 2000 in the same group of Adirondack lakes reported on by

Driscoll et al. (2007), but observed no trend for this period in New England lakes, Appalachian streams,

or Midwest lakes.

Monthly stream chemistry monitoring at the HBEF s howed decreases in Al i concentrations at four

locations along the reference stream for the experimental forest from 1982 to 2000, but no trends at two

other locations along this stream (Palmer et al., 2004). These data also showed a surprising decrease in pH

at two of the locations where Al i decreased, and no pH trend at the other two locations where Al i

decreased (Palmer et al., 2004). Comparison of total Al concentrations in 130 lakes in the northeastern

U.S. in 1984 with those measured in 2001 showed lower average concentrations in 2001 in the

Adirondack region, the Catskill/Pocono region, central New England, southern New England, and Maine

(Warby et al., 2005). Because these measurements are of total Al, they are not directly comparable to Al i

.

Most recently, Lawrence et al. (in review) found that 49 of 195 streams (25%) during August base flow in

the western Adirondack region had Al i concentrations above 2.0 μM, the level above which toxic effects

on biota have been shown (Driscoll et al., 2001b; Baldigo et al., 2007).



### Acid Neutralizing Capacity

In response to reduced levels of acidic deposition required by the CAA and other emissions control

legislation, Stoddard et al. (2003) found trends during the 1990s toward increasing Gran ANC (Figure B21) in all of the glaciated regions of the eastern U.S. (i.e., New England, Adirondacks, Northern

Appalachian Plateau) and Upper Midwest, and decreasing Gran ANC in the Ridge/Blue Ridge province.

Changes were relatively modest compared with observed reductions in SO 2−

4 concentrations. Only the

regional increases in the Adirondacks, Northern Appalachian Plateau, and Upper Midwest were

statistically significant (Figure B-21). Median increases of about +1 μeq/L/yr in the Northern Appalachian

Plateau, Adirondacks and Upper Midwest represent significant movement towards ecological recovery

from acidification (Stoddard et al., 2003).

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33 It has been hypothesized that decreases in acidic deposition will yield the most chemical recovery

in lakes and streams that have experienced the most severe acidification. Using data from all of the sites

in regions where decreases in surface water SO 2−

4 and NO −

3 have occurred, Stoddard et al. (2003) found

that acidic lakes and streams exhibited a highly significant median increase in Gran ANC of

+1.3 μeq/L/yr during the 1990s. Low-ANC sites (0 to 25 μeq/L) showed a smaller significant median

ANC increase of +0.8 μeq/L/yr. Moderate ANC sites, those with mean ANC values greater than 25 μeq/L,

showed no significant change in Gran ANC (Figure B-21).

All of the glaciated regions in the eastern U.S. showed declines in base cation (Ca 2+ + Mg 2+ )

concentrations during the 1990s, with the average changes in the range of −1.5 to −3.4 μeq/L/yr. All of

the regional trends were highly significant (Figure B-21). Across the eastern U.S., surface water SO 2−

4 has

decreased at a rate of about −2.5 μeq/L/yr (the mean of regional median slopes), and NO −

3 at a rate of

−0.5 μeq/L/yr, in surface waters on glaciated terrain during the 1990s. These rates of change set an upper

limit to our expectation of ANC recovery of +3 μeq/L/yr (i.e., the sum of SO 2− and NO

− 4 3 trend

magnitudes). The Gran ANC increase reported by Stoddard et al. (2003) was actually about one-third of

this maximum, +1 μeq/L/yr. The difference between the observed Gran ANC trend and the maximum

trend estimated from rates of acid anion change can largely be explained by the average regional median

decline in (Ca 2+ + Mg 2+ concentrations, which was about −2.0 μeq/L/yr (Stoddard et al., 2003).



### Episodic Effects

Episodic acidification can result naturally from the mobilization of organic acids and from dilution

of base cation concentrations, but decreases in pH and ANC associated with increases in SO 2−

4 and NO −

3

are largely attributable to acidic deposition (Wigington et al., 1996). Episodic acidification is most

common in the early spring and late fall as a result of snowmelt and rainstorms, and is least common in

summer, when high flows tend to be infrequent. Seasonal variations in stream flow also result in seasonal

patterns of surface water chemistry at base flow. Lakes and streams at base flow tend to be more acidic in

early spring than at other times of the year and low flows in late summer tend to be least acidic (Lawrence

et al., 2007).

The transient nature of high flows makes episodic acidification difficult to measure. Therefore,

assessments have generally estimated the number of lakes and streams prone to episodic acidification by

combining episode information from a few sites with base flow values of ANC determined in large

surveys (Eshleman et al., 1995; Bulger et al., 2000; Driscoll et al., 2001b). Inclusion of episodically

acidified water bodies in regional assessments substantially increases estimates of the extent of surface

water acidification. For example, baseflow samples collected from 1991 to 1994 through the EPA TIME

Program indicated that 10% of the 1,812 lakes (>1 ha surface area) in the Adirondack region of New York

could be considered chronically acidic on the basis of ANC values less than 0 μeq/L, but that an

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34 additional 31% of these lakes had baseflow ANC values less than 50 μeq/L and were, therefore, estimated

to be susceptible to episodic acidification (Driscoll et al., 2001b). Lawrence (2002) also estimated the

extent of episodically acidified stream reaches in a Catskill, NY watershed (area = 85 km 2 ) through the

use of an index site at the base of the watershed that became episodically acidified at high flows.

Upstream sites with a lower base flow ANC than the index site at the same date and time were found to

have a high likelihood of becoming episodically acidified. Base flow sampling of 122 upstream sites

indicated that approximately 16% of the total upstream reaches were chronically acidified (ANC <

10 μeq/L), but that 66% of the stream reaches became episodically acidified.

Stoddard et al. (2003) compared seasonal data from New England lakes, Adirondack lakes and

Northern Appalachian streams, collected monthly to quarterly, to evaluate the difference between the

chemistry of surface waters in the summer and in the spring. Results indicated that spring values of ANC

were an average of 30 μeq/L lower than summer ANC. This study referred to samples collected in spring

as “episodic samples,” although sampling was done independent of flow. Therefore, the 30 μeq/L

difference should be considered a seasonal effect rather than an episodic effect.

The most thorough characterization of episodic variations in stream chemistry was conducted

through the ERP, in which 13 low-order streams (watershed areas less than 24 km 2 ) in the Adirondack and

Catskill regions of New York, and the Appalachian Plateau in Pennsylvania were monitored from 1988 to

1990 (Wigington et al., 1996). Acid episodes with chemical concentrations within the 90th percentile

involved decreases in ANC of up to 200 μeq/L, decreases in pH of up to one unit, and increases in

concentrations of Al i of up to 15 μM (Wigington et al., 1996). Results also showed that acid episodes

reduced the size of fish populations and eliminated acid-sensitive species if median high-flow pH was less

than 5.2 and Al i concentrations exceeded 3.7 μM, despite the relatively short duration of episodes (Baker

et al., 1996). Baker et al. (1996) concluded that effect on biota from episodic acidification were likely to

be similar to those from chronic acidification. Elimination of an annual age class can result from an

episode that occurred in the presence of a sensitive life stage. Largely on the basis of this study, the EPA

concluded that reversal of effects from episodic acidification could be used as a key ecological endpoint

for an acid deposition standard for protection of the environment (U.S. Environmental Protection Agency,

1995).

Despite the significance of the findings of ERP, little assessment or monitoring of episodes was

done in the 1990s. One exception was the work of Hyer et al. (1995) in three watersheds of differing

geology in Shenandoah National Park. Results suggested that episodic acidification was occurring

throughout the park on all bedrock types, although acidification was not sufficient to cause elevated Al

concentrations. Lawrence (2002) also documented severe episodic acidification in August 1998 in a

tributary of an ERP stream, where Al i concentrations increased from 1.6 to 7.3 μM in 6.5 h.

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28 In the first large-scale study designed to sample streams during high-flow conditions, Lawrence

et al. (2007) found that 124 out of 188 (66%) western Adirondack streams were prone to acidification to

the level at which Al i becomes mobilized. Only streams accessible with less than a 60–min hike were

sampled in this study. The March, 2004 survey was chosen to represent episodic conditions, and a survey

conducted August 16–18, 2004 was chosen to represent base flow conditions. Based on this comparison,

35% of the streams were chronically acidified, 30% of the streams were episodically acidified, and 34%

were not acidified. Survey results were also used to estimate that 718 km of stream reaches were prone to

acidification, although 3085 km of stream reaches within the study region could not be assessed because

of inaccessibility.

There have been no studies in the U.S. that determine if either the severity or frequency of episodic

acidification has lessened. In a study of two streams in Nova Scotia (Laudon et al., 2002), trends in ANC

in four phases of storm hydrographs from 1983 to 1998, were not detected other than in the peak-flow

phase of one stream (an increase of 0.87 μeq/L). In Sweden, the anthropogenic contribution to episodic

decreases in ANC were estimated to range from 40 to 80% in five streams from 1990 to 1999 (Laudon

et al., 2002).



# B.5. Effects on Watersheds and Landscapes

B.5.1. Interactions among Terrestrial, Transitional, and Aquatic

Ecosystems

Acidification has pronounced effects on nutrient cycling in terrestrial, transitional, and aquatic

ecosystems. Of particular importance in this regard is the role of N deposition in influencing N cycling.

This topic is discussed in detail in Section 5. Also important are the influences of acidification on the

availability of Ca 2+ and other nutrient base cations (Mg 2+

, K + ).

In general, decomposition, nutrient cycling, productivity, and other system-level processes in

surface waters are not as sensitive as species composition and richness to relatively small amounts of

acidification. Such effects only seem to occur at high levels of acidification (e.g., pH < 5). This is because

acid-sensitive species are often replaced by more acid-tolerant species that perform the same function

until acidification becomes severe. For example, whereas changes in microbial composition and

abundance have been observed with acidification, they appear to have minimal effect on overall microbial

respiration and nutrient cycling. At extreme levels of acidity, however, these system-level functions may

also decrease. Thus, system-level functions are not generally good indicators of light to moderate levels

of acidification.

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32 Integrating the effects of atmospheric deposition across spatial scales is difficult. The response of a

single plant, or small group of plants, cannot be easily scaled up to examine effects on plant communities,

ecosystems, watersheds, or geographic regions. Integration typically requires a combination of

approaches, including ecosystem modeling, experimental manipulation studies, surveys across pollution

deposition gradients, and long-term monitoring studies. Similarly, aquatic effects at the population level

can be readily quantified, but extrapolation to the community or aquatic ecosystem level is problematic.

Linking research results across scales will be an important component of future research. Measurements

that correlate with ecosystem processes, such as foliar N concentration, leaf area index, or spectral

reflectance, can in some cases be remotely sensed. They offer great promise for future assessment of

terrestrial effects across spatial scales.

Effects of atmospheric deposition of acidifying substances on soil, vegetation, and surface water

are manifested in specific processes, affecting energy, water and nutrient flow, intra- and inter-species

competitive interactions, and ecosystem primary production. Therefore, effects on sensitive species (only

some of which have been documented) have the potential to cascade throughout the ecosystem and

become manifested at a variety of scales. Such ecosystem- to landscape-scale effects from atmospheric

deposition of acidifying substances are known to occur, but the results of these interacting processes have

not been conclusively demonstrated.

It is also evident that acidification from natural and human-caused disturbances, including climatic

stressors (temperature, moisture availability, wind), insect infestation, disease, fire, and timber harvest,

can affect the severity of effect of atmospheric deposition of SO X and NO

Y . Although it is clear that such

interactions can occur, there are no studies that have clearly documented that acidic deposition at levels

that commonly occur across broad landscapes in the U.S. has conclusively altered ecosystem structure or

function. Similarly, although it is widely believed that acidic atmospheric deposition can make plants

more susceptible to the adverse effects of other natural and human-caused stressors, such effects have not

been conclusively demonstrated in more than a few cases. The data demonstrating and quantifying the

extent to which SO X and NO

Y deposition are altering natural terrestrial ecosystems via acidification

pathways are sparse. In particular, effects of soil and soil water acidification on soil ecosystem processes

and nutrient cycling are poorly known. Even less is known about effects on soil microorganisms and food

webs, or how such effects interact with the above-ground vegetation community.



# B.5.2. Interactions with Land Use and Disturbance

The prevailing scientific consensus during the 1980s held that the majority of lakes in eastern

North America that had pH less than about 5.5 to 6.0 had been acidified by acidic deposition. Reports that

acidic lakes and streams were rare or absent in similar areas not receiving acidic deposition were used as

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35 evidence of acidification by acidic deposition in many regions (e.g., Neary and Dillon, 1988; Sullivan

et al., 1988; Baker et al., 1991a). An alternative hypothesis had been advanced by Rosenquist (1978),

Krug (1989, 1991), and Krug and Frink (1983) that land use changes could explain recent lake

acidification in southern Norway and the northeastern U.S. According to this hypothesis, natural soil

processes and changes in vegetation can generate more acidity than is received from atmospheric

deposition. For example, an increase in acidic humus formation in response to decreased upland

agriculture was suggested as being responsible for regional acidification in southern Norway, rather than

acidic deposition (Rosenqvist, 1978). Subsequent acidic deposition effects research in some cases seemed

to be designed to refute this hypothesis rather than to explore the relationships between land use and acidbase chemistry (cf. Havas et al., 1984; Birks et al., 1990). Research intended to discriminate between

acidic deposition and land use as the major cause of acidification generally concluded that acidic

deposition was the principal cause of regional acidification in certain areas of North America and Europe.

Perhaps more appropriate research questions might have focused on quantifying the relative importance

of land use activities or landscape change in exacerbating or ameliorating acidic deposition effects. The

importance of acidic deposition as an agent of acidification does not preclude the importance of land use

and landscape changes which, in some cases, may actually be more important than acidic deposition

(Sullivan et al., 1996b).

There has not been a regional evaluation of land use changes in areas of the U.S. susceptible to

surface water acidification from acidic deposition. It has therefore not been possible to quantify the extent

or magnitude of land use effects on acidification. It is clear, however, that such changes can have

important effects on acid-base status (Sullivan, 2000), especially as influenced by N deposition (Goodale

and Aber, 2001).

Changes in human land use activity, and associated changes in vegetative structure, influence

ecosystem response to external stressors such as acidic deposition, exposure to O 3 , natural disturbance

factors such as wind and fire, and climatic changes. Some activities contribute to the acidification of soil

and surface waters; other activities decrease acidity (Sullivan et al., 1996b) (Table B-21).

Forest management practices, especially those that have occurred over many generations of trees,

can have important effects on soil erosion, nutrient supplies, and organic material. Such effects can

influence the availability of base cations for acid neutralization and/or aspects of N cycling.

Forests are efficient at scavenging S and N from the atmosphere. Differences in forest canopy,

particularly between deciduous and coniferous trees, can cause large differences in dry deposition, and

therefore total deposition of S and N. In regions that receive high levels of acidic deposition, the presence

of forest vegetation, especially coniferous trees, enhances total deposition of acid-forming precursors

(Rustad et al., 1994). In addition to the enhanced deposition caused by the presence of large trees, there

are also differences in nutrient uptake. In particular, younger trees take up larger quantities of N and other

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33 nutrients than do trees in older forests. Therefore, changes in the occurrence and age or species

composition of the forest can influence the rates of atmospheric deposition to the site as well as the fate of

atmospherically deposited substances.

Landscape processes and watershed disturbance can influence soil and water acidification in many

ways. Land use practices and vegetation patterns have been changing in various parts of the U.S. for

decades to centuries. These changes in human activity can influence the response of forested ecosystems

to external stressors, including atmospheric deposition of S o r N, natural disturbance factors such as wind

and fire, and climatic changes. Some processes contribute to the acidification of soil and surface waters or

reduce the base saturation of the soils thereby increasing their sensitivity to acidic deposition. Other

processes cause decreased acidity (Sullivan et al., 1996b; (Sullivan, 2000).

Watershed disturbance from logging, blowdown, and fire disrupts the normal flow of water and can

cause increased contact between runoff water and soil surfaces, leading to increased base cation

concentration and ANC in drainage water. Recovery from disturbance can cause a decrease in drainage

water ANC as the system returns to pre-disturbance conditions. In particular, soil loss through erosion can

reduce the base cation pool size, thereby limiting the capacity of soils to neutralize atmospheric acidity. In

addition, forest harvesting has an important effect on forest N-demand, thereby reducing the likelihood of

future N-saturation in response to high N deposition. Forest management practices, especially those that

have occurred over many generations, have had important effects on soil chemistry, nutrient supplies, and

organic material.

Watershed disturbances, including road building, agriculture, mining, urbanization, logging,

blowdown, and fire can alter various aspects of ecosystems biogeochemistry. Such disturbances can

influence the water budget, base cation mobilization, routing of drainage water, nutrient input, and S a nd

N cycling in ways that affect the acid-base chemistry and nutrient dynamics of soils and drainage waters

(Sullivan et al., 1996b). The effects of such disturbances can greatly modify the response of a given

watershed to atmospheric inputs of S and N.



## B.5.2.1. Timber Harvest

Removal of the forest affects drainage water quality in several ways. Deposition of S a nd N are

reduced; leaching of NO − 3 increases and, in some cases, causes a pulse of surface water acidification.

Base cations tied up in wood are lost when wood is transported off-site. Regrowth of the forest may

further affect drainage water quality through vegetative uptake of N and base cations. Trees accumulate

base cations to a greater degree than anions. To balance the charge discrepancy, roots release an

equivalent amount of protons and acidify the soil. Base cation accumulation by trees is age-dependent.

Young forests grow faster and are therefore more acidifying than older forests (Nilsson et al., 1982;

Nilsson, 1993). They also retain greater amounts of N.

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34 Most forests in the northeastern U.S. are recovering from extensive human disturbance that

occurred over a period of about 200 years. Landscapes were mainly forested during pre-colonial times,

logged or cleared for agriculture in the mid to late 19th century, and are now largely early to mid-seral

stage regenerating forests (Niering, 1998).

In some areas that experience relatively high levels of acidic deposition, there is growing concern

about sustainable timber productivity (cf. Adams, 1999). Harvest-induced leaching losses have been

estimated to range from 6 to 60 kg/ha/yr of N, 28 to 48 kg/ha/yr of Ca 2+ , and 7 to 16 kg/ha/yr of Mg 2+

(Federer et al., 1989). Timber harvesting also increases leaching losses from the site because of the

reduction in transpirational water loss. The increased water flux after tree removal increases the

opportunity to leach base cations from the soil.

If base cations sequestered in tree wood are removed from the site by tree harvesting, the result is a

decrease in the available base cation pool on the site. Physical disturbances to forest soils during logging

operations and increased soil temperature that results from exposure of the forest floor to sunlight may

also cause a short-term increase in the rates of N mineralization and nitrification (Joslin et al., 1992). The

resulting increase in NO 3 − production and leaching further depletes base cations from the soil pool.

Johnson et al. (1991a) measured short-term (3 years) effects of logging at HBEF in New

Hampshire on soil acid-base chemistry. Base saturation of the mineral soil Bh horizon decreased from 14

to 11% and pH decreased by 0.24 pH units.

Likens et al. (2002) reported results of a 34–year study of the biogeochemistry of forest ecosystems

at HBEF. Part of the study evaluated the effects of tree removal on S cycling and related biogeochemical

processes. Vegetation removal resulted in increased decomposition of organic matter and nitrification.

These changes, in turn, lowered soil water pH, enhanced SO 4 2− adsorption on mineral soil, and therefore

decreased the flux of SO 4 2− in stream water. With subsequent vegetation regrowth, the adsorbed SO 4 2−

was released from the soil to drainage water, and streamwater SO 4 2− concentrations increased.

Baldigo et al. (2005) compared the effects of clear-cut and timber-stand improvement (TSI)

harvests on water chemistry and mortality of caged brook trout in three Catskill Mountain streams.

Harvests removed 73% of tree basal area from a clearcut subbasin, 5% basal area from a TSI subbasin,

and 14% basal area at a site below the confluence of both streams (the combined effect of the two harvest

methods). Water quality and trout mortality were affected only in the clearcut stream. Acidity and

concentrations of NO 3 − and Al i increased sharply during high flows after the first growing season (1997).

Acid-Al i episodes were severe during this period and decreased steadily in magnitude and duration

thereafter. All trout at the clearcut site died within 7 days during spring 1998, and 85% died during spring

1999. Only background mortality was observed in other years at this site and at the other three sites

during all tests. The effects of tree harvests on fish communities are of concern because they might

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32 interact with effects of acidic deposition and produce more substantial effects on biota than either stress

factor on its own.



## B.5.2.2. Insect Infestation

Effects of insect-caused defoliation on the N cycle can be pronounced. The foliar N consumed by

insects is deposited on the forest floor as insect feces (frass), greenfall, and insect biomass. Some of this

deposited N is subsequently taken up by tree roots and soil microbes, with little effect on the nutritional

condition of the trees or the site. Where a sizable component of this N is leached in drainage water, the

nutritional consequences can be more significant. There are also various feedback mechanisms. For

example, low N supply can slow the population growth of defoliating insects (Mason et al., 1992) and

enhance the tree’s chemical defenses against insects (Hunter and Schultz, 1995). The amount of N

leaching loss is generally small, relative to atmospheric deposition inputs and relative to the amount of N

transferred to the forest floor with the defoliation (Lovett and Ruesink, 1995; Lovett et al., 2002).

Nevertheless, it can be high enough to contribute to base cation depletion of soils and effects on

downstream receiving waters. The extent of NO − 3 leaching may be partly related to the extent of

defoliation and tree mortality that occurs and also the amount of precipitation that occurs immediately

after the defoliation (Lovett et al., 2002).

Forest insect infestation can have profound effects on the acid-base and nutrient chemistry of soils

and drainage waters. Effects of a gypsy moth infestation in Shenandoah National Park provide a good

example. Between the mid-1980s and the early 1990s, the southward expanding range of the European

gypsy moth traversed Shenandoah National Park and affected all of the University of Virginia’s SWAS

study watersheds (Webb, 1999). Some areas of the park were heavily defoliated 2 to 3 years in a row. The

White Oak Run watershed, for example, was more than 90% defoliated in both 1991 and 1992. This

insect infestation of forest ecosystems in Shenandoah National Park resulted in substantial effects on

streamwater chemistry. The most notable effects of the defoliation on park streams were dramatic

increases in the concentration and export of N and base cations in streamwater. Following defoliation,

NO − 3 export increased to previously unobserved levels and remained high for over 6 years before

returning to predefoliation levels. The very low levels of pre-disturbance NO − 3 export in park streams

were consistent with expectations for N-limited, regenerating forests (Aber et al., 1989; Stoddard, 1994).

Release of NO − 3 to surface waters following defoliation was likewise consistent with previous

observations of increased N export due to forest disturbance (Likens et al., 1970; Swank, 1988). The exact

mechanisms have not been determined, but it is evident that the repeated consumption and processing of

foliage by the gypsy moth larva disrupted the ordinarily tight cycling of N in Shenandoah National Park

forests.

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33 Although N is thought to play an important role in the chronic acidification of surface waters in

some areas (cf. Sullivan et al., 1997), the elevated concentrations of NO 3 − in Shenandoah National Park

streams following defoliation did not appear to contribute to baseflow acidification in White Oak Run.

This was due to a concurrent increase in concentrations of base cations in streamwater (Webb et al.,

1995). Both NO 3 − and base cation concentrations increased during high-runoff conditions, although the

increase in base cations did not fully compensate for the episodic increase in NO 3 − . Episodic acidification

following defoliation thus became more frequent and more extreme in terms of observed minimum ANC

(Webb et al., 1995).

The full effect of the gypsy moth on aquatic resources in Shenandoah National Park is not well

understood. One consequence may be a reduction in the supply of available soil base cations and

associated effects on streamwater ANC. Repeated periods of defoliation would probably increase the

effect of episodic acidification on sensitive aquatic fauna and may determine the conditions under which

some species are lost. Ultimately such effects may depend upon both the severity of future gypsy moth or

other insect outbreaks and possibly on the amount of atmospheric N deposition. Gypsy moth populations

typically display a pattern of periodic outbreaks and collapse (Cambell, 1981). It remains to be seen what

the long-term pattern will be (Sullivan et al., 2003).

Webb et al. (1994) compared pre- and post-defoliation streamwater chemistry for 23 VTSSS

watersheds. Nitrate concentrations, measured quarterly, increased in most of the streams in response to

defoliation, typically by 10 to 20 μeq/L or more. The increased streamwater NO 3 − concentration was

probably derived from the N content of the foliage that had been consumed by the gypsy moth larvae and

converted to feces on the forest floor. Sulfate concentrations and ANC also decreased in streamwater.

Although the mechanism for decreased SO 4 2− was not totally clear, Webb et al. (1994) hypothesized that

increased nitrification in response to the increased soil N pool may have caused soil acidification, which

would be expected to have increased soil S adsorption (cf. Johnson and Cole, 1980). Decreased S

deposition during the comparison period may also have contributed to the SO 4 2− response.

Eshleman et al. (1998) reported NO 3 − outputs from five small (<15 km 2 ) forested watersheds in

Virginia and Maryland from 1988 to 1995. The study watersheds varied in geology, vegetation, and acid

sensitivity, with baseflow ANC typically in the range of 0 to 10 μeq/L in Paine Run to the range of 150 to

350 μeq/L in Piney River. Oak species ( Quercus spp.), which are a preferred food source of gypsy moth

larvae, occupied about 60% to 100% of the study watersheds. Nitrate concentrations increased in at least

three of the watersheds in association with intense defoliation by the gypsy moth larva during the late

1980s to early 1990s, to peak annual average NO 3 − concentrations of about 30 to 55 μeq/L. Most of the

increased NO 3 − leaching occurred during storm flow conditions.

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30 A number of other studies have been conducted that examined the effects of gypsy moth, or other

forest insect pests, on watershed biogeochemistry. Defoliation of poplars ( Populus sp.) by gypsy moth

larvae in southwestern Michigan did not result in appreciable NO − 3 leaching (Russell et al., 2004).

Other pest species can have similar effects. For example, spruce-fir forests throughout the southern

Appalachian Mountains have been subjected to significant disturbance, especially from the balsam wooly

adelgid, a European pest which has infested Fraser fir since about the 1960s. Severe fir mortality has

occurred in many areas. This disturbance factor has the potential to interact with acidic deposition and

other ecosystem stresses, and contribute to multiple-stress tree mortality and to changes in

biogeochemical cycling.

Defoliation by the elm spanworm ( Ennomos subsignarius Hübner) larvae in old-growth hemlockhardwood forests on the Allegheny High Plateau of northwestern Pennsylvania increased streamwater

NO − 3 concentrations from pre-defoliation levels of about 29 μeq/L to peak values the summer after

defoliation of about 100 μeq/L (Lewis and Likens, 2007).



# B.5.3. Wind or Ice Storm Damage

Forest blowdown might affect surface water acid-base chemistry by changing the pathway

followed by drainage water through watershed soils (Dobson et al., 1990). Pipes formed in the soil by

decaying tree roots can alter hydrologic flow so that less water enters the soil matrix, where neutralization

processes buffer the acidity of rainwater and snowmelt. Pipes tend to occur most commonly in nearsurface soil horizons where most tree rooting occurs. Contact between drainage water and mineral soil is

reduced when runoff is routed through them. If enhanced pipeflow, resulting from sudden extensive tree

mortality, affects a large portion of a watershed, runoff water may have less opportunity for acid

neutralization than would be the case in the absence of such pipeflow.

Severe canopy damage occurred in 1998 in response to an ice storm at HBEF a nd surrounding

areas in the White Mountains. Houlton et al. (2003) reported effects of this disturbance on N cycling and

leaching losses. Subsequent to the ice storm, drainage water NO − 3 concentrations increased sevenfold to

tenfold. Peak streamwater NO − 3 concentrations during spring months reached or exceeded 50 μeq/L at

many sites. There were no significant differences, however, in N mineralization, nitrification, or

denitrification rates between damaged and undamaged areas. Houlton et al. (2003) interpreted these

results as an indication that increased NO − 3 leaching was probably due to decreased root uptake rather

than accelerated N cycling by soil microbes. The amount of NO − 3 leaching loss was estimated to be more

than half of the entire year’s worth of atmospheric N deposition.

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## B.5.3.1. Fire

Fire can increase concentrations of NO − and O 2−

3 S 4 in soils and drainage water (cf. Chorover et al.,

1994; Riggan et al., 1994). Fenn and Poth (1998) hypothesized that successful fire suppression efforts

may have contributed to the development of N-saturation in fire-adapted ecosystems in southern

California by allowing N to accumulate in soil and in the forest floor, and by maintaining dense mature

stands with reduced N demand.

The effects of fire on NO − 3 leaching in chaparral stands in the San Gabriel Mountains, CA that

received high atmospheric N deposition were investigated by Riggan et al. (1994). Study watersheds were

burned with fires of different intensity and, after rainfall, NO − 3 and NH + 4 were measured in watershed

streams. Nitrogen release was up to 40 times greater in burned watersheds than in unburned watersheds,

and the amount and concentration of N release were found to be related to fire intensity.

Chorover et al. (1994) evaluated the effects of fire on soil and stream water chemistry in Sequoia

National Park, CA. Burning increased concentrations of NO − and SO 2−

3 4 in soil water and stream water.

Sulfate concentrations increased 100 fold. Nitrate concentrations also increased and remained higher in

soils and stream water for about 3 years. These results suggest that successful fire suppression may have

contributed to the development of N saturation in fire-adapted ecosystems in southern California by

allowing N to accumulate in the soil and forest floor, and by maintaining dense mature stands with

reduced N demand (Fenn and Poth, 1998).



## B.5.3.2. Multiple Stress Response

Acidification-related effects of S and N deposition do not occur in isolation; they interact with

disturbances of various types, both natural and human-caused. They also influence a range of

biogeochemical processes that may be difficult to predict. Overall, the interactions between disturbance

and ecosystem acidification as a consequence of acidic deposition are not well understood.

It is believed that high rates of N deposition cause increased susceptibility of forests to other

stressors, including reducing the resistance of some tree species to frost, insect damage, or drought. The

effects of acidic deposition can interact with a variety of stressors, both natural and human-caused. The

end result might include adverse effects that would not occur solely in response to acidic deposition, or in

response to any one of the other stressors.

Watershed disturbance might also effect Hg cycling and its relationship to S d eposition. For

example, Garcia and Carignan (2000), in a study of 20 watersheds in Quebec, Canada, found that the

average Hg concentration in 560–mm northern pike ( Esox lucius ) was significantly higher in lakes whose

watersheds had recently (1995) been logged (3.4 μg/g), as compared with reference lake watersheds

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28 (1.9 μg/g), that had remained undisturbed for at least 40 years. Fish tissue Hg concentration also increased

with increasing DOC and lakewater SO 2−

4 concentration, and with decreasing pH.



# B.6. Ecological indicators of acidification

B.6.1. Biological Indicators

Surface water acidification from acidic deposition causes effects on organisms at all trophic levels.

Early studies focused on the loss of fish populations, especially salmonids. Later studies also reported that

many species of phytoplankton, zooplankton, insect larvae, crayfish, snails, and freshwater mussels are

sensitive and are often reduced or absent from acidified lakes and streams (Havas, 1986; Baker et al.,

1990a). Similarly, many species of microrhizal fungi and lichens have been reported to be particularly

sensitive to acidic deposition in terrestrial ecosystems.

Effects of acidification on aquatic biota have been demonstrated in laboratory and field bioassays

(e.g., Baker et al., 1996), whole-ecosystem acidification experiments (e.g., Schindler et al., 1985), and

field surveys (e.g., Baker and Schofield, 1982; Gallagher and Baker, 1990). Many of the species that

commonly occur in acid-sensitive surface waters susceptible to acidic deposition cannot reproduce or

survive if the water is acidic. Some sensitive species of fish, invertebrates, and algae cannot survive at

moderate levels of acidity. For example, some zooplankton predators, sensitive mayfly species, and

sensitive fish species are affected at pH values below the range of 5.6 to 6.0 (Baker and Christensen,

1991). Such pH values generally equate to ANC below about 25 to 50 μeq/L.

There are few published examples of long-term monitoring data for biological assemblages in acidsensitive surface waters, and none in the U.S. Therefore, conclusions about the effect of acidic deposition

on the distribution of sensitive species are based on other kinds of data (Stoddard et al., 2003). For

example, the number of fish species increases with increasing pH and ANC when evaluated for multiple

water bodies across the landscape. This result has been shown for streams in Virginia, lakes in the

Adirondacks, and both high-elevation and seepage lakes in Maine (Figure B-17).

Given the available data, it is clear that acidification from acidic deposition limits the distribution

of acid-sensitive fish, benthic invertebrate, phytoplankton, and zooplankton species, but a lack of

adequate data makes it difficult to quantify the magnitude of change in biota from historical condition or

in response to recent (past two to three decades) decreases in acidic deposition in individual lakes or

streams. Studies in Canada and Europe have illustrated the feasibility and complexity of biological

recovery in response to decreased acidity.

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33 Threshold pH levels for adverse biological effects have been summarized for a variety of aquatic

organisms (Haines and Baker, 1986; Baker et al., 1990a). The effects of low pH are specific to the

organism, and perhaps region, under consideration and depend also upon the concentrations of other

chemical constituents in the water, notably Al i and Ca 2+

. In general, populations of salmonid fish are not

found at pH levels less than 5.0, and smallmouth bass ( Micropterus dolomieu ) populations are usually not

found at pH values less than 5.2 to 5.5 (Haines and Baker, 1986). A number of synoptic surveys indicate

loss of species diversity and absence of many other fish species in the pH range of 5.0 to 5.5 (Haines and

Baker, 1986). Levels of pH less than 6.0 to 6.5 have been associated with adverse effects on populations

of dace, minnows, and shiners (family Cyprinidae), and bioassays suggest that given sufficient Al

concentrations, pH less than 6.5 can lead to increased egg and larval mortality in blueback herring ( Alosa

aestivalis ) and striped bass ( Morone saxatilis ) (Hall, 1987; Klauda et al., 1987).

Mycorrhizal fungi have been suggested as possible biological indicators of atmospheric deposition

effects by Løkke et al. (1996) because they are intimately associated with tree roots, depend on plant

assimilates, and play essential roles in plant nutrient uptake. Thus, mycorrhizal fungi can influence the

ability of their host plants to tolerate different anthropogenically generated stresses. Mycorrhizae and

associated fine roots have short lifespans and their turnover appears to be controlled by environmental

factors. Changes in mycorrhizal species composition, or the loss of dominant mycorrhizal species in areas

where diversity is already low, may cause increased susceptibility of plants to stress (Løkke et al., 1996).

Mycorrhizal fungi are dependent for their nutrition on the supply of assimilates from the host plant.

Stresses that shift the allocation of C reserves to the production of new leaves at the expense of supporting

tissues will be reflected rapidly in decreased fine root and mycorrhizal biomass (Winner and Atkinson,

1986). Decreased C allocation to roots could also affect soil carbon and rhizosphere organisms. For

example, earthworms are believed to decrease in abundance, and in species number, in acidified soils

(Løkke et al., 1996). Soil dwelling animals, including earthworms, are important for decomposition, soil

aeration, and nutrient redistribution in the soil. They contribute to decomposition and nutrient availability,

mainly by increasing the accessibility of dead plant material to microorganisms.



## B.6.1.1. Phytoplankton

Phytoplankton are the small microscopic plants or plant-like organisms that live suspended in the

water column of lakes and large rivers. Acidification results in decreased species richness and diversity of

phytoplankton communities. There is also a shift in the composition of dominant taxa, but species

composition shifts cannot be accurately predicted (though it is clear that community restructuring occurs

with acidification). This effect is most prevalent in the pH 5 to 6 range (Baker et al., 1990a). Acidification

has also been found to cause decreases in food web complexity (indicated by the number of trophic links

or species) in the Adirondack Mountains (Havens and Carlson, 1998). Both Al toxicity and P limitation

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34 may also be responsible for shifts in phytoplankton community composition. Neither grazing pressure nor

changes in water clarity associated with acidification seem to have a major effect on phytoplankton

community structure. There is no consistent pattern of acidification effects on phytoplankton biomass.

Various lakes have shown increases, decreases, or no change in phytoplankton biomass with acidification

(Baker et al., 1990a). Leavitt et al. (1999) suggested that the complex interactions between pH, DOC, and

light explain the high variability in the algal biomass-acidification relationship. In most lakes,

acidification has a negligible effect on primary productivity.

Diatoms, which comprise an important component of the phytoplankton, are excellent indicators of

environmental change in aquatic ecosystems, including acidity, nutrient status, salinity, and climatic

features (Sullivan and Charles, 1994; Stoermer and Smol, 1999). There are thousands of different species,

many of which have rather narrow ecological tolerance ranges. Diatoms have been widely used as

indicators of past lake acidification. Inference based on diatom fossil remains preserved in lake sediments

is an excellent approach for quantifying historical chemical change (Charles and Norton, 1986).

Paleolimnological reconstructions of past lakewater chemistry are based on transfer functions

derived from relationships between current lakewater chemistry and diatom (or, in some cases,

chrysophyte) algal remains in surface sediments. Predictive relationships are developed using regional

lake datasets, and are then applied to diatom assemblage data collected from horizontal slices of lake

sediment cores to infer past lakewater conditions (Charles et al., 1989; Husar and Sullivan, 1991).

Periphyton are the small microscopic plants (or plant-like organisms) that live on submerged

substrates in aquatic systems (e.g., stream or lake bottoms). As seen in phytoplankton communities,

acidification results in decreased species richness, community alteration, and emergence of new dominant

species in periphyton communities. Many diatom and blue-green bacterial periphyton species cannot

tolerate acidic conditions. On the other hand, green algae, particularly the filamentous Zygnemataceae,

increase in relative abundance at lower pH (Baker et al., 1990a). Unlike for phytoplankton, there is

evidence that the biomass of attached periphyton increases at lower pH.

Studies of phytoplankton recovery from acidification indicate that there is an increase in

phytoplankton species richness and diversity as pH increases. In the Experimental Lakes area of Ontario,

previously acidified lakes have been experimentally de-acidified. In Lake 223, there was little increase in

phytoplankton diversity as pH changed from 5.0 to 5.8 but a strong recovery of diversity at pH above 6

(Findlay and Kasian, 1996). In Lake 302S, profound change began at pH 5.5; phytoplankton assemblages

at pH below 5.5 resembled acidified lakes. Cyanobacteria were among the first to recover at pH 5.5 to 5.8

(Findlay et al., 1999). In the Killarney Park area of Ontario, Findlay (2003) reported that lakes that were

previously low in pH (5.0 to 5.5) and are now above pH 6 have shifted towards phytoplankton

assemblages typical of circumneutral environments.

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34 

## B.6.1.2. Zooplankton

Field survey and experimental lake studies both indicate that acidification reduces zooplankton

species richness. Effects of acidification on community biomass and abundance, however, were not

definitive. Some studies indicated a lower biomass under low pH conditions, whereas other studies

showed no consistent pattern in the biomass-pH relationship. Limited data indicated that acidification

does not alter zooplankton community grazing rates. Zooplankton species that have been shown to be

sensitive to low pH include Diaptomus sicilis, Epischura lacustris, Tropocyclops parsinus mexicanus,

Daphnia galeata mendotae, Daphnia rosea, Diaphanosoma birgei, Leptodora kindtii, Asplanchna

priodonta, and Conochilus unicornis

. In North America, species reported to have increased dominance in

acidic lakes (acid-tolerants) include Keratella taurocephala, Bosmina longirostris, and Diaptomus

minutus

. Possible mechanisms for zooplankton sensitivity to low pH include ion regulation failure,

reduced oxygen uptake, inability to reproduce, and Al toxicity. Indirect effects of acidification on

zooplankton communities are also possible due to pH-induced shifts in higher trophic level zooplankton

predators. This mechanism is probably of less importance than the direct effects of low pH. It is also

probable that under acidic conditions, zooplankton communities are less able to ameliorate nutrient

additions or control algal densities (Baker et al., 1990a).

Reported pH thresholds for zooplankton community alteration range from 5 to 6. Holt and Yan

(2003) reported a threshold of community change at pH 6 for lakes in southern Ontario. Locke and

Sprules (1994) reported that acidification below pH 5 in the 1970s overcame the resistance stability of the

zooplankton community in Ontario Precambrian Shield lakes. The subset of study lakes that showed pH

recovery from acidification 20 years later in 1990 also showed recovery in the stability of the zooplankton

community. Holt and Yan (2003) also noted recovery in zooplankton community composition (based on

similarity to neutral lakes) in the subset of Killarney Park (Ontario) lakes in which the pH increased to

over 6 during the 1971 to 2000 study period. They did not, however, note any time trend of increasing

species richness between recovering lakes and non-recovering lakes.

Recovery in experimentally acidified Lake 223 back to pH 6.1 was studied by Malley and Chang

(1995). They reported that the zooplankton community was still in a state of flux. Species diversity that

had been reduced during the acidification phase had partially returned to preacidification levels. Rotifers

had recovered less than crustaceans. One decade after cessation of the experimental acidification of Little

Rock Lake in Wisconsin, recovery of the zooplankton community was complete (Frost et al., 2006).

Recovery did not follow the same trajectory as the initial acidification, however, indicating a substantial

hysteresis in zooplankton community recovery. About 40% of the zooplankton species in the lake

exhibited a lag of 1 to 6 years to recover to levels noted in the neutral reference basin.

In situ enclosure studies were conducted for 35 days at Emerald Lake in the Sierra Nevada by

Barmuta et al. (1990). The lake sediments were included within the experimental enclosures. This allowed

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32 the investigators to document the response of zoobenthos as well as zooplankton. Treatments included a

control (pH 6.3) and acid addition to reach pH levels of 5.8, 5.4, 5.3, 5.0, and 4.7. Results indicated that

zooplankton were sensitive to acidification but zoobenthos were unaffected by the experimental

treatment. Daphnia rosea and Diaptomus signicauda decreased in abundance below the range of pH 5.5

to 5.8 and were eliminated below about pH 5.0. Bosnia longirostris and Keratella taurocephala generally

became more abundant with decreasing pH. Barmuta et al. (1990) concluded that even slight acidification

of high-elevation lakes in the Sierra Nevada might alter the structure of the zooplankton community.

Sullivan et al. (2006a) found that zooplankton taxonomic richness varied with ANC in Adirondack

lakes (Table B-22). Taxonomic richness expressed as number of species of crustaceans, rotifers, and total

zooplankton, increased with increasing ANC. In general, lakewater ANC explained nearly half of the

variation in total zooplankton and crustacean taxonomic richness, but less for rotifer richness. These

results (Table B-22) provided the basis for estimating changes in zooplankton richness in response to past

or future changes in lakewater ANC. Several zooplankton species found in lakes in the Sierra Nevada are

also known to be sensitive to acidity status (Gerritsen et al., 1998).



## B.6.1.3. Benthic Invertebrates

Within stream systems, macroinvertebrate communities are among the most sensitive life forms to

disturbances, including those associated with atmospheric deposition (Cairns and Pratt, 1993). In

addition, they are relatively easy to sample in the field (Plafkin et al., 1989; Resh et al., 1995; Karr and

Chu, 1999).

Acidification results in the loss of acid-sensitive benthic invertebrates and decreases in pH of one

unit or more typically result in species loss. Invertebrate taxa that are most sensitive to acidification are

mayflies, amphipods, snails, and clams. At low levels of acidification (pH 5.5 to 6.0), acid-sensitive

species are replaced by more acid-tolerant species, yielding little or no change in total community species

richness, diversity, density, or biomass. If pH decreases are larger, more species will be lost without

replacement, resulting in decreased richness and diversity. Many sites also note decreases in invertebrate

biomass and productivity (more so in streams than lakes). High levels of acidification (pH < 5) were

found to virtually eliminate all mayflies, crustaceans and mollusks from French streams (Guerold et al.,

2000). Examples of sensitive benthic invertebrate species include Baetis rhodani, Gammarus lacustris,

Hyalella azteca, Asellus aquaticus, Orconectes rusticus

, and O. propinquous

. Stoneflies are generally

more acid-tolerant than mayflies and caddisflies.

Possible mechanisms for acidification effects on invertebrates include direct toxicity of H + and Al,

disruption of ion regulation, and reproductive failure. Indirect effects due to acidification-induced changes

to invertebrate predator populations are also possible (Baker et al., 1990a). Acidic episodes in streams can

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35 cause increased downstream drift of acid-sensitive species, particularly Baetis (Kratz et al., 1994; Smock

and Gazzera, 1996).

It has been well documented that low streamwater pH can be associated with reductions in

invertebrate species richness or diversity (Townsend et al., 1983; Raddum and Fjellheim, 1984; Burton

et al., 1985; Kimmel et al., 1985; Hall and Ide, 1987; Peterson and Van Eechhaute, 1992; Rosemond et al.,

1992; Sullivan et al., 2003), and sometimes density (Hall et al., 1980; Townsend et al., 1983; Burton et al.,

1985; Kimmel et al., 1985). Effects on invertebrate density are not universal; a number of studies have

found no density effects (Harriman and Morrison, 1982; Simpson et al., 1985; Ormerod and Tyler, 1987;

Winterbourn and Collier, 1987). However, a decrease in species richness with decreasing pH has been

found in almost all such studies (Rosemond et al., 1992), and this finding has been especially pronounced

in streams for order Ephemeroptera (mayflies).

The Ephemeroptera-Plecoptera-Tricoptera (EPT) Index is a common measure of stream

macroinvertebrate community integrity. The EPT metric is the total number of families present in those

three insect orders (mayflies, stoneflies, and caddisflies, respectively). The total number of families is

generally lower at acidified sites because species within those families tend to exhibit varying acid

sensitivity (cf. SAMAB, 1996). Mayflies tend to be most sensitive of the three, and stoneflies tend to be

least sensitive (Peterson and Van Eechhaute, 1992).

There has been some recovery in benthic invertebrate communities in surface waters exhibiting

chemical recovery from acidification. In Scotland, Soulsby et al. (1995) reported an increase in acidsensitive mayflies in some streams that showed recent ANC increases. However, no increases in

invertebrates were observed in the most acidic streams despite observed increases in ANC. They

suggested that further acidic deposition reductions and sufficient time for reversal of soil acidification

may be required before biotic recovery can occur. Tipping et al. (2002) noted increases of invertebrate

richness and diversity in the English Lake District in their study streams that had pH increases of 0.3 to

0.5 units since about 1970.

Responses of aquatic macroinvertebrates to acidification were evaluated by Kratz et al. (1994) in

12 streamside channels in Sequoia National Park, CA. Replicated treatments included a control (pH 6.5 to

6.7) and experimental exposure at pH levels of 5.1 to 5.2 and 4.4 to 4.6. Invertebrate drift was monitored

continuously and benthic densities were determined before and after acidification. Single 8–hr acid pulses

increased the drift of sensitive taxa, and benthic densities were reduced. Baetis showed reduced density

post-treatment to less than 25% of control densities in both pH reduction treatments (5.2, 4.6) and two

different experimental exposures. Densities of Paraleptophlebia appeared to be reduced by the

acidification, but most treatment effects were not statistically significant. Kratz et al. (1994) suggested

that the effects of acid inputs on benthic species densities depended on microhabitat preferences. Baetis

nymphs are epibenthic and active. They are often found on the upper surfaces of rocks where they are

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33 directly exposed to acidified water. This may have been responsible for their greater response to

acidification.



## B.6.1.4. Fish

By 1990 it was well established that pH in the range of 4.0–6.5 could cause significant adverse

biological effects on fish. Low pH was one of the most important factors resulting in adverse effects. The

toxicity of pH was, in most cases, the result of impaired body salt regulation. Decreased pH in the water

inhibited the active uptake of Na + and Cl − and stimulated the passive loss of these ions (Baker et al.,

1990a).

The response to acidification was not uniform, however. Some species and life stages experienced

significant mortality in bioassays at relatively high pH (e.g., pH 6.0–6.5 for eggs and fry of striped bass

and fathead minnow) (Buckler et al., 1987; McCormick et al., 1989), whereas others were able to persist

at quite low pH without adverse effect (Mudminnow; [Umbra spp.] at pH 4.0 and Umbra pygmaea at pH

3.5) (Dederen, 1987). Many minnows and dace (Cyprinidae) are sensitive to acidity (threshold effects at

pH < 5.5 to 6.0), but some common game species such as brook trout, largemouth bass, and small mouth

bass are relatively insensitive (threshold effects at pH < 5.0 to 5.5). A summary of studies that

demonstrated the difference among species is shown in Table B-23. Table B-24 summarizes the results

from a variety of studies that determined the threshold values of pH for various taxa and kinds of effects.

The effect of acidification on aquatic organisms, especially fish, is due in large part to the toxic

effect of Al i that is released from watershed soils. A number of studies reviewed by Baker et al. (1990a)

reported threshold values of Al i for various species and effects. Those results are presented in Table B-25.

The effects of low pH and high Al i can be ameliorated to an extent in the presence of increased Ca 2+

concentration. A summary of the effect of increasing Ca 2+ concentration is presented in Table B-26.

Fish populations in acidified streams and lakes of Europe and North America have declined, and

some have become extinct as a result of atmospheric deposition of acids and the resulting changes in

water quality (Baker et al., 1990a). A variety of factors, including Al i , DOC, and Ca 2+

, along with the

timing and magnitude of episodic fluctuations in toxic acid and Al i concentrations, are related to the

degree to which surface water acidification influences fish survival in natural systems (Baker et al.,

1990a; Gagen et al., 1993; Siminon et al., 1993; Van Sickle et al., 1996; Baldigo and Murdoch, 1997).

Aluminum fractionation and Al i concentration are directly dependent upon pH levels (Driscoll et al.,

1985).

Fish communities of acid-sensitive streams and lakes may contain a variety of species, but are

often dominated by trout. Across the eastern U.S., brook trout is often selected as an indicator of

acidification effects on aquatic biota because it is native to many eastern streams and lakes and because

residents place great recreational and aesthetic value on this species. It must be emphasized, however, that

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35 brook trout is a relatively acid-tolerant species. Many other fish species, including rainbow and brown

trout, as well as a variety of other fish species, are more acid-sensitive than brook trout. In many

Appalachian Mountain streams that have been acidified by acidic deposition, brook trout is the last

species to disappear; it is generally lost at pH near 5.0 (MacAvoy and Bulger, 1995), which usually

corresponds in these streams with ANC near 0 (Sullivan et al., 2003).

Although there are known differences in acid sensitivity among fish species, experimentally

determined acid sensitivities are available for only a minority of freshwater fish species. Baker and

Christensen (1991) reported critical pH values for 25 species of fish. They defined critical pH as the

threshold for significant adverse effects on fish populations. The reported range of pH values represents

the authors’ estimate of the uncertainty of this threshold. The range of response within species depends on

differences in sensitivity among life stages, and on different exposure concentrations of Ca 2+ and Al. To

cite a few examples, blacknose dace is regarded as very sensitive to acid stress, because population loss

due to acidification has been documented in this species at pH values as high as 6.1; in field bioassays,

embryo mortality has been attributed to acid stress at pH values as high as 5.9. Embryo mortality has

occurred in common shiner at pH values as high as 6.0. Although the critical pH range for rainbow trout

is designated as 4.9–5.6, adult and juvenile mortality have occurred at pH values as high as 5.9. Brown

trout population loss has occurred over the pH range of 4.8–6.0, and brook trout fry mortality has

occurred over the range of 4.8–5.9 (Baker and Christensen, 1991). Relative sensitivities can be suggested

by regional surveys as well, although interpretation of such data is complicated by factors that correlate

with elevation. Such factors, including habitat complexity and refugia from high-flow conditions, often

vary with elevation in parallel with acid sensitivity. It is noteworthy, however that about half of the 53 fish

species found in Adirondack Mountain waters in New York never occur at pH values below 6.0 (Kretser

et al., 1989; Driscoll et al., 2001b); for those species whose acid tolerances are unknown, it is probable

that acid sensitivity is responsible for at least some of these absences. It is the difference in acid tolerance

among species that produces a gradual decline in species richness as acidification progresses, with the

most sensitive species lost first.

Effects on biota can be assessed as effects on a particular sensitive species or species perceived to

be important, or as effects on the richness or diversity of fish or other potentially sensitive life form. For

example, Bulger et al. (2000) developed ANC thresholds for brook trout in Virginia, which are presented

in (Table B-27). These values were based on annual average stream water chemistry, and therefore

represent chronic exposure conditions. The likelihood of additional episodic stress is incorporated into the

response categories in the manner in which they are interpreted. For example, the episodically acidic

response category, which has chronic ANC in the range of 0 to 20 μeq/L, represents streams that are

expected to acidify to ANC near or below 0 during rainfall or snowmelt episodes. In such streams,

sublethal and/or lethal effects on brook trout are possible (Bulger et al., 2000; Sullivan et al., 2003).

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35 Fish species richness, population density, condition factor, age distribution, size, and bioassay

survival have all been shown to be reduced in low-ANC streams as compared to intermediate-ANC and

high-ANC streams (Bulger et al., 1995; Dennis et al., 1995; Dennis and Bulger, 1995; MacAvoy and

Bulger, 1995). Fish species richness is a good indicator of acidification response. Lakes or streams having

pH below about 5.0 or ANC below about 0 generally do not support fish. Depending on the region, waters

having pH above about 6.5 and ANC above about 50 μeq/L support large, but variable, numbers of

species. There is often a positive relationship between pH and number of fish species, at least for pH

values between about 5.0 and 6.5, or ANC values between about 0 and 50 to 100 μeq/L (Bulger et al.,

1999; Sullivan et al., 2006a). Such observed relationships are complicated, however, by the tendency for

smaller lakes and streams, having smaller watersheds, to also support fewer fish species, irrespective of

acid-base chemistry. This pattern may be due to a decrease in the number of available niches as stream or

lake size decreases. Nevertheless, fish species richness is one of the most useful indicators of biological

effects of surface water acidification.

Acidification and the associated elevated concentrations of Al i in surface waters have adversely

affected fish populations and communities in parts of the Adirondack Mountains of northern New York

(Baker and Schofield, 1982; Johnson et al., 1987; (Schofield and Driscoll, 1987)Kretser et al., 1989;

Siminon et al., 1993) and in acid-sensitive streams of the Catskill Mountains of southeastern New York

(Stoddard and Murdoch, 1991) and the Appalachian Mountains from Pennsylvania to Tennessee and

South Carolina (SAMAB, 1996; Bulger et al., 1999, 2000).

Adverse effects of low pH and high Al i concentration on fish include increased mortality, decreased

growth, decreased reproductive potential, and ionoregulatory impairment. A partial list of studies

demonstrating such effects is provided in Table B-28 from Baker et al. (1990a). It has been shown,

however, that there is marked variability among species and among life stages within species in the

specific levels of pH and Al i that produce measurable responses.

Surface-water acidification can affect fish populations by a number of mechanisms ranging from

increased morality and emigration to decreased food supplies (Baker et al., 1990a). The primary reason

for population decline and extinction, however, is usually the failure of a species to successfully recruit

young-of-the-year fish (Mills et al., 1987; Brezonik et al., 1993). The response of aquatic communities to

acidification, therefore, should appear first as changes in age distribution and decreased health of

individual fish (growth and condition), then as decreased biomass and density in populations of acidintolerant fish species, and finally as elimination of sensitive species (Baker et al., 1990a).

The primary mechanism for the toxic effects of low pH and elevated Al on fish involves disruption

of normal ion regulation at the gill surface resulting in increased rates of ion loss and inhibition of ion

uptake (McWilliams and Potts, 1978; Leivestad, 1982; Wood and McDonald, 1987; Bergman et al.,

1988). Additional effects might include disruption of Ca 2+ metabolism (Peterson and Martin-Robichaud,

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35 1986; Gunn and Noakes, 1987; Reader et al., 1988), and decreased hatching success (Runn et al., 1977;

Peterson et al., 1980; Haya and Waiwood, 1981; Waiwood and Haya, 1983).

Prominent physiological disturbance for fish exposed to acid waters are iono- and osmoregulatory

failure, acid-base regulatory failure, and respiratory and circulatory failure. Most of these effects can be

directly attributed to effects on gill function or structure. The acute toxicity of low pH in acidic waters

results in the loss of Ca 2+ from important binding sites in the gill epithelium, which reduces the ability of

the gill to control membrane permeability (McDonald, 1983; Havas, 1986; Exley and Phillips, 1988).

The energy costs to fish for active iono-osmoregulation can be substantial (Farmer and Beamish,

1969; Bulger, 1986). The concentrations of serum electrolytes (such as Na + and Cl

− ) are many times

higher (often 100–fold higher) in fish blood than in the fresh waters in which they live. The active uptake

of these ions occurs at the gills. Because of the steep gradient in Na + and Cl − concentrations between the

blood and fresh water, there is constant diffusional loss of these ions, which must be replaced by energyrequiring active transport. Low pH increases the rate of passive loss of blood electrolytes (especially Na +

and Cl

− ); and Al elevates losses of Na + and Cl − above the levels due to acid stress alone (Wood, 1989).

For example, dace in an acidified stream maintain whole-body Na + at levels similar to dace in a highANC stream (Dennis and Bulger, 1995), despite probable higher gill losses of electrolytes due to acid/Al

stress. Therefore, the homeostatic mechanisms at the gill responsible for maintaining blood electrolyte

levels must work harder and use more energy to maintain these levels for dace in the acidified stream.

Whole lake experiments and artificial stream channel experiments have shown that acidification

can lead to loss of fish species. A summary of the work on Lake 223 in the Experimental Lakes Area in

Canada is provided in Table B-29. Work at Little Rock Lake in Wisconsin suggested that rock bass

suffered recruitment failure at pH 5.6 or below. Artificial channel studies showed poor survival and

reproductive success for fathead minnow at pH 5.9 to 6.0.

ANC criteria have been used for evaluation of potential acidification effects on fish communities.

The utility of these criteria lies in the association between ANC and the surface water constituents that

directly contribute to or ameliorate acidity-related stress, in particular pH, Ca 2+ , and Al. Bulger et al.

(2000) developed ANC thresholds for brook trout response to acidification in forested headwater

catchments in western Virginia (See Table B-27). Note that because brook trout are comparatively acid

tolerant, adverse effects on many other fish species should be expected at relatively higher ANC values.

Streams with chronic ANC greater than about 50 μeq/L are generally considered suitable for brook

trout in southeastern U.S. streams because they have a large enough buffering capacity that persistent

acidification poses no threat to this species, and there is little likelihood of storm-induced acidic episodes

lethal to brook trout. In such streams, reproducing brook trout populations are expected if the habitat is

otherwise suitable (Bulger et al., 2000), although some streams may periodically experience episodic

chemistry that affects species more sensitive than brook trout. Streams having annual average ANC from

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35 20 to 50 μeq/L may or may not experience episodic acidification during storms that can be lethal to

juvenile brook trout, as well as other fish. Streams that are designated as episodically acidic (chronic ANC

from 0 to 20 μeq/L) are considered marginal for brook trout because acidic episodes are likely (Hyer

et al., 1995), although the frequency and magnitude of episodes vary. Streams that are chronically acidic

(chronic ANC less than 0 μeq/L) are not expected to support healthy brook trout populations (Bulger

et al., 2000).

Field surveys provided a regional context for fish response to acidification. Although there were

some variations, the results of field surveys generally confirmed the results of bioassays, field

experiments, and other intensive field studies. The results of many field surveys were summarized in

Baker et al. (1990a) and are compiled in (Table B-30).

It is important to note, however, that the absence of fish from a given lake or stream in an area that

experiences surface water acidification does not necessarily imply that acidification is responsible for the

absence of fish. For example, results of fisheries research in the Adirondacks has indicated that many

Adirondack lakes always had marginal spawning habitat for brook trout (Schofield, 1993), and some of

the currently fishless acidic lakes probably never supported fish.

Many of the data for the assessment of fish status in the Adirondack region of New York come

from the reports by Kretser et al. (1989) and Baker et al. (1990a). The status of fish and of the presence of

individual species were related to a variety of lake characteristics. Of the lakes without fish, 42% had high

organic acid content that may have caused the observed low pH, 13% were bog lakes of high acidity and

naturally poor fish habitat, 9% had pH > 5.5 suggesting other factors were likely responsible for the lack

of fish, and 3% were small high-elevation lakes that were unlikely to have fish regardless of acid-base

chemistry. However, 34% of the lakes surveyed (112 lakes) that had no fish at the time of survey had low

pH that was most likely the result of acid deposition and no other obvious explanation for the lack of fish.

Multivariate regression of the presence/absence of brook trout in Adirondack waters produced a

ranking of factors that appeared to influence the presence of brook trout when biological factors were

excluded from the analysis (stocking, presence of associated species, and presence of competitors).

Among contributing factors, including SiO 2 , ANC, DOC, substrate, and distance to the nearest road, pH

ranked first as a predictor of brook trout presence (Christensen et al., 1990). The results of this analysis

supported the hypothesis that 1990 levels of pH and related variables restricted the distribution of some

fish in Adirondack waters.

Fish toxicity models have been developed as mathematical regression functions fit to observations

of fish mortality when exposed to constant levels of pH, Al i , and Ca 2+ in laboratory toxicity tests. These

models had the advantage that they dealt directly with the interaction effects of pH, Al, and Ca 2+ , but they

did not account for the effects of variations in other aspects of surface water quality, and they could not be

directly interpreted in terms of population-level response.

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32 The many bioassays conducted of pH effects were screened by Baker et al. (1990a) to provide data

most suitable for model development. Bioassays selected for inclusion were those that measured the

mortality of early life stages, those that incorporated different combinations of pH, Al, and Ca 2+

, and those

that used fish of varying sensitivity (Bergman et al., 1988).

Acidity and Al toxicity are not the only stress factors that influence the distribution of fish in acidsensitive streams. Other habitat characteristics, including water temperature and stream channel

morphology, can be important (Sullivan et al., 2003). In addition, it is probable that some trout

populations have been affected by competition with other introduced species (cf. Larson and Moore,

1985).



## B.6.1.5. Amphibians

Some species of amphibians are considered to be highly sensitive to changes in environmental

conditions and some species have probably been adversely effected by acidic deposition in some areas.

Furthermore, several species of amphibian have exhibited marked declines in abundance throughout the

western U.S. in recent decades and there has been much speculation concerning the cause(s) of these

declines in abundance.

Populations of many species of amphibians have declined or become eradicated throughout the

world in recent decades (Barinaga, 1990; Wake, 1991). The causes have not been evident and some of the

declines have occurred in remote pristine areas. For example, in the Sierra Nevada, at least two of five

species of aquatic-breeding amphibians, Rana muscosa (mountain yellow-legged frog) and Bufo canorus

(Yosemite toad) have been declining (Phillips, 1990). A number of hypotheses have been proposed for

amphibian decline, including acidic deposition. In the western U.S., however, acidic deposition has been

discounted as the primary cause of the decline of R. muscosa and B. canorus in the Sierra Nevada and of

R. pipiens and B. boreas in the Rocky Mountains (Corn et al., 1989; Bradford et al., 1992). Grant et al.

(2005) reported little relationship between streamwater ANC and the adjacent salamander community in

Shenandoah National Park.

In some cases, population fragmentation as a consequence of fish predation may be a more likely

cause (Bradford et al., 1993). It is generally recognized that R. muscosa was eliminated by introduced fish

early in the 20th century in many lakes and streams in Sequoia and Kings Canyon National Parks. The

amphibians have been eliminated from nearly all waters inhabited by fish, presumably by predation on

tadpoles. Prior to 1870, virtually all of the high-elevation (>2500 m) lakes in the Sierra Nevada were

barren of fish, but have since been stocked with fish. Fish introductions may have contributed to recent

amphibian declines because amphibian populations are now more isolated from each other than formerly.

The role of atmospheric deposition as an additional stressor is not clear.

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33 The acidification sensitivity of temporary ponds, where many amphibians live or reproduce, have

not been well studied. These ponds tend to fill directly from rain or snowmelt and thus can be more acidic

than surrounding lakes and streams. There is a correlation between pond acidity and amphibian

abundance.

There are both acid-sensitive and acid-tolerant amphibians. Examples of acid-sensitive amphibians

include the spotted salamander (Ambystoma maculatum) and Jefferson salamander ( Ambystoma

jeffersonianum ). Embryos of acid-sensitive species are killed by water with pH less than about 4.5. Acidtolerant embryos may survive at a pH of 3.7. Toxicity is not solely a matter of pH, but is also influenced

by Ca 2+

, Al i , and DOC concentrations. It is also dependent on the life stages present and water

temperature (Baker et al., 1990a). Large-scale amphibian extinctions in any geographic region due to

acidic deposition have not been detected.

Although acidic deposition may play a role in some areas, there is no evidence to suggest that it is a

primary factor. Other issues, including fish introductions, are probably more important as stressors on

amphibian populations across broad regional to national scales.



## B.6.1.6. Fish­Eating Birds

Relative to other trophic groups, there are few studies assessing acidification effects on fish-eating

birds. Limited data suggest that fish-eating birds are adversely affected by acidification. Acidification

effects on birds may be indirect, related to changes in the quantity and quality of food. Other potential

causal pathways include delayed egg laying, lighter/thinner egg shells, and reduced chick growth in acidic

waters (Tyler and Ormerod, 1992). There is also concern about increased metal and Hg concentrations in

fish-eating birds associated with bioaccumulation from contaminated fish in known areas of acidification

(Baker et al., 1990a).

Fish-eating birds can serve as biological indicators of lakes affected by acidic deposition (McNicol,

2002). Lack of prey resources, decreased food quality, and elevated lake water methylmercury (MeHg)

concentrations that could be associated with acidification may negatively effect foraging, breeding, and/or

reproduction for the common loon ( Gavia immer ), common merganser ( Mergus merganser ), belted

kingfisher ( Ceryle alcyon ), osprey ( Pandion haliatus ), American black duck ( Anas rubripes ), ring-necked

duck ( Aythya collaris ), eastern kingbird ( Tyrannus tyrannus ), and tree swallow ( Tachycineta bicolor )

(Table B-31) (Longcore and Gill, 1993). Breeding distribution for the common goldeneye ( Bucephala

clangula ), an insectivorous bird, may be positively effected by acidic deposition (Longcore and Gill,

1993). Reduced prey diversity and quantity have been observed to create feeding problems for nesting

pairs of loons on low-pH lakes in the Adirondacks (Parker, 1988).

Since the mid 1980s, a statistically significant increase in fish-eating birds has been observed in the

Sudbury region of Ontario, Canada, which has corresponded with a decreasing abundance of common

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32 � S1: sulfate emissions equal to those in the early 1980’s (base case)

� S2: sulfate emissions equal to that in 1994 (full Canadian emissions reductions based on the

1991 Canada/U.S. Air Quality Agreement)

� S3: expected sulfate emissions in 2010 (full implementation of U.S. emissions reductions

based on the 1991 agreement)

� S4: a hypothetical 50% reduction in expected 2010 sulfate emissions

� S5: a hypothetical 75% reduction in expected 2010 sulfate emissions g oldeneye (McNicol, 2 0 02). This interaction has been attributed to an increase in prey for pisciverous

birds and a decrease in available prey for insectivorous birds as a result of stricter S e missions controls in

the U.S. and Canada (McNicol, 2002). Logistic regression modeling with measured pH and species

occurrence data for acid-sensitive lakes in the Algoma region of Ontario showed that the occurrence of

fish, common loons, and common mergansers is positively related to lake water pH (McNicol, 2002).

Predictions of common loon and merganser recovery for this area were made using the Waterfowl

Acidification Response Modeling System (WARMS) under varying S e missions control scenarios

targeted for 2010 (McNicol, 2002). The modeled emissions scenarios include:

The number o f lakes projected to be suitable for supporting breeding pairs and broods increased

with lake pH and stricter emissions controls (Table B-32) (McNicol, 2002).

Marginal improvements to fish-eating bird habitat were predicted to occur by 2010 (S3), with more

significant improvements expected under hypothetical S e missions reductions of 50% and 75% (S4 and

S5) for lakes with pH below 6.5 (McNicol, 2002). Fundamental to the predicted improvement of these

fish-eating bird populations is the expected increase in food availability with lake pH recovery.

Elevated MeHg accumulation in fish-eating birds in Wisconsin and the northeastern U.S. has been

linked to lake acidification (Meyer et al., 1995; Hrabik and Watras, 2002; Evers et al., 2007). This form of

Hg is toxic, bioavailable, and accumulates in top predators to levels of concern for both human health and

the environment (Table B-33) (Evers et al., 2007).

Acidic deposition might contribute to Hg toxicity in fish-eating birds because SO 2−

4 addition to

wetland environments could stimulate the production of MeHg, thereby increasing lake water

concentrations of MeHg (Jeremiason et al., 2006). Kramar et al. (2005) determined that the extent of

wetland located in close proximity (less than150 m) to loon territory was positively correlated with Hg

concentrations in loon blood. Wetland MeHg production is discussed in greater detail in Section 6.3.

Accumulation of MeHg in fish-eating birds can result in damage to nervous, excretory, and

reproductive systems (Wolfe et al., 1998). Table B-34 (Wolfe et al., 1998) lists several studies indicating

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2

3

4 effects related to mercury bioaccumulation in avian eggs and tissues. Reproduction is considered one of

the most sensitive endpoints to chronic low-level MeHg exposure for fish-eating birds (Wolfe et al.,

1998). Reduced clutch size, increased number of eggs laid outside the nest, eggshell thinning, and

increased embryo mortality have all been documented (Wolfe et al., 1998).

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### Table B­1. N­saturated forests in North America, including estimated N inputs and outputs.

Location Forest Type Elevation (m) N Input

(kg/ha/yr) N Output

(kg/ha/yr) Reference

Adirondack Mts.,

northeastern New York

Catskill Mts., southeastern

New York

Turkey Lakes Watershed,

Ontario, Canada

Whitetop Mt., southwestern,

Virginia

Fernow, West Virginia

Great Smoky Mts. National

Park, Tennessee

Great Smoky Mts. National

Park, Becking Site, North

Carolina

Great Smoky Mts. National

Park, Tower Site, North

Carolina

Front Range, Colorado

San Dimas, San Gabriel Mts.,

southern California

Camp Paivika, San

Bernardino Mts., southern

California

Location

Klamath Mts., northern

California

Thompson Forest, Cascade

Mts., Washington Northern hardwoods or

hardwood/conifer mix

Mainly hardwood; some

eastern hemlock

Sugar maple and yellow

birch

Red spruce

Mixed hardwood

American beech

Red spruce

Red spruce

Alpine tundra, subalpine

conifer

Chaparral and grasslands

Mixed conifer

Forest Type

Western coniferous

Red alder 396–661

335–675

350–400

1,650

735–870

1,600

1,800

1,740

3,000–4,000

580–1,080

1,600

Elevation

(m)

NA

220 9.3 a

10.2 a

7.0–7.7

(as throughfall)

32 c

15–20

3.1 d

10.3 d

26.6

7.5–8.0

23.3 e

30

N Input

(kg/ha/yr)

Mainly geologicg

4.7 plus > 100 as

N fixation

2 b Stage 1 N loss

Stage 1 and 2 N

b loss

17.9–23.6

47 c

6.1

2.9

19.2

20.3

7.5

0.04–19.4

7–26

N Output

(kg/ha/yr)

NAg

38.9 Driscoll and Van

Dreason (1993)

Stoddard (Stoddard,

1994)

Foster et al. (1989);

Johnson and Lindberg

(1992)

Joslin and Wolfe (1992);

Joslin et al. (1992)

Gilliam et al. (1996);

Peterjohn et al. (1996)

Johnson and Lindberg

(1992)

Johnson et al. (1991c)

Johnson et al. (1991c)

Williams et al. (1996)

Riggan et al. (1985)

Fenn et al. (1996)

Reference

Dahlgren (1994)

Johnson and Lindberg

(1992)

a Estimated total N deposition from wet deposition data is from Driscoll et al. (1991) for the Adirondacks, and from Stoddard and Murdoch (1991) for

the Catskills. Total deposition was estimated based on the wet deposition: total N deposition ratio (0.56) at Huntington Forest in the Adirondacks

(1992). N deposition can be higher in some areas, especially at high­elevation sites such as Whiteface Mountain (15.9 kg/ha/yr); Johnson, 1992).

b Stage 1 and 2 of N loss according to the watershed conceptual model of Stoddard (Stoddard, 1994). N discharge (kg/ha/yr) data are not available,

− only stream water NO 3 concentration trend data were collected.

c Values appear high compared to other sites, especially N leaching losses. Joslin and Wolfe (1992) concede that “there is considerable uncertainty

− associated with the estimates of atmospheric deposition and leaching fluxes.” However, elevated NO 3 concentrations in soil solution and lack of a growth response to N fertilization ratio (Joslin and Wolfe, 1994) support the hypothesis that the forest at Whitetop Mountain is N ­saturated.

d Estimated total N deposition from throughfall data. Total deposition was estimated based on the throughfall/total N deposition ratio from the nearby

Smokes Tower site (Johnson, 1992).

e Annual throughfall deposition to the chaparral ecosystem.

f N output is from unpublished stream water data (Fenn and Poth, 1999). The low value represents a year of average precipitation, and the high value

is for 1995, when precipitation was nearly double the long­term average. N output includes N export in stream water and to groundwater.

g Annual input and output data are not known, although N deposition in this forest is probably typical for much of the rural western U.S. (2–3 kg

N/ha/yr; Young et al., 1988). Excess N is from weathering of ammonium in mica schist bedrock. The ammonium was rapidly nitrified, leading to high

− NO 3 concentrations in soil solution (Dahlgren, 1994). 

### 1

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### Table

in the B­2. Summary of

six high­interest measured ANC,

areas. pH, and Al concentrations compared with reference values

Area n* N* Percent of Population with

ANC ≥ 0 pH ≥ 5.5 � g/ Ali > 100 L

AD IRONDACKS

Southwest lakes

Other lakes 52

84 450

707 38

0 51

3 36

0

NEW ENGLAND

Seaboard Lowlands lakes

Highland lakes 94

354 848

3,574 8

2 11

5 0

2

MID­ATLANTIC HIGHLANDS

Forested lakes

Other lakes

Forested streams

Other streams 91

52

78

69 433

791

11,631

10,172 10

0

12

0 9

0

17

2 1

0

8

0

ATLANTIC COASTAL PLAIN

Northeast lakes

Pine Barrens streams

Other streams 22

12

31 187

675

7,452 11

56

10 15

92

24 7

56

15

FLORIDA

Northern Highland lakes

Northern Highland streams 32

18 522

669 63

28 53

55 10

0

EASTERN UPPER MIDWEST

Low silica lakes

High silica lakes 155

125 1,254

1,673 16

3 19

4 1

2

* n = sample size, N = estimated number

Source: Baker et al. (1990b). of lakes or upstream reach ends in population.

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### Table B­3. Sources of data and sample sizes for datasets analyzed by Stoddard et

with estimates of the condition of surface waters in each region in the 1980s. al. (2003), along

Source of Data and Region No. of 1 Sites Size of 2 Population Percent Acidic in 3 1980s

Statistical Surveys

4 New England Lakes

4 Adirondack Lakes

Appalachian Plateau Streams 30

43

31 4,327 lakes

1,290 lakes

72,000 stream miles 5%

14%

6%

Sensitive Surface Waters

New England Lakes

Adirondack Lakes

Northern Appalachian Streams

Upper Midwest Lakes

Ridge/Blue Ridge Streams 24

48

9

38

69 N.A.

N.A.

N.A.

N.A.

N.A. 5%

14%

6%

3%

5%

1 Number of monitoring sites with monitoring data available (1990–2000)

2 Total number of lakes, or stream length, for which statistical survey results can be inferred. Site selection for LTM (sensitive surface waters) is not

statistically based, and results cannot be expanded to population level.

3 Estimates of extent of acidification, based on National Surface Water Survey results (Linthurst et al., 1986; Kaufmann et al., 1988).

4 Estimates are for lakes with surface areas > 4 ha; estimates based on populations including smaller lakes are likely to be higher, due to the increased

incidence of acidification in small lakes.

Statistical survey data are from the EMAP and TIME projects. Sensitive surface water data are from the LTM project, as well as other contributed

studies.

Source: Stoddard et al. (2003).



### Table B­4.

regions of

estimates Estimates of change in number and proportion of acidic surface waters in acid­sensitive

the North and East, based on applying current rates of change in Gran ANC to past

of population characteristics from probability surveys.

Region

New England

Adirondacks. Results of Regional Survey Results of Monitoring during 1990s

Population Number % Time Period

1 2 Size Acidic Acidic of Estimate

6,834 lakes 386 lakes 5.6% 1991–94

1830 lakes 238 lakes 13.0% 1991–94 Rate of ANC

3 change

+0.3

+0.8 Estimated % Acidic

Number Acidic in in 2000

2000

374 lakes 5.5%

149 lakes 8.1% % Change in

Number of Acidic

Systems

−2%

−38%

No.

Appalachians

Ridge/Blue

Ridge

Upper

Midwest 42,426 km 5014 km 11.8% 1993−94

32,687 km 1634 km 5.0% 1987

8,574 lakes 251 lakes 2.9% 1984 +0.7

−0.0

+1.0 3600 km 8.5%

1634 km 5.0%

80 lakes 0.9% −28%

0%

−68%

Source: Stoddard et al. (2003)

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### Table B­6. Slopes of trends

for the period 1990–2000. in Gran ANC in acidic, low ANC and moderate ANC lakes and streams

ANC Class Number of Sites Change in Gran ANC ( � eq/L/yr)

A cidic (ANC <

Low ANC (0 <

Moderate ANC � 0 e q/L)

ANC < 25 � eq/L)

(25 < ANC < 200 � eq/ L) 26

51

43 +1.29**

+0.84**

+0.32 ns

ns trend not significant

** p < 0.01

Note: Analysis includes

Source: Stoddard et al. (p > 0.05)

all sites in New

(2003) England, Adirondacks, Appalachian Plateau, and Upper Midwest; Ridge and Blue Ridge sites excluded. 

### Table B­5. Regional trend results for long­term monitoring sites for the period 1990 through 2000.

Region 2− SO 4 ( � eq/L/yr) − NO 3 ( � eq/L/yr) B as e C ations

[Ca2+ + Mg2+]

( � eq/L/yr) G ran ANC

( � eq/L/yr) H ydr ogen ( � eq/L/yr) D OC

( mg/L/yr) A luminum

( � g/L/yr)

N ew England Lakes

Adirondack Lakes −1.77**

−2.26** +0.01ns

−0.47** −1.48**

−2.29** +0.11ns

+1.03** −0.01ns

−0.19** +0.03*

+0.06** +0.09ns

−1.12**

Appalachian Streams

Upper Midwest Lakes

Ridge/Blue Ridge

Streams −2.27*

−3.36**

+0.29** −1.37**

+0.02ns

−0.07** −3.40**

−1.42**

−0.01ns +0.79*

+1.07**

−0.07ns −0.08*

−0.01*

+0.01ns +0.03ns

+0.06**

NA +0.56ns

−0.06ns

NA

ns regional trend not significant

* p < 0.05

** p < 0.01

NA insufficient data

Note: Values are median slopes

Source: Stoddard et al. (2003). (p

for > 0.05)

the group of sites in each region.

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### Table B­7.

Maine. Changes in key chemical characteristics during periods of record in aquatic systems in

Acadia NP lakes (22)

LTM lakes @ Tunk Mtn (6)

− spring

LTM lakes @ Tunk Mtn (6)

− fall

LTM lakes since 1990

− fall only

High elevation lakes (90)

Seepage lakes (120)

East Bear Brook at BBWM

RLTM lakes (16) Years

17

17

17

8

12

12

11

7 Change in (all in � eq/L)

S ulfate

−10

−9

−7

−9

−16

−9

−22

−6 Nitrate

0

0

0

0

1

1

−16

1 Base Cations

−17

−10

−9

−10

−23

−1

−44

−17 Calculated

−7

−1

−2

−1

−8

7

−6

−12 ANC a ANC

0

1

−2

−1

−2

7

−4

−4 DOC b

−2

1

1

0

4

4

1

2

A Calculated ANC

= [change in base cations]

B DOC ( � eq/L)

= DOC in mg/l * 4 (e.g., Kahl

Source: Kahl et al. (1999) m inus

et al., [change

1999). in (sulfate + nitrate)]



### Table B­8. Projected changes (μeq/L) in median values of streamwater chemistry at the regional

modeling sites from 1995 to 2040 in each of the three emissions control strategies, stratified into

two segments of the SAMI region (northeast and southwest) and by physiographic province.

Physiographic Province Number of Sites Δ Sulfate Δ Nitrate Δ SBC ΔANC

A2 STRATEGY 1

Virginia and West Virginia

Blue Ridge

Valley and Ridge

Appalachian Plateau

North Carolina, Tennessee,

Blue Ridge

Appalachian Plateau

Virginia and West Virginia

Blue Ridge

Valley and Ridge

Appalachian Plateau

North Carolina, Tennessee,

Blue Ridge

Appalachian Plateau South

South Carolina,

Carolina, Georgia,

Georgia, 16

41

34

and Alabama

33

6

16

41

34

and Alabama

33

6 1.8

−0.45

−31.2

8.8

15.3

−2.7

−5.6

−36.3

5.6

11.6 0.03

0.02

−3.5

0.15

0.15

−0.04

−0.37

−4.9

−0.48

−0.23 −2.2

−6.8

−33.8

1.0

−1.2

−3.0

−8.2

−38.4

−0.60

−1.9 −4.0

−6.6

−4.4

−8.0

−15.9

−1.0

−4.7

−1.4

−5.4

−13.3

B3 STRATEGY 1

Virginia and West Virginia

Blue Ridge

Valley and Ridge

Appalachian Plateau

North Carolina, Tennessee, South Carolina, Georgia, 16

41

34

and Alabama −7.4

−13.8

−40.4 −0.09

−0.36

−5.8 −5.1

−10.3

−39.3 2.9

−0.83

2.6

August 2008 B­140 DRAFT—DO NOT QUOTE OR CITEPhysiographic Province Number of Sites Δ Sulfate Δ Nitrate Δ SBC ΔANC

Blue Ridge 33 3.2 −1.0 −2.3 −3.2

Appalachian Plateau 6 7.2 −0.53 −3.1 −10.4

1 Emissions control

Source: Sullivan et strategies

al. (2004) were based on existing regulations (A2), moderate additional controls (B1), and more aggressive additional controls (B3)



### Table B­9. Population estimates

a 

### western U.S. of water chemistry percentiles for selected lake populations in the

Population n N pH A NC

( � eq/L) S BC

( � eq/L) 2− SO 4 ( � eq/L) − NO 3 ( � eq/L) D OC

(mg/L)

P1 P5 P1 P5 P1 P5 P95 P99 P95 P99 P50 P99

Sierra Nevada 114 2,119 5.84 6.31 15 16 21 26 90 386 8 10 0.8 2.7

Cascades 146 1,473 5.95 6.25 11 18 20 31 60 97 3 6 1.3 2.6

Idaho Batholith 88 937 6.34 6.42 21 33 30 45 30 43 3 4 1.2 2.4

NW Wyoming 38 648 6.56 6.56 38 38 64 66 41 2,909 13 32 1.0 4.8

Colorado Rockies 121 1,173 6.02 6.65 25 42 58 80 915 2,212 10 13 1.3 5.7

a Data from Landers et al. (1987).

b Excluding Fern Lake (4D3−017) which is naturally acidic

Note: The 1st and 5th percentiles (P1, P5) are presented for pH, ANC ( � eq/L), and SBC ( � eq/L) and the 95th and

2

− shown for SO 4 ­ ( � eq/L) and NO 3 ( � eq/L). The median (P50) and 90th percentiles are shown for DOC (mg/L). 9 9th (P95, P99) percentiles are



### Table B­10. Population estimates of the

− 

### ANC and NO

3 

### within defined ranges. percentage of lakes in selected subregions of the West with

ANC ( � eq/L) NO 3 ­ ( � eq/L)

< 0 <25 <50 >5 >10

Sierra Nevada 0 8.7 39.3 10.6 1.5

Cascades 0 10.2 22.4 1.5 0.0

Idaho Batholith 0 2.0 23.6 4.6 3.9

NY Wyominga

Colorado Rockies 0

0 2.3

0.9 12.8

5.5 22.8

9.8 8.9

1.8

a Excluding Fern

Source: Landers Lake

et al. (4D3−017)

(1987) which is a naturally acidic lake

August 2008 B­141 DRAFT—DO NOT QUOTE OR CITE

### Table B­11. Median streamwater ANC and watershed area

that have water chemistry and fish species richness data. of streams in Shenandoah National Park

Site ID Watershed Area 2 (km ) Median ANC ( � eq/L) N umber of

Fish Species

Smaller Watersheds (<10 km 2 )

North Fork Dry Run

Deep Run

White Oak Run

Two Mile Run

Meadow Run

Larger Watersheds (>10 km 2 )

Brokenback Run

Staunton River

Piney River

Paine Run

Hazel River

White Oak Canyon

N. Fork Thornton River

Jeremy’s Run

Rose River 2.3

3.6

4.9

5.4

8.8

10.1

10.6

12.4

12.7

13.2

14.0

18.9

22.0

23.6 48.7

0.3

16.2

10.0

−3.1

74.4

76.8

191.9

3.7

86.8

119.3

249.1

158.5

133.6 2

a N.D.

3

2

1

3

5

7

3

6

7

9

6

8

a No data were available

Source: Sullivan (2003) regarding the number of fish species in Deep Run



### Table B­12. Reference levels for the Acidic Stress Index (ASI) based on logistic

presence as a function of the sensitive intermediate and tolerant ASI values for

brook trout, lake trout, and common shiner. regression of fish

brown bullhead,

Reference Acid Stress Index Fish Response

Lakes Streams

Tolerant ASI

Tolerant ASI > 30

> 10 Intermediate

Sensitive ASI ASI >

> 30 30 Loss

Loss of

of all fish species

brook trout

Intermediate

Sensitive ASI ASI >

> 80 80

Sensitive ASI > 10 Loss

Loss of

of other sport fish, such as smallmouth bass

acid­sensitive species, such as minnows. and lake trout

Source: Baker et al. (1990a)

August 2008 B­142 DRAFT—DO NOT QUOTE OR CITE

### Table B­13. General summary of biological changes

expressed as a decrease in surface water pH. anticipated with surface water acidification,

pH

Decrease General Biological Effects

6.5 to 6.0

6.0 to 5.5

5.5 to 5.0

5.0 to 4.5

5.0 to 4.5

(cont’d) Small decrease in species richness of plankton and benthic invertebrate communities resulting from the loss of a few highly acidsensitive species, but no measurable change in total community abundance or production.

Some adverse effects (decreased reproductive success) may occur for highly acid­sensitive fish species (e.g., fathead minnow, striped

bass).

Loss of sensitive species of minnows and dace, such as fathead minnow and blacknose dace; in some waters, decreased reproductive

success of lake trout and walleye, which are important sport fish species in some areas.

Visual accumulation of filamentous green algae in the near­shore zone of many lakes and in some streams.

Distinct decrease in species richness and change in species composition of plankton and benthic invertebrate communities, although little

if any change in total community abundance or production.

Loss of some common invertebrate species from zooplankton and benthic communities, including many species of snails, clams,

mayflies, and amphipods, and some crayfish.

Loss of several important sport fish species, including lake trout, walleye, rainbow trout, and smallmouth bass, as well as additional

nongame species such as creek chub.

Further increase in the extent and abundance of filamentous green algae in lake near­shore areas and streams.

Continued shift in species composition and decline in species richness of plankton, periphyton, and benthic invertebrate communities;

decreases in total abundance and biomass of benthic invertebrates and zooplankton may occur in some waters.

Loss of several additional invertebrate species common in surface waters, including all snails, most species of clams, and many species

of mayflies, stoneflies, and other benthic invertebrates.

Inhibition of nitrification.

Loss of most fish species, including most important sport fish species such as brook trout and Atlantic salmon. A few fish species are

able to survive and reproduce in water below pH 4.5 (e.g., central mudminnow, yellow perch, and in some waters, largemouth bass).

Measurable decline in the whole­system rates of decomposition of some forms of organic matter, potentially resulting in decreased rates

of nutrient cycling.

Substantial decrease in number of species of plankton and benthic invertebrates and further decline in species richness of plankton and

periphyton communities; measurable decrease in total community biomass of plankton and benthic invertebrates of most waters.

Loss of additional species of plankton and benthic invertebrate species, including all clams and many insects and crustaceans.

Reproductive failure of some acid­sensitive species of amphibians, such as spotted salamanders, Jefferson salamanders, and the leopard

frog.

Source: Baker et al. (1990a)



### Table B­14. Estimated percentage of Adirondack lakes with and Acidic Stress Index exceeding the

reference levels for effects on fish populations, based on diatom­inferred historical (pre­industrial)

chemistry and present­day measured and inferred acid­base chemistry.

ASI Reference Level DDRP Target Population

ELS/NSWS Target Population Measured Diatom Inferred Measured

Historical Current Net Change

Tolerant ASI > 30 0.0 3.6 +3.6 1.8 2.2

Tolerant ASI > 10 0.0 9.1 +9.1 10.9 6.5

Intermediate ASI > 80 7.3 21.8 +14.5 21.8 15.2

Sensitive ASI > 80 28.5 41.2 +12.7 32.7 20.0

Source: Baker et al. (1990a)



### 1

August 2008 B­143 DRAFT—DO NOT QUOTE OR CITE

### Table B­15. Estimated percentage of Adirondack lakes with acid­base chemistry unsuitable for fish

population survival, based on diatom­inferred historical (pre­industrial) chemistry and present­day

measured and inferred acid­base chemistry.

Fish Species

Model a

Brook Trout

Bayesian

LAF framework

pH

pCa/pH

pCa/pH, Al/DO C

Lake Trout

pH

pCa/pH

Inorg. Al

Common Shiner

pH

pCa/pH DDRP b Target Population ELS/NWS

b Target Population

Measured

10.1

15.8

9.3

10.3

13.9

14.4

18.9

17.1

21.3

29.1 ALSC

Measured

21.8

24.6

22.2

23.0

23.5

30.9

­

­

42.3

­ Diatom­Inferredc

Historical

2.7

­

2.3

16.0

16.6

6.4

31.7

23.9

19.2

45.8 Current

13.0

­

11.3

13.3

15.6

18.1

25.1

38.6

29.6

37.7 Net Change

+10.3

­

+9.0

−2.6

−1.0

+11.2

−6.6

+14.7

+10.5

−8.1 Measured

14.2

­

12.8

14.5

19.2

21.4

29.0

26.2

33.5

40.2

a All models, except the brook trout Bayesian model (Section 3.5)

defined in Section 3.3.3.

b ELS/NSWS target population in Subregion 1A, defined in Section

sediment diatom analyses, for example, excluding lakes with ANC

further details.

c Estimates of acid­base chemistry inferred from sediment diatom

­ Analysis not conducted.

Source: Baker et al. (1990a) a nd LAF framework (Section 3.4), are field­based acidification response models as

3.1 (N

= 1,290 lakes); a subset of these lakes was considered for the DDRP and

> 400 � eq/L and with site depths < 1.5 m (N

= 675 lakes). See Sullivan (1990) for

analysis; methods and water chemistry described in Sullivan (1990).



### Table B­16. Estimated percentage of the lakes in the Northeast and Upper Midwest, ELS/NSWS

target population with an Acidic Stress Index exceeding the reference levels for fish populations

defined in Table C­12.

ASI Reference Level Subregion 1A Northeast Region Upper Midwest Region

Tolerant ASI > 30 2.2 1.0 0.5

Tolerant ASI > 10 6.5 2.4 1.0

Intermediate ASI > 80 15.2 5.7 2.0

Sensitive ASI > 80 20.0 8.6 3.1

Source: Baker et al. (1990a)

August 2008 B­144 DRAFT—DO NOT QUOTE OR CITE

### Table B­17. Estimated percentage of lakes in the Northeast,

base chemistry unsuitable for fish population survival. ELS/NSWS target populations with acidFish Species/Model Subregion 1A Entire Northeast

Brook trout

Bayesian

LAF Framework

pH

pCa/pH

pCa/pH, Al/DOC 10.1

15.8

9.3

10.3

13.9 3.7

8.9

3.5

4.4

7.0

Lake trout

pH

pCa/pH

Inorganic Al 14.4

18.9

17.1 5.8

8.9

6.3

Common shiner

pH

pCa/pH 21.3

29.1 9.5

19.7

Source: Baker et al. (1990a)

August 2008 B­145 DRAFT—DO NOT QUOTE OR CITE

### Table B­18. Distribution of acidic stress index values among the NSS­1 Target populations for the

mid­Appalachian region.

Number (%)

Total Length (%)

Lower Node Upper Node

Sensitive ASI

≥ 10 84.6 66.7 76.1

10–30 10.1 18.9 14.0

30–50 1.4 1.9 1.9

50–80 1.8 2.6 1.6

>80 2.0 9.8 6.4

Intermediate ASI

≥ 10 97.8 89.3 88.9

10–30 0.2 2.5 0.7

30–50 0.6 1.3 0.4

50–80 0.1 1.4 0.4

>80 1.3 5.4 1.9

Tolerant ASI

≥ 10 99.4 97.1 98.1

10–30 0.6 1.4 0.9

30–50 0.0 0.6 0.3

50–80 0.0 0.9 0.7

>80 0.0 0.0 0.0

Source: Baker et al. (Baker, 1990)

August 2008 B­146 DRAFT—DO NOT QUOTE OR CITE

### Table B­19. Distribution of

interior Southeast region. acidic stress index values among the NSS­1 target populations for the

Number (%)

Total Length (%)

Lower Node Upper Node

Sensitive

≥ 10

10–30

30–50

50–80

>80 ASI

79.4

18.8

0.0

1.7

0.0 70.1

21.1

1.7

5.2

1.7 75.9

18.8

2.0

2.5

0.7

Intermediate

≥ 10

10–30

30–50

50–80

>80 ASI

100.0

0.0

0.0

0.0

0.0 98.3

0.0

0.0

0.0

1.7 99.3

0.0

0.0

0.0

0.7

Tolerant

≥ 10

10–30

30–50

50–80

>80

Source: ASI

Baker et al. 100.0

0.0

0.0

0.0

0.0 100.0

0.0

0.0

0.0

0.0 100.0

0.0

0.0

0.0

0.0

(Baker, 1990)



### 1

Table B­20. Comparison of solution and tissue chemistries at threshold treatment levels where

significant impacts on tree growth or nutrient content were first observed. In many cases, adverse

impacts were observed at the lowest Al treatment level. Hence, the actual threshold Ca/Al ratio may

be higher than that reported. Results are from a variety of studies reported in the literature.

Study Solution

Al

( � mol/L) Solution

Ca/Al

(M) Foliar Ca

(mg/km) Foliar Al

(mg/km) Foliar

Ca/Al

(M) Root Ca

(mg/km) Root Al

(mg/kg) Root

Ca/Al

(M) Type of Study

or

a Experiment Response

b Variable Al

Analysis

Used in

c Ratio

Norway spruce

Godbold et al.

(1988)

Matzner et al.

(1989)

Stienen and

Bauch (1988)

Schroder et

al. (1988) 100

100+

1500

2000 1.3

0.3 to

0.66

1 1.8

1470 32 31 770 1890 0.28 H

F

H

H N

N

N,B

N Ali

Alt

Alt

Ali

August 2008 B­147 DRAFT—DO NOT QUOTE OR CITEStudy Solution

Al

( � mol/L) Solution

Ca/Al

(M) Foliar Ca

(mg/km) Foliar Al

(mg/km) Foliar

Ca/Al

(M) Root Ca

(mg/km) Root Al

(mg/kg) Root

Ca/Al

(M) Type of Study

or

a Experiment Response

b Variable Al

Analysis

Used in

c Ratio

Red spruce

Thornton et 250

al. (1987)

Hutchinson et 185

al. (1986)

Joslin and 200

Wolfe (1988)

Schier (1985) 1850

Ohno et al. 250

(1988)

Joslin and

Wolfe (1992)

White spruce

Nosko et al. 50

(1988)

Red oak

Joslin and 300

Wolfe (1989)

DeWald et al. 115

(1990)

McCormick 7405

and Steiner

(1978)

Honeylocust

Thornton et 50

al. (1986b,c)

Sucoff et al. 100

(1990)

Wolfe and 100

Joslin (1989) 1

2.2

nd

1.35

0.8

0.45

0.2

4.05

4.48

0.54

1.1

1.4

4.3 1100

~3000

3630 65

75 11.4

12.9

14

11.9

32.4

8.4 650

~2000

3630 6000

6415 0.07

0.43

0.06

0.38

0.21 to

0.32

0.35

0.71 H

S

S

H

S

F

H

S

S

H

H

S

S B

B

B

B,N

N

B

B

B

B

B

B

B

B Ali

Alt

Ali

Ali

Ala in

paste

Alt

Alt

Alt in

Alt

Alt

Ali

Ali

Ali soil

SrCl2

Sugar maple

Thornton et 100–600

al. (1986a) 0.42

2.5 to ~2000 ~190 9.9 ~1500 ~2700 0.4 H B,N Ali

Loblolly pine

Cronan et al. 500–3000

(1989)

Thornton

(unpubl.)

American beech

C ronan et al. 500–3000

(1989)

European beech

A sp and 300

Berggren

(1990)

C ronan et al. 500

(1989)

Peach

Edwards a nd 222

Horton (1977)

Scotch pi ne 0.5

0.5

0.35

0.5 900

2670 260

69 2.3

26.1

3.8

10.8 3700

1140 7770

7930 0.32

0.1

0.2

0.008 H

H

H

H

H N

N

N

N

N Ali

Ali

Ali

Ali

Ali

August 2008 B­148 DRAFT—DO NOT QUOTE OR CITESolution Al Solution Foliar Root Type of Study Foliar Ca Foliar Al Root Ca Root Al Response Analysis Study Al Ca/Al Ca/Al Ca/Al or b (mg/km) (mg/km) (mg/km) (mg/kg) a Variable Used in (M) (M) (M) Experiment ( � mol/L) c Ratio

Ilvesniemi 185 nd 400 300 0.9 2300 S N,B Alt

(1992)

McCormick 2960 1.35 H B Alt

and Steiner

(1978)

Virginia pine

McCormick 2960 1.35 H B Alt

and Steiner

(1978)

Pitch pine

McCormick 2960 1.35 H B Alt

and Steiner

(1978)

Cumming and 50 20 S N,B Alt

Weinstein

(1990)

Birches: gray, paper and yellow

McCormick 4444 0.9 H B Alt

and Steiner

(1978)

European birch

Goransson 1000 0.02 0.17 H N Ali

and Eldhuset

(1987)

Radiata pine

Truman et al. 17 10.5 2120 800 1.8 1320 1850 0.48 H N Alt

(1986)

Douglas­fir

Keltjens and 370 0.54 2300 300 5.2 H B Alt

Van Loenen

(1989)

Larch

Keltjens and 555 0.36 1800 250 4.9 H B Alt

Van Loenen

(1989)

a Types of study include hydroponic (H), soil or sand culture (S), or existing forest (F).

b Response variables include biomass (B), or nutrient content (N).

c Aluminum measurements include Ali (Ali), monomeric Al (Ala), and total Al (Alt). Since most of the lab studies were conducted under

conditions of low pH and minimal DOC, measurements of total Al, Ali, and labile Al are very comparable.

Source: Cronan and Grigal (Cronan, 1995)



### 1

August 2008 B­149 DRAFT—DO NOT QUOTE OR CITE

### Table

water B­21. Overview of selected

acid­base chemistry major processes by which landscape change can alter drainage

Landscape Change Effect on Acid­Base Chemistry

Logging, blowdown

Road building and construction

Drainage of wetlands

Drought

Lake shore development

Insect damage Dilution

Lower deposition, less acidity

Pulse of NO 3 − acidity initially

Less base cation neutralization, more acidity

Less water contact with mineral soils, less neutralization of acidic deposition inputs

More base cation neutralization, less acidity initially

Depletion of base cation reserves in soils, more acidity long­term

Re­oxidation of stored S, pulses of acidity with increased discharge

Reduced groundwater inputs to seepage lakes with consequent increased acidity

Increased relative baseflow to drainage waters with consequent decreased acidity

Decreased acidity

Pulse of NO 3 − acidity initially

Source: Sullivan (2000)



### Table B­22. Observed relationships between zooplankton species richness (R) and lakewater ANC.

2 Taxonomic Group Equation r p

Total Zooplankton R = 15.65 + 0.089ANC 0.46 0.001

Crustaceans R = 6.35 + 0.028ANC 0.47 0.001

Rotifers R = 9.04 + 0.053ANC 0.30 0.001

Source: Sullivan et al. (2006a)

August 2008 B­150 DRAFT—DO NOT QUOTE OR CITE

### Table

most B­23. Threshold response

sensitive. of increased mortality of fish to low pH listed from least sensitive to

Study Species Increased Mortality

Threshold, pH Study Conditions

Johnson et al. (1987)

Holtze and Hutchinson

(1989)

Johansson et al. (1977)

Swenson et al. (1989)

Mills e t al. (1987) Blacknose da ce, creek chub

Brook trout

Common shiner

Lake whitefish, white sucker,

walleye

Smallmouth bass

Atlantic salmon

Brown trout

Brook Trout

Black crappie

Rock bass

Yellow perch, largemouth bass

Fathead minnow

Slimy sculpin

L ake Trout

Pearl d ace

W hite sucker 5 .9

4.8

5.4

5.1

4.8

5.0

4.5

4.5

5.5

5.0

4.5

5.9

5.6

5.6

5.1

5.0 ­

­

­

­

­

­

­ 6 .0

5.1

6.0

5.2

5.0

5.9

5.1 I n situ bioassay with early life stages in Adirondack

surface waters

Laboratory exposure of early life stages to pH and Al.

Laboratory tests with eggs exposed to low pH, no Al.

Laboratory tests with early life stages exposed to pH

and Al.

Whole­lake treatment (fish population recruitment

f ailure)

Source: Baker e t al. (1990a)



### 1

August 2008 B­151 DRAFT—DO NOT QUOTE OR CITE

### Table B­24. Threshold values of pH for various taxa and effects.

Reference Type of Study Taxa pH Observed Effects

Buckler et al. (1987)

McCormick et al. (1989)

Mills et al. (1987)

Klauda et al. (1987)

Holtze and Hutchinson (1989)

Baker and Schofield (1980)

Kane and Rabeni (1987)

Leino et al. (1987)

Lacroix (1985a)

McDonald and Milligan (1988)

McWilliams and Potts (1978)

Tietge et al. (1988)

Booth et al. (1988)

Audet and Wood (1988)

Peterson and Martin­Robichaud

(1986)

Powell and McKeown (1986) Lab bioassay

Lab bioassay

Whole­lake

experiment

Lab bioassay

Lab bioassay

Lab bioassay

Lab bioassay

Whole­Lake

experiment

Field survey

Lab bioassay

Lab bioassay

Lab bioassay

Lab bioassay

Lab bioassay

Lab experiment

Lab bioassay Striped bass

Fathead minnow

Fathead minnow

Blueback herring

Common shiner

White sucker

Smallmouth bass

Adult fathead minnow

Atlantic salmon parr (age 1+)

Adult brook trout

Adult brown trout

Adult brook trout

Adult brook trout

Adult rainbow trout

Atlantic salmon larvae

Coho salmon parr and

smolts 6.5

6.0

5.9

5.7

5.4

5.2

5.1

5.2–

5.8

4.9–

5.3

5.2

5.0

4.9

4.8

4.8

4.5

4.4 >50% larval mortality

Significant decrease in embryo survival

Population recruitment failure

>50% mortality of larvae

>50% embryo mortality

Substantial reduction in embryo survival

>50% mortality of larvae after 30–day exposure

Increased numbers of chloride (ionoregulatory) cells

on the gills

Significantly lower blood Cl levels; high K levels

Reduced Na transport activity

Net Na loss; major shift in the gill tansepithelial

potential

Increased volume density of lamellar chloride cells on gills

Net loss of Na and Cl

Decreased plasma Na and Cl levels

Reduced accumulation of Na, K, and Ca

Net decrease in plasma Na

Source: Baker et al. (1990a).

August 2008 B­152 DRAFT—DO NOT QUOTE OR CITE

### Table B­25.

studies). Threshold values of Al for various species and effects (form of A not specified for most

Reference Type of Study Taxa pH A l

( � g/L) O bserved Effect (at similar pH without added Al)

Sadler and Lynam

(Sadler, 1988)

Turnpenny et al.

(Turnpenny, 1987)

Holtze and Hutchinson

(1989)

Skogheim and Roseland

(1986)

Klauda and Palmer

(1987)

Rosseland and Skogheim

(1984)

Baker and Schofield

(Baker, 1982)

Fjellheim et al. (1985)

Brown (1983)

Schofield and Trojnar

(1980)

Ormerod et al. (1987) LB

Field survey

LB

Field mesocosm

experiment

LB

LB

LB

LB

LB

Field study

Whole­stream

experiment Brown trout

Broen trout

Walleye

Atlantic salmon

Blueback herring

Atlantic salmon

White sucker

Eel

Brown trout

Brook trout

Atlantic salmon and

brown trout 5.2

—­

4.9

5.1

5.5–

5.6

4.9–

5.0

5.2

5.1

4.5–

5.4

4.9

5.0 30

40

50

75

100

130

200

230

250

286

350 Significant reduction in fish growth

Fish absent or rare in streams in Wales and England

>50% mortality of embryos to 4–d post­hatch

>50% mortality of smolts

>50% larval mortality

Significant increase in mortality of presmolts

>50% larval mortality

Significant increase in elver mortality

>50% fry mortality

No survival of trout stocked into lakes with higher total

(even after accounting for pH effects).

>50% mortality of young­of­the­year. Al

Source: Baker et al. (1990a)

August 2008 B­153 DRAFT—DO NOT QUOTE OR CITE

### Table B­26. The effects of 2+ 

### increasing Ca to ameliorate low pH and high Al.

Reference Type of

Study Taxa pH A l Range

( � g/L) 2+ Ca Range

( � eq/L) O bserved Response to Increasing 2+ Ca

Brown (1982, 1983)

Wright and Snekvik

(1978)

Brown (1983)

McDonald (1983)

Edwards et al.

(1987)

Freda and McDonald

(1988)

Mount et al. (1988)

Sadler and Lynam

(Sadler, 1988) LB

Field

survey

LB

LB

LB

LB

LB

LB Brown trout

Brown trout

Brown trout

Rainbow trout

Brown trout

Common shiner,

rainbow trout

Brook trout

Brown trout 4.5–

5.1

4.5–

7.5

4.5–

5.4

4.3

4.2

4.0

5.0–

6.3

5.2 —­

—­

0–500

—­

—­

—­

0–500

0–80 12–400

20–200

12–100

69–223

100–5600

70–1000

25–400

8–800 Increased embryo survival and hatch

2+ Trouot status significantly correlated with log Ca and

pH.

Increased fry survival in low pH or high Al waters

Decreased adult mortality and net loss of Na and Cl

Lower loss of plasma Na and Cl ions

Significant decrease in Na loss

Increased survival and growth of adults; increased

progeny survival with adult exposure to low and high Al

Increased yearling survival and growth in waters with

elevated Al.

Source: Baker et al. (1990a)



### Table B­27.

for streams Brook trout acidification response

in Virginia (2000). categories developed by Bulger et al. (XXXXXXXXXX)

Response Category Chronic ANC Range ( � eq/L) E xpected Response

Suitable

Indeterminate

Episodically acidic

Chronically acidic >50

20 to 50

0 to 20

<0 Reproducing brook trout expected if other habitat

Brook trout response expected to be variable

Sub­lethal and/or lethal effects on brook trout are

Lethal effects on brook trout probable features

possible are also suitable

August 2008 B­154 DRAFT—DO NOT QUOTE OR CITE

### Table B­28. Partial listing of bioassays

and (or) elevated aluminum. demonstrating decreased fish survival in waters with low pH

Reference Species Life Stage Lab/Field

Johansson and Kihlström (1975)

Johansson and Milbrink (1976)

Johansson et al. (1977)

Trojnar (1977)

Peterson et al. (1980)

Schofield and Trojnar (1980)

Baker and Schofield (Baker, 1982)

Brown (1983)

Hulsman et al. (1983)

Sharpe et al. (1983)

Jagoe et al. (1984)

Lacroix (1985b)

Ingersoll (1986)

Buckler et al. (1987)

Johnson et al. (Johnson, 1987)

Klauda and Palmer (1987)

Lacroix and Townsend (1987)

Wales and Liimatainen (1987)

Palmer et al. (1988)

Gunn (1989)

Holtze and Hutchinson (1989)

Hutchinson et al. (1989) Northern pike

Roach

European perch

Brown trout

Brook trout

White sucker

Atlantic salmon

Brook trout

Brook trout

White sucker

Brown trout

Walleye

Rainbow trout

Brook trout

Brown trout

Rainbow trout

Mottled sculpin

Arctic char

Atlantic salmon

Brook trout

Striped bass

Brook trout

Lake trout

Creek chub

Blacknose dace

Blueback herring

Atlantic salmon

Walleye

Bluegill

Fathead minnow

Lake trout

Common shiner

Lake whitefish

White sucker

Walleye

Smallmouth bass

Largemouth bass

Lake trout

Brook trout Fry

Egg

Egg and fry

Egg and fry

Egg

Fry

Egg and fry

Fry

Egg

Fry

Fry and adult

Egg and fry

Egg and fry

Egg and fry

Fry

Egg, fry, and young­of­year

Egg and fry

Juvenile

Egg

Juvenile

Egg and fry

Egg and fry

Egg and fry Lab

Lab,

Lab

Lab

Lab

Lab

Lab

Lab

Field

Field

Lab

Field

Lab

Lab

Field

Lab

Lab

Field

Lab

Lab,

Lab

Lab field

field

August 2008 B­155 DRAFT—DO NOT QUOTE OR CITEAugust 2008 B­156 DRAFT—DO NOT QUOTE OR CITE 

### Table B­29. Mills et al., 1987. Shows effect of various pH on fish forage fish and lake trout.

1977 1978 1979 1980 1981 1982 1983 Biota pH 6.13 pH 5.93 pH 5.64 pH 5.59 pH 5.02 pH 5.09 pH 5.13

Forage Fathead Fathead minnow I ncrease i n W hite sucker Recruitment f ailure Recruitment f ailure

f ish minnow near extinction; a bundance o f pearl recruitment f ailure; for all species for all species

experience slimy sculpin da ce, Suckers very n o e ffect on a dult

r ecruitment decline in a bundant. gr owth and survival

failure abundance

Lake I ncrease in Increased L ake trout Recruitment f ailure; L ake trout condition L ake trout condition

t rout condition abundance o f recruitment f ailure; n o e ffect on a dult po or; recruitment very po or;

i .e., “fatter” young­of­the­year condition similar to gr owth and survival f ailure; r educed recruitment f ailure;

preacidification adult survival reduced adult

survival

S ource: ( Baker, 1990)



### Table B­30. Range of minimum pH of fish species occurrence in 11 lake surveys.

Family a nd Species High Minimum pH Low Minimum pH

Cyprinidae

B luntnose minnow

Fathead minnow

B lacknose da ce

Pearl dace

Northern redbelly d ace

Common shiner

Golden shiner

Creek chub 6.6

6.3

6.8

5.9

5.9

6.2

5.5

5.9 5.6

5.1

5.6

4.7

4.7

4.9

4.5

4.6

Salmonidae

Brook trout

L ake trout

Brown trout

Atlantic salmon 5.6

5.2

5.0

6.3 4.6

4.9

4.6

5.3

Centrarchidae

Smallmouth bass

Largemouth bass

Pumpkinseed

Bluegill

Rock bass

B lack crappie 7.0

5.0

6.6

4.5

6.2

5.6 4.9

4.6

4.6

4.5

4.6

5.6

Percidae

Yellow perch

W alleye 5.8

6.9 4.4

5.2Family and Species High Minimum pH Low Minimum pH

Johnny darter

Iowa darter 6.2

6.2 4.9

4.6

Esocidae

Northern pike 5.9 4.0

Catastomidae

White sucker 5.5 4.6

Ictaluridae

Brown bullhead 5.6 4.5

Umbridae

Central mudminnow 4.5 4.2

Gasterosteidae

Brook stickleback 5.4 4.6

Source: Baker et al. (1990a)

a 

### Table B­31. Studies that either did (yes)

affected certain species of birds or did not (no) yield evidence that acidic deposition

Species

Common loon

Common merganser

Belted kingfisher

Osprey

Black duck

Common goldeneye

Ring­necked duck

Eurasian dipper

Eastern kingbird

Tree swallow Diet/ Foraging Breeding Distribution Reproductive Measures

a Reference

1–3, 19,20

19

4

5,6

7–9

8

10,11

12–14

15

16–18 Yes

x

x

x

x

x

x No Yes

x

x

x

x

X

b x

x No

x

x

x Yes

x

x

x

b x

x

x

x

x No

x

a 1= Alvo et al. (1988); 2 = Parker (1988); 3 = Wayland and McNicol (1990); 4 = Goriup (1989); 5 = Eriksson (1983); 6 = Eriksson (1986); 7 = Hunter

et al. (1986); 8

= DesGranges and Darveau (1985); 9 = Rattner et al. (1987); 10,11 = McAuley and Longcore (1988a,b) 12,13 = Ormerod et al.

(1985, 1986); 14 = Ormerod and Tyler (1987); 15 = Glooschenko et al. (1986); 16,17 = Blancher and McNicol (1988, 1991); 18

= St. Louis et al.

(1990); 19 = Blancher and McNicol (1991); 20 = Blair (1990).

b The effect was beneficial

Source: Longcore and Gill (1993)

August 2008 B­157 DRAFT—DO NOT QUOTE OR CITE

### Table B­32. Predicted habitat suitability for lakes in the Algona Model Dataset

Group

Fish

Common loon

pairs

Common loon

broods

Common

merganser pairs

Common

merganser broods Total Model

Lakes

526

433

433

433

433 Current

Suitable Lakes

338

100

36

52

31 Number of Lakes with Suitable Habitat Under Each Emission Scenario

S1

29

12

2

6

6 Current pH <

S2 S3 S4

34 40 77

14 14 16

2 2 2

12 27 68

10 18 69 6

S5

100

17

2

86

89 S1

97

22

6

14

14 Current pH 6–6.5 Current pH > 6.5

S2 S3 S4 S5 S1 S2 S3 S4

100 107 124 133 196 196 197 197

22 23 24 24 66 66 66 67

6 7 7 7 28 28 28 28

17 20 33 44 31 31 32 34

15 16 21 29 12 11 10 10 S5

197

67

28

35

10

Results are expressed as the number of lakes with suitable habitat for fish, common loons (pairs and broods)

broods) under each emission scenario (S1, S2, S3, S4, S5) according to current pH classes (<6, 6–6.5, >6.5).

probability of presence at time t from WARMS output (# of suitable lakes at time t/total number of lakes), for

mergansers [n

= 433]. and common mergansers (pairs and

Habitat suitability is calculated by

fish [n

= 526], and for loons and



### Table B­33. Summary statistics of biological data layers for mercury

wildlife (μg/g) in the northeastern U.S. and southeastern Canada. (Hg) concentrations in fish and

Hg Concentrations

Category/Species

Human health

Yellow perch a

b Largemouth bass

Ecological health

Brook trout

Yellow perch c

e Common loon

Bald eagle

Mink

River otter Sample

Size

4089

934

319

(841) d

1546

217

126

80 Data layer

Designation

Primary

Secondary

Secondary

Secondary

Primary

Secondary

Secondary

Secondary Mean ±

Standard

Deviation

0.39 ± 0.49

0.54 ± 0.35

0.31 ± 0.28

0.23 ± 0.35

1.74 ± 1.20

0.52 ± 0.20

19.50 ± 12.1

20.20 ± 9.30 Range

< 0.05–5.24

<0.05–2.66

<0.05–2.07

<0.05–3.18

0.11–14.20

0.08–1.27

2.80–68.50

1.14–37.80 Hg Level

of Concern

(Tissue Type)

0.30 (fillet)

0.30 (fillet)

0.16 (whole fish)

0.16 (whole fish)

3.0 (blood)

1.0 (blood)

30.0 (fur)

30.0 (fur) Percentage of

Samples with

Concentration >

Level of Concern

50

75

75

48

11

6

11

15

Note: All data are in wet weight except for fur, which is on a fresh­weight basis

a Fillet Hg in yellow perch is based on individuals with a standardized length of 20 cm.

b Fillet Hg in largemouth bass is based on individuals with a standardized length of 36 cm.

c Whole­fish Hg in yellow perch is based on individuals with a standardized length of 13 cm. Whole­fish Hg for

d The sample population of 841 yellow perch examined for whole­fish Hg is included with the 4089 fillets (i.e.,

does not double­count yellow perch).

e Egg Hg for the common loon was converted to the adult blood equivalent yellow perch was

the total number converted to fillet Hg.

of all biotic data layers

August 2008 B­158 DRAFT—DO NOT QUOTE OR CITE

### Table B­34. Mercury concentrations in avian eggs and tissues and related effects.

Tissue Concen. (ppm) Wet (w)

or Dry (d) Endpoint Species Reference

Liver

Liver

Liver

Liver

Liver

Liver

Liver

Liver

Liver

Liver

Liver

Liver

Liver

Brain

Brain

Egg

Egg

Egg

Egg

Egg

Egg

Egg

Kidney

Kidney

Kidney

Kidney 1.06

22.2

5

7.2

9.08

20.7

30

35

54.5

97.7

103.6

126.5

306 total/

20.4 MeHg

4–6

20

1–5/0.2–1.0

0.5–1.5

0.86

1.0

1.0–3.6

2–16

3.65

37.4 total/

6.2 MeHg

40.4

74.3

86.4 w

w

w

w

w

w

w

w

w

w

w

w

d

w

w

d

w

w

w

w

w

w

d

w

w

w No effect

Abnormal feather loss in juveniles

Conservative threshold for major toxic effects

Increased disease and emaciation

Nesting success

Hatching success

Neurologic effects

Death

LD33 a

Death

LD33

LD33

No adverse effects observed

Failure to hatch

25% mortality

Reduced productivity in one half of the

population

Decreased hatchability

Aberrant nesting behavior

Successful reproduction

Residue threshold for significant toxic effects

No decreased hatchability

27% hatching, 10–12% fledging

No adverse effect observed

LD33

LD33

LD33 Common tern

Common tern

Water birds

Common tern

Common tern

Common tern

Osprey

Common loon

European starling

Gannet

European starling

Red­winged blackbird

Black­footed

albatross

Black duck

Zebra finch

Merlin

Pheasant

Common loon

Common tern

Variety of water birds

Herring gull

Common tern

Black­footed

albatross

Grackle

Red­winged blackbird

European starling Gochfeld (1980)

Gochfeld (1980)

Zillioux et al. (1993)

Spalding and Forrester

(1991)

Finley and Stendall (1978)

Finley and Stendall (1978)

Heinz (1974)

Wiemeyer et al. (1987)

Finley et al. (1979)

Finley et al. (1979)

Finley et al. (1979)

Gochfeld (1980)

Hoffman and Moore (1979)

Scheuhammer (1988)

Newton and Hass (1988)

Heinz (1979)

Heinz (1979)

Finley and Stendall (1978)

Zillioux et al. (1993)

Finley and Stendall (1978)

Finley and Stendall (1978)

Kim et al. (1996)

Finley et al. (1979)

Finley et al. (1979)

Finley et al. (1979)

a LD33 = lethal

Source: Wolfe dose, 33%

et al. (1998)



### 1

2

August 2008 B­159 DRAFT—DO NOT QUOTE OR CITE

### Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N d eposition of N

cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11(4):

639-648.

Aber, J. D.; Nadelhoffer, K. J.; Steudler, P.; Melillo, J. M. (1989) Nitrogen saturation in northern

forest ecosystems: excess nitrogen from fossil fuel combustion may stress the biosphere.

BioScience 39: 378-386.

Aber, J. D.; Mellilo, J. M.; Nadelhoffer, K. J.; Pastor, J.; Boone, R. D. (1991) Factors controlling

nitrogen cycling and nitrogen saturation in northern temperate forest ecosystems. Ecol.

Appl. 1: 303-315.

Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.; McNulty, S.;

Currie, W.; Rustad, L.; Fernandez, I. (1998) Nitrogen saturation in temperate forest

ecosystems. BioScience 48: 921-934.

Aber, J. D.; Ollinger, S. V.; Driscoll, C. T.; Likens, G. E.; Holmes, R. T.; Freuder, R. J.;

Goodale, C. L. (2002) Inorganic nitrogen losses from a forested ecosystem in response to

physical, chemical, biotic, and climatic perturbations. Ecosystems 5: 648-658.

Aber, J. D.; Goodale, C. L.; Ollinger, S. V.; Smith, M.-L.; Magill, A. H.; Martin, M. E.; Hall, R.

A.; Stoddard, J. L. (2003) Is nitrogen deposition altering the nitrogen status of

northeastern forests? Bioscience 53: 375-389.

Adams, M. B. (1999) Acidic deposition and sustainable forest management in the central

Appalachians, USA. For. Ecol. Manage. 122: 17-28.

Adams, M. B.; Angradi, T. R.; Kochenderfer, J. N. (1997) Steam water and soil solution

responses to 5 years of nitrogen and sulfur additions at the Fernow Ex perimental Forest,

West Virginia. Forest Ecol. Manage. 95: 79-91.

Adams, M. B.; Burger, J. A.; Jenkins, A. B.; Zelazny, L. (2000) Impact of harvesting and

atmospheric pollution on nutrient depletion of eastern U.S. hardwood forests. For. Ecol.

Manage. 138: 301-319.

Alewell, C.; Gehre, M. (1999) Patterns of stable S isotopes in a forested catchment as indicators

for biological S turnover. Biogeochemistry 47: 319-333.

Allen, E. B.; Sirulnik, A. G.; Egerton-Warburton, L.; Kee, S. N.; Bytnerowicz, A.; Padgett, P. E.;

Temple, P. J.; Fenn, M. E.; Poth, M. A.; Meixner, T. (2005) Air pollution and vegetation

change in southern California coastal sage scrub: a comparison with chaparral and

coniferous forest. In: Kus, B. E.; Beyers, J. L., eds. Planning for biodiversity: bringing

research and management together. Albany, CA: U.S. Department of Agriculture, Forest

Service, Pacific Southwest Research Station; general technical report PSW-GTR-195; pp.

79-95.

Almer, B.; Dickson, W.; Ekström, C.; Hörnström, E.; Miller, U. (1974) Effects of acidification

on Swedish lakes. Ambio 3: 30-36. 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37 

# ANNEX B

­ References

August 2008 B­160 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Altshuller, A. P.; Linthurst, R. A., eds. (1984) The acidic deposition phenomenon and its effects:

critical assessment review p apers. Washington, DC: U.S. Environmental Protection

Agency; report no. EPA-600/8-83-016-BF.

Alvo, R.; Hussell, D. J. T.; Berrill, M. (1988) The breeding success of common loons (Gavia

immer) in relation to alkalinity and other lake characteristics in Ontario. Can. J. Zool. 66:

746-752.

Arrington, D. V.; Lindquist, R. C.; Beck, B. F.; Wilson, W. L. (1987) Thickly mantled karst of

the Interlachen, Florida area. In: Balkema, A. A., ed. Karst hydrology: engineering and

environmental applications; pp. 31-39.

Asp, H.; Berggren, D. (1990) Phosphate and calcium uptake in beech (Fagus sylvatica) in the

presence of aluminium and natural fulvic acids. Physiol. Plant. 80: 307-314.

Audet, C.; Wood, C. M. (1988) Do rainbow t rout (Salmo gairdneri) acclimate to low p H? Can. J.

Fish. Aquat. Sci. 45: 1399-1405.

Bailey, S. W.; Hornbeck, J. W.; Driscoll, C. T.; Gaudette, H. E. (1996) Calcium inputs and

transport in a base-poor forest ecosystem as interpreted by Sr isotopes. Water Resour.

Res. 32: 707-719.

Bailey, S. W.; Horsley, S. B.; Long, R. P.; Hallet, R. A., eds. (1999) Influence of geologic and

pedologic factors on health of sugar maple on the Allegheny Plateau. U.S. Department of

Agriculture, Forest Service; general technical report NE-261; pp. 63-65.

Bailey, S. W.; Buso, D. C.; Likens, G. E. (2003) Implications of sodium mass balance for

interpreting the calcium cycle of a forested ecosystem. Ecology 84: 471-484.

Bailey, S. W.; Horsley, S. B.; Long, R. P.; Hallett, R. A. (2004) Influence of edaphic factors on

sugar maple nutrition and health on the Allegheny Plateau. Soil Sci. Soc. Am. J. 68: 243­

252.

Bailey, S. W.; Horsley, S. B.; Long, R. P. (2005) Thirty years of change in forest soils of the

Allegheny Plateau, Pennsylvania. Soil Sci. Soc. Am. J. 69: 681-690.

Baker, L. A.; Brezonik, P. L. (1988) Dynamic model of in-lake alkalinity generation. Water

Resour. Res. 24: 65-74.

Baker, L. A.; Christensen, S. W. (1991) Effects of acidification on biological communities. In:

Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case studies. New

York, NY: Springer-Verlag; pp. 83-106.

Baker, J. P.; Schofield, C. L. (1980) Aluminum toxicity to fish as related to acid precipitation

and Adirondack surface water quality. In: Drabløs, D.; Tollan, A., eds. Ecological impact

of acid precipitation: proceedings of an international conference; March; Sandefjord,

Norway. Oslo-As, Norway: SNSF project; pp. 292-295.

Baker, J. P.; Schofield, C. L. (1982) Aluminum toxicity to fish in acidic waters. Water Air Soil

Pollut. 18: 289-309.

Baker, J. P.; Schofield, C. L. (1985) Acidification impacts on fish populations: a review. In:

Adams, D. D.; Page, W. P., eds. Acid deposition: environmental, economic, and policy

issues. New Y ork, NY: Plenum Press; pp. 183-221.

August 2008 B­161 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Baker, L. A.; Pollman, C. D.; Eilers, J. M. (1988) Alkalinity regulation in softwater Florida

lakes. Water Resour. Res. 24: 1069-1082.

Baker, J. P.; Bernard, D. P.; Christensen, S. W.; Sale, M. J. (1990a) Biological effects of changes

in surface water acid-base chemistry. Washington, DC: National Acid Precipitation

Assessment Program. State of Science/Technology Report 13.

Baker, J. P.; Gherini, S. A.; Christensen, S. W.; Driscoll, C. T.; Gallagher, J.; Munson, R. K.;

Newton, R. M.; Reckhow, K. H.; Schofield, C. L. (1990b) Adirondack lakes survey: an interpretive analysis of fish communities and water chemistry, 1984-1987. Ray Brook,

NY: Adirondack Lakes Survey Corporation.

Baker, L. S.; Kaufmann, P. R.; Herlihy, A. T.; Eilers, J. M. (1991a) Current status of surface

water acid-base chemistry. In: Irving, P. M., ed. Acidic deposition: state of science and

technology, v. II, aquatic processes and effects. Washington, DC: National Acid

Precipitation Assessment Program; NAPAP State of Science/Technology report 9; pp. 9­

5

- 9-367, 9-A1, 9-B1, 9-C1, 9-D1, and 9-CP1.

Baker, L. A.; Herlihy, A. T.; Kaufmann, P. R.; Eilers, J. M. (1991b) Acidic lakes and streams in

the United States: the role of acidic deposition. Science (Washington, DC, U.S.) 252:

1151-1154.

Baker, T. R.; Allen, H. L.; Schoeneberger, M. M.; Kress, L. W. (1994) Nutritional response of

loblolly pine exposed to ozone and simulated acid rain. Can. J. For. Res. 24: 453-461.

Baker, J. P.; Van Sickle, J.; Gagen, C. J.; DeWalle, D. R.; Sharpe, W. E.; Carline, R. F.; Baldigo,

B. P.; Murdoch, P. S.; Bath, D. W.; Kretser, W. A.; Simonin, H. A.; Wigington, P. J., Jr.

(1996) Episodic acidification of small streams in the northeastern United States: effects

on fish populations. Ecol. Appl. 6: 423-437.

Baldigo, B. P.; Murdoch, P. S. (1997) Effect of stream acidification and inorganic aluminum on mortality of brook trout (Salvelinus fontinalis) in the Catskill Mountains, New Y ork.

Can. J. Fish. Aquat. Sci. 54: 603-615.

Baldigo, B. P.; Murdoch, P. S.; Burns, D. A. (2005) Stream acidification and mortality of brook

trout (Salvelinus fontinalis) in response to timber harvest in Catskill Mountain

watersheds, New Y ork, USA. Can. J. Fish. Aquat. Sci. 62: 1168-1183.

Baldigo, B. P.; Lawrence, G. B.; Simonin, H. A. (2007) Persistent mortality of brook trout in

episodically acidified streams of the southwestern Adirondack Mountains, New Y ork.

Trans. Am. Fish. Soc. 136: 121-134.

Barinaga, M. (1990) Where have all the froggies gone? Science (Washington, DC, U.S.) 247:

1033-1034.

Barmuta, L. A.; Cooper, S. D.; Hamilton, S. K.; Kratz, K. W.; Melack, J. M. (1990) Responses

of zooplankton and zoobenthos to experimental acidification in a high-elevation lake

(Sierra Nevada, California, U.S.A.). Freshwater Biol. 23: 571-586.

Baron, J.; Norton, S. A.; Beeson, D. R.; Hermann, R. (1986) Sediment diatom and metal

stratigraphy from Rocky Mountain lakes with special reference to atmospheric

deposition. Can. J. Fish. Aquat. Sci. 43: 1350-1362.

August 2008 B­162 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Baron, J. S.; Ojima, D. S.; Holland, E. A.; Parton, W. J. (1994) Analysis of nitrogen saturation

potential in Rocky Mountain tundra and forest: implications for aquatic systems.

Biogeochemistry 27: 61-82.

Baron, J. S.; Rueth, H. M.; Wolfe, A. M.; Nydick, K. R.; Allstott, E. J.; Minear, J. T.; Moraska,

B. (2000) Ecosystem responses to nitrogen deposition in the Colorado Front Range.

Ecosystems 3: 352-368.

Battoe, L. E.; Lowe, E. F. (1992) Acidification of Lake Annie, Highlands Co., FL. Water Air

Soil Pollut. 65: 69-80.

Beamish, R. J. (1976) Acidification of lakes in Canada by acid precipitation and the resulting

effects on fishes. Water Air Soil Pollut. 6: 501-514.

Beamish, R. J.; Harvey, H. H. (1972) Acidification of the La Cloche mountain lakes, Ontario,

and resulting fish mortalities. J. Fish. Res. Board Can. 29: 1131-1143.

Beggs, G. L.; Gunn, J. M. (1986) Response of lake trout (Salvelinus namaycush) and brook trout

(S. fontinalis) to surface water acidification in Ontario. Water Air Soil Pollut. 30: 711­

717.

Bell, J. N. B. (1985) SO 2 

### effects on the productivity of grass species. In: Winner, W. E.;

Mooney, H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation: physiology,

ecology, and policy issues. Stanford, CA: Stanford University Press; pp. 250-263.

Belnap, J.; Sigal, L.; Moir, W.; Eversman, S. (1993) Identification of sensitive species, in lichens

as bioindicators of air quality. In: Huckaby, L. S., ed. Lichens as bioindicators of air

quality. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky

Mountain Forest and Range Experimental Station; pp. 67-88.

Berggren, D.; Mulder, J. (1995) The role of organic matter in controlling aluminum solubility in

acidic mineral soil horizons. Geochim. Cosmochim. Acta 59: 4167-4180.

Bergman, H. L.; Mattice, J. S.; Brown, D. J. A. (1988) Lake acidification and fisheries project:

adult brook trout (Salvelinus fontinalis). Can. J. Fish. Aquat. Sci. 45: 1561-1562.

Billett, M. F.; Parker-Jervis, F.; Fitzpatrick, E. A.; Cresser, M. S. (1990) Forest soil chemical

changes between 1949/50. J. Soil Sci. 41: 133-145.

Billings, W. D. (1978) Plants and the ecosystem. 3rd ed. Belmont, CA: Wadsworth Publishing

Company, Inc.; pp. 1-62, 83-108.

Birks, H. J. B.; Berge, F.; Boyle, J. F.; Cumming, B. F. (1990) A p alaeoecological test of the

land-use hypothesis for recent lake acidification in south-west Norway using hill-top

lakes. J. Paleolimnol. 4: 69-65.

Blair, R. B. (1990) Water quality and the summer distribution of common loons in Wisconsin.

Passenger Pigeon 52: 119-126.

Blake, L.; Goulding, K. W. T.; Mott, C. J. B.; Johnston, A. E. (1999) Changes in soil chemistry

accompanying acidification over more than 100 years under woodland and grass at

Rothamsted Experimental Station, UK. Eur. J. Soil Sci. 50: 401-412.

Blancher, P. J.; McNicol, D. K. (1988) Breeding biology of tree swallows in relation to wetland

acidity. Can. J. Zool. 66: 842-849.

August 2008 B­163 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Blancher, P. J.; McNicol, D. K. (1991) Tree swallow d iet in relation to wetland activity. Can. J.

Zool. 69: 2629-2637.

Blum, J. D.; Johnson, C. E.; Siccama, T. G.; Eagar, C.; Fahey, T. J.; Likens, G. E.; Klaue, A.;

Nezat, C. A.; Driscoll, C. T. (2002) Mycorrhizal weathering of apatite as an important

calcium source in base-poor forest ecosystems. Nature (London, U.K.) 417: 729-731.

Bobbink, R.; Hornung, M.; Roelofs, J. G. M. (1998) The effects of air-borne nitrogen pollutants

on species diversity in natural and semi-natural European vegetation. J. Ecol. 86: 717­

738.

Bondietti, E. A.; McLaughlin, S. B. (1992) Evidence of historical influences of acidic deposition

on wood and soil chemistry. In: Johnson, D. W.; Lindberg, S. E., eds. Atmospheric

deposition and forest nutrient cycling: a synthesis of the integrated forest study. New

York, NY: Springer-Verlag: pp. 358-377. (Billings, W. D.; Golley, F.; Lange, O. L.;

Olson, J. S.; Remmert, H., eds. Ecological studies analysis and synthesis: v. 91).

Booth, C. E.; McDonald, D. G.; Simons, B. P.; Wood, C. M. (1988) Effects of aluminum and

low p H o n net ion fluxes and ion balance in the brook trout (Salvelinus fontinalis). Can.

J. Fish. Aquat. Sci. 45: 1563-1574.

Bowman, W. D.; Gartner, J. R.; Holland, K.; Wiedermann, M. (2006) Nitrogen critical loads for

alpine vegetation and terrestrial ecosystem response: are we there yet? Ecol. Appl. 16:

1183-1193.

Bradford, D. F.; Swanson, C.; Gordon, M. S. (1992) Effects of low p H a nd aluminum on two

declining species of amphibians in the Sierra Nevada, California. J. Herpetol. 26: 369­

377.

Bradford, D. F.; Tabatabai, F.; Graber, D. M. (1993) Isolation of remaining populations of the

native frog, Rana muscosa, by introduced fishes in Sequoia and Kings Canyon National

Parks, California. Conserv. Biol. 7: 882-888.

Brenner, M.; Binford, M. W. (1988) Relationships between concentrations of sedimentary

variables and trophic state in Florida lakes. Can. J. Fish. Aquat. Sci. 45: 294-300.

Brezonik, P. L.; Eaton, J. G.; Frost, T. M.; Garrison, P. J.; Kratz, T. K.; Mach, C. E.;

McCormick, J. H.; Perry, J. A.; Rose, W. A.; Sampson, C. J.; Shelley, B. C. L.; Swenson,

W. A.; Webster, K. E. (1993) Experimental acidification of Little Rock Lake, Wisconsin:

chemical and biological changes over the pH range 6.1 to 4.7. Can. J. Fish. Aquat. Sci.

50: 1101-1121.

Bricker, O. P.; Rice, K. C. (1989) Acidic deposition to streams: a geology-based method predicts

their sensitivity. Environ. Sci. Technol. 23: 379-385.

Brooks, P. D.; Williams, M. W.; Schmidt, S. K. (1996) Microbial activity under alpine

snowpacks, Niwot Ridge, Colorado. Biogeochemistry 32: 93-113.

Brown, D. J. A. (1982) The effect of pH and calcium on fish and fisheries. Water Air Soil Pollut.

18: 343-351.

Brown, D. J. A. (1983) Effect of calcium and aluminum concentrations on the survival of brown

trout (Salmo trutta) at low p H. Bull. Environ. Contam. Toxicol. 30: 582-587.

August 2008 B­164 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Buckler, D. R.; Mehrle, P. M.; Cleveland, L.; Dwyer, F. J. (1987) Influence of pH o n the toxicity

of aluminum and other inorganic contaminants to east coast striped bass. Water Air Soil

Pollut. 35: 97-106.

Bugas, P. E., Jr.; Mohn, L. O.; Kauffman, J. W. (1999) Impacts of acid deposition on fish

populations in St. Marys River, Augusta County, Virginia. Banisteria 13: 191-200.

Bukaveckas, P.; Shaw, W. (1998) Phytoplankton responses to nutrient and grazer manipulations

among northeastern lakes of varying pH. Can. J. Fish. Aquat. Sci. 55: 958-966.

Bulger, A. J. (1986) Coincident peaks in serum osmolality and heat-tolerance rhythms in

seawater-acclimated killifish (Fundulus heteroclitus). Physiol. Zool. 59: 169-174.

Bulger, A. J.; Lien, L.; Cosby, B. J.; Henriksen, A. (1993) Trout status and chemistry from the

Norwegian thousand lake survey: statistical analysis. Can. J. Fish. Aquat. Sci. 50: 575­

585.

Bulger, A. J.; Dolloff, C. A.; Cosby, B. J.; Eshleman, K. N.; Webb, J. R.; Galloway, J. N. (1995)

The Shenandoah National Park: fish in sensitive habitats (SNP:FISH) project. An

integrated assessment of fish community responses to stream acidification. Water Air

Soil Pollut. 85: 309-314.

Bulger, A. J.; Cosby, B. J.; Dolloff, C. A.; Eshleman, K. N.; Webb, J. R.; Galloway, J. N. (1999)

SNP:FISH, Shenandoah National Park: fish in sensitive habitats. Project final report.

Volume I: project description and summary of results; Volume II: stream water chemistry

and discharge, and synoptic water quality surveys. Volume III: basin-wide habitat and

population inventories, and behavioral responses to acid in a laboratory stream. Volume

IV: stream bioassays, aluminum toxicity, species richness and stream chemistry, and

models of susceptibility to acidification. Charlottesville, VA: University of Virginia,

Department of Environmental Sciences. Project Completion Report to the National Park

Service. Cooperative Agreement CA-4000-2-1007, Supplemental Agreement #2.

Bulger, A. J.; Cosby, B. J.; Webb, J. R. (2000) Current, reconstructed past, and projected future

status of brook trout (Salvelinus fontinalis) streams in Virginia. Can. J. Fish. Aquat. Sci.

57: 1515-1523.

Burton, T. M.; Stanford, R. M.; Allan, J. W. (1985) Acidification effects on stream biota and

organic matter processing. Can. J. Fish. Aquat. Sci. 42: 669-675.

Bytnerowicz, A. (2002) Physiological/ecological interactions between ozone and nitrogen

deposition in forest ecosystems. Phyton 42: 13-28.

Bytnerowicz, A.; Fenn, M. E. (1996) Nitrogen deposition in California forests: a review.

Environ. Pollut. 92: 127-146.

Caine, N. (1989) Hydrograph separation in a small alpine basin based on inorganic solute

concentrations. J. Hydrol. 1: 89-101.

Cairns, J., Jr.; Pratt, J. R. (1993) A h istory of biological monitoring using benthic

macroinvertebrates. In: Rosenberg, D. M.; Resh, V. H., eds. Freshwater biomonitoring

and benthic macroinvertebrates. New Y ork, NY: Chapman and Hall; pp. 10-28.

Campbell, D. H.; Clow, D. W.; Ingersoll, G. P.; Mast, M. A.; Spahr, N. E.; Turk, J. T. (1995)

Processes controlling the chemistry of two snowmelt-dominated streams in the Rocky

Mountains. Water Resour. Res. 31(11): 2811-2821.

August 2008 B­165 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Campbell, D. H.; Kendall, C.; Chang, C. C. Y.; Silva, S. R.; Tonnessen, K. A. (2002) Pathways

for nitrate release from an alpine watershed: determination using 15 

### δ N a nd 18 

### δ O. Water

Resour. Res. 31: 2811-2821.

Campbell, D. H.; Muths, E.; Turk, J. T.; Corn, P. S. (2004) Sensitivity to acidification of

subalpine ponds and lakes in north-western Colorado. Hydrol. Processes 18: 2817-2834.

Cape, J. N. (1993) Direct damage to vegetation caused by acid rain and polluted cloud: definition

of critical levels for forest trees. Environ. Pollut. 82: 167-180.

Cappellato, R.; Peters, N. E.; Meyers, T. P. (1998) Above-ground sulfur cycling in adjacent

coniferous and deciduous forests and watershed sulfur retention in the Georgia Piedmont,

USA. Water Air Soil Pollut. 103: 151-171.

Castro, M. S.; Morgan, I. R. P. (2000) Input-output budgets of major ions for a forested

watershed in western Maryland. Water Air Soil Pollut. 119(1-4): 121-137.

Chapin, F. S., III; Ruess, R. W. (2001) The roots of the matter. Nature (London) 411: 749, 751­

752.

Charles, D. F., ed. (1991) Acidic deposition and aquatic eosystems: regional case studies. New

York, NY: Springer-Verlag.

Charles, D. F.; Norton, S. A. (1986) Paleolimnological evidence for trends in atmospheric

deposition of acids and metals. In: Acid deposition: long-term trends. Washington, DC:

National Academy Press, Committee on monitoring and assessment of trends in acid

deposition; pp. 335-435.

Charles, D. F.; Batarbee, R. W.; Renberg, I.; Dam, H. V.; Smol, J. P.; Norton, S. A.; Linberg, S.

E.; Pages, A. L. (1989) Paleoecological analysis of lake acidification trends in North

America and Europe using diatoms and chrysophytes. In: Aquatic processes and lake

acidification. New Y ork, NY: Springer-Verlag; pp. 207-276.

Charles, D. F.; Binford, M. W.; Furlong, E. T.; Hites, R. A.; Mitchell, M. J.; Norton, S. A.;

Oldfield, F.; Paterson, M. J.; Smol, J. P.; Uutala, A. J.; White, J. R.; Whitehead, D. F.;

Wise, R. J. (1990) Paleoecological investigation of recent lake acidification in the

Adirondack Mountains, N.Y. J. Paleolimnol. 3: 195-241.

Chen, L.; Driscoll, C. T. (2005) Regional assessment of the response of the acid-base status of

lake-watersheds in the Adirondack region of New York to changes in atmospheric

deposition using PnET-BGC. Environ. Sci. Technol. 39: 787-794.

Chen, C. W.; Gherini, S. A. P. N. E.; Murdoch, P. S.; Newton, R. M.; Goldstein, R. A. (1984)

Hydrologic analyses of acidic and alkaline lakes. Water Resour. Res. 20: 1875-1882.

Chorover, J.; Visousek, P. M.; Everson, D. A.; Esperanza, A. M.; Turner, D. (1994) Solution

chemistry profiles of mixed-conifer forests before and after fire. Biogeochem. 26: 115­

144.

Christensen, S. W.; Beauchamp, J. J.; Shaakir-Ali, J. A.; Coe, J.; Baker, J. P.; Smith, E. P.;

Gallagher, J. (1990) Patterns of fish distribution in relation to lake/watershed

characteristics: regression analyses and diagnostics. In: Interpretative analysis of the

Adirondack Lakes Survey. Final report to the Adirondack Lakes Survey Corporation; pp.

A-1-A-62.

August 2008 B­166 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Church, M. R.; Shaffer, P. W.; Thornton, K. W.; Cassell, D. L.; Liff, C. I.; Johnson, M. G.;

Lammers, D. A.; Lee, J. J.; Holdren, G. R.; Kern, J. S.; Liegel, L. H.; Pierson, S. M.;

Stevens, D. L.; Rochelle, B. P.; Turner, R. S. (1992) Direct/delayed response project:

future effects of long-term sulfur deposition on stream chemistry in the mid-Appalachian

region of the eastern United States. Corvallis, OR: U.S. Environmental Protection

Agency; report no. EPA/600/R-92/186.

Clow, D. W.; Mast, M. A. (1999) Long-term trends in stream water and precipitation chemistry

at five headwater basins in the northeastern United States. Water Resour. Res. 35: 541­

554.

Cook, R. B.; Jager, H. I. (1991) Upper midwest: the effects of acidic deposition on lakes. In:

Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case studies. New

York, NY: Springer-Verlag, Inc.

Cook, R. B.; Kreis, R. G., Jr.; Kingston, J. C.; Camburn, K. E.; Norton, S. A.; Mitchell, M. J.;

Fry, B.; Shane, L. C. K. (1990) An acidic lake in northern Michigan. J. Paleolimnol. 3:

13-34.

Cook, R. B.; Elwood, J. W.; Turner, R. R.; Bogle, M. A.; Mulholland, P. J.; Palumbo, A. V.

(1994) Acid-base chemistry of high-elevation streams in the Great Smoky Mountains.

Water Air Soil Pollut. 72: 331-356.

Corn, P. S.; Stolzenburg, W.; Bury, R. B. (1989) Acid precipitation studies in Colorado and

Wyoming: interim report of surveys of mountain amphibians and water chemistry. U.S.

Fish and Wildlife Service; biological report 80 (40.26).

Cosby, B. J.; Hornberger, G. M.; Galloway, J. N.; Wright, R. F. (1985) Modeling the effects of

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

chemistry. Water Resour. Res. 21: 51-63.

Cosby, B. J.; Wright, R. F.; Gjessing, E. (1995) An acidification model (MAGIC) with organic

acids evaluated using whole-catchment manipulations in Norway. J. Hydrol. 170: 101­

122.

Cosby, B. J.; Norton, S. A.; Kahl, J. S. (1996) Using a paired-catchment manipulation

experiment to evaluate a catchment-scale biogeochemical model. Sci. Total Environ. 183:

49-66.

Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Wright, R. F. (2001) Modelling the effects of acid

deposition: refinements, adjustments and inclusion of nitrogen dynamics in the MAGIC

model. Hydrol. Earth Syst. Sci. 5: 499-517.

Courchesne, F.; Cote, B.; Fyles, J. W.; Hendershot, W. H.; Biron, P. M.; Roy, A. G.; Turmel,

M.-C. (2005) Recent changes in soil chemistry in a forested ecosystem of southern

Quebec, Canada. Soil Sci. Soc. Am. J. 69: 1298-1313.

Cowardin, L. M.; Golet, F. C.; LaRoe, E. T. (1979) Classification of wetland and deepwater

habitats of the United States. U.S. Department of the Interior: Fish and Wildlife Service;

report no. FWS/OBS-79/31.

Cowling, E.; Dochinger, L. S. (1980) Effects of acidic precipitation on health and productivity of

forests. U.S. Department of Agriculture, Forest Service; technical report no. PSW-43; pp.

165-173.

August 2008 B­167 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

1 0

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Craig, B. W.; Friedland, A. J. (1991) Spatial patterns in forest composition and standing dead red

spruce in montane forests of the Adirondacks and northern Appalachians. Environ.

Monit. Assess. 18: 129-140.

Cronan, C. S.; Aiken, G. R. (1985) Chemistry and transport of soluble humic substances in

forested watersheds of the Adirondack Park, New York. Geochim. Cosmochim. Acta 49:

1697-1705.

Cronan, C. S.; Goldstein, R. A. (1989) ALBIOS: a comparison of aluminum biogeochemistry in

forested watersheds exposed to acidic deposition. In: Acidic precipitation, vol. 1: case

studies. New Y ork, NY: Springer-Verlag; pp. 113-135. (Advances in environmental

science).

Cronan, C. S.; Grigal, D. F. (1995) Use of calcium/aluminum ratios as indicators of stress in

forest ecosystems. J. Environ. Qual. 24: 209-226.

Cronan, C. S.; Schofield, C. L. (1979) Aluminum leaching response to acid precipitation: effects

on high elevation watersheds in the Northeast. Science (Washington, DC, U.S.) 204: 304­

306.

Cronan, C. S.; Reiners, W. A.; Reynolds, R. C. J.; Lang, G. E. (1978) Forest floor leaching:

contributions from mineral, organic, and carbonic acids in New H ampshire subalpine

forests. Science (Washington, DC, U.S.) 200: 309-311.

Cronan, C. S.; Walker, W. J.; Bloom, P. R. (1986) Predicting aqueous aluminum concentrations

in natural waters. Nature (London, U.K.) 324: 140-143.

Cronan, C. S.; April, R.; Bartlett, R. J.; Bloom, P. R.; Driscoll, C. T.; Gherini, S. A.; Henderson,

G. S.; Joslin, J. D.; Kelly, J. M.; Newton, R. M.; Parnell, R. A.; Patterson, H. H.; Raynal,

D. J.; Schaedle, M.; Schofield, C. L.; Sucoff, E. I.; Tepper, H. B.; Thornton, F. C. (1989)

Aluminum toxicity in forests exposed to acidic deposition: the ALBIOS results. Water

Air Soil Pollut. 48: 181-192.

Cronan, C. S.; Driscoll, C. T.; Newton, R. M.; Kelly, J. M.; Schofield, C. L.; Bartlett, R. J.;

April, R. (1990) A c omparative analysis of aluminum biogeochemistry in a northeastern

and a southeastern forested watershed. Water Resour. Res. 26: 1413-1430.

Cumming, J. R.; Weinstein, L. H. (1990) Aluminum-mycorrhizal interactions in the physiology

of pitch pine seedlings. Plant Soil 125: 7-18.

Cumming, B. F.; Smol, J. P.; Kingston, J. C.; Charles, D. F.; Birks, H. J. B.; Camburn, K. E.;

Dixit, S. S.; Uutala, A. J.; Selle, A. R. (1992) How m uch acidification has occurred in

Adirondack region lakes (New Y ork, USA) since preindustrial times? Can. J. Fish.

Aquat. Sci. 49: 128-141.

Cumming, B. F.; Davey, K. A.; Smol, J. P.; Birks, H. J. B. (1994) When did acid-sensitive

Adirondack lakes (New York, USA) begin to acidify and are they still acidifying? Can. J.

Fish. Aquat. Sci. 51: 1550-1568.

Dahlgren, R. A. (1994) Soil acidification and nitrogen saturation from weathering of ammoniumbearing rock. Nature (London) 368: 838-841.

Dahlgren, R. A.; Walker, W. J. (1993) Aluminum release rates from selected Spodosol Bs

horizons: effect of pH and solid-phase aluminum pools. Geochim. Cosmochim. Acta 57:

57-66.

August 2008 B­168 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Dail, D. B.; Davidson, E. A.; Chorover, J. (2001) Rapid abiotic transformation of nitrate in an

acid forest soil. Biogeochemistry 54: 131-146.

Dangles, O.; Malmqvist, B.; Laudon, H. (2004) Naturally acid freshwater ecosystems are diverse

and functional: evidence from boreal streams. Oikos 104: 149-155.

David, M. B.; Driscoll, C. T. (1984) Aluminum speciation and equilibria in soil solutions of a Haplorthod in the Adirondack Mountains ( New Y ork, USA). Geoderma 33: 297-318.

David, M. B.; Lawrence, G. B. (1996) Soil and soil solution chemistry under red spruce stands

across the northeastern United States. Soil Sci. 161: 314-328.

David, M. B.; Mitchell, M. J.; Scott, T. J. (1987) Importance of biological processes in the sulfur

budget of a northern hardwood ecosystem. Biol. Fert. Soils 5(3): 258-264.

David, M.; Vance, G.; Kahl, J. (1999) Chemistry of dissolved organic carbon at Bear Brook

Watershed, Maine: stream water response to (NH 4 

### ) 2 

### SO 4 

### additions. Environ. Monit.

Assess. 55: 149-163.

Davidson, E. A.; Chorover, J.; Dail, D. B. (2003) A m echanism of abiotic immobilization of

nitrate in forest ecosystems: the ferrous wheel hypothesis. Global Change Biol. 9: 228­

236.

Davies, L.; Bates, J. W.; Bell, J. N. B.; James, P. W.; Purvis, O. W. (2007) Diversity and

sensitivity of epiphytes to oxides of nitrogen in London. Environ. Pollut. 146: 299-310.

Davis, R. B.; Anderson, D. S.; Berge, F. (1985a) Loss of organic matter, a fundamental process

in lake acidification: paleolimnological evidence. Nature (London, U.K.) 316: 436-438.

Davis, R. B.; Anderson, D. S.; Berge, F. (1985b) Paleolimnological evidence that lake

acidification is accompanied by loss of organic matter. Nature (London, U.K.) 316: 436­

438.

Davis, R. B.; Anderson, D. S.; Norton, S. A.; Whiting, M. C. (1994) Acidity of twelve northern

New En gland (USA) lakes in recent centuries. J. Paleolimnol. 12: 103-154.

De Wit, H. A.; Mulder, J.; Nygaard, P. H.; Aamlid, D. (2001) Testing the aluminium toxicity

hypothesis: A f ield manipulation experiment in mature spruce forest in Norway. Water

Air Soil Pollut. 130: 995-1000.

DeConinck, F. (1980) Major mechanisms in formation of spodic horizons. Geoderma 24: 101­

128.

DeHayes, D. H.; Schaberg, P. G.; Hawley, G. J.; Strimbeck, G. R. (1999) Acid rain impacts on

calcium nutrition and forest health. BioScience 49: 789-800.

Deevey, E. S.; Binford, M. W.; Brenner, M.; Whitmore, T. J. (1986) Sedimentary records of

accelerated nutrient loading in Florida lakes. Hydrobiologia 143: 49-53.

Denning; A.S, B.; J. Mast, M. A.; Arthur, M. (1991) Hydrologic pathways and chemical

composition of runoff during snowmelt in Loch Vale watershed, Rocky Mountain

National Park, Colorado, USA. Water Air Soil Pollut. 59: 107-123.

Dennis, T. E.; Bulger, A. J. (1995) Condition factor and whole-body sodium concentrations in a

freshwater fish: evidence for acidification stress and possible ionoregulatory overcompensation. Water Air Soil Pollut. 85: 377-382.

August 2008 B­169 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 D ennis, T. E.; Bulger, A. J. (1999) The susceptibility of blacknose dace (Rhinichthys atratulus)

to acidification in Shenandoah National Park. In: Bulger, A. J.; Cosby, B. J.; Dolloff, C.

A.; Eshleman, K. N.; Galloway, J. N.; Webb. , J. R., eds. Shenandoah National Park: fish

in sensitive habitats. Project final report, Volume IV: stream bioassays, aluminum

toxicity, species richness and stream chemistry, and models of susceptibility to

acidification. Chapter 6B. Project completion report to the National Park Service.

Cooperative agreement CA-4000-2-1007.

Dennis, T. D.; MacAvoy, S. E.; Steg, M. B.; Bulger, A. J. (1995) The association of water

chemistry variables and fish condition in streams of Shenandoah National Park (USA).

Water Air Soil Pollut. 85: 365-370.

DesGranges, J.-L.; Darveau, M. (1985) Effect of lake acidity and morphometry on the

distribution of aquatic birds in southern Quebec. Holarctic Ecol. 8: 181-190.

Deviney, F. A. J.; Rice, K. C.; Hornberger, G. M. (2006) Time series and recurrence interval

models to predict the vulnerability of streams to episodic acidification in Shenandoah

National Park, Virgnia. Water Resour. Res. 42(W09405): 10.1029/2005WR004740.

DeWald, L. E.; Sucoff, E. I.; Ohno, T.; Buschena, C. A. (1990) Response of northern red oak

(Quercus rubra L.) seedlings to soil solution aluminum. Can. J. For. Res. 20: 331-336.

DeWalle, D. R.; Swistock, B. R. (1994) Causes of episodic acidification in five Pennsylvania

streams on the northern Appalachian Plateau. Water Resour. Res. 30: 1955-1963.

DeWalle, D. R.; Dinicola, R. S.; Sharpe, W. E. (1987) Predicting baseflow alkalinity as an index

to episodic stream acidification and fish presence. Water Resour. Bull. 23: 29-35.

Dickson, W. (1978) Some effects of the acidification of Swedish lakes. Verh. Int. Ver. Theor.

Angew. Limnol. 20: 851-856.

Dillon, P. J.; Reid, R. A.; Girard, R. (1986) Changes in the chemistry of lakes near Sudbury,

Ontario, following reduction of SO 2 

### emissions. Water Air Soil Pollut. 31: 59-65.

Dise, N. B.; Wright, R. F. (1995) Nitrogen leaching from European forests in relation to nitrogen

deposition. For. Ecol. Manage. 71: 153-161.

Dise, N. B.; Matzner, E.; Gundersen, P. (1998) Synthesis of nitrogen pools and fluxes from

European forest ecosystems. Water Air Soil Pollut. 105: 143-154.

Dixit, S. S.; Keller, W.; Dixit, A.; Smol, J. P. (2001) Diatom-inferred dissolved organic carbon

reconstructions provide assessments of past UV-B penetration in Canadian Shield lakes.

Can. J. Fish. Aquat. Sci. 58: 543-550.

Dobson, J. E.; Rush, R. M.; Peplies, R. W. (1990) Forest blowdown and lake acidification. Ann.

Assoc. Amer. Geogr. 80: 343-361.

Drever, J. I.; Hurcomb, D. R. (1986) Neutralization of atmospheric acidity by chemical

weathering in an alpine drainage basin in the North Cascade Mountains. Geology 14:

221-224.

Driscoll, C. T.; Bisogni, J. J. (1984) Weak acid/base systems in dilute acidified lakes and streams

of the Adirondack region of New York State. In: Schnoor, J. L., ed. Modeling of total

acid precipitation. Boston, MA: Butterworth Publishers; pp. 53-72.

August 2008 B­170 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Driscoll, C. T.; Newton, R. M. (1985) Chemical characteristics of Adirondack lakes. Environ.

Sci. Technol. 19: 1018-1024.

Driscoll, C. T.; Van Dreason, R. (1993) Seasonal and long-term temporal patterns in the

chemistry of Adirondack lakes. Water Air Soil Pollut. 67: 319-344.

Driscoll, C. T., Jr.; Baker, J. P.; Bisogni, J. J., Jr.; Schofield, C. L. (1980) Effect of aluminum

speciation on fish in dilute acidified waters. Nature (London) 284: 161-164.

Driscoll, C. T.; Van Breemen, N.; Mulder, J. (1985) Aluminum chemistry in a forested spodosol.

Soil Sci. Soc. Am. J. 49: 437-444.

Driscoll, C. T.; Wyskowski, B. J.; Consentini, C. C.; Smith, M. E. (1987) Processes regulating

temporal and longitudinal variations in the chemistry of a low-order woodland stream in

the Adirondack region of New Y ork. Biogeochemistry 3: 225-241.

Driscoll, C. T.; Johnson, N. M.; Likens, G. E.; Feller, M. C. (1988) Effects of acidic deposition

on the chemistry of headwater streams: a comparison between Hubbard Brook, New

Hampshire, and Jamieson Creek, British Columbia. Water Resour. Res. 24: 195-200.

Driscoll, C. T.; Likens, G. E.; Hedin, L. O.; Eaton, J. S.; Bormann, F. H. (1989) Changes in the

chemistry of surface waters. Environ. Sci. Tech. 23: 137-143.

Driscoll, C. T.; Newton, R. M.; Gubala, C. P.; Baker, J. P.; Christensen, S. W. (1991)

Adirondack mountains. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems:

regional case studies. New Y ork, NY: Springer-Verlag; pp. 133-202.

Driscoll, C. T.; Lehtinen, M. D.; Sullivan, T. J. (1994) Modeling the acid-base chemistry of

organic solutes in Adirondack, New Y ork, lakes. Water Resour. Res. 30: 297-306.

Driscoll, C. T.; Postek, K. M.; Kretser, W.; Raynal, D. J. (1995) Long-term trends in the

chemistry of precipitation and lake water in the Adirondack region of New York, USA.

Water Air Soil Pollut. 85: 583-588.

Driscoll, C. T.; Lawrence, G. B.; Bulger, A. J.; Butler, T. J.; Cronan, C. S.; Eagar, C.; Lambert,

K. F.; Likens, G. E.; Stoddard, J. L.; Weather, K. C. (2001a) Acid rain revisited:

advances in scientific understanding since the passage of the 1970 and 1990 Clean Air

Act Amendments. Hanover, NH: Hubbard Brook Research Foundation. Science links

publication vol. 1, no. 1.

Driscoll, C. T.; Lawrence, G. B.; Bulger, A. J.; Butler, T. J.; Cronan, C. S.; Eagar, C.; Lambert,

K. F.; Likens, G. E.; Stoddard, J. L.; Weathers, K. C. (2001b) Acidic deposition in the

northeastern United States: sources and inputs, ecosystem effects, and management

strategies. BioScience 51: 180-198.

Driscoll, C.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C.; Groffman, P.;

Hopkinson, C.; Lambert, K.; Lawrence, G.; Ollinger, S. (2003a) Nitrogen pollution:

sources and consequences in the U.S. Northeast. Environment 45: 8-22.

Driscoll, C. T.; Driscoll, K. M.; Roy, K. M.; Mitchell, M. J. (2003b) Chemical response of lakes

in the Adirondack region of New Y ork to declines in acidic deposition. Environ. Sci.

Technol. 37: 2036-2042.

Driscoll, C. T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C. L.;

Groffman, P.; Hopkinson, C.; lambert, K.; Lawrence, G.; Ollinger, S. (2003c) Nitrogen

August 2008 B­171 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 pollution in the northeastern United States: sources, effects, and management options.

BioScience 53: 357-374.

Driscoll, C. T.; Driscoll, K. M.; Roy, K. M.; Dukett, J. (2007) Changes in the chemistry of lakes

in the Adirondack Region of New Y ork following declines in acidic deposition. Appl.

Geochem. 22: 1181-1188.

Drohan, J. R.; Sharpe, W. E. (1997) Long-term changes in forest soil acidity in Pennsylvania,

U.S.A. Water Air Soil Pollut. 95: 299-311.

Drohan, P. J.; Stout, S. L.; Petersen, G. W. (2002) Sugar maple (Acer saccharum Marsh.) decline

during 1979-1989 in northern Pennsylvania. For. Ecol. Manage. 170: 1-17.

Eagar, C.; Adams, M. B., eds. (1992) Ecology and decline of red spruce in the eastern United

States. New Y ork, NY: Springer-Verlag. (Ecological studies: v. 96).

Eagar, C.; Van Miegroet, H.; McLaughline, S. B.; Nicholas, N. S. (1996) Evaluation of effects of

acidic deposition to terrestrial ecosystems in class I areas of the Southern Appalachians.

Southern Appalachian Mountains Initiative; technical report.

Edwards, J. H.; Horton, B. D. (1977) Aluminum-induced calcium deficiency in peach seedlings.

J. Am. Soc. Hortic. Sci. 102: 459-461.

Edwards, P. J.; Helvey, J. D. (1991) Long-term ionic increases from a central Appalachian

forested watershed. J. Environ. Qual. 20: 250-255.

Edwards, D.; Brown, J. A.; Whitehead, C. (1987) Endocrine and other physiological indicators

of acid stress in the brown trout. Ann. Soc. R. Zool. Belg. 117(suppl. 1): 331-342.

Egerton-Warburton, L. M.; Allen, E. B. (2000) Shifts in arbuscular mycorrhizal communities

along an anthropogenic nitrogen deposition gradient. Ecol. Appl. 10: 484-496.

Eilers, J. M.; Glass, G. E.; Webster, K. E.; Rogalla, J. A. (1983) Hydrologic control of lake

susceptibility to acidification. Can. J. Fish. Aquat. Sci. 40: 1896-1904.

Eilers, J. M.; Brakke, D. F.; Landers, D. H. (1988) Chemical and physical characteristics of lakes

in the upper midwest, United States. Environ. Sci. Technol. 22: 164-172.

Elwood, J. W.; Sale, M. J.; Kaufmann, P. R.; Cada, G. F. (1991) The Southern Blue Ridge

Province. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case

studies. New Y ork, NY: Springer-Verlag; pp. 319-364.

Emmett, B. A.; Boxman, D.; Bredemeier, M.; Gunderson, P.; Kjøaas, O. J.; Moldan, F.;

Schleppi, P.; Tietema, A.; Wright, R. F. (1998) Predicting the effects of atmospheric

nitrogen deposition in conifer stands: evidence from the NITREX e cosystem-scale

experiments. Ecosystems 1: 352-360.

Eriksson, E. (1981) Aluminium in groundwater, possible solution equilibria. Nord. Hydrol. 12:

43-50.

Eriksson, M. O. G. (1983) The role of fish in the selection of lakes by non-piscivorous ducks:

mallard, teal and goldeneye. Wildfowl 34: 27-32.

Eriksson, M. O. G. (1986) Fish delivery, production of young, and nest density of Osprey

(Pandion haliaetus) in southwest Sweden. Can. J. Zool. 64: 1961-1965.

Eshleman, K. N. (1988) Predicting regional episodic acidification of surface waters using

empirical models. Water Resour. Res. 24: 1118-1126.

August 2008 B­172 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Eshleman, K. N.; Hyer, K. E. (2000) Discharge and water chemistry at the three intensive sites.

In: Bulger, A. J.; Cosby, B. J.; Dolloff, C. A.; Eshleman, K. N.; Webb, J. R.; Galloway, J.

N., eds. Shenandoah National Park: fish in sensitive habitats. Project Final Report

- Vol.

II. Stream water chemistry and discharge, and synoptic water quality surveys; pp. 51-92.

Eshleman, K. N.; Davies, T. D.; Tranter, M.; Wigington, P. J., Jr. (1995) A t wo-component

mixing model for predicting regional episodic acidification of surface waters during

spring snowmelt periods. Water Resour. Res. 31: 1011-1021.

Eshleman, K. N.; Morgan II, R. P.; Webb, J. R.; Deviney, F. A.; Galloway, J. N. (1998)

Temporal patterns of nitrogen leakage from mid-Appalachian forested watersheds: role of

insect defoliation. Water Resour. Res. 34(8): 2005-2116.

Evans, C. D.; Monteith, D. T. (2001) Chemical trends at lakes and streams in the UK A cid

Waters Monitoring Network, 1988-2000: evidence for recent recovery at a national scale.

Hydrol. Earth Syst. Sci. 5: 351-366.

Everhart, W. H.; Youngs, W. D. (1981) Principles of fishery science. Ithaca, NY: Cornell

University Press.

Evers, D. C.; Han, Y.; Driscoll, C. T.; Kamman, N. C.; Goodale, M. W.; Lambert, K. F.; Holsen,

T. M.; Chen, C. Y.; Clair, T. A.; Butler, T. (2007) Biological mercury hotspots in the

northeastern United States and southeastern Canada. BioScience 57: 29-43.

Exley, C.; Phillips, M. J. (1988) Acid rain: implications for the farming of salmonids. In: Muir, J.

F.; Roberts, R. J., eds. Recent advances in aquaculture. London, United Kingdom: Croom

Helm; pp. 225-342.

Falkengren-Grerup, U.; Erikson, H. (1990) Changes in soil, vegetation, and forest yield between

1947 and 1988 in beech and oak sites of southern Sweden. For. Ecol. Manage. 38: 37-53.

Farag, A. M.; Woodward, D. F.; Little, E. E.; Steadman, B.; Vertucci, F. A. (1993) The effects of

low p H a nd elevated aluminum on Yellowstone cutthroat trout (Oncorhynchus clarki

bouvieri). Environ. Toxicol. Chem. 12: 719-731.

Farmer, G. J.; Beamish, F. W. H. (1969) Oxygen consumption of Tilapia nilotica in relation to

swimming speed and salinity. J. Fish. Res. Board Can. 26: 2807-2821.

Farmer, A. M.; Bates, J. W.; Bell, J. N. B. (1992) Ecophysiological effects of acid rain on bryophytes and lichens. In: Bates, J. W.; Farmer, A. M., eds. Bryophytes and lichens in a changing environment. Oxford: Claredon Press.

Federer, C. A.; Hornbeck, J. W.; Tritton, L. M.; Martin, C. W.; Pierce, R. S. (1989) Long-term

depletion of calcium and other nutrients in eastern US forests. Environ. Manage. (N. Y.)

13: 593-601.

Fenn, M. E.; Poth, M. A. (1998) Indicators of nitrogen status in California forests. U.S.D.A.

Forest Service Gen. Tech. Rep. PSW-GTR-166.

Fenn, M. E.; Poth, M. A. (1999) Temporal and spatial trends in streamwater nitrate

concentrations in the San Bernardino Mountains, southern California. J. Environ. Qual.

28: 822-836.

Fenn, M. E.; Poth, M. A.; Johnson, D. W. (1996) Evidence for nitrogen saturation in the San

Bernardino Mountains in southern California. For. Ecol. Manage. 82: 211-230.

August 2008 B­173 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 F enn, M. E.; Poth, M. A.; Aber, J. D.; Baron, J. S.; Bormann, B. T.; Johnson, D. W.; Lemly, A.

D.; McNulty, S. G.; Ryan, D. F.; Stottlemyer, R. (1998) Nitrogen excess in North

American ecosystems: predisposing factors, ecosystem responses, and management

strategies. Ecol. Appl. 8: 706-733.

Fenn, M. E.; Baron, J. S.; Allen, E. B.; Rueth, H. M.; Nydick, K. R.; Geiser, L.; Bowman, W. D.;

Sickman, J. O.; Meixner, T.; Johnson, D. W.; Neitlich, P. (2003) Ecological effects of

nitrogen deposition in the western United States. BioScience 53: 404-420.

Fenn, M. E.; Geiser, L.; Bachman, R.; Blubaugh, T. J.; Bytnerowicz, A. (2007) Atmospheric

deposition inputs and effects on lichen chemistry and indicator species in the Columbia

River Gorge, USA. Environ. Pollut. 146: 77-91.

Fernandez, I. J.; Rustad, L. E.; Norton, S. A.; Kahl, J. S.; Cosby, B. J. (2003) Experimental

acidification causes soil base-cation depletion at the Bear Brook Watershed in Maine.

Soil Sci. Soc. Am. J. 67: 1909-1919.

Findlay, D. L. (2003) Response of phytoplankton communities to acidification and recovery in

Killarney Park and the Experimental Lakes Area, Ontario. Ambio 32: 190-195.

Findlay, D. L.; Kasian, S. E. M. (1996) The effect of incremental pH recovery on the Lake 223

phytoplankton community. Can. J. Fish. Aquat. Sci. 53: 856-864.

Findlay, D. L.; Hecky, R. E.; Kasian, S. E. M.; Stainton, M. P.; Hendzel, L. L.; Schindler, E. U.

(1999) Effects on phytoplankton of nutrients added in conjunction with acidification.

Freshwater Biol. 41: 131-145.

Finley, M. T.; Stendell, R. C. (1978) Survival and reproductive success of black ducks fed

methyl mercury. Environ. Pollut. 16: 51-64.

Finley, M. T.; Stickel, W. H.; Christensen, R. E. (1979) Mercury residues in tissues of dead and

surviving birds fed methylmercury. Bull. Environ. Contam. Toxicol. 21: 105-110.

Fisher, D. C.; Oppenheimer, M. (1991) Atmospheric nitrogen deposition and the Chesapeake

Bay estuary. Ambio 20: 102-108.

Fitzhugh, R. D.; Lovett, G. M.; Venterea, R. T. (2003) Biotic and abiotic immobilization of

ammonium, nitrite, and nitrate in soils developed under different tree species in the

Catskill Mountains, New York, USA. Global Change Biol. 9: 1591-1601.

Fjellheim, A.; Raddum, G. G.; Sagen, T. (1985) Effect of aluminum at low pH on the mortality

of elvers. J. Exp. Zool. 262(3): 247-254. **CHECK REFERENCE LIST FOR TITLE**

Flower, R. J.; Battarbee, R. W. (1983) Diatom evidence for recent acidification of two Scottish

Lochs. Nature (London, U.K.) 305: 130-133.

Flum, T.; Nodvin, S. C. (1995) Factors affecting streamwater chemistry in the Great Smoky

Mountains, USA. Water Air Soil Pollut. 85: 1707-1712.

Ford, J.; Stoddard, J. L.; Powers, C. F. (1993) Perspectives in environmental monitoring: an

introduction to the U.S. EPA Long-Term Monitonring (LTM) project. Water Air Soil

Pollut. 67: 247-255.

Foster, N. W.; Nicolson, J. A.; Hazlett, P. W. (1989) Temporal variation in nitrate and nutrient

cations in drainage waters from a deciduous forest. J. Environ. Qual. 18: 238-244.

August 2008 B­174 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Freda, J.; McDonald, D. G. (1988) Physiologic correlates of interspecific variation in acid

tolerance in fish. J. Exp. Biol. 136: 243-258.

Fremstad, E.; Paal, J.; Möls, T. (2005) Impacts of increased nitrogen supply on Norwegian

lichen-rich alpine communities: a 10-year experiment. J. Ecol. 93: 471-481.

Frenette, J. J.; Richard, Y.; Moreau, G. (1986) Fish responses to acidity in Quebéc lakes: a

review. Water Air Soil Pollut. 30: 461-475.

Frey, S. D.; Knorr, M.; Parrent, J. L.; Simpson, R. T. (2004) Chronic nitrogen enrichment affects

the structure and function of the soil microbial community in temperate hardwood and

pine forests. For. Ecol. Manage. 196: 159-171.

Frost, T. M.; Fischer, J. M.; Klug, J. L.; Arnott, S. E.; Montz, P. K. (2006) Trajectories of

zooplankton recovery in the Little Rock Lake whole-lake acidification experiment. Ecol.

Appl. 16: 353-367.

Fuller, R. D.; Driscoll, C. T.; Lawrence, G. B.; Nodvin, S. C. (1987) Processes regulating

sulphate flux after whole-tree harvesting. Nature (London) 325: 707-710.

Gagen, C. J.; Sharpe, W. E.; Carline, R. F. (1993) Mortality of brook trout, mottled sculpins, and

slimy sculpins during acidic episodes. Trans. Am. Fish. Soc. 122: 616-628.

Gallagher, J.; Baker, J. L. (1990) Current status of fish communities in Adirondack lakes. In:

Adirondack Lakes survey: an interpretative analysis of fish communities and water

chemistry, 1984-87. Ray Brook, NY: Adirondack Lake Survey Corporation; pp. 3-11

- 3­

48.

Galloway, J. N. (1996) Anthropogenic mobilization of sulphur and nitrogen: immediate and

delayed consequences. Annu. Rev. Energy Environ. 21: 261-292.

Garcia, E.; Carignan, R. (2000) Mercury concentrations in northern pike (Esox lucius) from

boreal lakes with logged, burned, or undisturbed catchments. Can. J. Fish. Aquat. Sci.

57(suppl. 2): 129-135.

Garner, J. H. B. (1994) Nitrogen oxides, plant metabolism, and forest ecosystem response. In:

Alscher, R. G.; Wellburn, A. R., eds. Plant responses to the gaseous environment:

molecular, metabolic and physiological aspects, [3rd international symposium on air

pollutants and plant metabolism]; June 1992; Blacksburg, VA. London, United Kingdom:

Chapman & Hall; pp. 301-314.

Gbondo-Tugbawa, S. S.; Driscoll, C. T.; Mitchell, M. J.; Aber, J. D.; Likens, G. E. (2002) A

model to simulate the response of a northern hardwood forest ecosystem to changes in S

deposition. Ecol. Appl. 12: 8-23.

Gensemer, R. W.; Playle, R. C. (1999) The bioavailability and toxicity of aluminum in aquatic

environments. Crit. Rev. Environ. Sci. Tech. 29: 315-450.

Gerritsen, J.; Carlson, W. E.; Dycus, D. L.; Faulkner, C.; Gibson, G.; Harcum, J.; Markowitz, S.

A. (1998) Lake and reservoir bioassessment and biocriteria. Washington, DC: U.S.

Environmental Protection Agency; report no. EPA 8 41-B-98-007.

Gilliam, F. S.; Adams, M. B.; Yurish, B. M. (1996) Ecosystem nutrient responses to chronic

nitrogen inputs at Fernow Ex perimental Forest, West Virginia. Can. J. For. Res. 26: 196­

205.

August 2008 B­175 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Glooschenko, V.; Blancher, P.; Herskowitz, J.; Fulthorpe, R.; Rang, S. (1986) Association of

wetland acidity with reproductive parameters and insect prey of the Eastern Kingbird

(Tyrannus tyrannus) near Sudbury, Ontario. Water Air Soil. Pollut. 30: 553-567.

Gochfeld, M. (1980) Tissue distribution of mercury in normal and abnormal young common

terns. Mar. Pollut. Bull. 11: 362-366.

Godbold, D. L.; Fritz, E.; Hüttermann, A. (1988) Aluminum toxicity and forest decline. Proc.

Natl. Acad. Sci. U. S. A. 85: 3888-3892.

Goede, R. W.; Barton, B. A. (1990) Organismic indices and an autopsy-based assessment as

indicators of health and condition of fish. Am. Fish. Soc. Symp. 8: 80-93.

Goodale, C. L.; Aber, J. D. (2001) The long-term effects of land-use history on nitrogen cycling

in northern hardwood forests. Ecol. Appl. 11: 253-267.

Goodale, C. L.; Aber, J. D.; Vitousek, P. M. (2003) An unexpected nitrate decline in New

Hampshire streams. Ecosystems 6: 75-86.

Göransson, A.; Eldhuset, T. D. (1987) Effects of aluminum on growth and nutrient uptake of

Betula pendula seedlings. Physiol. Plant. 69: 193-199.

Gordon, C.; Wynn, J. M.; Woodin, S. J. (2001) Impacts of increased nitrogen supply on high

Arctic heath: the importance of bryophytes and phosphorus availability. New P hytol.

149: 461-471.

Gorham, E.; Janssens, J. A.; Wheeler, G. A.; Glaser, P. H. (1987) Natural and anthropogenic

acidification of peatlands: an overview. In: Hutchinson, T. C.; Meema, K., eds. Effects of

atmospheric pollutants on forests, wetlands, and agricultural ecosystems. New Y ork, NY:

Springer-Verlag; pp. 493-512.

Goriup, P. D. (1989) Acidic air pollution and birds in Europe. Oryx 23: 82-86.

Graetz, D. A.; Pollman, C. D.; Roof, B.; Will, E. (1985) Effects of acidic treatments on soil

chemistry and microbiology. In: Florida acid deposition study. Phase IV r eport.

Gainesville, FL: Environmental Science and Engineering, Inc. Vol. I. ESE No. 83-152­

0106/0207/0307; pp. 4-5

- 4-123.

Grant, E. H. C.; Jung, R. E.; Rice, K. C. (2005) Stream salamander species richness and

abundance in relation to environmental factors in Shenandoah National Park, Virginia.

Am. Midl. Nat. 153: 348-356.

Griffiths, R. W.; Keller, W. (1992) Benthic macroinvertebrate changes in lakes near Sudbury,

Ontario following a reduction in acid emissions. Can. J. Fish. Aquat. Sci. 49(suppl. 1):

63-75.

Gubala, C. P.; Driscoll, C. T.; Newton, R. M.; Schofield, C. F. (1991) The chemistry of a nearshore lake region during spring snowmelt. Environ. Sci. Technol. 25: 2024-2030.

Guderian, R.; Tingey, D. T.; Rabe, R. (1985) Effects of photochemical oxidants on plants. In:

Guderian, R., ed. Air pollution by photochemical oxidants: formation, transport, control,

and effects on plants. Berlin, Federal Republic of Germany: Springer-Verlag; pp. 127­

333. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds.

Ecological studies: analysis and synthesis, v. 52).

August 2008 B­176 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Guerold, F.; Boudot, J. P.; Jacqumin, G.; Vein, D.; Merlet, D.; Rouiller, J. (2000)

Macroinvertebrate community loss as a result of headwater stream acidification in the

Vosges Mountains (N-E France). Biodiversity Conserv. 9: 767-783.

Gundersen, P.; Rasmussen, L. (1995) Nitrogen mobility in a nitrogen limited forest at

Klosterhede, Denmark, examined by NH 4 

### NO 3 

### addition. For. Ecol. Manage. 71: 75-88.

Gunn, J. M. (1989) Survival of lake charr (Salvelinus namaycush) embryos under pules exposure

to acidic runoff water. In: Nriagu, J. O.; Lakshminarayana, J. S. S., eds. Aquatic

toxicology and water quality management. New Y ork, NY: John Wiley & Sons; pp. 23­

45. (Advances in environmental science and technology: v. 22).

Gunn, J. M.; Keller, W. (1984a) In situ manipulation of water chemistry using crushed limestone

and observed effects on fish. Fisheries 9: 19-24.

Gunn, J. M.; Keller, W. (1984b) Spawning site water chemistry and lake trout (Salvelinus

namaycush) sac fry survival during spring snowmelt. Can. J. Fish. Aquat. Sci. 41: 319­

329.

Gunn, J. M.; Keller, W. (1990) Biological recovery of an acid lake after reductions in industrial

emissions of sulphur. Nature (London, U.K.) 355: 431-433.

Gunn, J. M.; Mills, K. H. (1998) The potential for restoration of acid-damaged lake trout lakes.

Restor. Ecol. 6: 390-397.

Gunn, J. M.; Noakes, D. L. G. (1987) Latent effects of pulse exposure to aluminum and low p H

on size, ionic composition and feeding efficiency of lake trout (Salvelinus fontinalis)

alevins. Can. J. Fish. Aquat. Sci. 44: 1418-1424.

Gunn, J. M.; McMurtry, M. J.; Casselman, J. M.; Keller, W.; Powell, M. J. (1988) Changes in

the fish community of a limed lake near sudbury, Ontario: effects of chemical

neutralization or reduced atmospheric deposition of acids? Water Air Soil Pollut. 41:

113-136.

Gunn, J.; Keller, W.; Negusanti, J.; Potvin, R.; Beckett, P.; Winterhalder, K. (1995) Ecosystem

recovery after emission reductions: Sudbury, Canada. Water Air Soil Pollut. 85: 1783­

1788.

Haines, T. A.; Baker, J. P. (1986) Evidence of fish population responses to acidification in the

eastern United States. Water Air Soil Pollut. 31: 605-629.

Hall, L. W. (1987) Acidification effects on larval striped bass, Morone saxatilis, in Chesapeake

Bay tributaries: a review. Water Air Soil Pollut. 35: 87-96.

Hall, R. J.; Ide, F. P. (1987) Evidence of acidification effects on stream insect communities in

central Ontario between 1937 and 1985. Can. J. Fish. Aquat. Sci. 44: 1652-1657.

Hall, R. J.; Likens, G. E.; Fiance, S. B.; Hendrey, G. R. (1980) Experimental acidification of a

stream in the Hubbard Brook Experimental Forest, New H ampshire. Ecology 61: 976­

989.

Hallett, R. A.; Bailey, S. W.; Horsley, S. B.; Long, R. P. (2006) Influence of nutrition and stress

on sugar maple at a regional scale. Can. J. For. Res. 36: 2235-2246.

August 2008 B­177 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Hamburg, S. P.; Yanai, R. D.; Arthur, M. A.; Blum, J. D.; Siccama, T. G. (2003) Biotic control

of calcium cycling in northern hardwood forests: acid rain and aging forests. Ecosystems

6: 399-406.

Harriman, R.; Morrison, B. R. S. (1982) Ecology of streams draining forested and non-forested

catchments in an area of central Scotland subject to acid precipitation. Hydrobiologia 88:

251-263.

Hartman, M. D.; Baron, J. S.; Ojima, D. S. (2005) Calculating pre-measurement atmospheric

deposition, stream chemistry, and soil chemistry for an alpine watershed in Rocky

Mountain National Park, Colorado [abstract]. In: Abstracts of the Ecological Society of

America 90th annual meeting; Montreal, Canada; Aug 7-12.

Hartman, M. D.; Baron, J. S.; Ojima, D. S.; Parton, W. J. (2005) Modeling the timeline for

surface water acidification from excess nitrogen deposition for Rocky Mountain National

Park [abstract]. In: Abstracts of the George Wright Society biennial conference;

Philadelphia, PA.

Hartman, M. D.; Baron, J. S.; Ojima, D. S. (2007) Application of a coupled ecosystem-chemical

equilibrium model, DayCent-Chem, to stream and soil chemistry in an alpine watershed.

Ecol. Modell. 200: 493-510.

Havas, M. (1985) Aluminum bioaccumulation and toxicity to Daphnia magna in soft water at

low p H. Can. J. Fish. Aquat. Sci. 42: 1741-1748.

Havas, M. (1986) Effects of acid deposition on aquatic ecosystems. In: Stern, A., ed. Air

pollution. New Y ork, NY: Academic Press; pp. 351-389.

Havas, M.; Hutchinson, T. C.; Likens, G. E. (1984) Red herrings in acid rain research. Environ.

Sci. Technol. 18: 176A-186A.

Havas, M.; Woodfine, D. G.; Lutz, P.; Yung, K.; MacIsaac, H. J.; Hutchinson, T. C. (1995)

Biological recovery of two previously acidified, metal-contaminated lakes near Sudbury,

Ontario, Canada. Water Air Soil Pollut. 85: 791-796.

Havens, K. E.; Carlson, R. E. (1998) Functional complementarity in plankton communities along

a gradient of acid stress. Environ. Pollut. 101: 427-436.

Hawley, G. J.; Schaberg, P. G.; Eagar, C.; Borer, C. H. (2006) Calcium addition at the Hubbard

Brook Experimental Forest reduced winter injury to red spruce in a high-injury year. Can.

J. For. Res. 36: 2544-2549.

Haya, K.; Waiwood, B. A. (1981) Acid pH and chorionase activity of Atlantic salmon (Salmo

salar) eggs. Bull. Environ. Contam. Toxicol. 27: 7-12.

Hedin, L. O.; Likens, G. E.; Postek, K. M.; Driscoll, C. T. (1990) A f ield experiment to test

whether organic acids buffer acid deposition. Nature (London, U.K.) 345: 798-800.

Hedin, L. O.; Granat, L.; Likens, G. E.; Buishand, T. A.; Galloway, J. N.; Butler, T. J.; Rodhe,

H. (1994) Steep declines in atmospheric base cations in regions of Europe and North

America. Nature (London) 367: 351-354.

Hedin, L. O.; Armesto, J. J.; Johnson, A. H. (1995) Patterns of nutrient loss from unpolluted, oldgrowth temperate forests: evaluation of biogeochemical theory. Ecology 76: 493-509.

August 2008 B­178 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 H eil, G. W.; Bruggink, M. (1987) Competition for nutrients between Calluna vulgaris (L.) Hull

and Molinia caerulea (L.) Moench. Oecologia 73: 105-108.

Heinz, G. (1974) Effects of low dietary levels of methyl mercury on mallard reproduction. Bull.

Environ. Contam. Toxicol. 11: 386-392.

Heinz, G. H. (1979) Methylmercury: reproductive and behavioral effects on three generations of

mallard ducks. J. Wildl. Manage. 43: 394-401.

Hemond, H. F. (1990) Wetlands as the source of dissolved organic carbon to surface waters. In:

Perdue, E. M.; Gjessing, E. T., eds. Organic acids in aquatic ecosystems. New York, NY:

John Wiley & Sons; pp. 301-313.

Hemond, H. F. (1994) Role of organic acids in acidification of fresh waters. In: Steinberg, C. E.

W.; Wright, R. F., eds. Acidification of freshwater ecosystems: implications for the

future: report of the Dahlem workshop on acidification of freshwater ecosystems; Berlin,

Germany; September-October 1992. New York, NY: Wiley; pp. 103-116.

Hendry, C. D.; Brezonik, P. L. (1984) Chemical composition of softwater Florida lakes and their

sensitivity to acid precipitation. Water Resour. Bull. 20: 75-86.

Henriksen, A. (1980) Acidification of freshwaters - a large scale titration. In: Drablos, D.;

Tollan, A., eds. Ecological impact of acid precipitation: proceedings of an international

conference; March; Sandefjord, Norway. Oslo, Norway: The Norwegian Interdisciplinary

Research Programme; pp. 68-74. (SNSF Project).

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

Verh. - Int. Ver. Theor. Angew. Limnol. 22: 692-698.

Herlihy, A. T.; Kaufman, P. R.; Mitch, M. E. (1991) Stream chemistry in the eastern United

States. 2. Current sources of acidity in acidic and low acid-neutralizing capacity streams.

Water Resour. Res. 27: 629-642.

Herlihy, A. T.; Kaufmann, P. R.; Church, M. R.; Wigington, P. J., Jr.; Webb, J. R.; Sale, M. J.

(1993) The effects of acidic deposition on streams in the Appalachian Mountain and

Pidemont Region of the mid-Atlantic United States. Water Resour. Res. 29: 2687-2703.

Hoffman, D. J.; Moore, J. M. (1979) Teratogenic effects of external egg applications of methyl

mercury in the mallard, Anas platyrynchos. Teratology 20: 453-461.

Holmes, R. S.; Whiting, M. L.; Stoddard, J. L. (1989) Changes in diatom-inferred pH and acid

neutralizing capacity in a dilute, high elevation, Sierra Nevada lake since A.D. 1825.

Freshwater Biol. 21: 295-310.

Holt, C.; Yan, N. D. (2003) Recovery of crustacean zooplankton communities from acidification

in Killarney Park, Ontario, 1971-2000: pH 6 as a recovery goal. R. Swed. Acad. Sci. 32:

203-207.

Holtze, K. E.; Hutchinson, N. J. (1989) Lethality of low pH and Al to early life stages of six fish

species inhabiting precambrian shield waters in Ontario. Can. J. Fish. Aquat. Sci. 46:

1188-1202.

Horsley, S. B.; Long, R. P.; Bailey, S. W.; Hallet, R. A.; Hall, T. J. (1999) Factors contributing

to sugar maple decline along topographic gradients on the glaciated and unglaciated

Allegheny Plateau. In: Horsely, S. B.; Long, R. P., eds. Sugar maple ecology and health:

August 2008 B­179 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 proceedings of an international symposium; June 1998; Warren, PA. Radnor, PA: U.S.

Department of Agriculture, Forest Service; pp. 60-62. (General technical report NE-261).

Horsley, S. B.; Long, R. P.; Bailey, S. W.; Hallett, R. A.; Hall, T. J. (2000) Factors associated

with the decline disease of sugar maple on the Allegheny Plateau. Can. J. Forest. Res. 30:

1365-1378.

Hosker, R. P., Jr.; Lindberg, S. E. (1982) Review: atmospheric deposition and plant assimilation

of gases and particles. Atmos. Environ. 16: 889-910.

Houle, D.; Carignan, R.; Ouimet, R. (2001) Soil organic sulfur dynamics in a coniferous forest.

Biogeochemistry 53(1): 105-124.

Houlton, B. Z.; Driscoll, C. T.; Fahey, T. J.; Likens, G. E.; Groffman, P. M.; Bernhardt, E. S.;

Buso, D. C. (2003) Nitrogen dynamics in ice storm-damaged forest ecosystems:

implications for nitrogen limitation theory. Ecosystems 6: 431-443.

Hrabik, T. R.; Watras, C. J. (2002) Recent declines in mercury concentration in a freshwater

fishery: isolating the effects of de-acidification and decreased atmospheric mercury deposition in Little Rock Lake. Sci. Total Environ. 297: 229-237.

Hulsman, P. F.; Powles, P. M.; Gunn, J. M. (1983) Mortality of walleye eggs and rainbow t rout

yolk-sac larvae in low-pH w aters of the LaCloche Mountain area, Ontario. Trans. Am.

Fish. Soc. 112: 680-683.

Hultberg, H.; Andersson, I. (1982) Liming of acidified lakes: induced long-term changes. Water

Air Soil Pollut. 18: 311-331.

Hunter, M. D.; Schultz, J. C. (1995) Fertilization mitigates chemical induction and herbivore

responses within damaged oak trees. Ecology 76: 1226-1232.

Hunter, M. L.; Jones, J. J.; Gibbs, K. E.; Moring, J. R. (1986) Duckoing responses to lake

acidification: do black ducks and fish compete? Oikos 47: 26-32.

Huntington, T. G. (2000) The potential for calcium depletion in forest ecosystems of

southeastern United States: review a nd analysis. Global Biogeochem. Cycles 14: 623­

638.

Husar, R. B.; Sullivan, T. J. (1991) Historical trends in atmospheric sulfur deposition and

methods for assessing long-term trends in surface water chemistry. In: Charles, D. F., ed.

Acidic deposition and aquatic ecosystems: regional case studies. New Y ork, NY:

Springer-Verlag; pp. 65-82.

Hutchinson, T. C.; Bozic, L.; Munoz-Vega, G. (1986) Responses of five species of conifer

seedlings to aluminum stress. Water Air Soil Pollut. 31: 283-294.

Hutchinson, N. J.; Holtze, K. E.; Munro, J. R.; Pawson, T. W. (1989) Modifying effects of life

stage, ionic strength and post-exposure mortality n lethality of H + 

### and Al to lake trout and

brook trout. Aquat. Toxicol. 15: 1-26.

Hutchinson, J.; Maynard, D.; Geiser, L. (1996) Air quality and lichens

- a literature review

emphasizing the Pacific Northwest, USA. U.S. Department of Agriculture, Forest

Service, Pacific Northwest Region Air Resource management Program.

Hyer, K. E.; Webb, J. R.; Eshleman, K. N. (1995) Episodic acidification of three streams in

Shenandoah National Park, Virginia, USA. Water Air Soil Pollut. 85: 523-528.

August 2008 B­180 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 I lvesniemi, H. (1992) The combined effect of mineral nutrition and soluble aluminum on Pinus

sylvestris and Picea abies seedlings. For. Ecol. Manage. 51: 227-238.

Ingersoll, C. G. (1986) The effects of pH, aluminum, and calcium on survival and growth of

brook trout (Salvelinus fontinalis) early life stages [dissertation]. Laramie, WY:

University of Wyoming, Department of Zoology and Physiology. Available from:

ProQuest, Ann Arbor, MI; publication no. AAD86-23090.

Ingersoll, C. G.; Gulley, D. D.; Mount, D. R.; Mueller, M. E.; Fernandez, J. D.; Hockett, J. R.;

Bergman, H. L. (1990) Aluminum and acid toxicity to two strains of brook trout

(Salvelinus fontinalis). Can. J. Fish. Aquat. Sci. 47: 1641-1648.

Ingersoll, C. G.; Mount, D. R.; Gulley, D. D.; La Point, T. W.; Bergman, H. L. (1990) Effects of

pH, aluminum, and calcium on survival and growth of eggs and fry of brook trout

(Salvelinus fontinalis). Can. J. Fish. Aquat. Sci. 47: 1580-1592.

Ito, M.; Mitchell, M. J.; Driscoll, C. T.; Roy, K. M. (2005) Factors affecting acid neutralizing

capacity in the Adirondack region of New York: a solute mass balance approach.

Environ. Sci. Technol. 39: 4076-4081.

Jackson, D. A.; Harvey, H. H. (1995) Gradual reduction and extinction of fish populations in

acid lakes. Water Air Soil Pollut. 85: 389-394.

Jagoe, C. H.; Haines, T. A.; Kircheis, F. W. (1984) Effects of reduced pH on three life stages of

Sunapee char Salvelinus alpinus. Bull. Environ. Contam. Toxicol. 33: 430-438.

Jeffries, D. S.; Clair, T. A.; Couture, S.; Dillon, P. J.; Dupont, J.; Keller, W.; McNicol, D. K.;

Turner, M. A.; Vet, R.; Weeber, R. (2003) Assessing the recovery of lakes in

southeastern Canada from the effects of acidic deposition. R. Swedish Acad. Sci. 32:

176-182.

Jeremiason, J. D.; Engstrom, D. R.; Swain, E. B.; Nater, E. Q.; Johnson, B. M.; Almendinger, J.

E.; Monson, B. A.; Kolka, R. K. (2006) Sulfate addition increases methylmercury

production in an experimental wetland. Environ. Sci. Technol. 40: 3800-3806.

Johannessen, M.; Henriksen, A. (1978) Chemistry of snow meltwater: changes in concentration

during melting. Water Resour. Res. 14: 615-619.

Johansson, N.; Kihlström, J. E. (1975) Pikes (Esox lucius L.) shown to be affected by low pH

values during first weeks after hatching. Environ. Res. 9: 12-17.

Johansson, N.; Milbrink, G. (1976) Some effects of acidified water on the early development of

roach (Rutilus rutilus L.) and perch (Perca fluviatilis L.). Water Resour. Bull. 12: 39-48.

Johansson, N.; Runn, P.; Milbrink, G. (1977) Early development of three salmonid species in

acidified water. Zoon 5: 127-132.

Johnson, G. V. (1991) General model for predicting crop response to fertilizer. Agron. J. 83:

367-373.

Johnson, D. W.; Cole, D. W. (1980) Mobility in soils: relevance to nutrient transport from forest

ecosystems. Environ. Int. 3: 79-90.

Johnson, D. W.; Fernandez, I. J. (1992) Soil-mediated effects of atmospheric deposition on

eastern U.S. spruce-fir forests. In: Eagar, C.; Adams, M. B., eds. Ecology and decline of

August 2008 B­181 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 red spruce in the eastern United States. New Y ork, NY: Springer-Verlag; pp. 235-270.

(Ecology studies: v. 96).

Johnson, D. W.; Lindberg, S. E., eds. (1992a) Atmospheric deposition and forest nutrient

cycling: a synthesis of the integrated forest study. New Y ork, NY: Springer-Verlag, Inc.

(Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. Ecological

studies: analysis and synthesis: v. 91).

Johnson, D. W.; Lindberg, S. E. (1992b) Nitrogen chemistry, deposition, and cycling in forests.

In: Johnson, D. W.; Lindberg, S. E., eds. Atmospheric deposition and forest nutrient

cycling: a synthesis of the integrated forest study. New Y ork, NY: Springer-Verlag, Inc.;

pp. 150-213. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds.

Ecological studies: analysis and synthesis: v. 91).

Johnson, D. W.; Mitchell, M. J. (1998) Responses of forest ecosystems to changing sulfur inputs.

In: Maynard, D. G., ed. Sulfur in the environment. New Y ork, NY: Marcel Dekker, Inc;

pp. 219-262. (Books in soils, plants, and the environment v. 67).

Johnson, N. M.; Driscoll, C. T.; Eaton, J. S.; Likens, G. E.; McDowell, W. H. (1981) 'Acid rain,'

dissolved aluminum and chemical weathering at the Hubbard Brook Experimental Forest,

New H ampshire. Geochim. Cosmochim. Acta 45: 1421-1437.

Johnson, D. W.; Simonin, H. A.; Colquhoun, J. R.; Flack, F. M. (1987) In situ toxicity tests of

fishes in acid waters. Biogeochemistry 3: 181-208.

Johnson, C. E.; Johnson, A. H.; Siccama, T. G. (1991a) Whole-tree clear-cutting effects on exchangeable cations and soil acidity. Soil Sci. Soc. Am. J. 55: 502-508.

Johnson, D. W.; Cresser, M. S.; Nilsson, S. I.; Turner, J.; Ulrich, B.; Binkley, D.; Cole, D. W.

(1991b) Soil changes in forest ecosystems: evidence for and probable causes. Proc. R.

Soc. Edinburgh Sect. B: Biol. Sci. 97B: 81-116.

Johnson, D. W.; Van Miegroet, H.; Lindberg, S. E.; Todd, D. E.; Harrison, R. B. (1991c)

Nutrient cycling in red spruce forests of the Great Smoky Mountains. Can. J. For. Res.

21: 769-787.

Johnson, A. H.; Andersen, S. B.; Siccama, T. G. (1994a) Acid rain and soils of the Adirondacks.

I. Changes in pH a nd available calcium, 1930-1984. Can. J. For. Res. 24: 39-45.

Johnson, A. H.; Friedland, A. J.; Miller, E. K.; Siccama, T. G. (1994b) Acid rain and soils of the

Adirondacks. III. Rates of soil acidification in a montane spruce-fir forest at Whiteface

Mountain, New Y ork. Can. J. For. Res. 24: 663-669.

Joslin, J. D.; Wolfe, M. H. (1988) Responses of red spruce seedlings to changes to soil aluminum

in six amended forest soil horizons. Can. J. For. Res. 18: 1614-1623.

Joslin, J. D.; Wolfe, M. H. (1989) Aluminum effects on northern red oak seedling growth in six

forest soil horizons. Soil Sci. Soc. Am. J. 53: 274-281.

Joslin, J. D.; Wolfe, M. H. (1992) Red spruce soil chemistry and root distribution across a cloud

water deposition gradient. Can. J. For. Res. 22: 893-904.

Joslin, J. D.; Wolfe, M. H. (1994) Foliar deficiencies of mature Southern Appalachian red spruce

determined from fertilizer trials. Soil Sci. Soc. Am. J. 58: 1572-1579.

August 2008 B­182 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Joslin, J. D.; Mays, P. A.; Wolfe, M. H.; Kelly, J. M.; Garber, R. W.; Brewer, P. F. (1987)

Chemistry of tension lysimeter water and lateral flow i n spruce and hardwood stands. J.

Environ. Qual. 16: 152-160.

Joslin, J. D.; Kelly, J. M.; Van Miegroet, H. (1992) Soil chemistry and nutrition of North

American spruce-fir stands: evidence for recent change. J. Environ. Qual. 21: 12-30.

Kahl, S. (1999) Responses of Maine surface waters to the Clean Air Act Amendments of 1990.

Orono, ME: University of Maine, Water Research Institute. Final report; EPA p roject

CX826563-01-0.

Kahl, J. S.; Scott, M. (1994) High elevation lake monitoring in Maine: 1986-1989. August, ME:

Maine Department of Environmental Protection.

Kahl, J. S.; Norton, S. A.; Cronan, C. S.; Fernandez, I. J.; Bacon, L. C.; Haines, T. A. (1991)

Maine. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case

studies. New Y ork, NY: Springer-Verlag; pp. 203-235.

Kahl, J. S.; Norton, S. A.; Haines, T. A.; Rochette, E. A.; Heath, R. H.; Nodvin, S. C. (1992)

Mechanisms of episodic acidification in low-order streams in Maine, USA. Environ.

Pollut. 78: 37-44.

Kahl, J. S.; Norton, S. A.; Fernandez, I. J.; Nadelhoffer, K. J.; Driscoll, C. T.; Aber, J. D. (1993a)

Experimental inducement of nitrogen saturation at the watershed scale. Environ. Sci.

Technol. 27: 565-568.

Kahl, J. S.; Haines, T. A.; Norton, S. A.; Davis, R. B. (1993b) Recent trends in the acid-base

status of surface waters in Maine, USA. Water Air Soil Pollut. 67: 281-300.

Kahl, J.; Norton, S.; Frenandez, I.; Rustad, L.; Handley, M. (1999) Nitrogen and sulfur inputoutput budgets in the experimental and reference watersheds, Bear Brook Watershed in

Maine (BBWM). Environ. Monit. Assess. 55: 113-131.

Kane, D. A.; Rabeni, C. F. (1987) Effects of aluminum and pH o n the early life stages of

smallmouth bass (Micropterus dolemieui). Water Res. 21: 633-639.

Karr, J. R.; Chu, L. W. (1999) Restoring life in running rivers: better biological monitoring.

Washington, DC: Island Press.

Kaufmann, P. R.; Herlihy, A. T.; Elwood, J. W.; Mitch, M. E.; Overton, W. S.; Sale, M. J.;

Messer, J. J.; Cougan, K. A.; Peck, D. V.; Reckhow, K. H.; Kinney, A. J.; Christie, S. J.;

Brown, D. D.; Hagley, C. A.; Jager, H. I. (1988) Chemical characteristics of streams in

the mid-Atlantic and southeastern United States (national stream survey

- phase I).

Volume I: population descriptions and physico-chemical relationships. Washington, DC:

U.S. Environmental Protection Agency, Office of Acid Deposition, Environmental

Monitoring and Quality Assurance; EPA r eport no. EPA-600/3-88-021a.

Kaufmann, P. R.; Herlihy, A. T.; Mitch, M. E.; Messer, J. J.; Overton, W. S. (1991) Stream

chemistry in the eastern United States: 1. synoptic survey design, acid-base status, and

regional patterns. Water Resour. Res. 27: 611-627.

Kauppi, P. E.; Mielikaeinen, K.; Kuusela, K. (1992) Biomass and carbon budget of European

forests, 1971 to 1990. Science (Washington, DC) 256: 70-74.

Keller, W. (1992) Introduction and overview t o aquatic acidificaiton studies in the Sudbury,

Ontario, Canada, area. Can. J. Fish. Aquat. Sci. 49(suppl. 1): 3-7.

August 2008 B­183 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 K eller, W.; Gunn, J. M. (1995) Lake water quality improvements and recovering aquatic

communities. In: Gunn, J. M., ed. Restoration and recovery of an industrial region:

progress in restoring the smelter-damaged landscape near Sudbury, Canada. New York,

NY: Springer-Verlag; pp. 67-80.

Keller, W.; Pitblado, J. R. (1986) Water quality changes in Sudbury area lakes: a comparison of

synoptic surveys in 1974-1976 and 1981-1983. Water Air Soil Pollut. 29: 285-296.

Keller, W.; Yan, D. (1991) Recovery of crustacean zooplankton species richness in Sudbury area

lakes following water quality improvements. Can. J. Fish. Aquat. Sci. 48: 1635-1644.

Keller, W.; Pitblado, J. R.; Conroy, N. I. (1986) Water quality improvements in the Sudbury,

Ontario, Canada area related to reduced smelter emissions. Water Air Soil Pollut. 31:

765-774.

Kelly, C. A.; Rudd, J. W. M.; Hesslein, R. H.; Driscoll, C. T.; Gherini, S. A.; Hecky, R. E.

(1987) Prediction of biological acid neutralization in acid sensitive lakes.

Biogeochemistry 3: 129-140.

Kelso, J. R. M.; Jeffries, D. S. (1988) Response of headwater lakes to varying atmospheric

deposition in north-central Ontario. Can. J. Fish. Aquat. Sci. 45: 1905-1911.

Keltjens, W. G.; Van Loenen, E. (1989) Effects of aluminum and mineral nutrition on growth

and chemical composition of hydroponically grown seedlings of five different forest tree

species. Plant Soil. 119: 39-40.

Kim, E. Y.; Murakami, T.; Saeki, K.; Tatsukawa, R. (1996) Mercury levels and its chemical

form in tissues and organs of seabirds. Arch. Environ. Contam. Toxicol. 30: 259-266.

Kimmel, W. G.; Murphey, D. J.; Sharpe, W. E.; DeWalle, D. R. (1985) Macroinvetebrate

community structure and detritus processing rates in two southwestern Pennsylvania

streams acidified by atmospheric deposition. Hydrobiologia 124: 97-102.

Kingston, J. C.; Birks, H. J. B. (1990) Dissolved organic carbon reconstruction from diatom

assemblages in PIRLA project lakes, North America. Philos. Trans. R. Soc. London Ser.

B 327: 279-288.

Kingston, J. C.; Cook, R. B.; R.G. Kreis, J.; Camburn, K. E.; Norton, S. A.; Sweets, P. R.;

Binford, M. W.; Mitchell, M. J.; Schindler, S. C.; Shane, L. C. K.; King, G. A. (1990)

Paleoecological investigation of recent lake acidification in the northern Great Lakes

states. J. Paleolimnol. 4: 153-201.

Klauda, R. J.; Palmer, R. E. (1987) Responses of blueback herring eggs and larvae to pulses of

acid and aluminum. Trans. Am. Fish. Soc. 116: 561-569.

Klauda, R. J.; Palmer, R. E.; Lenkevich, M. J. (1987) Sensitivity of early life stages of blueback

herring to moderate acidity and aluminum in soft freshwater. Estuaries 10: 44-53.

Knights, J. S.; Zhao, F. J.; Spiro, B.; McGrath, S. P. (2000) Long-term effects of land use and

fertilizer treatments on sulfur cycling. J. Environ. Qual. 29: 1867-1874.

Kobe, R. K.; Likens, G. E.; Eagar, C. (2002) Tree seedling growth and mortality responses to

manipulations of calcium and aluminum in a northern hardwood forest. Can. J. Forest.

Res. 32: 954-966.

August 2008 B­184 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Köchy, M.; Wilson, S. D. (2001) Nitrogen deposition and forest expansion in the Northern Great

Plains. J. Ecol. 89: 807-817.

Kozlowski, T. T. (1985) SO 2 

### effects on plant community structure. In: Winner, W. E.; Mooney,

H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation: physiology, ecology, and

policy issues. Stanford, CA: Stanford University Press; pp. 431-453.

Kramar, D.; Goodale, W. M.; Kennedy, L. M.; Carstensen, L. W.; Kaur, T. (2005) Relating land

cover characteristics and common loon mercury levels using geographic information

systems. Ecotoxicology 14: 253-262.

Kratz, K. W.; Cooper, S. D.; Melack, J. M. (1994) Effects of single and repeated experimental

acid pulses on invertebrates in a high altitude Sierra Nevada stream. Freshwater Biol. 32:

161-183.

Kretser, W.; Gallagher, J.; Nicolette, J. (1989) Adirondack lakes study, 1984-1987: an evaluation

of fish communities and water chemistry. Ray Brook, NY: Adirondack Lakes Survey

Corporation.

Krug, E. C. (1989) Assessment of the theory and hypotheses of the acidification of watersheds.

Champaign, IL: Illinois State water Survey; contract report 457.

Krug, E. C. (1991) Review o f acid-deposition-catchment interaction and comments on future

research needs. J. Hydrol. 128: 1-27.

Krug, E. C.; Frink, C. R. (1983) Acid rain on acid soil: a new p erspective. Science (Washington,

DC) 221: 520-525.

Krug, E. C.; Isaacson, P. J.; Frink, C. R. (1985) Appraisal of some current hypotheses describing

acidification of watersheds. J. Air Pollut. Control Assoc. 35: 109-114.

Lacroix, G. L. (1985a) Plasma ionic composition of the Atlantic salmon (Salmo salar), white

sucker (Catostomus commersoni), and alewife (Alosa pseudoharengus) in some acidic

rivers of Nova Scotia. Can. J. Zool. 63: 2254-2261.

Lacroix, G. L. (1985b) Survival of eggs and alevins of Atlantic salmon (Salmo salar) in relation

to the chemistry of interstitial water in redds in some acidic streams of Altantic Canada.

Can. J. Fish. Aquat. Sci. 42: 292-299.

Lacroix, G. L.; Townsend, D. R. (1987) Responses of juvenile Atlantic salmon (Salmo salar) to

episodic increases in acidity of Nova Scotia rivers. Can. J. Fish. Aquat. Sci. 44: 1475­

1484.

Lancaster, J.; Real, M.; Juggins, S.; Monteith, D. T.; Flower, R. J.; Beaumont, W. R. C. (1996)

Monitoring temporal changes in the biology of acid waters. Freshwater Biol. 36: 179­

201.

Landers, D. H.; Eilers, J. M.; Brakke, D. F.; Overton, W. S.; Kellar, P. E.; Silverstein, M. E.;

Schonbrod, R. D.; Crowe, R. E.; Linthurst, R. A.; Omernik, J. M.; Teague, S. A.; Meier,

E. P. (1987) Western lake survey phase I: characteristics of lakes in the western United

States, volume I: population descriptions and physico-chemical relationships.

Washington, DC: U.S. Environmental Protection Agency, Office of Acid Deposition,

Environmental Monitoring and Quality Assurance; EPA r eport no. EPA/600/3-86-054A.

Available from: NTIS, Springfield, VA; PB88-146824.

August 2008 B­185 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Lapenis, A. G.; Lawrence, G. B.; Andreev, A. A.; Bobrov, A. A.; Torn, M. S.; Harden, J. W.

(2004) Acidification of forest soil in Russia: from 1893 to present. Glob. Biogeochem.

Cycles 18(GB1037): 10.1029/2003GB002107.

Larson, G. L.; Moore, S. E. (1985) Encroachment of exotic rainbow t rout into stream populations

of native brook trout in the Southern Appalachian Mountains. Trans. Am. Fish. Soc. 114:

195-203.

Larsen, D. P.; Urquhart, N. S. (1993) A f ramework for assessing the sensitivity of the EMAP

design. In: Larsen, D. P.; Christie, S. J., eds. EMAP-surface waters 1991 pilot report.

Corvallis, OR: U.S. Environmental Protection Agency; pp. 4.1-4.37.

Larsen, D. P.; Thornton, K. W.; Urquhart, N. S.; Paulsen, S. G. (1994) The role of sample

surveys for monitoring the condition of the nation's lakes. Environ. Monit. Assess. 32:

101-134.

Laudon, H.; Clair, T. A.; Hemond, H. F. (2002) Long-term response in episodic acidification to

declining SO 2-

4 

### deposition in two streams in Nova Scotia. Hydrol. Earth Syst. Sci. 6: 773­

782.

Lauenroth, W. K.; Milchunas, D. G. (1985) SO 2 

### effects on plant community function. In: Winter,

W. E.; Mooney, H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation: physiology,

ecology, and policy issues. Stanford, CA: Stanford University Press; pp. 454-477.

Lawrence, G. B. (2002) Persistent episodic acidification of streams linked to acid rain effects on

soil. Atmos. Environ. 36: 1589-1598.

Lawrence, G. B.; David, M. B. (1977) Response of aluminum solubility to elevated nitrification

in soil of a red spruce stand in eastern Maine. Environ. Sci. Technol. 31: 825-830.

Lawrence, G. B.; Huntington, T. G. (1999) Soil-calcium depletion linked to acid rain and forest

growth in the eastern United States. Reston, VA: U.S. Geological Survey. Available:

http://bqs.usgs.gov/acidrain/WRIR984267.pdf [29 September, 2004].

Lawrence, G. B.; Fuller, R. D.; Driscoll, C. T. (1987) Release of aluminum following whole-tree

harvesting at the Hubbard Brook Experimental Forest, New H ampshire. J. Environ. Qual.

16: 383-390.

Lawrence, G. B.; David, M. B.; Shortle, W. C. (1995) A n ew m echanism for calcium loss in

forest-floor soils. Nature (London) 378: 162-165.

Lawrence, G. B.; David, M. B.; Bailey, S. W.; Shortle, W. C. (1997) Assessment of soil calcium

status in red spruce forests in the northeastern United States. Biogeochemistry 38: 19-39.

Lawrence, G. B.; David, M. B.; Shortle, W. S.; Bailey, S. W.; Lovett, G. M. (1999a)

Mechanisms of base-cation depletion by acid deposition in forest soils of the northeastern

US. In: Horsely, S. B.; Long, R. P., eds. Sugar maple ecology and health: proceedings of

an international symposium; June 2-4 1998. Radner, PA: U.S. Department of Agriculture,

Forest Service; pp. 75-87.

Lawrence, G. B.; David, M. B.; Lovett, G. M.; Murdoch, P. S.; Burns, D. A.; Stoddard, J. L.;

Baldigo, B. P.; Porter, J. H.; Thompson, A. W. (1999b) Soil calcium status and the

response of stream chemistry to changing acidic deposition rates. Ecol. Appl. 9: 1059­

1072.

August 2008 B­186 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Lawrence, G. B.; Momen, B.; Roy, K. M. (2004) Use of stream chemistry for monitoring acidic

deposition effects in the Adirondack Region of New Y ork. J. Environ. Qual. 33: 1002­

1009.

Lawrence, G. B.; Lapenis, A. G.; Berggren, D.; Aparin, B. F.; Smith, K. T.; Shortle, W. C.;

Bailey, S. W.; VarlyGuin, D. L.; Babikov, B. (2005) Climate dependency of tree growth

suppressed by acid deposition effects on soils in northwest Russia. Environ. Sci. Technol.

39: 2004-2010.

Lawrence, G. B.; Sutherland, J. W.; Boylen, C. W.; Nierzwicki-Bauer, S. A.; Momen, B.;

Baldigo, B. P.; Simonin, H. A. (2007) Acid rain effects on aluminum mobilization

clarified by inclusion of strong organic acids. Environ. Sci. Technol. 41: 93-98.

Leavitt, P. R.; Findlay, D. L.; Hall, R. I.; Smol, J. P. (1999) Algal responses to dissolved organic

carbon loss and pH d ecline during whole-lake acidification: evidence from

paleolimnology. Limnol. Oceanogr. 44: 757-773.

Leino, R. L.; Wilkinson, P.; Anderson, J. G. (1987) Histopathlogical changes in the gills of pearl

dace, Semotilus margarita, and fathead minnows, Pimephales promleas, from

experimentally acidified Canadian lakes. Can. J. Fish. Aquat. Sci. 44(suppl. 1): 126-134.

Leivestad, H. (1982) Physiological effects of acid stress on fish. In: Haines, T. A.; Johnson, E.,

eds. Acid rain/fisheries. Bethesda, MD: American Fisheries Society; pp. 157-164.

Levlin, S. A. (1998) Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1:

431-436.

Lewis, G. P.; Likens, G. E. (2007) Changes in stream chemistry associated with insect

defoliation in a Pennsylvania hemlock-hardwoods forest. For. Ecol. Manage. 238: 199­

211.

Ley, R. E.; Williams, M. W.; Schmidt, S. K. (2004) Microbial population dynamics in an

extreme environment: controlling factors in talus soils at 3750 m in the Colorado Rocky

Mountains. Biogeochemistry 68: 313-335.

Likens, G. E.; Bormann, F. H.; Johnson, N. M.; Fisher, D. W.; Pierce, R. S. (1970) Effects of

forest cutting and herbicide treatment on nutrient budgets in the Hubbard Brook

watershed-ecosystem. Ecol. Monogr. 40: 23-47.

Likens, G. E.; Driscoll, C. T.; Buso, D. C. (1996) Long-term effects of acid rain: response and

recovery of a forest ecosystem. Science (Washington, DC) 272: 244-246.

Likens, G. E.; Driscoll, C. T.; Buso, D. C.; Siccama, T. G.; Johnson, C. E.; Lovett, G. M.; Fahey,

T. J.; Reiners, W. A.; Ryan, D. F.; Martin, C. W.; Bailey, S. W. (1998) The

biogeochemistry of calcium at Hubbard Brook. Biogeochemistry 41: 89-173.

Likens, G. E.; Butler, T. J.; Buso, D. C. (2001) Long- and short-term changes in sulfate

deposition: effects of the 1990 Clean Air Act Amendments. Biogeochemistry 52: 1-11.

Likens, G. E.; Driscoll, C. T.; Buso, D. C.; Mitchell, M. J.; Lovett, G. M.; Bailey, S. W.;

Siccama, T. G.; Reiners, W. A.; Alewell, C. (2002) The biogeochemistry of sulfur at

Hubbard Brook. Biogeochemistry 60: 235-316.

Lilleskov, E. A.; Fahey, T. J.; Horton, T. R.; Lovett, G. M. (2002) Belowground ectomycorrhizal

fungal community change over a nitrogen deposition gradient in Alaska. Ecology 83:

104-115.

August 2008 B­187 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Lind, C. J.; Hem, J. D. (1975) Effects of organic solutes on chemical reactions of aluminum.

U.S. Geological Survey Water Supply Paper 1827-G.

Linthurst, R. A.; Landers, D. H.; Eilers, J. M.; Brakke, D. F.; Overton, W. S.; Meier, E. P.;

Crowe, R. E. (1986a) Characteristics of lakes in the eastern United States. Volume I.

Population descriptions and physico-chemical relationships. Washington, DC: U.S.

Environmental Protection Agency, Office of Acid Deposition, Environmental

Monitoring, and Quality Assurance; EPA r eport no. EPA-600/4-86-007a. Available from:

NTIS, Springfield, VA; PB87-110383.

Linthurst, R. A.; Landers, D. H.; Eilers, J. M.; Kellar, P. E.; Brakke, D. F.; Overton, W. S.;

Crowe, R.; Meier, E. P.; Kanciruk, P.; Jeffries, D. S. (1986b) Regional chemical

characteristics of lakes in North America part II: eastern United States. Water Air Soil

Pollut. 31: 577-591.

Little, E. L., Jr. (1971) Atlas of United States trees. v. 1. conifers and important hardwoods.

Washington, DC: U.S. Department of Agriculture, Forest Service. [Miscellaneous

Publication, no. 1146].

Locke, A.; Sprules, W. G. (1994) Effects of lake acidification and recovery on the stability of

zooplankton food webs. Ecology 75: 498-506.

Lofts, S.; Woof, C.; Tipping, E.; Clarke, N.; Mulder, J. (2001) Modelling pH b uffering and

aluminium solubility in European forest soils. Eur. J. Soil Sci. 52: 189-204.

Løkke, H.; Bak, J.; Falkengren-Grerup, U.; Finlay, R. D.; Ilvesniemi, H.; Nygaard, P. H.; Starr,

M. (1996) Critical loads of acidic deposition for forest soils: is the current approach

adequate. Ambio 25: 510-516.

Longcore, J. R.; Gill, J. D. (1993) Acidic depositions: effects on wildlife and habitats. Bethesda,

MD: The Wildlife Society. (Wildlife Society technical review 9 3-1).

Lovett, G. M.; Ruesink, A. E. (1995) Carbon and nitrogen assimilation in red oaks (Quercus

rubra L.) subject to defoliation and nitrogen stress. Tree Physiol. 12: 259-269.

Lovett, G. M.; Rueth, H. (1999) Soil nitrogen transformations in beech and maple stands along a nitrogen deposition gradient. Ecol. Appl. 9: 1330-1344.

Lovett, G. M.; Traynor, M. M.; Pouyat, R. V.; Carreiro, M. M.; Zhu, W.-X.; Baxter, J. W.

(2000a) Atmospheric deposition to oak forest along an urban-rural gradient. Environ. Sci.

Technol. 34: 4294-4300.

Lovett, G. M.; Weathers, K. C.; Sobczak, W. V. (2000b) Nitrogen saturation and retention in

forested watersheds of the Catskill Mountains, New Y ork. Ecol. Appl. 10: 73-84.

Lovett, G. M.; Christenson, L. M.; Groffman, P. M.; Jones, C. G.; Hart, J. E; Mitchell, M. J.

(2002) Insect defoliation and nitrogen cycling in forests. BioScience 52: 335-341.

Lydersen, E.; Fjeld, E.; Gjessing, E. T. (1996) The Humic Lake Acidification Experiment

(HUMEX): main physico-chemical results after five years of artificial acidification.

Environ. Int. 22: 591-604.

Lynch, J. A.; Corbett, E. S. (1989) Hydrologic control of sulfate mobility in a forested

watershed. Water Resour. Res. 25: 1695-1703.

August 2008 B­188 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 MacAvoy, S. W.; Bulger, A. J. (1995) Survival of brook trout (Salvelinus fontinalis) embryos

and fry in streams of different acid sensitivity in Shenandoah National Park, USA. Water

Air Soil Pollut. 85: 445-450.

Magill, A. H.; Aber, J. D.; Berntson, G. M.; McDowell, W. H.; Nadelhoffer, K. J.; Melillo, J. M.;

Steudler, P. (2000) Long-term nitrogen additions and nitrogen saturation in two

temperate forests. Ecosystems 3: 238-253.

Magill, A. H.; Aber, J. D.; Currie, W. S.; Nadelhoffer, K. J.; Martin, M. E.; McDowell, W. H.;

Melillo, J. M.; Steudler, P. (2004) Ecosystem response to 15 years of chronic nitrogen

additions at the Harvard Forest LTER, Massachusetts, USA. For. Ecol. Manage. 196: 7­

28.

Malley, D. F.; Chang, P. S. (1995) Assessing the health of a zooplankton community in a small

Precambrian Shield lake during recovery from experimental acidification. J. Aquat.

Ecosyst. Health 3: 273-286.

Mansfield, T. A.; Jones, T. (1985) Growth/environment interactions in SO 2 

### responses of grasses.

In: Winter, W. E.; Mooney, H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation:

physiology, ecology, and policy issues. Stanford, CA: Stanford University Press; pp. 332­

345.

Markewitz, D.; Richter, D. D.; Allen, H. L.; Urrego, J. B. (1998) Three decades of observed soil

acidification in the Calhoun Experimental Forest: has acid rain made a difference? Soil

Sci. Soc. Am. J. 62: 1428-1439.

Martinson, L.; Lamersdorf, N.; Warfvinge, P. (2005) The Solling roof revisited

- slow r ecovery

from acidification observed and modeled despite a decade of "clean-rain" treatment.

Environ. Pollut. 135: 293-302.

Mason, J.; Seip, H. M. (1985) The current state of knowledge on acidification of surface waters

and guidelines for further research. Ambio 14: 45-51.

Mason, R. R.; Wickman, B. E.; Beckwith, R. C.; Paul, H. G. (1992) Thinning and nitrogen

fertilization in a grand fir stand infested with spruce budworm. part I: insect response.

For. Sci. 38: 235-251.

Matuszek, J. E.; Beggs, G. L. (1988) Fish species richness in relation to lake area, pH, and other

abiotic factors in Ontario lakes. Can. J. Fish. Aquat. Sci. 45: 1931-1941.

Matzner, E.; Blanck, K.; Hartmann, G.; Stock, R. (1989) Needle chlorosis pattern in relation to

soil chemical properties in two Norway spruce (Picea abies, Karst) forests of the German

Harz Mountains. In: Bucher, J. B.; Bucher-Wallin, I., eds. Air pollution and forest

decline, proceedings of the 14th international meeting for specialists in air pollution

effects of forest ecosystems; October 1988; Interlaken, Switzerland. Birmensdorf,

Switzerland: International Union of Forestry Research Organizations; pp. 195-199.

McAuley, D. G.; Longcore, J. R. (1988a) Survival of juvenile ring-necked ducks on wetlands of

different pH. J. Wildlife Manage. 52: 169-176.

McAuley, D. G.; Longcore, J. R. (1988b) Foods of juvenile ring-necked ducks: relationship to

wetland pH. J. Wildlife Manage. 52: 177-185.

McClenahen, J. R. (1987) Effects of simulated throughfall pH a nd sulfate concentration on a

deciduous forest soil. Water Air Soil Pollut. 35: 319-333.

August 2008 B­189 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 McCormick, L. H.; Steiner, K. C. (1978) Variation in aluminum tolerance among six genera of

trees. For. Sci. 24: 565-568.

McCormick, J. H.; Jensen, K. M.; Anderson, L. E. (1989) Chronic effects of low p H a nd

elevated aluminum on survival, maturation, spawning, and embryo-larval development of

the fathead minnow i n soft water. Water Air Soil Pollut. 43: 293-307.

McCune, B. (1988) Lichen communities along O 3 

### and SO 2 

### gradients in Indianapolis. Bryologist

91: 223-228.

McDonald, D. G. (1983) The effects of H + 

### upon the gills of freshwater fish. Can. J. Zool. 61:

691-703.

McDonald, D. G.; Milligan, C. L. (1988) Sodium transport in the brook trout, Salvelinus

fontinalis: effects of prolonged low p H e xposure in the presence and absence of

aluminum. Can. J. Fish. Aquat. Sci. 45: 1606-1613.

McLaughlin, S. B.; Tjoelker, M. J. (1992) Growth and physiological changes in red spruce saplings associated with acidic deposition at high elevations in the southern

Appalachians, USA. For. Ecol. Manage. 51: 43-51.

McLaughlin, S. B.; Wimmer, R. (1999) Tansley review n o.104: Calcium physiology and

terrestrial ecosystem processes. New P hytol. 142: 373-417.

McNicol, D. K. (2002) Relation of lake acidification and recovery to fish, common loon and

common merganser occurrence in Algoma Lakes. Water Air Soil Pollut: Focus 2: 151­

168.

McNicol, D. K.; Mallory, M. L.; Wedeles, C. H. R. (1995) Assessing biological recovery of

acid-sensitive lakes in Ontario, Canada. Water Air Soil Pollut. 85: 457-462.

McNulty, S. G.; Aber, J. D.; Newman, S. D. (1996) Nitrogen saturation in a high elevation New

England spruce-fir stand. For. Ecol. Manage. 84: 109-121.

McWilliams, P. G.; Potts, W. T. W. (1978) The effects of pH and calcium concentrations on gill

potentials in the brown trout, Salmo trutta. J. Comp. Physiol. B 126: 277-286.

Melack, J. M.; Stoddard, J. L. (1991) Sierra Nevada, California. In: Charles, D. F., ed. Acidic

deposition and aquatic ecosystems: regional case studies. New Y ork, NY: SpringerVerlag; pp. 503-530.

Melack, J. M.; Stoddard, J. L.; Ochs, C. A. (1985) Major ion chemistry and sensitivity to acid

precipitation of Sierra Nevada lakes. Water Resour. Res. 21: 27-32.

Melack, J. M.; Cooper, S. D.; Holmes, R. W.; Sickman, J. O.; Kratz, K.; Hopkins, P.;

Hardenbergh, H.; Thieme, M.; Meeker, L. (1987) Chemical and biological survey of

lakes and streams located in the Emerald Lake watershed, Sequoia National Park. Final

report. Sacramento, CA: California Air Resources Board; contract no. A3-096-32.

Melack, J. M.; Sickman, J. O.; Leydecker, A. (1988) Comparative analyses of high-altitude lakes

and catchments in the Sierra Nevada: susceptibility to acidification. Final report.

Sacramento, CA: California Air Resources Board; contract A032-188.

Melack, J. M.; Cooper, S. C.; Jenkins, T. M.; Barmuta, L., Jr.; Hamilton, S.; Kratz, K.; Sickman,

J.; Soiseth, C. (1989) Chemical and biological characteristics of Emerald Lake and the

August 2008 B­190 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 streams in its watershed, and the response of the lake and streams to acidic deposition.

Final report. Sacramento, CA: California Air Resources Board; contract A6-184-32.

Melack, J. M.; Sickman, J. O.; Setaro, F.; Dawson, D. (1997) Monitoring of wet deposition in

alpine areas in the Sierra Nevada. Sacramento, CA: California Air Resources Board;

contract A932-081.

Meyer, M. W.; Evers, D. C.; Daulton, T.; Braselton, W. E. (1995) Common loons (Gavia immer)

nesting on low p H l akes in nothern Wisconsin have elevated blood mercury content.

Water Air Soil Pollut. 80: 871-880.

Miller, E. K.; Blum, J. D.; Friedland, A. J. (1993) Determination of soil exchangeable-cation loss

and weatheirng rates using Sr isotopes. Nature (London, U.K.) 362: 438-441.

Mills, K. H.; Chalanchuk, S. M.; Mohr, L. C.; Davies, I. J. (1987) Responses of fish populations

in Lake 223 to 8 years of experimental acidification. Can. J. Fish. Aquat. Sci. 44(suppl.

1): 114-125.

Minocha, R.; Shortle, W. C.; Lawrence, G. B.; David, M. B.; Minocha, S. C. (1997)

Relationships among foliar chemistry, foliar polyamines, and soil chemistry in red spruce

trees growing across the northeastern United States. Plant Soil 191: 109-122.

Mitchell, M. J.; Driscoll, C. T.; Kahl, J. S.; Likens, G. E.; Murdoch, P. S.; Pardo, L. H. (1996)

Climatic control of nitrate loss from forested watersheds in the northeast United States.

Environ. Sci. Technol. 30: 2609-2612.

Mitchell, R. J.; Sutton, M. A.; Truscott, A.-M.; Leith, I. D.; Cape, J. N.; Pitcairn, C. E. R.; Van

Dijk, N. (2004) Growth and tissue nitrogen of epiphytic Atlantic bryophytes: effects of

increased and decreased atmospheric N d eposition. Funct. Ecol. 18: 322-329.

Mitsch, W. J.; Gosselink, J. G. (2000) Wetlands. 3rd ed. New Y ork, NY: John Wiley & Sons,

Inc.

Molot, L. A.; Dillon, P. J.; LaZerte, B. D. (1989) Changes in ionic composition of streamwater

during snowmelt in central Ontario. Can. J. Fish. Aquat. Sci. 46: 1658-1666.

Momen, B.; Lawrence, G. B.; Nierzwicki-Bauer, S. A.; Sutherland, J. W.; Eichler, L. W.;

Harrison, J. P.; Boylen, C. W. (2006) Trends in summer chemistry linked to productivity

in lakes recovering from acid deposition in the Adirondack region of New York.

Ecosystems 9: 1306-1317.

Moncoulon, D.; Probst, A.; Party, J. (2004) Weathering, atmospheric deposition and vegetation

uptake: role for ecosystem sensitivity to acid deposition and critical load. C.R. Geosci.

336: 1417-1426.

Mörth, C. M.; Torssander, P.; Kjønaas, O. J.; Stuanes, A. O.; Moldan, F.; Giesler, R. (2005)

Mineralization of organic sulfur delays recovery from anthropogenic acidification.

Environ. Sci. Tech. 39: 5234-5240.

Mount, D. R.; Ingersoll, C. G.; Gulley, D. D.; Fernandez, J. D.; LaPoint, T. W.; Bergman, H. L.

(1988) Effect of long-term exposure to acid, aluminum, and low c alcium on adult brook

trout (Salvelinus fontinalis). I. Survival, growth, fecundity, and progeny survival. Can. J.

Fish. Aquat. Sci. 45: 1623-1632.

Mulder, J.; Stein, A. (1994) The solubility of aluminum in acidic forest soils: long-term changes

due to acid deposition. Geochim. Cosmochim. Acta 58: 85-94.

August 2008 B­191 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Mulder, J.; Van Breemen, N.; Eijck, H. C. (1989) Depletion of soil aluminium by acid deposition

and implications for acid neutralization. Nature (London, U.K.) 337: 247-249.

Muniz, I. P. (1991) Freshwater acidification: its effects on species and communities of

freshwater microbes, plants and animals. R. Soc. Edinburgh Sect. B: Biol. Sci. 97: 227­

254

Munson, R. K.; Gherini, S. A. (1993a) Analysis of the mineral acid-base components of acidneutralizing capacity in Adirondack Lakes. Water Resour. Res. 29: 881-890.

Munson, R. K.; Gherini, S. A. (1993b) Influence of organic acids on the pH and acidneutralizing capacity of Adirondack Lakes. Water Resour. Res. 29: 891-899.

Murdoch, P. S.; Shanley, J. B. (2006) Flow-specific trends in river-water quality resulting from

the effects of the Clean Air Act in three mesoscale, forested river basins in the

northeastern United States through 2002. Environ. Monit. Assess. 120: 1-25.

Murdoch, P. S.; Stoddard, J. L. (1992) The role of nitrate in the acidification of streams in the

Catskill Mountains of New Y ork. Water Resour. Res. 28: 2707-2720.

Murdoch, P. S.; Stoddard, J. L. (1993) Chemical characteristics and temporal trends in eight

streams of the Catskill Mountains, New Y ork. Water Air Soil Pollut. 67: 367-395.

Murdoch, P. S.; Burns, D. A.; Lawrence, G. B. (1998) Relation of climate change to the

acidification of surface waters by nitrogen deposition. Environ. Sci. Technol. 32: 1642­

1647.

Musselman, R. C.; Hudnell, L.; Williams, M. W.; Sommerfeld, R. A. (1996) Water chemistry of

Rocky Mountain Front Range aquatic ecosystems. Ft. Collins, CO: USDA Rocky

Mountain Forest and Range Experiment Station; research paper RM-RP-325.

National Acid Precipitation Assessment Program. (1991) National Acid Precipitation

Assessment Program 1990 integrated assessment report. Washington, DC: National Acid

Precipitation Assessment Program.

National Acid Precipitation Assessment Program. (1998) NAPAP biennial report to Congress: an integrated assessment. Silver Spring, MD: National Science and Technology Council,

Committee on Environment and Natural Resources.

Neary, B. P.; Dillon, P. J. (1988) Effects of sulphur deposition on lake-water chemistry in

Ontario, Canada. Nature (London, U.K.) 333: 340-343.

Nelson, P. O. (1991) Cascade Mountains: lake chemistry and sensitivity of acid deposition. In:

Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case studies. New

York, NY: Springer-Verlag; pp. 531-563.

Newton, I.; Hass, M. B. (1988) Pollutants in Merlin eggs and their effects on breeding. Br. Birds

81: 258-269.

Niering, W. A. (1998) Forces that shaped the forests of the northeastern United States. Northeast.

Nat. 5: 99-110.

Nilsson, S. I. (1993) Acidification of Swedish ologotrophic lakes

- interactions between

deposition, forest growth and effects on lake-water quality. Ambio 22: 272-276.

Nilsson, S. I.; Miller, J. G.; Miller, J. D. (1982) Forest growth as a possible cause of soil and

water acidification: an examination of the concepts. Oikos 39: 40-49.

August 2008 B­192 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Nodvin, S. C.; Miegroet, H. V.; Lindberg, S. E.; Nicholas, N. S.; Johnson, D. W. (1995) Acidic

deposition, ecosystem processes, and nitrogen saturation in a high elevation southern

Appalachian watershed. Water Air Soil Pollut. 85: 1647-1652.

Norton, S. A.; Akielaszek, J. J.; Haines, T. A.; Stromborg, K. J.; Longcore, J. R. (1982) Bedrock

geologic control of sensitivity of aquatic ecosystems in the United States to acidic

deposition. Fort Collins, CO: National Atmospheric Deposition Program; pp. 1-13.

Norton, S. A.; Wright, R. F.; Kahl, J. S.; Scofield, J. P. (1992) The MAGIC simulation of surface

water acidification at, and first year results from, the Bear Brook Watershed

Manipulation, Maine, USA. Environ. Pollut. 77: 279-286.

Nosko, P.; Brassard, P.; Kramer, J. R.; Kershaw, K. A. (1988) The effect of aluminum on seed

germination and early seedling establishment, growth, and respiration of white spruce

(Picea glauca). Can. J. Bot. 66: 2305-2310.

Novák, M.; Bottrell, S. H.; P ř echová, E. (2001) Sulfur isotope inventories of atmospheric

deposition, spruce forest floor and living sphagnum along a NW-SE transect across

Europe. Biogeochemistry 53: 23-50.

Odman, M. T.; Boylan, J. W.; Wilkinson, J. G.; Russell, A. G.; Mueller, S. F.; Imhoff, R. E.;

Doty, K. G.; Norris, W. B.; McNider, R. T. (2002) SAMI air quality modeling: final

report. Asheville, NC: Southern Appalachian Mountains Initiative.

Odum, E. P. (1989) Ecology and our endangered life-support systems. Sunderland, MA: Sinauer

Associates, Inc.; pp. 58-66, 108-118.

Ohno, T.; Sucoff, E. I.; Erich, M. S.; Bloom, P. R.; Buschena, C. A.; Dixon, R. K. (1988)

Growth and nutrient content of red spruce seedlings in soil amended with aluminum. J.

Environ. Qual. 17: 666-672.

Økland, J.; Økland, K. A. (1986) The effects of acid deposition on benthic animals in lakes and

streams. Experientia 42: 471-486.

Oliver, B. G.; Thurman, E. M.; Malcolm, R. L. (1983) The contribution of humic substances to

the acidity of colored natural waters. Geochim. Cosmochim. Acta 47: 2031-2035.

Ollinger, S. V.; Aber, J. D.; Lovett, G. M.; Millham, S. E.; Lathrop, R. G.; Ellis, J. M. (1993) A

spatial model of atmospheric deposition for the northeastern U.S. Ecol. Appl. 3: 459-472.

Ormerod, S. J.; Tyler, S. J. (1987) Dippers (Cinclus cinclus) and grey wagtails (Motacilla

cinerea) as indicators of stream acidity in upland Wales. In: Diamond, A. W.; Filion, F.

L., eds. The value of birds. Cambridge, United Kingdom: International Council for Bird

Preservation; pp. 191-208. (ICBP technical publication no. 6).

Ormerod, S. J.; Tyler, S. J. (1991) Exploitation of prey by a river bird, the dipper Cinclus cinclus

(L.), along acidic and circumneutral streams in upland Wales. Freshwater Biol. 25: 105­

116.

Ormerod, S. J.; Tyler, S. J.; Lewis, J. M. S. (1985) Is the breeding distribution of dippers

influenced by stream acidity? Bird Study 32: 32-39.

Ormerod, S. J.; Allinson, N.; Hudson, D.; Tyler, S. J. (1986) The distribution of breeding

Dippers (Cinclus cinclus [L.]; Aves) in relation to stream acidity in upland Wales.

Freshwater Biol. 16: 501-507.

August 2008 B­193 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 O rmerod, S. J.; Boole, P.; McCahon, C. P.; Weatherley, N. S.; Pascoe, D.; Edwards, R. W.

(1987) Short-term experimental acidification of a Welsh UK stream comparing the

biological effects of hydrogen ions and aluminum. Freshwater Biol. 17: 341-356.

Palmer, R. E.; Klauda, R. J.; Lewis, T. E. (1988) Comparative sensitivities of bluegill, channel

catfish and fathead minnow to pH and aluminum. Environ. Toxicol. Chem. 7: 505-516.

Palmer, S. M.; Driscoll, C. T.; Johnson, C. E. (2004) Long-term trends in soil solution and

stream water chemistry at the Hubbard Brrok Experiment Forest: relationship with

landscape position. Biogeochemistry 68: 51-70.

Parker, K. E. (1988) Common loon reproduction and chick feeding on acidified lakes in the

Adirondack Park, New York. Can. J. Zool. 66: 804-810.

Parker, D. R.; Zelazny, L. W.; Kinraide, T. B. (1989) Chemical speciation and plant toxicity of

aqueous aluminum. In: Lewis, T. E., ed. Environmental chemistry and toxicology of

aluminum. Washington, DC: American Chemical Society; pp. 117-145.

Perdue, E. M.; Reuter, J. H.; Parrish, R. S. (1984) A statistical model of proton binding by

humus. Geochim. Cosmochim. Acta 48: 1257-1263.

Peterjohn, W. T.; Adams, M. B.; Gilliam, F. S. (1996) Symptoms of nitrogen saturation in two

central Appalachian hardwood forest ecosystems. Biogeochemistry 35: 507-522.

Peterson, R. H.; Martin-Robichaud, D. J. (1986) Growth and major inorganic cation budgets of

Atlantic salmon alevins at three ambient acidities. Trans. Am. Fish. Soc. 115: 220-226.

Peterson, R. H.; Van Eechhaute, L. (1992) Distributions of Ephemeroptera, Plecoptera, and

Trichoptera of three maritime catchments differing in pH. Freshwater Biol. 27: 65-78.

Peterson, R. H.; Daye, P. G.; Metcalfe, J. L. (1980) Inhibition of Atlantic salmon (Salmo salar)

hatching at low pH. Can. J. Fish. Aquat. Sci. 37: 770-774.

Peterson, D. L.; Sullivan, T. J.; Eilers, J. M.; Brace, S.; Horner, D. (1998) Assessment of air

quality and air pollutant impacts in national parks of the Rocky Mountains and northern

Great Plains. Denver, CO: U.S. Department of the Interior, National Park Service, Air

Resources Division. Available: http://www2.nature.nps.gov/air/Pubs/regionPark.cfm [16

June, 2006].

Phillips, K. (1990) Where have all the frogs and toads gone? BioScience 40: 422-424.

Phillips, R. P.; Yanai, R. D. (2004) The effects of AlCl 3 

### additions on rhizosphere soil and fine

root chemistry of sugar maple (Acer saccharum). Water Air Soil Pollut. 159: 339-356.

Plafkin, J. L.; Barbour, M. T.; Porter, K. D.; Gross, S. K.; Hughes, R. M. (1989) Rapid

bioassessment protocols for use in streams and rivers: benthic macroinvertebrates and

fish. Washington, DC: U.S. Environmental Protection Agency, Office of Water

Regulations and Standards; report no. EPA 440-89-001.

Pollman, C. D.; Canfield, D. E. (1991) Florida: hydrologic and biogeochemical controls on

seepage lake chemistry. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems:

regional case studies. New York, NY: Springer-Verlag, Inc.

Pollman, C. D.; Sweets, P. R. (1990) Hindcasting of pre-cultural ANC and pH of seepage lakes

in the Adirondacks, Upper Midwest, and Florida. Corvallis, OR: U.S. Environmental

Protection Agency; report no. EPA 89007B1/RES-1.

August 2008 B­194 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39 Powell, J. F. F.; McKeown, B. A. (1986) The effects of acid exposure on the ion regulation and

seawater adaptation of Coho salmon (Oncorhynchus kisutch) parrs and smolts. Comp.

Biochem. Physiol. C: Comp. Pharmacol. 83: 45-52.

Power, S. A.; Green, E. R.; Barker, C. G.; Bell, J. N. B.; Ashmore, M. R. (2006) Ecosystem

recovery: heathland response to a reduction in nitrogen deposition. Glob. Change Biol.

12: 1241-1252.

Pregitzer, K. S.; Burton, A. J.; Mroz, G. D.; Leighty, H. O.; MacDonald, N. W. (1992) Foliar

sulfur and nitrogen along an 800-km pollution gradient. Can. J. Forest. Res. 22: 1761­

1769.

Raddum, G. G.; Fjellheim, A. (1984) Acidification and early warning organisms in freshwater in

western Norway. Verh.

- Int. Ver. Theor. Angew. Limnol. 22: 1973-1980.

Raddum, G. G.; Brettum, P.; Matzow, D.; Nilssen, J. P.; Skov, A.; Svealy, T.; Wright, R. F.

(1986) Liming the acid lake Hovvatn, Norway: a whole lake study. Water Air Soil Pollut.

31: 721-763.

Rago, P. J.; Wiener, J. G. (1986) Does pH affect fish species richness when lake area is

considered? Trans. Am. Fish. Soc. 115: 438-447.

Rapport, D. J.; Whitford, W. G. (1999) How e cosystems respond to stress: common properties of

arid and aquatic systems. BioScience 49: 193-203.

Rattner, B. A.; Haramis, G. M.; Chu, D. S.; Bunck, C. M.; Scanes, C. G. (1987) Growth and

physiological condition of black ducks reared on acidified wetlands. Can. J. Zool. 65:

2953-2958.

Reader, J. P.; Dalziel, T. R. K.; Morris, R. (1988) Growth, mineral uptake and skeletal calcium

deposition in brown trout, Salmo trutta L., yolk-sac fry exposed to aluminum and

manganese in soft acid water. J. Fish Biol. 32: 607-624.

Reckhow, K. H.; Black, R. W.; Stockton, T. B., Jr.; Vogt, J. D.; Wood, J. G. (1987) Empirical

models of fish response to lake acidification. Can. J. Fish. Aquat. Sci. 44: 1432-1442.

Resh, V. H.; Norris, R. H.; Barbour, M. T. (1995) Design and implementation of rapid

assessment approaches for water resource monitoring using benthic macroinvertebrates.

Aust. J. Ecol. 20: 108-121.

Reuss, J. O. (1983) Implications of the calcium-aluminum exchange system for the effect of acid

precipitation on soils. J. Environ. Qual. 12: 591-595.

Reuss, J. O.; Johnson, D. W. (1985) Effect of soil processes on the acidification of water by acid

deposition. J. Environ. Qual. 14: 26-31.

Reuss, J. O.; Vertucci, F. A.; Musselman, R. C.; Sommerfeld, R. A. (1995) Chemical fluxes and

sensitivity to acidification of two high-elevation catchments in southern Wyoming. J.

Hydrol. 173: 165-189.

Richardson, D. H. S.; Cameron, R. P. (2004) Cyanolichens: their response to pollution and

possible management strategies for their conservation in notheastern North America.

Northeast. Nat. 11: 1-22.

August 2008 B­195 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Richter, D. D.; Markewitz, D.; Wells, C. G.; Allen, H. L.; April, R.; Heine, P. R.; Urrego, B.

(1994) Soil chemical change during three decades in an old-field loblolly pine (Pinus

taeda L.) ecosystem. Ecology 75: 1463-1473.

Richter, D. D.; Markewitz, D.; Heine, P. R.; Jin, V.; Raikes, J.; Tian, K.; Wells, C. G. (2000)

Legacies of agriculture and forest regrowth in the nitrogen of old-field soils. For. Ecol.

Manage. 138: 233-248.

Riggan, P. J.; Lockwood, R. N.; Lopez, E. N. (1985) Deposition and processing of airborne

nitrogen pollutants in Mediterranean-type ecosystems of southern California. Environ.

Sci. Technol. 19: 781-789.

Riggan, P. J.; Lockwood, R. N.; Jacks, P. M.; Colver, C. G.; Weirich, F.; DeBano, L. F.; Brass, J.

A. (1994) Effects of fire severity on nitrate mobilization in watersheds subject to chronic

atmospheric deposition. Environ. Sci. Technol. 28: 369-375.

Rochelle, B. P.; Church, M. R. (1987) Regional patterns of sulphur retention in watersheds of the

eastern U.S. Water Air Soil Pollut. 36(1-2): 61-73.

Rochelle, B. P.; Church, M. R.; David, M. B. (1987) Sulfur retention at intensively studied sites

in the U.S. and Canada. Water Air Soil Pollut. 33: 73-83.

Rosemond, A. D.; Reice, S. R.; Elwood, J. W.; Mulholland, P. J. (1992) The effects of stream

acidity on benthic invertebrate communities in the south-eastern United States.

Freshwater Biol. 27: 193-209.

Rosenqvist, I. (1978) Acid precipitation and other possible sources for acidification of rivers and

lakes. Sci. Total Environ. 10: 271-272.

Ross, D. S.; Lawrence, G. B.; Fredriksen, G. (2004) Mineralization and nitrification patterns at

eight northeastern USA forested research sites. For. Ecol. Manage. 188: 317-335.

Rosseland, B. O. (1986) Ecological effects of acidification on tertiary consumers: fish population

response. Water Air Soil Pollut. 30: 451-460.

Rosseland, B. O.; Skogheim, O. K. (1984) A comparative study on salmonid fish species in acid

aluminum-rich water. II. Physiological stress and mortality of one- and two-year-old fish.

Drottningholm, Sweden: Institute of Freshwater Research, report no. 61; pp. 186-194.

Rosseland, B. O.; Staurnes, M. (1994) Physiological mechanisms for toxic effects and resistance

to acidic water: an ecophysiological and ecotoxicological approach. In: Steinberg, C. E.

W.; Wright, R. F., eds. Acidification of freshwater ecosystems: implications for the

future. New Y ork, NY: John Wiley & Sons Ltd.; pp. 227-246.

Rosseland, B. O.; Eldhuset, T. D.; Staurnes, M. (1990) Environmental effects of aluminum.

Environ. Geochem. Health 12: 17-27.

Rueth, H.; Baron, J. S. (2002) Differences in Englemann spruce forest biogeochemistry east and

west of the Continental Divide in Colorado, USA. Ecosystems 5: 45-57.

Rueth, H. M.; Baron, J. S.; Allstott, E. J. (2003) Responses of Engelmann spruce forests to

nitrogen fertilization in the Colorado Rocky Mountains. Ecol. Appl. 13: 664-673.

Rundle, S. D.; Hildrew, A. G. (1990) The distribution of micro-anthropods in some southern

English streams: the influence of physicochemistry. Freshwater Biol. 23: 411-431.

August 2008 B­196 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 R unn, P.; Johansson, N.; Milbrink, G. (1977) Some effects of low pH on the hatchability of eggs

of perch, Perca fluviatilis L. Zoon 5: 115-125.

Russell, C. A.; Kosola, K. R.; Paul, E. A.; Robertson, G. P. (2004) Nitrogen cycling in poplar

stands defoliated by insects. Biogeochemistry 68: 365-381.

Rustad, L. E.; Kahl, J. S.; Norton, S. A.; Fernandez, I. J. (1994) Underestimation of dry

deposition by throughfall in mixed northern hardwood forests. J. Hydrol. 162: 319-336.

Sadler, K.; Lynam, S. (1988) The influence of calcium on aluminium-induced changes in the

growth rate and mortality of brown trout, Salmo trutta L. J. Fish Biol. 33: 171-179.

Saros, J. E.; Interlandi, S. J.; Wolfe, A. P.; Engstrom, D. R. (2003) Recent changes in the diatom

community structure of lakes in the Beartooth Mountain Range, USA. Arct. Anarct. Alp.

Res. 35: 18-23.

Scheuhammer, A. M. (1988) Chronic dietary toxicity of methylmercury in the zebra finch,

Poephila guttata. Bull. Environ. Contam. Toxicol. 40: 123-130.

Schier, G. A. (1985) Response of red spruce and balsam fir seedlings to aluminum toxicity in

nutrient solutions. Can. J. For. Res. 15: 29-33.

Schimel, J. P.; Bennett, J. (2004) Nitrogen mineralization: challenges of a changing paradigm.

Ecology 85: 591-602.

Schindler, D. W. (1988) Effects of acid rain on freshwater ecosystems. Science (Washington,

DC) 239: 232-239.

Schindler, D. W. (1990) Experimental perturbations of whole lakes as tests of hypotheses

concerning ecosystem structure and function. Oikos 57: 25-41.

Schindler, D. W.; Mills, K. H.; Malley, D. F.; Findlay, D. L.; Schearer, J. A.; Davies, I. J.;

Turner, M. A.; Lindsey, G. A.; Cruikshank, D. R. (1985) Long-term ecosystem stress:

effects of years of experimental acidification on a small lake. Science (Washington, DC,

1979-1986) 228: 1395-1401.

Schlegel, H.; Amundson, R. G.; Hüttermann, A. (1992) Element distribution in red spruce (Picea

rubens) fine roots; evidence for aluminum toxicity at Whiteface Mountain. Can. J. Forest.

Res. 22: 1132-1138.

Schnitzer, M.; Skinner, S. I. M. (1963) Organo-metallic interactions in soils: 2. Reactions

between different forms of iron and aluminum and the organic matter of a podzol Bh

horizon. Soil Sci. 96: 181-186.

Schnoor, J. L.; Nikolaidis, N. P.; Glass, G. E. (1986) Lake resources at risk to acidic deposition

in the Upper Midwest. J. Water Pollut. Control Fed. 58: 139-148.

Schofield, C. L. (1993) Habitat suitability for Brook Trout (Salvelinus fontinalis) reproduction in

Adirondack lakes. Water Resour. Res. 29: 875-879.

Schofield, C. L.; Driscoll, C. T. (1987) Fish species distribution in relation to water quality

gradients in the North Branch of the Moose River Basin. Biogeochemistry 3: 63-85.

Schofield, C. L.; Keleher, C. (1996) Comparison of brook trout reproductive success and

recruitment in an acidic Adirondack lake following whole lake liming and watershed

liming. Biogeochemistry 32: 323-337.

August 2008 B­197 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Schofield, C. L.; Trojnar, J. R. (1980) Aluminum toxicity to brook trout (Salvelinus fontinalis) in

acidified waters. In: Toribara, T. Y.; Miller, M. W.; Morrow, P. E., eds. Polluted rain:

proceedings of the twelfth Rochester international conference on environmental toxicity;

May 1979; Rochester, NY. New Y ork, NY: Plenum Press; pp. 341-366. (Hollaender, A.;

Probstein, R. F.; Welch, B. L., eds. Environmental science research: v. 17).

Schreck, C. B. (1981) Stress and rearing of salmonids. Aquaculture 28: 241-249.

Schreck, C. B. (1982) Stress and compensation in teleostean fishes: response to social and

physical factors. In: Pickering, A. D., ed. Stress and fish. New Y ork, NY: Academic

Press; pp. 295-321.

Schröder, W. H.; Bauch, J.; Endeward, R. (1988) Microbeam analysis of Ca exchange and

uptake in the fine roots of spruce: influence of pH and aluminum. Trees (Heidelberg,

Ger.) 2: 96-103.

Scott, M. G.; Hutchinson, T. C.; Feth, M. J. (1989a) A c omparison of the effects on Canadian

boreal forest lichens of nitric and sulphuric acids as sources of rain acidity. New P hytol.

111: 663-671.

Scott, M. G.; Hutchinson, T. C.; Feth, M. J. (1989b) Contrasting responses of lichens and

Vaccinium angustifolium to long-term acidification of a boreal forest ecosystem. Can. J.

Bot. 67: 579-588.

Seastedt, T. R.; Bowman, W. D.; Caine, T. N.; McKnight, D.; Townsend, A.; Williams, M. W.

(2004) The landscape continuum: a model for high elevation ecosystems. BioScience 54:

111-121.

Sharpe, W. E.; Kimmel, W. G.; Young, E. S., Jr.; DeWalle, D. R. (1983) In-situ bioassays of fish

mortality in two Pennsylvania streams acidified by atmospheric deposition. Northeast.

Environ. Sci. 2: 171-178.

Shortle, W. C.; Smith, K. T. (1988) Aluminum-induced calcium deficiency syndrome in

declining red spruce. Science (Washington, DC) 240: 1017-1018.

Shortle, W. C.; Smith, K. T.; Minocha, R.; Lawrence, G. B.; David, M. B. (1997) Acidic

deposition, cation mobilization, and biochemical indicators of stress in healthy red

spruce. J. Environ. Qual. 26: 871-876.

Shugart, H. H.; McLaughlin, S. B. (1985) Modeling SO 2 

### effects on forest growth and community

dynamics. In: W.E. Winter, H. A. M., and R.A. Goldstein, ed. Sulfur dioxide and

vegetation: physiology, ecology, and policy issues. Stanford, CA: Stanford University

Press; pp. 478-491.

Siminon, H. A.; Kretser, W. A.; Bath, D. W.; Olson, M.; Gallagher, J. (1993) In situ bioassays of

brook trout (Salvelinus fontinalis) and blacknose dace (Rhinichthys atratulus) in

Adirondack streams affected by episodic acidification. Can. J. Forest. Res. 50: 902-912.

Simonsson, M.; Berggren, D. (1998) Aluminium solubility related to secondary solid phases in

upper B horizons with spodic characteristics. Eur. J. Soil Sci. 49: 317-326.

Simpson, K. W.; Bode, R. W.; Colquhoun, J. R. (1985) The macroinvertebrate fauna of an acidstressed headwater stream system in the Adirondack Mountains, New York. Freshwater

Biol. 15: 671-681.

August 2008 B­198 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Skjelkvåle, B. L.; Wright, R. F.; Henriksen, A. (1998) Norwegian lakes show w idespread

recovery from acidification; results from national surveys of lakewater chemistry 1986­

1997. Hydrol. Earth Syst. Sci. 2: 555-562.

Skjelkvåle, B. L.; Stoddard, J. L.; Andersen, T. (2001) Trends in surface water acidification in

Europe and North America (1989-1998). Water Air Soil Pollut. 130: 787-792.

Skogheim, O. K.; Rosseland, B. O. (1986) Mortality of smolt of Atlantic salmon Salmo-salar L.,

at low l evels of aluminum in acidic softwater. Bull. Environ. Contam. Toxicol. 37: 258­

265.

Skyllberg, U. (1999) pH and solubility of aluminum in acidic forest soils: a consequence of

reactions between organic acidity and aluminum alkalinity. Eur. J. Soil Sci. 50: 95-106.

Smith, W. H. (1990) Forests as sinks for air contaminants: soil compartment. In: Air pollution

and forests: interactions between air contaminants and forest ecosystems. 2nd ed. New

York, NY: Springer-Verlag; pp. 113-146. (Springer series on environmental

management).

Smith, G. F.; Nicholas, N. S. (1999) Post disturbance spruce-fir forest stand dynamics at seven disjunct sites. Castanea 64: 175-186.

Smock, L. A.; Gazzera, S. B. (1996) Effects of experimental episodic acidification on a

southeastern USA b lackwater stream. J. Freshwater Ecol. 11: 81-90.

Soulsby, C.; Turnbull, D.; Langan, S. J.; Owen, R.; Hirst, D. (1995) Long-term trends in stream

chemistry and biology in northeast Scotland: evidence for recovery. Water Air Soil

Pollut. 85: 689-694.

Southern Appalachian Man and the Biosphere (SAMAB). (1996) The Southern Appalachian

Assessment. Aquatics technical report. Knoxville, TN: SAMAB Cooperative. Available:

http://samab.org/saa/reports/aquatic/aquatic.html [29 November, 2007].

Spalding, M. G.; Forrester, D. J. (1991) Effects of parasitism and disease on the nesting success

of colonial wading birds (Ciconiiformes) in southern Florida. Tallahassee, FL: Florida

Game and Fresh Water Fish Commission, Nongame Wildlife Program; project NG88­

008. Prepared by: University of Florida, College of Veterinary Medicine, Department of

Infectious Diseases.

St. Louis, V. L.; Breebaart, L.; Barlow, J. C. (1990) Foraging behaviour of tree swallows over

acidified and nonacidic lakes. Can. J. Zool. 68: 2385-2392.

Stauffer, R. E. (1990) Granite weathering and the sensitivity of alpine lakes to acid deposition.

Limnol. Oceanogr. 35: 1112-1134.

Stauffer, R. E.; Wittchen, B. D. (1991) Effects of silicate weathering on water chemistry in

forested, upland, felsic terrain of the USA. Geochim. Cosmochim. Acta 55: 3253-3271.

Stienen, H.; Bauch, J. (1988) Element content in tissues of spruce seedlings from hydroponic

cultures simulating acidification and deacidification. Plant Soil 106: 231-238.

Stoddard, J. L. (1990) Plan for converting the NAPAP aquatic effects long-term monitoring

(LTM) project to the Temporally Integrated Monitoring of Ecosystems (TIME) project.

International report. Corvallis, OR: U.S. Environmental Protection Agency.

August 2008 B­199 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Stoddard, J. L. (1991) Trends in Catskill stream water quality: evidence from historical data.

Water Resour. Res. 27: 2855-2864.

Stoddard, J. L. (1994) Long-term changes in watershed retention of nitrogen: its causes and

aquatic consequences. In: Baker, L. A., ed. Environmental chemistry of lakes and

reservoirs. Washington, DC: American Chemical Society; pp. 223-284. (Advances in

chemistry series no. 237).

Stoddard, J. L. (1995) Episodic acidification during snowmelt of high elevation lakes in the

Sierra Nevada mountains of California. Water Air Soil Pollut. 85: 353-358.

Stoddard, J. L.; Kellogg, J. H. (1993) Trends and patterns in lake acidification in the state of

Vermont: evidence from the long-term monitoring project. Water Air Soil Pollut. 67:

301-317.

Stoddard, J. L.; Murdoch, P. S. (1991) Catskill Mountains. In: Charles, D. F., ed. Acidic

deposition and aquatic ecosystems: regional case studies. New Y ork, NY: SpringerVerlag; pp. 237-271.

Stoddard, J. L.; Urquhart, N. S.; Newell, A. D.; Kugler, D. (1996) The Temporally Interated

Monitoring of Ecosystems (TIME) project design. 2. Detection of regional acidification

trends. Water Resour. Res. 32: 2529-2538.

Stoddard, J. L.; Driscoll, C. T.; Kahl, J. S.; Kellogg, J. H. (1998) A r egional analysis of lake

acidification trends for the northeastern U.S., 1982-1994. Environ. Monit. Assess. 51:

399-413.

Stoddard, J. L.; Jeffries, D. S.; Lükewille, A.; Clair, T. A.; Dillon, P. J.; Driscoll, C. T.; Forsius,

M.; Johannessen, M.; Kahl, J. S.; Kellogg, J. H.; Kemp, A.; Mannio, J.; Monteith, D. T.;

Murdoch, P. S.; Patrick, S.; Rebsdorf, A.; Skjelkvale, B. L.; Stainton, M. P.; Traaen, T.;

Van Dam, H.; Webster, K. E.; Wieting, J.; Wilander, A. (1999) Regional trends in

aquatic recovery from acidification in North American and Europe. Nature (London,

U.K.) 401: 575-578.

Stoddard, J.; Kahl, J. S.; Deviney, F. A.; DeWalle, D. R.; Driscoll, C. T.; Herlihy, A. T.;

Kellogg, J. H.; Murdoch, P. S.; Webb, J. R.; Webster, K. E. (2003) Response of surface

water chemistry to the Clean Air Act Amendments of 1990. Research Triangle Park, NC:

U.S. Environmental Protection Agency, Office of Research and Development, National

Health and Environmental Effects Research Laboratory. EPA 6 20/R-03.001.

Stoermer, E. F.; Smol, J. P., eds. (1999) The diatoms: applications for the environmental and

earth sciences. New Y ork, NY: Cambridge University Press.

Strengbom, J.; Nordin, A.; Näsholm, T.; Ericson, L. (2001) Slow r ecovery of boreal forest

ecosystem following decreased nitrogen input. Funct. Ecol. 15: 451-457.

Strickland, T. C.; Holdren, G. R., Jr.; Ringold, P. L.; Bernard, D.; Smythe, K.; Fallon, W. (1993)

A n ational critical loads framework for atmospheric deposition effects assessment: I.

Method summary. Environ. Manage. (N. Y.) 17: 329-334.

Sucoff, E. I.; Thornton, F. C.; Joslin, J. D. (1990) Sensitivity of tree seedlings to aluminum: I.

Honeylocust. J. Environ. Qual. 19: 163-171.

August 2008 B­200 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Sullivan, T. J. (1990) Historical changes in surface water acid-base chemistry in response to

acidic deposition. Washington, DC: National Acid Precipitation Assessment Program;

State of the Science; SOS/T11.

Sullivan, T. J. (1993) Whole ecosystem nitrogen effects research in Europe. Environ. Sci.

Technol. 27: 1482-1486.

Sullivan, T. J. (2000) Aquatic effects of acidic deposition. Boca Raton, FL: Lewis Publishers.

Sullivan, T. J.; Charles, D. F. (1994) The feasibility and utility of a paleolimnology/paleoclimate

data cooperative for North America. J. Paleolimnol. 10: 265-273.

Sullivan, T. J.; Cosby, B. J. (1998) Modeling the concentration of aluminium in surface waters.

Water Air Soil Pollut. 105: 643-659.

Sullivan, T. J.; Eilers, J. M.; Church, M. R.; Blick, D. J.; Eshleman, K. N.; Landers, D. H.;

DeHaan, M. S. (1988) Atmospheric wet sulphate deposition and lakewater chemistry.

Nature (London) 331: 607-609.

Sullivan, T. J.; Charles, D. F.; Smol, J. P.; Cumming, B. F.; Selle, A. R.; Thomas, D. R.; Bernert,

J. A.; Dixit, S. S. (1990) Quantification of changes in lakewater chemistry in response to

acidic deposition. Nature (London, U.K.) 345: 54-58.

Sullivan, T. J.; Turner, R. S.; Charles, D. F.; Cumming, B. F.; Smol, J. P.; Schofield, C. L.;

Driscoll, C. T.; Cosby, B. J.; Birks, H. J. B.; Uutala, A. J.; Kingston, J. C.; Dixit, S. S.;

Bernert, J. A.; Ryan, P. F.; Marmorek, D. R. (1992) Use of historical assessment for

evaluation of process-based model projections of future environmental change: lake

acidification in the Adirondack Mountains, New York, U.S.A. Environ. Pollut. 77: 253­

262.

Sullivan, T. J.; Cosby, B. J.; Driscoll, C. T.; Charles, D. F.; Hemond, H. F. (1996a) Influence of

organic acids on model projections of lake acidification. Water Air Soil Pollut. 91: 271­

282.

Sullivan, T. J.; McMartin, B.; Charles, D. F. (1996b) Re-examination of the role of landscape

change in the acidification of lakes in the Adirondack Mountains, New York. Sci. Total

Environ. 183: 231-248.

Sullivan, T. J.; Eilers, J. M.; Cosby, B. J.; Vaché, K. B. (1997) Increasing role of nitrogen in the

acidification of surface waters in the Adirondack Mountains, New Y ork. Water Air Soil

Pollut. 95: 313-336.

Sullivan, T. J.; Cosby, B. J.; Webb, J. R.; Snyder, K. U.; Herlihy, A. T.; Bulger, A. J.; Gilbert, E.

H.; Moore, D. (2002) Assessment of the effects of acidic deposition on aquatic resources

in the Southern Appalachian Mountains. Report prepared for the Southern Appalachian

Mountains Initiative (SAMI). Corvallis, OR: E&S Environmental Chemistry, Inc.

Sullivan, T. J.; Cosby, B. J.; Laurence, J. A.; Dennis, R. L.; Savig, K.; Webb, J. R.; Bulger, A. J.;

Scruggs, M.; Gordon, C.; Ray, J.; Lee, H.; Hogsett, W. E.; Wayne, H.; Miller, D.; Kern,

J. S. (2003) Assessment of air quality and related values in Shenandoah National Park.

Philadelphia, PA: U.S. Department of the Interior, National Park Service, Northeast

Region; technical report NPS/NERCHAL/NRTR-03/090. Available:

http://www.nps.gov/nero/science/FINAL/shen_air_quality/shen_airquality.html [19 June,

2006].

August 2008 B­201 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43 S ullivan, T. J.; Cosby, B. J.; Herlihy, A. T.; Webb, J. R.; Bulger, A. J.; Snyder, K. U.; Brewer, P.

F.; Gilbert, E. H.; Moore, D. L. (2004) Regional model projections of future effects of

sulfur and nitrogen deposition on streams in the southern Appalachian Mountains. Water

Resour. Res. 40(W02101): 10.1029/2003WR001998.

Sullivan, T. J.; Cosby, B. J.; Tonnessen, K. A.; Clow, D. W. (2004) Surface water acidification

responses and critical loads of sulfur and nitrogen deposition in Loch Vale watershed,

Colorado. Water Resour. Res. 41(W01021): 10.1029/2004WR003414.

Sullivan, T. J.; Driscoll, C. T.; Cosby, B. J.; Fernandez, I. J.; Herlihy, A. T.; Zhai, J.; Stemberger,

R.; Snyder, K. U.; Sutherland, J. W.; Nierzwicki-Bauer, S. A.; Boylen, C. W.;

McDonnell, T. C.; Nowicki, N. A. (2006a) Assessment of the extent to which

intensively-studied lakes are representative of the Adirondack Mountain region. Final

report. Albany, NY: New York State Energy Research and Development Authority

(NYSERDA); report 06-17. Available:

http://nysl.nysed.gov/uhtbin/cgisirsi/Qcwd6NzFby/NYSL/138650099/8/4298474 [1

November, 2007].

Sullivan, T. J.; Fernandez, I. J.; Herlihy, A. T.; Driscoll, C. T.; McDonnell, T. C.; Nowicki, N.

A.; Snyder, K. U.; Sutherland, J. W. (2006b) Acid-base characteristics of soils in the

Adirondack Mountains, New York. Soil Sci. Soc. Am. J. 70: 141-152.

Sullivan, T. J.; Webb, J. R.; Snyder, K. U.; Herlihy, A. T.; Cosby, B. J. (2007) Spatial

distribution of acid-sensitive and acid-impacted streams in relation to watershed features

in the southern Appalachian mountains. Water Air Soil Pollut. 182: 57-71.

Swank, W. T. (1988) Stream chemistry responses to disturbance. In: Swank, W. T.; Crossley, D.

S., eds. Forest hydrology and ecology at Coweeta. New York, NY: Springer-Verlag,

Inc.; pp. 339-358.

Sweets, P. R. (1992) Diatom paleolimnological evidence for lake acidification in the Trail Ridge

region of Florida. Water Air Soil Pollut. 65: 43-57.

Sweets, P. R.; Binert, R. W.; Cusimono, T. L.; Binford, M. W. (1990) Paleoecological

investigations of recent lake acidification in northern Florida. J. Paleolimnol. 4: 103-137.

Swenson, W. A.; McCormick, J. H.; Simonson, T. D.; Jensen, K. M.; Eaton, J. G. (1989)

Experimental acidification of Little Rock Lake (Wisconsin): fish research approach and

early responses. Arch. Environ. Contam. Toxicol. 18: 167-174.

Taylor, G. E., Jr.; Pitelka, L. F. (1992) Genetic diversity of plant populations and the role of air

pollution. In: Barker, J. R.; Tingey, D. T., eds. Air pollution effects on biodiversity. New

York, NY: Van Nostrand Reinhold; pp. 111-130.

Taylor, G. E., Jr.; Hanson, P. J.; Baldocchi, D. D. (1988) Pollutant deposition to individual

leaves and plant canopies: sites of regulation and relationship to injury. In: Heck, W. W.;

Taylor, O. C.; Tingey, D. T., eds. Assessment of crop loss from air pollutants. New York,

NY: Elsevier Applied Science; pp. 227-257.

Temple, P. J.; Riechers, G. H.; Miller, P. R. (1992) Foliar injury responses of ponderosa pine

seedlings to ozone, wet and dry acidic deposition, and drought. Environ. Exp. Bot. 32:

101-113.

Thornton, F. C.; Schaedle, M.; Raynal, D. J. (1986a) Effect of aluminum on the growth of sugar

maple in solution culture. Can. J. For. Res. 16: 892-896.

August 2008 B­202 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Thornton, F. C.; Schaedle, M.; Raynal, D. J. (1986b) Effects of aluminum on the growth,

development, and nutrient composition of honeylocust (Gleditia triacanthos L.) seedling.

Tree Physiol. 2: 307-316.

Thornton, F. C.; Schaedle, M.; Raynal, D. J.; Zipperer, C. (1986c) Effects of aluminum on honeylocust (Gleditsia triacanthos L.) seedlings in solution culture. J. Exp. Bot. 37: 775­

785.

Thornton, F. C.; Schaedle, M.; Raynal, D. J. (1987) Effects of aluminum on red spruce seedlings

in solution culture. Environ. Exp. Bot. 27: 489-498.

Tietema, A.; Beier, C. (1995) A c orrelative evaluation of nitrogen cycling in the forest

ecosystems of the EC projects NITREX a nd EXMAN. For. Ecol. Manage. 71: 143-151.

Tietge, J. E.; Johnson, R. D.; Bergman, H. L. (1988) Morphometric changes in gill secondary

lamellae of brook trout (Salvelinus fontinalis) after long-term exposure to acid and

aluminum. Can. J. Fish. Aquat. Sci. 45: 1643-1648.

Tipping, E.; Berggren, D.; Mulder, J.; Woof, C. (1995) Modelling the solid-solution distributions

of protons, aluminium, base cations and humic substances in acid soils. Eur. J. Soil Sci.

46: 77-94.

Tipping, E.; Bass, J. A. B.; Hardie, D.; Haworth, E. Y.; Hurley, M. A.; Wills, G. (2002)

Biological responses to the reversal of acidification in surface waters of the English Lake

District. Environ. Pollut. 116: 137-146.

Tonnessen, K. A. (1991) The Emerald Lake Watershed Study: introduction and site description.

Water Resour. Res. 27: 1537-1539.

Townsend, C. R.; Hildrew, A. G.; Francis, J. (1983) Community structure in some southern

English streams: the influence of physiochemical factors. Freshw. Biol. 13: 521-544.

Treshow, M. (1980) Pollution effects on plant distribution. Environ. Conserv. 7: 279-286.

Trojnar, J. R. (1977) Egg and larval survival of white suckers (Catostomus commersoni) at low

pH. J. Fish. Res. Board Can. 34: 262-266.

Truman, R. A.; Humphreys, F. R.; Ryan, P. J. (1986) Effect of varying solution ratios of Al to Ca

and Mg on the uptake of phosphorus by Pinus radiata. Plant Soil 96: 109-123.

Turk, J. T.; Spahr, N. E. (1991) Rocky Mountains. In: Charles, D. F., ed. Acidic deposition and

aquatic ecosystems: regional case studies. New Y ork, NY: Springer-Verlag, Inc.; pp.

471-501.

Turk, J. T.; Campbell, D. H.; Spahr, N. E. (1992) Initial findings of synoptic snowpack sampling

in Colorado Rocky Mountains. Denver, CO: U.S. Department of the Interior, Geologic

Survey; open-file report 92-645.

Turk, J. T.; Campbell, D. H.; Spahr, N. E. (1993) Use of chemistry and stable sulfur isotopes to

determine sources of trends in sulfate of Colorado lakes. Water Air Soil Pollut. 67: 415­

431.

Turner, R. S.; Cook, R. B.; Van Miegroet, H.; Johnson, D. W.; Elwood, J. W.; Bricker, O. P.;

Lindberg, S. E.; Hornberger, G. M. (1991) Watershed and lake processes affecting

surface water acid-base chemistry. In: Irving, P. M., ed. Acidic deposition: state of

science and technology, volume II, aquatic processes and effects. Washington, DC: The

August 2008 B­203 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 U.S. National Acid Precipitation Assessment Program. (Acidic deposition: state of

science and technology report 10).

Turner, R. S.; Ryan, P. F.; Marmorek, D. R.; Thornton, K. W.; Sullivan, T. J.; Baker, J. P.;

Christensen, S. W.; Sale, M. J. (1992) Sensitivity to change for low-ANC eastern US

lakes and streams and brook trout populations under alternative sulfate deposition

scenarios. Environ. Pollut. 77: 269-277.

Turnpenny, A. W. H.; Sadler, K.; Aston, R. J.; Milner, A. G. P.; Lynam, S. (1987) The fish

populations of some streams in Wales and northern England in relation to acidity and

associated factors. J. Fish Biol. 31: 415-434.

Tyler, S. J.; Ormerod, S. J. (1992) A r eview o f the likely causal pathways relating the reduced

density of breeding dippers Cinclus cinclus to the acidification of upland streams.

Environ. Pollut. 78: 49-55.

U.S. Environmental Protection Agency. (1982) Air quality criteria for particulate matter and

sulfur oxides. Research Triangle Park, NC: Office of Health and Environmental

Assessment, Environmental Criteria and Assessment Office; EPA r eport no. EPA-600/8­

82-029aF-cF. 3v. Available from: NTIS, Springfield, VA; PB84-156777.

U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen.

Research Triangle Park, NC: Office of Health and Environmental Assessment,

Environmental Criteria and Assessment Office; report nos. EPA/600/8-91/049aF-cF. 3v.

Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517.

U.S. Environmental Protection Agency. (1995) Acid deposition standard feasibility study. Report

to Congress. Washington, DC: Office of Air and Radiation; report no. EPA 4 30-R-95­

001a.

U.S. Environmental Protection Agency. (2004) Air quality criteria for particulate matter.

Research Triangle Park, NC: National Center for Environmental Assessment; report no.

EPA/600/P-99/002aF-bF. 2v. Available: http://cfpub.epa.gov/ncea/ [9 November, 2004].

U.S. Environmental Protection Agency. (2006) Wadeable streams assessment: a collaborative

survey of the nation's streams. Washington, DC: Office of Research and Development,

Office of Water; report no. EPA 8 41-B-06-002. Available:

http://www.epa.gov/owow/streamsurvey/pdf/WSA_Assessment_May2007.pdf [30

November, 2007].

Ulrich, B. (1983) Soil acidity and its relations to acid deposition. In: Ulrich, B.; Pankrath, J., eds.

Effects of accumulation of air pollutants in forest ecosystems: proceedings of a workshop; May 1982; Göttingen, Federal Republic of Germany. Dordrecht, The

Netherlands: D. Reidel Publishing Company; pp. 127-146.

Ulrich, B.; Mayer, R.; Khanna, P. K. (1980) Chemical changes due to acid precipitation in loessderived soil in central Europe. Soil Sci. 130: 193-199.

Urquhart, N. S.; Paulsen, S. G.; Larsen, D. P. (1988) Monitoring for regional and policy-relevant

trends over time. Ecol. Appl. 8: 246-257.

Van Breemen, N.; Mulder, J.; Driscoll, C. T. (1983) Acidification and alkalinization of soils.

Plant Soil 75: 283-308.

August 2008 B­204 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Van Breemen, N.; Giesler, R.; Olsson, M.; Finlay, R.; Lundström, U.; Jongmans, A. G. (2000)

Mycorrhizal weathering: A t rue case of mineral plant nutrition? Biogeochemistry 49: 53­

67.

Van Haluwyn, C.; van Herk, C. M. (2002) Bioindication: the community approach. In: Nimis, P.

L.; Scheidegger, C.; Wolseley, P. A., eds. Monitoring with lichens-monitoring lichens.

Dordrecht, The Netherlands: Kluwer Academic Publishers; pp. 39-64.

Van Herk, C. M. (2001) Bark pH a nd susceptibility to toxic air pollutants as independent causes

of changes in epiphytic lichen composition in space and time. Lichenologist 33(5): 419­

441.

Van Miegroet, H.; Cole, D. W.; Foster, N. W. (1992a) Nitrogen distribution and cycling. In:

Johnson, D. W.; Lindberg, S. E., eds. Atmospheric deposition and forest nutrient cycling:

a synthesis of the integrated forest study. New Y ork, NY: Springer-Verlag; pp. 178-196.

(Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. Ecological

studies: analysis and synthesis: v. 91).

Van Miegroet, H.; Johnson, D. W.; Cole, D. W. (1992b) Analysis of N c ycles in polluted vs unpolluted environment. In: Johnson, D. W.; Lindberg, S. E., eds. Atmospheric

deposition and forest nutrient cycling: a synthesis of the integrated forest study. New

York, NY: Springer-Verlag; pp. 199-202. (Billings, W. D.; Golley, F.; Lange, O. L.;

Olson, J. S.; Remmert, H., eds. Ecological studies: analysis and synthesis: v. 91).

Van Miegroet, H. V.; Johnson, D. W.; Burt, T. P.; Heathwaite, A. L.; Trudgill, S. T. (1993)

Nitrate dynamics in forest soils. In: Burt, T. P.; Heathwaite, A. L.; Trudgill, S. T., eds.

Nitrate: processes, patterns and management. New Y ork, NY: John Wiley & Sons; pp.

75-97.

Van Miegroet, H.; Moore, P. T.; Tewksbury, C. E.; Nicholas, N. S. (2007) Carbon sources and

sinks in high-elevation spruce-fir forests of the southeastern US. For. Ecol. Manage. 238:

249-260.

Van Sickle, J.; Church, M. R. (1995) Nitrogen bounding study. methods for estimating the

relative effects of sulfur and nitrogen depostion on surface water chemistry. Corvallis,

OR: U.S. Environmental Protection Agency, National Health and Environment Effects

Research Laboratory; report no. EPA/600/R-95/172. Available from: NTIS, Springfield,

VA; PB96-133921.

Van Sickle, J.; Baker, J. P.; Simonin, H. A.; Baldigo, B. P.; Kretser, W. A.; Sharpe, W. E. (1996)

Episodic acidification of small streams in the northeastern United States: fish mortality in

field bioassays. Ecol. Appl. 6: 408-421.

Vertucci, F. A.; Eilers, J. M. (1993) Issues in monitoring wilderness lake chemistry: a case study

in the Sawtooth Mountains, Idaho. Environ. Monit. Assess. 28: 277-294.

Vitousek, P. M.; Aber, J. D.; Howarth, R. W.; Likens, G. E.; Matson, P. A.; Schindler, D. W.;

Schlesinger, W. H.; Tilman, D. G. (1997) Human alteration of the global nitrogen cycle:

sources and consequences. Ecol. Appl. 7: 737-750.

Vogt, K. A.; Dahlgren, R.; Ugolini, F.; Zabowski, D.; Moore, E. E.; Zasoski, R. (1987a)

Aluminum, Fe, Ca, Mg, K, Mn, Cu, Zn and P in above- and belowground biomass. I.

Abies amabilis and Tsuga mertensiana. Biogeochemistry 4: 277-294.

August 2008 B­205 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Vogt, K. A.; Dahlgren, R.; Ugolini, F.; Zabowski, D.; Moore, E. E.; Zasoski, R. (1987b)

Aluminum, Fe, Ca, Mg, K, Mn, Cu, Zn and P in above- and belowground biomass. II.

Pools, and circulation in a subalpine Abies amabilis stand. Biogeochemistry 4: 295-311.

Waiwood, B. A.; Haya, K. (1983) Levels of chorionase activity during embryonic development

of Salmo salar under acid conditions. Bull. Environ. Contam. Toxicol. 30: 511-515.

Wake, D. B. (1991) Declining amphibian populations. Science (Washington, DC) 253: 860.

Wales, D. L.; Liimatainen, V. A. (1987) Preliminary assessment of the current impact and

potential risk of acidic deposition on walleye populations in Ontario. Toronto, ON,

Canada: Ministry of Natural Resources; Ontario Fisheries acidification report series no.

87-11.

Wallace, J. B.; Webster, J. R.; Lowe, R. L. (1992) High-gradient streams of the Appalachians.

In: Hackney, C. T.; Adams, S. M.; Martin, W. H., eds. Biodiversity of southeastern

United States aquatic communities. New Y ork, NY: John Wiley & Sons; pp. 133-192.

Warby, R. A. F.; Johnson, C. E.; Driscoll, C. T. (2005) Chemical recovery of surface waters

across the northeastern U.S. from reduced inputs of acidic deposition: 1984-2001.

Environ. Sci. Tech. 39: 6548-6554.

Wargo, P. M.; Tilley, J.; Lawrence, G.; David, M.; Vogt, K.; Vogt, D.; Holifield, Q. (2003)

Vitality and chemistry of roots of red spruce in forest floors of stands with a gradient of

soil Al/Ca ratios in the northeastern United States. Can. J. Forest Res. 33: 635-652.

Wayland, M.; McNicol, D. K. (1990) Status report on the effects of acid precipitation on

common loon reproduction in Ontario: the Ontario Lakes loon survey. Ottawa, ON,

Canada: Canadian Wildlife Service.

Weathers, K. C.; Lovett, G. M.; Likens, G. E.; Caraco, N. F. M. (2000) Cloudwater inputs of

nitrogen to forest ecosystems in southern Chile: forms, fluxes, and sources. Ecosystems

3: 590-595.

Webb, J. R. (1999) Synoptic stream water chemistry. In: Bulger, A. J.; Cosby, B. J.; Dolloff, C.

A.; Eshleman, K. N.; Webb, J. R.; Galloway, J. N., eds. Shenandoah National Park: fish

in sensitive habitats (SNP:FISH); final report vol. II, Ch. 3. Prepared for: National Park

Service. Charlottesville, VA: University of Virginia, Department of Environmental

Sciences.

Webb, J. R.; Deviney, F. A.; Galloway, J. N.; Rinehart, C. A.; Thompson, P. A.; Wilson, S.

(1994) The acid-base status of native brook trout streams in the mountains of Virginia.

Charlottesville, VA: University of Virginia, Department of Environmental Sciences.

Webb, J. R.; Cosby, B. J.; Deviney, F. A., Jr.; Eshleman, K. N.; Galloway, J. N. (1995) Change

in the acid-base status of an Appalachian Mountain catchment following forest

defoliation by the gypsy moth. Water Air Soil Pollut. 85: 535-540.

Webb, J. R.; Cosby, B. J.; Deviney, F. A.; Galloway, J. N.; Maben, S. W.; Bulger, A. J. (2004)

Are brook trout streams in western Virginia and Shenandoah National Park recovering

from acidification? Environ. Sci. Technol. 38: 4091-4096.

Webster, K. E.; Newell, A. D.; Baker, L. A.; Brezonik, P. L. (1990) Climatically induced rapid

acidification of a softwater seepage lake. Nature (London, U.K.) 347: 374-376.

August 2008 B­206 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Webster, K. E.; Brezonik, P. L.; Holdhusen, B. J. (1993) Temporal trends in low a lkalinity lakes

of the Upper Midwest (1983-1989). Water Air Soil Pollut. 67: 397-414.

Webster, K. L.; Creed, I. F.; Nicholas, N. S.; Miegroet, H. V. (2004) Exploring interactions

between pollutant emissions and climatic variability in growth of red spruce in the Great

Smoky Mountains National Park. Water Air Soil Pollut. 159: 225-248.

Wedemeyer, G. A.; Barton, B. A.; MeLeay, D. J. (1990) Stress and acclimation. In: Schreck, C.

B.; Moyle, P. B., eds. Methods for fish biology. Bethesda, MD: American Fisheries

Society; pp. 178-196.

Westman, W. E.; Preston, K. P.; Weeks, L. B. (1985) SO 2 

### effects on the growth of native plants.

In: Winter, W. E.; Mooney, H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation:

physiology, ecology, and policy issues. Stanford, CA: Stanford University Press; pp. 264­

279.

Wetmore, C. M. (1985) Lichens and air quality in Isle Royale National Park. Denver, CO: U.S.

Department of the Interior, National Park Service.

Whiting, M. C.; Whitehead, D. R.; Holmes, R. W.; Norton, S. A. (1989) Paleolimnological

reconstruction of recent acidity changes in four Sierra Nevada lakes. J. Paleolimnol. 2:

285-304.

Wiemeyer, S. N.; Schmeling, S. K.; Anderson, A. (1987) Environmental pollutant and necropsy

data for ospreys from the eastern United States, 1975-1982. J. Wildl. Dis. 23: 279-291.

Wigington, P. J., Jr. (1999) Episodic acidification: causes, occurrence and significance to aquatic

resources. In: Drohan, J. R., ed. The effects of acidic deposition on aquatic ecosystems in

Pennsylvania: proceedings of the 1998 PA acidic deposition conference. University Park,

PA: Environmental Resources Research Institute; pp. 1-5.

Wigington, P. J., Jr.; Davies, T. D.; Tranter, M.; Eshleman, K. (1991) Episodic acidification of

surface waters due to acidic deposition. In: Irving, P. M., ed. Acidic deposition: state of

science and technology, volume II, aquatic processes and effects. Washington, DC: The

U.S. National Acid Precipitation Assessment Program. (State of science and technology

report 12).

Wigington, P. J.; Baker, J. P.; DeWalle, D. R.; Kretser, W. A.; Murdoch, P. S.; Siminon, H. A.;

Van Sickle, J.; McDowell, M. K.; Peck, D. V.; Barchet, W. R. (1993) Episodic

acidification of streams in the northeastern United States: chemical and biological results

of the Episodic Response Project. Washington, DC: U.S. Environmental Protection

Agency; report no. EPA/600/R-93/190.

Wigington, P. J., Jr.; DeWalle, D. R.; Murdoch, P. S.; Kretser, W. A.; Simonin, H. A.; Van

Sickle, J.; Baker, J. P. (1996) Episodic acidification of small streams in the northeastern

United States: ionic controls of episodes. Ecol. Appl. 6: 389-407.

Williams, M. W.; Melack, J. M. (1991) Precipitation chemistry in and ionic loading to an alpine

basin, Sierra Nevada. Water Resour. Res. 27: 1563-1574.

Williams, M.; Tonnessen, K. (2000) Critical loads for inorganic nitrogen deposition in the

Colorado Front Range, USA. Ecol. Appl. 10: 1648-1665.

August 2008 B­207 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39 Williams, M. W.; Bales, R. C.; Brown, A. D.; Melack, J. M. (1995) Fluxes and transformation of

nitrogen in a high-elevation catchment, Sierra Nevada. Biogeochemistry 28: 1-31.

Williams, M. W.; Baron, J. S.; Caine, N.; Sommerfeld, R.; Sanford, R. (1996a) Nitrogen

saturation in the Rocky Mountains. Environ. Sci. Technol. 30: 640-646.

Williams, M. W.; Platts-Mills, T.; Caine, N. (1996b) Landscape controls on surface water nitrate

concentrations at catchment and regional scales in the Colorado Rocky Mountains. Sun

River, OR: Chapman conference: nitrogen cycling in forested catchments.

Winner, W. E.; Atkinson, C. J. (1986) Absorption of air pollution by plants, and consequences

for growth. Trends Ecol. Evol. 1: 15-18.

Winterbourn, M. J.; Collier, K. J. (1987) Distribution of benthic invertebrates in acid, brown

water streams in the South Island of New Zealand. Hydrobiologia 153: 255-286.

Wodzinski, R. S.; Labeda, D. P.; Alexander, M. (1977) Toxicity of SO 2 

### and NO

x 

### : selective

inhibition of blue-green algae by bisulfite and nitrite. J. Air Pollut. Control Assoc. 27:

891-893.

Wolfe, M. H.; Joslin, J. D. (1989) Honeylocust (Gleditsia triacanthos L.) root response to

aluminum and calcium. Plant Soil 119: 181-185.

Wolfe, M. F.; Schwarzbach, S.; Sulaiman, R. A. (1998) Effects of mercury on wildlife: a comprehensive review. Environ. Toxicol. Chem. 17: 146-160.

Wolfe, A. P.; Van Gorpe, A. C.; Baron, J. S. (2003) Recent ecological and biogeochemical

changes in alpine lakes of Rocky Mountain National Park (Colorado, USA): a response to

anthropogenic nitrogen deposition. Geobiology 1(2): 153-168.

Wood, C. M. (1989) The physiological problems of fish in acid waters. In: Morris, R.; Taylor, E.

W.; Brown, D. J. A.; Brown, J. A., eds. Acid toxicity and aquatic animals. Cambridge,

United Kingdom: Cambridge University Press; pp. 125-152.

Wood, C. M.; McDonald, D. G. (1982) Physiological mechanisms of acid toxicity to fish. In:

Johnson, R. E., ed. Acid rain/fisheries. Bethesda, MD: American Fisheries Society; pp.

197-226.

Wood, C. M.; McDonald, D. G. (1987) The physiology of acid/aluminum stress in trout. Ann.

Soc. R. Zool. Belg. 177(suppl 1): 399-410.

Wood, C. M.; McDonald, D. G.; Ingersoll, C. G.; Mount, D. R.; Johannsson, O. E.; Landsberger,

S.; Bergman, H. L. (1990) Effects of water acidity, calcium, and aluminum on whole

body ions of brook trout (Salvelinus fontinalis) continuously exposed from fertilization to

swim-up: a study by instrumental neutron activation analysis. Can. J. Fish. Aquat. Sci.

47: 1593-1603.

Woodward, D. F. (1991) Sensitivity of greenback cutthroat trout to acidic pH a nd elevated

aluminum. Trans. Am. Fish. Soc. 120: 34-42.

Woodward, D. F.; Farag, A. M.; Mueller, M. E.; Little, E. E.; Vertucci, F. A. (1989) Sensitivity

of endemic Snake River cutthroat trout to acidity and elevated aluminum. Trans. Am.

Fish. Soc. 118: 630-641.

August 2008 B­208 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

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12

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16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35 W oodwell, G. M. (1970) Effects of pollution on the structure and physiology of ecosystems:

changes in natural ecosystems caused by many different types of disturbances are similar

and predictable. Science (Washington, DC) 168: 429-433.

Wright, R. F.; Snekvik, E. (1978) Acid precipitation: chemistry and fish populations in 700 lakes

in southernmost Norway. Verh. Int. Ver. Theor. Angew. Limnol. 20: 765-775.

Wright, R. F.; Lotse, E.; Semb, A. (1988a) Reversibility of acidification shown by wholecatchment experiments. Nature (London, U.K.) 334: 670-675.

Wright, R. F.; Norton, S. A.; Brakke, D. F.; Frogner, T. (1988b) Experimental verification of

episodic acidification of freshwaters by sea salts. Nature (London) 334: 422-424.

Wright, R. F.; Lotse, E.; Semb, A. (1993) RAIN Project: results after 8 years of experimentally

reduced acid ceposition to a whole catchment. Can. J. Fish. Aquat. Sci. 50: 258-268.

Wright, R. F.; Larssen, T.; Camarero, L.; Cosby, B. J.; Ferrier, R. C.; Helliwell, R.; Forsius, M.;

Jenkins, A.; Kopacek, J.; Majer, V.; Moldan, F.; Posch, M.; Rogora, M.; Schopp, W.

(2005) Recovery of acidified European surface waters. Environ. Sci. Technol. 39: 64A72A.

Yan, N. D.; Keller, W.; Somers, K. M.; Pawson, T. W.; Girard, R. E. (1996a) Recovery of

crustacean zooplankton communities from acid and metal contamination: comparing

manipulated and reference lakes. Can. J. Fish. Aquat. Sci. 53: 1301-1327.

Yan, N. D.; Welsh, P. G.; Lin, H.; Taylor, D. J.; Filion, J.-M. (1996b) Demographic and genetic

evidence of the long-term recovery of Daphnia galeata mendotae (Crustacea: Daphniidae)

in Sudbury lakes following additions of base: the role of metal toxicity. Can. J. Fish.

Aquat. Sci. 53: 1328-1344.

Yanai, R. D.; Siccama, T. G.; Arthur, M. A.; Federer, C. A.; Friedland, A. J. (1999)

Accumulation and depletion of base cations in forest floors in the northeastern United

States. Ecology 80: 2774-2787.

Yanai, R. D.; Phillips, R. P.; Arthur, M. A.; Siccama, T. G.; Hane, E. N. (2005) Spatial and

temporal variation in calcium and aluminum in northern hardwood forest floors. Water

Air Soil Pollut. 160: 109-118.

Zhai, J.; Driscoll, C. T.; Sullivan, T. J.; Cosby, B. J. (2008) Regional application of the PnETBGC model to assess historical acidification of Adirondack Lakes. Water Resour. Res.:

submitted.

Zillioux, E. J.; Porcella, D. B.; Benoit, J. M. (1993) Mercury cycling and effects in freshwater

wetland ecosystems. Environ. Toxicol. Chem. 12: 2245-2264.

Zulla, Y.; Billet, M. F. (1994) Long-term changes in chemical-weathering rates between 1949-50

and 1987 in forest soils from northeast Scotland. Eur. J. Soil Sci. 45: 327-335.

August 2008 B­209 DRAFT—DO NOT QUOTE OR CITE

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# Annex C. Nutrient Enrichment Effects

from N

C.1. Effects on Biogeochemical Pathways and Cycles

C.1.1. N Cycling in Terrestrial Ecosystems

C.1.1.1. N Deposition Effects on DON Leaching

Some N fertilization experiments suggest that increasing N deposition drives an increase in

production of dissolved organic nitrogen (DON) in soil (e.g., Seely and Lajtha, 1997; McDowell et al.,

2004), but there is little evidence that elevated N deposition increases the export and loss of DON from

terrestrial ecosystems. Essentially all of the increase in N export across gradients of N deposition occurs

as an increase in NO − 3 rather than DON export. The latter is typically less than 2 kg N/ha/yr from most

northeastern-forested watersheds (Campbell et al., 2000; Goodale et al., 2000; Lovett et al., 2000; Aber

et al., 2003).



## C.1.1.2. Interactions between snow melt and nitrate leaching

Changes in other environmental parameters can also be important. Measurement of nutrient

concentrations in Emerald Lake (Sierra Nevadas) over a period of 19 years suggested that NO − 3

concentration declined between 1983 and 1995. This was likely caused by changes in the snow regime

induced by a drought during the period 1987 to 1992 (Sickman et al., 2003a). Years that had shallow and

early melting snowpacks generally had lower snowmelt NO − 3 concentration. In addition, declines in

NO − 3 concentration during the growing season even in the wet years of 1993 through 2000 were likely the

result of increased P loading to Emerald Lake and the consequent release of phytoplankton from P

limitation (Sickman et al., 2003a).



## C.1.1.3. Denitrification: NO and N

2 

## O Flux

Davidson et al. (2000) described N gas loss from terrestrial ecosystems using a conceptual model

called “hole-in-the-pipe.” In this model, production of NO, N 2 O, and N 2 gas are functions of the general

rate of N cycling processes through soil (i.e., the N flux “flowing through the pipe”), combined with

information on soil water content, a key determinant of the ratio of NO:N 2 O (relative “hole size” for NO

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35 and N 2 O gas “leakage”). The model formulation has been supported by a range of field measurements in

temperate and especially tropical ecosystems (Davidson et al., 2000), and suggests that processes that

increase the rate of N cycling through soils should also increase the rate of N gas loss from these systems.

Production of NO and N 2 O tend to be lower in temperate than in tropical ecosystems, largely because of

colder temperatures and slower rates of N cycling in temperate systems, and the frequency of P rather

than N limitation in tropical systems. However, increased availability of N through fertilization can

increase the rate of NO and N 2 O gas loss from temperate forests.

Early studies of N gas emission in response to N fertilization experiments at the Harvard Forest,

MA, found small increases in N 2 O production in response to the highest N treatment (150 kg N/ha/yr) to a

red pine (Pinus resinosa) stand, but N 2 O losses accounted for <0.4% of N additions (Magill et al., 2000).

However, later studies found that NO emission rates can be more than an order of magnitude greater than

N 2 O emissions, with NO emissions amounting to 3-4% to 8% (4 to 5 kg N/ha/yr) of the N additions to the

fertilized pine stands (Venterea et al., 2003, 2004). Emissions of NO and N 2 O increased with fertilization

rate (0, 50, and 150 kg N/ha/yr) in both the red pine and a nearby red oak (Quercus rubra)/red maple

(Acer rubrum) stand (Venterea et al., 2003, 2004). A study of the response of Scots pine (Pinus

sylvestris) stands across a gradient of N deposition in Germany found a threefold to fourfold increase in

the rate of NO and N 2 O production as N deposition increased from 15 to 22 kg N/ha/yr. In these forests,

both gases were produced in roughly equal amounts, although as N deposition increased, the rate of NO

production increased more steeply than did the rate of N 2 O production (Butterbach-Bahl et al., 2002a).

At Höglwald, a German site receiving 20 to 30 kg N/ha/yr in throughfall, Butterbach-Bahl et al.

(2002b) reported higher emissions of NO than N 2 O in both a spruce and a beech stand, with N oxide

emissions totaling 4.5 to 6.8 kg N/ha/yr. Intensive laboratory studies suggested additional emissions of

N 2 gas amounting to 7.2 and 12.4 kg N/ha/yr in the spruce and beech stands, respectively. This is the

only known forest site for which a complete (NO, NO 2 , N 2 O, and N 2 ) N gas budget has been estimated,

and in total, these measurements suggest that soil emissions may balance 46% to 78% of the N received

in throughfall at this site. This result suggests somewhat higher rates of N gas loss than might be inferred

from a series of 15 N tracer studies in conifer stands across Europe, which spanned a range of rates of N

input from atmospheric and experimental sources of 3 to 91 kg N/ha/yr. Across all sites, total recovery of

added 15 N in soil, vegetation, and lysimeter leachate after 9 to 21 months amounted to 65% to 105% of

added 15 N (Tietema et al., 1998), providing a broad constraint on N gas emissions of no more than 35% of

added 15 N. The lowest rates of 15 N recovery (65% to 67%) occurred at Speuld and Ysselsteyn, two sites

in The Netherlands with the highest rates of chronic throughfall N input (35 to 53 kg N/ha/yr). Although

much more work is needed on complete N gas budgets, several lines of evidence suggest that trace gas

emissions of N may constitute an increasing pathway of N loss with increasing rates of N deposition.

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## C.1.1.4. Climate and N

2 

## O Interactions

Rainfall events are an important feature controlling N 2 O produced via denitrification. Rainfall

increases soil moisture. This inhibits oxygen diffusion creating anoxic conditions, which increases rates

of denitrification. A study of a spruce forest sites under ambient and elevated N deposition (20 and 30 kg

N/ha/yr, respectively) indicated through most of the study period N 2 O emission was equivalent between

the sites (Mohn et al., 2000). However, after rainfall events the maximum rate of N 2 O emission was

much higher for the +N plots, especially when rainfall caused low soil redox potential (an indicator of

anoxic conditions). Another study of mixed spruce, pine and birch forest (100 yrs old) under well- and

poorly drained soil moisture conditions indicated poorly drained soils produced 1/3 more N 2 O (118 g N 2 O

N ha/yr) than well drained soils. In this study, N deposition was increased in the poorly drained soil from

ambient (12 kg N/ha/yr) to elevated (42 kg N/ha/yr), N 2 O emissions increased by a factor of more than 2

(254 kg N/ha/yr) (Klemedtsson et al., 1997).

In addition to soil moisture, temperature also influences denitrification. The PnET model

(Photosynthesis-Evapotranspiartion-Model) is coupled with Denitrification-Decomposition (DNDC)

model, and an N module that are further described in Li et al. (1992, 1996, 2000), Li (2000), and Stange

et al. (2000) to form the PnET-N-DNDC model. The PnET-N-DNDC model is designed to simulate and

predict soil C and N biogeochemistry in temperate forest ecosystems and to simulate the emissions of

N 2 O and NO from forest soils. Denitrification is described in the model as a series of sequential

reductions driven by microorganisms using N oxides as electron acceptors under anaerobic conditions.

As intermediates of the processes, NO and N 2 O are tightly controlled by the kinetics of each step in the

sequential reactions. The capacity of this model to simulate N trace gas emissions from forest soils was

tested by comparing model results with results form field measurements at 19 different field sites across

Europe and 1 site in the United States (Kesik et al., 2005). Possible feedbacks of temperature and

precipitationchange on forest soil NO and N 2 O emissions in Europe were investigated using PnET-NDNDC (Kesik et al., 2006). The model results indicated decreasing precipitation and increasing

temperature in areas with light texture soils (below 15%) resulted in decreased soil moisture values; in

turn, N 2 O production by denitrification decreases. Under these same environmental conditions, NO

production by nitrification increases. Most laboratory studies show increasing temperature increases N 2 O

production, however if water filled pore space (WFPS) increases to 70-80%, then N 2 rather than N 2 O is

the main product of denitrification and N 2 O emissions go down. This illustrates how N 2 O emissions

increase with increasing soil moisture until soil moisture become more conducive to N 2 emission.

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# C.1.2. N Cycling in Transitional Ecosystems

C.1.2.1. Denitrification: Measurement Techniques

There are a variety of methods for measuring denitrification rates in wetland, freshwater and

marine sediments, including measurements of NO − 3 loss, N 2 production, N 2 O accumulation in response to

acetylene inhibition of N 2 O reduction, isotopic methods, and N 2 :argon (Ar) measurement by membrane

inlet mass spectrometry (MIMS) (Smith et al., 2006). The majority of direct measurements of

denitrification have measured only rates of production of N 2 O, or used the “acetylene block” technique of

inhibiting transformation of N 2 O to N 2 and monitoring the accumulation of N 2 O as a surrogate of the sum

of N 2 O and N 2

. The acetylene block method is highly problematic, however, as it also inhibits rates of

nitrification, and so denitrification rates are strongly underestimated where nitrification and denitrification

processes are coupled closely in space or time (Groffman et al., 2006).

Techniques can be based on laboratory incubation of sediment cores or in situ studies. Each

method has advantages and disadvantages and many studies have been conducted to compare results

among the various methods (e.g., Seitzinger, 1988; Seitzinger et al., 1993, 2002; Bernot et al., 2003;

Groffman et al., 2006; Smith et al., 2006). Kana et al. (1998) described the MIMS m ethod to measure

small changes in dissolved N 2 caused by denitrification in sediments. This technique allows measurement

of N 2 flux in unperturbed sediment cores with high temporal resolution (Kana et al., 1998). This is

especially useful during summer conditions when NO − 3 concentration in the water is typically low, but

the high temperature can support high rates of denitrification. Under such conditions, it is likely that a

coupled sequence of nitrification and denitrification accounts for substantial N loss from estuarine

sediments (Kemp et al., 1990). Constraints regarding field and analytical methods have seriously limited

understanding of the magnitude and controls on denitrification (Groffman et al., 2006).



## C.1.2.2. N Deposition Effects on Methane

Increased N loading to transitional ecosystems can affect both methane (CH 4 )-producing and CH 4 ­

oxidizing microbial activity. The difference between the CH 4 production and oxidation determines the

magnitude of CH 4 emission from soils. There is evidence to support that ammonium compounds reduce

CH 4 oxidation (Steudler et al., 1989; King and Schnell, 1994; Gulledge et al., 1997), but ammonium

compounds have also been observed to increase methanotropic bacterial activity (Bodelier et al., 2000). In

general CH 4 emissions from saturated soils have been observed to increase with N addition (Granberg

et al., 2001; Saarnio et al., 2003; Zhang et al., 2007). The prevailing hypothesis for explaining this effect

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30 is that increases in vegetative cover caused by N addition increase C availability through root exudates,

which in turn stimulates methanogenic bacteria and CH 4 emissions (Granberg et al., 2001; Saarnio et al.,

2003; Zhang et al., 2007).

Saarnio et al. (2003) observed moderate increases in CH 4 emissions from boreal wetland soils with

N fertilization rates of 30 kg N/ha/yr as ammonium nitrate (NH 4 NO 3 ). Comparable N application rates

and effects on CH 4 emissions were also observed by Granberg et al. (2001) in a similar ecosystem type.

Zhang et al. (2007) observed elevated CH 4 emissions from freshwater wetland soils with experimental N

additions of 240 kg N/ha/yr. They postulated that additional N increased abundance of Deyeucia

angustifolia which increased CH 4 emissions by supplying methanogenic bacteria with additional substrate

in the form of root exudates. Other studies have shown that N addition had little or no effect on CH 4

emissions across a variety of ecosystem types (Saarnio et al., 2000; Silvola et al., 2003; Ambus and

Robertson, 2006). Note that the N enrichment rates employed in all of the above reported studies related

to N effects on soil CH 4 emissions were greater (30 to 240 kg N/ha/yr) than atmospheric N inputs in most

areas of the United States that are heavily effected by elevated atmospheric N deposition. See ISA

section 3.3 for a discussion of methane flux from terrestrial, transition and aquatic ecosystems.



# C.1.3. N Cycling in Estuarine Ecosystems

C.1.3.1. Denitrification and Anammox in Estuarine Ecosystems

Denitrification is a major factor governing the loss of N from estuarine ecosystems. Denitrification

by microbes found in estuarine and marine sediments releases much of the added N inputs back into the

atmosphere (Vitousek et al., 1997a; Arrigo, 2005). Collection of quantitative data on this process has

been hampered, however, by the complexity of environmental controls on the denitrification process and

difficulties in measuring denitrification rates (Kana et al., 1998). Major environmental controls include

temperature and the availability of NO

− 3 , O 2 , and organic materials (Seitzinger, 1988; Rysgaard et al.,

1994).

Marine microbial ecology is highly complex and poorly understood. Relatively new knowledge

about anammox bacteria has completely altered scientific understanding of N cycling in the oceans.

Although it was previously believed that denitrification was responsible for virtually all of the transfer of

Nr in the ocean to the atmosphere as N 2 gas, it now appears that anaerobic ammonium oxidation

(anammox) may account for up to 50% of the N 2 production in the oceans (Devol, 2003; Ward, 2003;

Dalsgaard et al., 2005; Kuypers et al., 2005). This reaction uses NO

- 2 as the primary electron acceptor and

is catalyzed by planctomycete bacteria of the genera Brocadia, Kuenenia, and Scalindua. That NH +

4

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25 could be oxidized under anoxic conditions was theorized several decades ago based on calculations of the

ratios among N, P, and C in marine ecosystems. Nevertheless, the process was not experimentally

documented until the 1990s (van de Graaf et al., 1995). More recently, anammox has been detected in a

variety of freshwater, estuarine, and marine waters and sediments (Devol, 2003; Jetten et al., 2003; Ward,

2003; Rysgaard et al., 2004; Dalsgaard et al., 2005; Engstrom et al., 2005; Jetten et al., 2005; Kuypers

et al., 2005; Pilcher, 2005; Op Den Camp et al., 2006).



## C.1.3.2. N Budgets

The greatest uncertainty in the development of detailed N budgets for coastal ecosystems is

quantifying how much of the N deposited on the watershed is transferred through the terrestrial watershed

to the estuary. The difficulty stems from (1) multiple agricultural and mobile and stationary fuel

combustion emission sources in an estuary watershed, (2) quantifying dry deposition to the estuary

surface and to the watershed, (3) measuring gaseous losses of NH 3 and NO X compounds to the

atmosphere, and (4) complex N flow pathways through the watershed (NRC, 2000). Published estimates

of the contribution of atmospheric deposition to estuary N load exhibit wide variability. Such estimates

for a specific estuary may differ. Some examples are summarized in Table C-1. Despite the variability, it

appears that atmospheric sources of N loading to estuaries in the U.S. can be quantitatively important.

The major sources of N to estuaries and near-coastal marine waters in the U.S., in addition to atmospheric

deposition, include wastewater effluent derived mainly from food imports and consumption, fertilizer

application, livestock feed imports, and N-fixing crops (Boyer et al., 2002; Driscoll et al., 2003a).

Boyer et al. (2002) estimated that atmospheric deposition averaged 31% of total N inputs over the

combined area of the 16 northeastern river basins. Contributions from atmospheric deposition ranged

from 60% of N inputs for the basins in northern Maine to 15 to 20% for the Schuylkill and Potomac River

Basins, the latter of which had large agricultural N inputs. Across all basins, estimated riverine export of

N amounted to 25% of total N inputs, and ranged from 11% to 40%. This result is consistent with a

similar analysis by Howarth et al. (1996), who found that basins draining to the North Atlantic exported

approximately 25% of anthropogenic N inputs on average.

August 2008 C­6 DRAFT—DO NOT QUOTE OR CITE

### Table C­1. Estimated percent of total N load to Delaware

contributed by atmospheric deposition. Bay and Hudson River/Raritan Bay

Reference

Paerl (1985)

Hinga et al. (1991)

Scudlark and Church (1993)

Paerl (Paerl, 1995)

Jaworski et al. (Jaworski, 1997)

Alexander et al. (Alexander, 2001)

Castro et al. (2001)

Stacey et al. (Stacey, 2001)

Land­based

Sparrow model

Castro and Driscoll (2002)

Castro et al. (2003) Delaware Percent of N Load Contributed by Atmospheric Deposition

Bay

44

—

15

—

44

22

—

16

25

20

23 Hudson River/Raritan Bay

—

33

—

68

68

26

10

10

27

17

18



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20 Turner et al. (2001) found a strong correlation between population density (persons/ km 2 ) and the

total N loading from watershed to estuary (r 2

= 0.78) for coastal watersheds in the United States. This

finding is likely due to the prevalence of automobiles in heavily populated areas, along with their

associated N emissions and deposition, plus the myriad non-atmospheric sources of N from human

activities, particularly sewage releases. They also determined that direct atmospheric deposition becomes

increasingly more important as a contributor to the total N loading to an estuary as the water surface area

increases relative to total watershed area (terrestrial plus water surfaces). Turner et al. (2001) found that,

on average, direct atmospheric deposition of N accounted for more than an estimated 25% of the estuarine

N load when the estuary surface occupied 20% or more of the overall watershed area. Few of the

estuaries in the eastern U.S. comprise such a large percentage of their watershed (Castro et al., 2001).

The estimates of the effect of direct atmospheric deposition to estuary surfaces are hampered by

uncertainties in dry deposition rates. Many published studies have assumed that dry N deposition is equal

to measured wet deposition (e.g., Fisher and Oppenheimer, 1991; Hinga et al., 1991; Scudlark and

Church, 1993), and this is probably biased high (cf. Baker, 1991). However, other studies have assumed

dry N deposition rates for estuarine and near coastal areas are equal to 40% of wet (Jaworski et al., 1997)

or 67% of wet (Meyers et al., 2001). Of particular importance, the rate of dry deposition to open water

surfaces is much lower than the rate of dry deposition to vegetated terrestrial surfaces. Paerl et al. (2001)

estimated that dry deposition to open estuarine surfaces is three to five times lower than to vegetated

surfaces. This difference is seldom considered in N-budgeting studies, and can have a substantial effect

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28 on estimates of direct atmospheric loading to estuary surfaces, which is especially important for estuaries

having low watershed area to estuary surface area ratio.

A number of empirical approaches have been developed to quantify N fluxes to the coastal zone

which rely on estimates of N sources within the watershed and characteristics of the landscape.

Alexander et al. (2002) compared several of these empirical methods, the most accurate and least biased

of which was that of Howarth et al. (1996). A modified version of the Howarth et al. (1996) methodology

was published by Boyer et al. (2006). More mechanistic approaches include those of Bouwman et al.

(2005), Van Drecht et al. (2003), and Green et al. (2004).



# C.1.4. Timing of Chemical Change

C.1.4.1. Interannual Change: Nitrate Leaching

Interannual changes in N cycling can be reflected in changes in streamwater chemistry. NO − 3

leaching from terrestrial ecosystems throughout the 1980s was observed in many of the original lakes in

EPA’s Adirondack Long Term Monitoring (ALTM) program (cf. Driscoll and Van Dreason, 1993),

which was followed by a decline during the 1990s. As a consequence of this subsequent decline, Driscoll

(2003b) reported an overall significant (p < 0.1) decrease in NO − 3 concentration for the period 1982 to

2000 for 8 of the 16 original ALTM monitoring sites. Only the one mounded seepage lake in the study

(Little Echo Pond) had a small, but statistically significant, increase in NO − 3 concentration (0.01 μeq/L/yr,

p < 0.06). It is not clear why many Adirondack watershed soils leached NO − 3 to a lesser extent during the

1990s than they did during the 1980s (Driscoll et al., 2003b). Decreasing stream NO − 3 concentrations

during the 1990s was also observed in the Catskill Mountains (Stoddard et al., 2003) and in New

Hampshire (Goodale et al., 2003). There was not a substantial change in N emissions or deposition in the

Northeast region over that period. Climatic factors, insect defoliation, increases in atmospheric CO 2 , and

interactions with increasing availability of DOC have been proposed as possible contributing factors to

regional decreases in NO − 3 leaching (cf. Mitchell et al., 1996; Aber et al., 2002; Driscoll et al., 2003b;

Goodale et al., 2003, 2005), but the driver of this decadal scale pattern remains uncertain.



## C.1.4.2. Episodic Change

Nutrient enrichment effects of N deposition are controlled to a large degree by biological and

hydrological processes that operate on episodic (hours to days), seasonal, and interannual time scales.

Nitrogen uptake and transformation reactions and processes vary greatly with season and with climatic

factors. In particular, N export from terrestrial and transitional ecosystems to aquatic ecosystems is

governed by seasonal fluctuations in temperature and biological uptake, and episodic fluctuations in water

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29 movement associated with rainstorms and snowmelt. The role of N in driving biotic change in stream

ecosystems due to episodic pulses of NO − 3 associated with spring snowmelt are discussed in detail in ISA

section 3.2.



## C.1.4.3. Reversibility of Impacts

Although there are relatively few studies of the reversibility of the biogeochemical effects of

elevated N deposition, the few to date suggest the possibility of recovery. Experimental studies in Europe

suggest that some ecosystem processes and characteristics are likely to recover rapidly following a

reduction in N deposition. In a study in northern Sweden, high levels of fertilization (90 kg N/ha/yr) over

20 yrs induced substantial soil acidification, including loss of over half of the base cations in the mineral

soil, a decrease in pH, and an increase in soluble Al (Högberg et al., 2006). However, 10 yrs after this

treatment was stopped, the pH of the mineral soil had increased, and extractable NO − 3 was no higher than

in the control plot. Stem volume growth did not substantially increase relative to the acidification period.

“Clean roof” experiments that prevent N deposition inputs at sites receiving >40 kg N/ha/yr ambient

atmospheric N deposition in The Netherlands increased wood and root production soon after the roof was

installed (Boxman et al., 1998a), and NO − 3 exports below the rooting zone were reduced dramatically

within 2 yrs (Bredemeier et al., 1998).

A study of alpine lake sediment cores in Rocky Mountain National Park, CO (Wolfe et al., 2003)

suggested the possible reversibility of N enrichment effects on lake biota. Although increased dominance

of mesotrophic diatom species was correlated with increased N inputs during the 20th century, it did not

appear that any of the oligotrophic species had been totally lost from study lakes. Thus, reduced future N

loading may allow renewed dominance by oligotrophic diatom species.

It is not necessarily true, however, that nutrient enrichment effects of N deposition will, in all

cases, be easily reversible. For example, it has been suggested that vegetation conversion in the coastal

sage scrub community in California has altered hydrologic function to an extent that may be difficult to

reverse. The depth of rainwater percolation into soil has been reduced as a result of invasion of nonnative annual grasses. This hydrologic change inhibits the growth of deep-rooting native shrubs (Wood

et al., 2006).



# C.1.5. Tables Supporting Cross Ecosystem Evaluation of N

2 

# O, CH

4 

# and CO

2 

# Flux

Table C-2 summarizes key information from the experiments included in the meta- analysis

presented in ISA section 3.3.4.

August 2008 C­9 DRAFT—DO NOT QUOTE OR CITE

### Table C­2. The study site, experimental condition, ecosystem type, N form,

and citations is presented for all studies used in NEE, EC, CH

4 

### uptake, CH

4 

### emission meta analyses. amount of N addition

emission and N

2 

### O

Site experimental

condition ecosystem N form N addition

(kg ha/yr) Reference

Net Ecosystem Carbon Exchange

Swiss FACE (Lollium)

Swiss FACE (Trifolium)

Ottawa, Canada

Ottawa, Canada

Ottawa, Canada

Ottawa, Canada

Ottawa, Canada

Laqueuille, France

Oensingen, Switzerland

Toolik Lake, AK

Toolik Lake, AK

Orange county,CA

Orange county,CA

Switzerland

Finland

Abisko, Sweden

Ecosystem Carbon Content (EC)

WA

Placerville CA

Placerville CA

Clayey, AL

Sandy, MS

Harvard forest, MA

Harvard forest, MA

Harvard forest, MA

Harvard forest, MA

Harvard forest, MA

Harvard forest, MA

BBWM

MI (site A)

MI (site B)

MI (site C)

MI (site D)

FL (NEE)

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field

field field

field

field

field grassland

grassland

wetland

wetland

wetland

wetland

wetland

grassland

grassland

tundra

tundra

grassland

grassland

grassland

wetland

tundra

coniferous

coniferous

coniferous

coniferous

coniferous

deciduous

coniferous

deciduous

deciduous

coniferous

coniferous

deciduous

deciduous

deciduous

deciduous

deciduous

coniferous NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

.

slurry

NH 4 NO 3

NH 4 NO 3

.

.

.

NH 4 NO 3

NH 4 NO 3

.

NH 4

NH 4

DAP

DAP

NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

NH 4 NO 3

NH 4

NO3

NO3

NO3

NO3

. 16

32

64

175

200

100

100

100

100

40

.

100

.

100

200

45

45

150

150

50

50

50

50

25.2

30

30

30

30

. 320

320

32

64 Aeschlimann et al. 2005

Aeschlimann et al. 2005

Basiliko et al. 2006

Basiliko et al. 2006

Bubrie et al. 2007

Bubrie et al. 2007

Bubrie et al. 2007

Soussana et al. 2007

Soussana et al. 2007

Shaver et al. 1998

Shaver et al. 1998

Harpole et al 2007

Harpole et al 2007

Diemer 1997

Saarnio et al. 2003

Christensen et al. 1997

Canary et al. 2000

Johnson et al. 2006

Johnson et al. 2006

Leggett et al. 2006

Leggett et al. 2006

Magill et al. 2004

Magill et al. 2004

Magill et al. 2004

Magill et al. 2004

Magill et al. 2004

Magill et al. 2004

Parker et al. 2001; Elvir et al. 2006

Pregitzer et al. 2008; BURTON et al.

2000

Pregitzer et al. 2008; BURTON et al.

2000

Pregitzer et al. 2008; BURTON et al.

2000

Pregitzer et al. 2008; BURTON et al.

2000

Shan et al. 2001

August 2008 C­10 DRAFT—DO NOT QUOTE OR CITESite experimental

condition ecosystem N form N addition

(kg ha/yr) Reference

CH4 emission

Netherland incubation wetland NH 4

. Aerts and Caluwe 1999

Netherland incubation wetland NH 4

. Aerts and Caluwe 1999

Polish incubation wetland NH 4

. Aerts and Caluwe 1999

Netherland incubation wetland NH 4

. Aerts and Toet 1997

Swiss FACE field grassland NH 4 NO 3 84 Baggs and Blum 2004

Swiss FACE field grassland NH 4 NO 3 84 Baggs and Blum 2004

Minnesota incubation wetland NH 4 20 Keller et al 2005

Minnesota incubation wetland NH 4 60 Keller et al 2005

Niwot Ridge, CO field grassland urea 250 Neef et al. 1994

Finland incubation wetland NH 4 NO 3 30 Nykanen et al. 2002

Finland incubation wetland NH 4 NO 3 100 Nykanen et al. 2002

Salmisuo, Finland field wetland NH 4 NO 3 30 Sarrnio and Silvola 1999

Salmisuo, Finland field wetland NH 4 NO 3 30 Sarrnio and Silvola 1999

Sanjiang mire, China field wetland NH 4 NO 3 240 Zhang et al. 2007

CH4 uptake

Quebec, Canada incubation coniferous NH 4

. Adamsen and King 1993

Quebec, Canada incubation coniferous NO3

. Adamsen and King 1993

Michigan field coniferous NH 4 NO 3 10 Ambus and Robertson 2006

Michigan field coniferous NH 4 NO 3 30 Ambus and Robertson 2006

Michigan field deciduous NH 4 NO 3 10 Ambus and Robertson 2006

Michigan field deciduous NH 4 NO 3 30 Ambus and Robertson 2006

Michigan field grassland NH 4 NO 3 10 Ambus and Robertson 2006

Solling, Germany field coniferous NH 4 NO 3 18.5 Borken et al. 2002

Solling, Germany field coniferous NH 4 NO 3 24.2 Borken et al. 2002

Solling, Germany field coniferous NH 4 NO 3 18.5 Borken et al. 2002

Solling, Germany field coniferous NH 4 NO 3 24.2 Borken et al. 2002

Bousson, PA field deciduous NH 4 NO 3 100 Bowen et al. 2000

Perridge Forest, UK incubation deciduous NO3

. Bradford et al. 2001

Perridge Forest, UK incubation deciduous NO3

. Bradford et al. 2001

MT Ascutney, VT field coniferous NH 4 31.4 Castro et al. 1992

Bousson, PA field deciduous NH 4 NO 3 100 Chan et al. 2005

Abisko, Sweden field tundra NH 4 NO 3 100 Christensen et al. 1997

Finland field wetland NH 4 100 Crill et al. (Crill, 1994)

Finland field wetland NO3 100 Crill et al. (Crill, 1994)

Finland field wetland urea 100 Crill et al. (Crill, 1994)

Villingen, Germany field coniferous NH 4 150 Gulledge and Schimel 2000

LTER Alaska field deciduous NH 4 NO 3 66.7 Gulledge and Schimel 2000

LTER Alaska field coniferous NH 4 NO 3 12.3 Gulledge and Schimel 2000

LTER Alaska field deciduous NH 4 NO 3 171.4 Gulledge and Schimel 2000

LTER Alaska field coniferous NH 4 NO 3 142.9 Gulledge and Schimel 2000

Swiss FACE field grassland NH 4 NO 3 560 Ineson et al. 1998

Swiss FACE field grassland NH 4 NO 3 560 Ineson et al. 1998

August 2008 C­11 DRAFT—DO NOT QUOTE OR CITESite experimental

condition ecosystem N form N addition

(kg ha/yr) Reference

Scotland incubation heathland NH 4 40 Macdonald et al. 1997

Scotland incubation heathland NH 4 NO 3 40 Macdonald et al. 1997

Scotland incubation heathland NO3 40 Macdonald et al. 1997

Finland field coniferous NH 4 NO 3 200 Maljane et al. 2006

Swale, CO field grassland urea 450 Mosier et al 1991

Midslope, CO field grassland urea 450 Mosier et al 1991

Passture, CO field grassland urea 450 Mosier et al 1991

Niwot Ridge, CO field grassland urea 250 Neef et al. 1994

Villingen, Germany field coniferous NH 4 150 Papen et. al 2001

Finland field wetland NH 4 NO 3 30 Saarnio et al. 2003

Gjovelandsnesset, Norway incubation coniferous NH 4 NO 3 30 Sitaula et al. 1995

Gjovelandsnesset, Norway incubation coniferous NH 4 NO 3 90 Sitaula et al. 1995

Harvard forest, MA field coniferous NH 4 NO 3 37 Steudler et al. 1989

Harvard forest, MA field deciduous NH 4 NO 3 37 Steudler et al. 1989

Harvard forest, MA field coniferous NH 4 NO 3 120 Steudler et al. 1989

Harvard forest, MA field deciduous NH 4 NO 3 120 Steudler et al. 1989

Belgium incubation wetland NH 4

. VanderNat et al. 1997

Belgium incubation wetland NH 4

. VanderNat et al. 1997

Costa Rica field tropical forest

. 65 Weitz et al (Weitz, 1999)

Costa Rica field tropical forest

. 65 Weitz et al (Weitz, 1999)

N 2 O emission

Michigan biological Station incubation deciduous NH 4 NO 3

. Ambus and Robertson 1999

Michigan biological Station incubation deciduous NH 4 NO 3

. Ambus and Robertson 1999

Michigan biological Station incubation deciduous NH 4 NO 3

. Ambus and Robertson 1999

Michigan biological Station incubation deciduous NH 4 NO 3

. Ambus and Robertson 1999

Michigan field coniferous NH 4 NO 3 10 Ambus and Robertson 2006

Michigan field deciduous NH 4 NO 3 10 Ambus and Robertson 2006

Michigan field grassland NH 4 NO 3 10 Ambus and Robertson 2006

Michigan field coniferous NH 4 NO 3 30 Ambus and Robertson 2006

Michigan field deciduous NH 4 NO 3 30 Ambus and Robertson 2006

Hyytiälä incubation coniferous NH 4

. Ambus et al. 2006

Speulderbos incubation coniferous NH 4

. Ambus et al. 2006

San Rossore incubation coniferous NH 4

. Ambus et al. 2006

Glencorse incubation coniferous NH 4

. Ambus et al. 2006

Nyirjes incubation coniferous NH 4

. Ambus et al. 2006

Achenkirch incubation coniferous NH 4

. Ambus et al. 2006

Höglwald incubation coniferous NH 4

. Ambus et al. 2006

Sorø incubation deciduous NH 4

. Ambus et al. 2006

Bosco negri incubation deciduous NH 4

. Ambus et al. 2006

Schottenwald incubation deciduous NH 4

. Ambus et al. 2006

Hyytiälä incubation coniferous NO3

. Ambus et al. 2006

Speulderbos incubation coniferous NO3

. Ambus et al. 2006

San Rossore incubation coniferous NO3

. Ambus et al. 2006

August 2008 C­12 DRAFT—DO NOT QUOTE OR CITESite experimental

condition ecosystem N form N addition

(kg ha/yr) Reference

Glencorse incubation coniferous NO3

. Ambus et al. 2006

Nyirjes incubation coniferous NO3

. Ambus et al. 2006

Achenkirch incubation coniferous NO3

. Ambus et al. 2006

Höglwald incubation coniferous NO3

. Ambus et al. 2006

Sorø incubation deciduous NO3

. Ambus et al. 2006

Bosco negri incubation deciduous NO3

. Ambus et al. 2006

Schottenwald incubation deciduous NO3

. Ambus et al. 2006

Swiss FACE field grassland NH 4 NO 3 84 Baggs and Blum 2004

Swiss FACE field grassland NH 4 NO 3 84 Baggs and Blum 2004

Swiss FACE field grassland NH 4 NO 3 420 Baggs et al 2003

Swiss FACE field grassland NH 4 NO 3 420 Baggs et al 2003

Swiss FACE field grassland NH 4 NO 3 420 Baggs et al 2003

Swiss FACE field grassland NH 4 NO 3 420 Baggs et al 2003

Swiss FACE field grassland NH 4 NO 3 420 Baggs et al 2003

Swiss FACE field grassland NH 4 NO 3 420 Baggs et al 2003

Solling, Germany field coniferous NH 4 NO 3 18.5 Borken et al. 2002

Solling, Germany field coniferous NH 4 NO 3 24.2 Borken et al. 2002

Solling, Germany field coniferous NH 4 NO 3 18.5 Borken et al. 2002

Solling, Germany field coniferous NH 4 NO 3 24.2 Borken et al. 2002

Harvard forest field deciduous NH 4 NO 3 37 bowden et al. 1991

Harvard forest field coniferous NH 4 NO 3 37 bowden et al. 1991

Harvard forest field deciduous NH 4 NO 3 120 bowden et al. 1991

Harvard forest field coniferous NH 4 NO 3 120 bowden et al. 1991

Solling, Germany field deciduous NH 4 140 Brumme and Beese 1992

Gullane incubation deciduous NH 4

. Castaldi and Smith 1998

Gullane incubation deciduous NO3

. Castaldi and Smith 1998

Ascutney

, VI field coniferous NH 4 31.4 Castro et al. 1993

Allt, UK field heathland NH 4 NO 3 20 Curitis et al 2006

Allt, UK field heathland NH 4 NO 3 20 Curitis et al 2006

Afon Gwy, UK field grassland NH 4 NO 3 20 Curitis et al 2006

Afon Gwy, UK field grassland NH 4 NO 3 20 Curitis et al 2006

Afon Gwy, UK field grassland NH 4 NO 3 20 Curitis et al 2006

River Etherow, UK field heathland NH 4 NO 3 20 Curitis et al 2006

River Etherow, UK field heathland NH 4 NO 3 20 Curitis et al 2006

St. James Parish, LO field wetland NO3 100 Delaune et al. 1998

St. James Parish, LO field wetland NH 4 100 Delaune et al. 1998

Saratoga field grassland NH 4 NO 3 168 Delgado et al. 1996

Puerto Rico field tropical forest urea 300 Erickson et al. 2001

Jasper Ridge

, CA incubation grassland urea 200 Hungate et al. 1997

Jasper Ridge

, CA incubation grassland urea 200 Hungate et al. 1997

Swiss FACE field grassland NH 4 NO 3 560 Ineson et al. 1998

Swiss FACE field grassland NH 4 NO 3 560 Ineson et al. 1998

Manaus, Brazil field tropical forest NO 3

. Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NO 3

. Keller et al. (Keller, 1988)

August 2008 C­13 DRAFT—DO NOT QUOTE OR CITESite experimental

condition ecosystem N form N addition

(kg ha/yr) Reference

Manaus, Brazil field tropical forest NO 3

. Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NO 3

. Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NH 4

. Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NH 4

. Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NH 4

. Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NH 4

. Keller et al. (Keller, 1988)

gardsjon watershed field coniferous NH 4 NO 3 35 Klemedtsson et. al 1997

gardsjon watershed field coniferous NH 4 NO 3 35 Klemedtsson et. al 1997

Avoyelles, LO field wetland NH 4 100 Lindau et al. 1994

Avoyelles, LO field wetland NH 4 300 Lindau et al. 1994

Avoyelles, LO field wetland NO 3 100 Lindau et al. 1994

Avoyelles, LO field wetland NO 3 300 Lindau et al. 1994

Harvard forest field coniferous NH 4 NO 3 113 Magill et al. 1997

Finland field coniferous

. 200 Maljane et al. 2006

Swale, CO field grassland urea 450 Mosier et al 1991

Midslope, CO field grassland urea 450 Mosier et al 1991

Passture, CO field grassland urea 450 Mosier et al 1991

Puerto Rico field wetland NH 4 15.4 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NH 4 130.2 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NH 4 266 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NO3 15.4 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NO3 130.2 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NO3 266 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NH 4 15.4 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NH 4 266 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NO3 15.4 Munoz­Hincapie et al. 2002

Niwot Ridge, CO field grassland urea 250 Neef et al. 1994

Niwot Ridge, CO field grassland urea 250 Neef et al. 1994

Villingen field coniferous NH 4 150 Papen et. al (2001)

Duke FACE incubation coniferous NO3

. Phillips et al. 2001

Duke FACE incubation coniferous NO3

. Phillips et al. 2001

Finland field wetland NO3 100 Regina et al. 1998

Finland field wetland NH 4 100 Regina et al. 1998

Finland field wetland urea 100 Regina et al. 1998

Mojave incubation desert NO3

. Schaeffer et al. 2003

Gjovelandsnesset, Sweden field coniferous NH 4 NO 3 30 Sitaula et al. 1995

Gjovelandsnesset, Sweden field coniferous NH 4 NO 3 90 Sitaula et al. 1995

Gjovelandsnesset, Sweden field coniferous NH 4 NO 3 30 Sitaula et al. 1995

Gjovelandsnesset, Sweden field coniferous NH 4 NO 3 90 Sitaula et al. 1995

Gjovelandsnesset, Sweden field coniferous NH 4 NO 3 30 Sitaula et al. 1995

Gjovelandsnesset, Sweden field coniferous NH 4 NO 3 90 Sitaula et al. 1995

Scotland field coniferous NH 4 NO 3 48 Skiba et al. 1998

Deepsyke forest field coniferous NH 4 NO 3 48 Skiba et al. 1999

Deepsyke forest field coniferous NH 4 NO 3 96 Skiba et al. 1999

August 2008 C­14 DRAFT—DO NOT QUOTE OR CITESite

Costa Rica experimental

condition ecosystem N form N addition

(kg ha/yr) Reference

field tropical forest

. 65 Weitz et al (Weitz, 1999)

Costa Rica field tropical forest

. 65 Weitz et al (Weitz, 1999)

Sanhuabg Mire, China field wetland NH 4 NO 3 240 Zhang et al. 2007



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# C.2. Terrestrial Ecosystems

The following sections are organized by ecosystem type and combine information that is

supplemental to section 3.3 of the ISA.



# C.2.1. General C cyling

C cycling is a complex process that includes C capture from the atmosphere by autotrophic biota,

the primary producers of the ecosystem, and respiration (autotrophic + heterotrophic). In general,

atmospheric nutrient (e.g., N) deposition on an ecosystem that is deficient in that nutrient will often cause

an increase in growth, at least initially, especially of the primary producers. If that same nutrient is

deposited on an ecosystem that has an adequate supply of that nutrient, there may be no appreciable

nutrient enrichment effect, at least up to a point. Nutrient input that is greatly in excess of biological

demand will often cause toxicity, reduced growth, or problems other than those associated with nutrient

enrichment (i.e., N-saturation, acidification, base cation depletion) (Figure C-1).



# C.2.2. Forest Growth Interactions with Herbivores

Light availability, nutrient balance, and C:N:P s toichiometry are closely relate, and affect the

composition of autotrophic species that will occupy a particular habitat. The resulting stoichiometric

balance of C:N:P i n the autotrophic community can have additional feedbacks on nutrient cycling by

herbivores, detritivores, and decomposers (Sterner and Elser, 2002). Such effects also extend to

herbivores, and likely other members of the food web. Forkner and Hunter (2000) altered plant growth of

oak ( Quercus prinus and Q. rubra ) saplings through fertilizer (N, P, K) addition and then censused the

densities of insect herbivore guilds and predaceous anthropods on experimental and control trees. In

general, leaf chewers, phloem feeders, and leaf miners were more common on fertilized, as compared

with non-fertilized, trees. Predaceous arthropods were also more abundant on fertilized trees and their

densities were correlated with herbivore densities.

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17 Source: EPA (1993).



### Figure C­1. Schematic representation of the response of vegetation to nutrient addition.



# C.2.3. Forest Growth Interactions with Herbivores

Light availability, nutrient balance, and C:N:P s toichiometry are closely relate, and affect the

composition of autotrophic species that will occupy a particular habitat. The resulting stoichiometric

balance of C:N:P i n the autotrophic community can have additional feedbacks on nutrient cycling by

herbivores, detritivores, and decomposers (Sterner and Elser, 2002). Such effects also extend to

herbivores, and likely other members of the food web. Forkner and Hunter (2000) altered plant growth of

oak ( Quercus prinus and Q. rubra ) saplings through fertilizer (N, P, K) addition and then censused the

densities of insect herbivore guilds and predaceous anthropods on experimental and control trees. In

general, leaf chewers, phloem feeders, and leaf miners were more common on fertilized, as compared

with non-fertilized, trees. Predaceous arthropods were also more abundant on fertilized trees and their

densities were correlated with herbivore densities.



# C.2.4. Southern California Coniferous Forest

Wet N deposition is generally low throughout the region, in the range of 1 to 3 kg N/ha/yr.

However, dry deposition is highly variable, but ranges up to about 30 kg N/ha/yr or more (Bytnerowicz

and Fenn, 1996; Fenn and Bytnerowicz, 1997; Takemoto et al., 2001). Available data (e.g., Minnich

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30 et al., 1995) suggest progression toward less needle retention, higher shoot:root biomass ratios, increasing

depth of litter, and high NO − 3 in soil solution in response to high N deposition. These changes may

eventually lead to replacement of pine species with nitrophilous and O 3 -tolerant species such as fir and

cedar (Takemoto et al., 2001).

Streamwater NO − 3 concentrations in montane watersheds that are downwind of the greater Los

Angeles area are the highest in North America. Some streams in the San Gabriel and the San Bernardino

Mountains have been documented to have levels of NO − 3 in stream water with peaks as high as 370 μeq / L

(Fenn and Poth, 1999), reflecting very high N deposition and N-saturation of the terrestrial ecosystem. In

contrast, N leaching is low in most watersheds in the Sierra Nevada, and NO − 3 concentrations in streams

are usually below 1 μeq/L. Nevertheless, some of the higher elevation watersheds in the Sierra Nevada

export appreciable NO − 3 from the terrestrial environment, particularly during the early phases of

snowmelt. Fenn et al. (2002) reported springtime peaks of NO

− 3 concentration in lakewater up to 38

μeq/L at high elevation and for watersheds dominated by talus. At lower elevation areas, however, most

of the inorganic N deposition loading is retained within the watersheds and concentrations of NO − 3 in

stream and lake waters are low (Fenn et al., 2003a). Surface water NO − 3 concentrations in these areas

provide an index reflecting the general levels of N deposition. For example, where surface water NO − 3

concentrations are high, N deposition to the terrestrial watershed is also high.



# C.2.5. Boreal forests

The boreal forest represents the largest terrestrial biome on Earth, and as such can have a large

influence on global cycling of N and other nutrients. Plant growth in the boreal forest is limited mainly

by N availability, in part because of slow mineralization of organic materials in the harsh climate

(Vitousek and Howarth, 1991). Conceptual models of N cycling in the boreal forest have typically

assumed that mineralization of organic N is required for plant uptake of N (Näsholm et al., 1998).

However, it has been demonstrated in laboratory studies (Chapin et al., 1993) and field studies (Näsholm

et al., 1998) that some boreal plants are capable of directly taking up amino acids from the soil, and

therefore bypassing the need for prior mineralization. The interactions among soil abiotic processes,

mycorrhizal associations, microbes, and plants are complex and poorly understood. Nevertheless, these

interactions are important to global N cycling and to boreal plant species composition because organic N

concentrations are typically high in the soil of boreal forests. It appears that atmospheric N deposition

and climate warming have the potential to alter boreal forest plant communities by shifting nutritional

processes from organic to inorganic N uptake (Näsholm et al., 1998).

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# C.2.6. Alpine

The Western U.S. contains extensive land areas that receive low levels of atmospheric N

deposition, interspersed with hot spots of relatively higher N deposition downwind of large metropolitan

centers and agricultural areas (Fenn, 2003). Alpine plant communities occur in some of the areas that

receive moderately elevated atmospheric N deposition such as those located in the Sierra Nevada in

southern California, the Front Range in Colorado, and the Cascade Mountains in Washington (Figure C2).

Alpine plant species are typically adapted to low nutrient availability and their soil-forming

processes are poorly developed, therefore they are often sensitive to effects from N enrichment (Bowman

et al. 2006) including changes in species composition (Bowman et al., 1995; Seastedt and Vaccaro, 2001).

Other reasons alpine tundra are sensitive to N enrichment include factors such as low rates of primary

production, short growing season, low temperature, and a wide variation in moisture availability

(Bowman and Fisk, 2001).

N cycling in alpine environments is strongly tied to variations in moisture regime (Bowman et al.,

1993; Bowman, 1994; Fisk et al., 1998). Blowing snow is transported across alpine landscapes by wind

and tends to accumulate in certain depression areas. These areas receive much higher levels of moisture

and winter season N deposition than other more wind-swept portions of the alpine environment

(Bowman, 1992). Fenn et al. (2003a) suggested that as much as 10 kg N/ha/yr may leach through the

snow during the initial phases of snowmelt in some of the alpine areas in Colorado that accumulate

substantial snowpack. It is these moist meadow areas that may be most affected by N deposition and are

also the areas most likely to show changes in plant species composition and impacts on N cycling

(Bowman and Steltzer, 1998).

N deposition to the alpine tundra of Niwot Ridge in the Colorado Front Range altered N cycling

and provided the potential for replacement of some native plant species by more competitive, fastergrowing native species (Bowman and Steltzer, 1998; Baron et al., 2000; Bowman, 2000). Many plants

that grow in alpine tundra, as is true of plants growing in other low resource environments (e.g., infertile

soil, desert), tend to have some similar characteristics, including slow growth rate, low photosynthetic

rate, low capacity for nutrient uptake, and low soil microbial activity (Bowman and Steltzer, 1998;

Bowman, 2000). Such plants generally continue to grow slowly when provided with an optimal supply

and balance of resources (Pearcy et al., 1987; Chapin, 1991).

August 2008 C­18 DRAFT—DO NOT QUOTE OR CITESource: Vegetative distribution data were taken from the national map LANDFIRE (September 2006) ( http://gisdata.usgs.net/website/landfire/ ).



### Figure C­2. Distribution of alpine vegetation in three western regions that are in close proximity to

urban and agricultural sources of atmospheric N emissions: a) the Denver­Fort Collins region of

Colorado, b) the Seattle­Tacoma region of Washington, and c) the Fresno­Los Angeles area of

California (the blue line on the map is the California/Nevada border). Alpine vegetation in these

areas is sensitive to nutrient enrichment effects from atomospheric N deposition.

1

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4 In addition, plants adapted to cold, moist environments grow more leaves than roots as the relative

availability of N increases. These patterns of vegetative development and their response to added N

affect plant capacity to respond to variation in available resources and to environmental stresses such as

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32 frost, high winds, and drought. Vegetation in the southern Rocky Mountains responds to increased N

supply by increasing plant productivity for some species, but this increase in productivity is also

accompanied by changes in species composition and abundance (Bowman et al., 1993). Many of the

dominant plant species do not respond to additional N supply with increased production. Rather, many

subdominant species, primarily grasses and some forbs, increase in abundance when the N supply is

increased (Fenn et al., 2003a).

In alpine ecosystems, changes in plant species composition due to N deposition can result in

increased leaching of NO − 3 from the soils because the plant species favored by higher N supply are often

associated with greater rates of N mineralization and nitrification than the pre-existing species (Bowman

et al., 1993, 2006; Steltzer and Bowman, 1998; Suding et al., 2006).

Total organic N pools in the soils of dry alpine meadows are large compared to pools of NH +

4 and

NO − 3 (Fisk and Schmidt, 1996). However, positive response to inorganic N fertilization has been

demonstrated, and thus some plant species appear to be restricted in their ability to take up organic N

from the soil and are growth-limited by the availability of inorganic N (Bowman et al., 1993, 1995;

Theodose and Bowman, 1997). Miller and Bowman (2002) analyzed patterns of foliar 15 N, NO − 3

reductase activity, and mycorrhizal infection compared with N uptake quantified by stable isotope tracer

additions in the greenhouse. 13 C enrichment subsequent to 13 C, 15 N-glycine addition indicated that all of

the 11 genera studied were able to take-up labeled glycine to some extent. Glycine uptake ranged from

about 35% to more than 100 % of NH +

4 uptake. Only Festuca ( fescue grass ) showed glycine uptake

exceeding both NH + − 4 and NO 3 uptake (Miller and Bowman, 2002).



# C.2.7. Arctic Tundra

Soluble N in tundra soil solution is dominated by organic N, including free amino acids, rather than

NH +

4 or NO − 3 (Kielland, 1995). Tundra plants appear to exhibit a range of interspecific differences that

allow coexistence under conditions that reflect a single limiting element. Species differ in rooting depth,

phenology, and uptake preferences for organic and inorganic forms of N (Shaver and Billings, 1975;

Chapin et al., 1993; Kielland, 1994; McKane et al., 2002). McKane et al. (2002) demonstrated, based on

15 N field experiments, that arctic tundra plant species were differentiated in timing, depth, and chemical

form of N utilization. Furthermore, the species that exhibited greatest productivity were those that

efficiently used the most abundant N forms.

Ericoid mycorrhizae provide host plants with the capacity to take up N in the form of amino acids

(Stribley and Read, 1980; Bajwa and Read, 1985). This is important in arctic plant communities that

occur on acidic organic soils because amino acids are typically readily available in such soils, and N

availability generally limits primary productivity.

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29 Future climate warming could have important effects on N cycling in arctic tundra ecosystems. In

the past, organic materials have accumulated in tundra soils, largely because decomposition has been

slower than plant growth. Climate warming may increase the decomposition of soil organic matter,

thereby increasing the availability of stored N (Weintraub and Schimel, 2005). The distributions of

woody plant species are also increasing in response to warming, with likely feedbacks on C and N

cycling. For example, the dominant shrub species in the arctic tundra in Alaska, Betula nana, is

expanding its distribution in tussock vegetation communities (Weintraub and Schimel, 2005).

Poor soil aeration is caused by permafrost, resulting in poor water drainage and the development of

anaerobic conditions. Vegetation composition and primary productivity vary in response to differences in

soil moisture and aeration (Everett and Brown, 1982; Gebauer et al., 1995). Reduced soil O 2 can limit

nutrient availability. For example, under anaerobic conditions, N mineralization and nitrification rates

decrease while denitrification increases (Ponnamperuma, 1972; Gebauer et al., 1995).



# C.2.8. Arid Land

From 1989 to 2004 in the Chihuahuan desert, Baez et al. (2007) observed a 43% increase in

ambient N deposition, from 1.71 to 2.45 kg N/ha/yr, resulting in an additional 5.88 kg N/ha/yr deposition

over that time period. They suggest that these deposition trends may result in significant plant community

changes, as indicated by fertilization studies of blue gramma ( Bouteloua gracilis ) and black gramma ( B.

eriopoda ). In a field addition with additions of 20 kg N/ha/yr in one season, blue gramma was favored

over black gramma, the current dominant species (Baez et al., 2007).



# C.2.9. Lichens

There are several potential uses of lichens for air pollution and deposition monitoring. These

include measurement of tissue lichen concentrations of specific pollutants (i.e., lichens as passive

monitors), determination of changes in species composition or the presence/absence of sensitive species,

and identification of areas having relatively high levels of air pollution, where monitoring instrumentation

could be installed to more quantitatively measure pollution levels. Assessment of long-term change in the

epiphytic lichen community can be especially valuable to provide an early indication of either improving

or deteriorating air quality and atmospheric deposition. Such monitoring was incorporated in 1994 into

the USFS F orest Inventory and Analysis (FIA) Program (See Annex A).

Lichen communities in the Pacific Northwest show signs of air pollution damage under current air

pollution levels. Symptons include decreases in the occurrences of sensitive taxa and replacement by

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Table C­3. Principal Air Quality Indicator Lichen Species in Oregon and Washington

Group Sub­Group Indicator Species

Clean Air Regional distribution Bryoria capillaris, Lobaria oregana, Sphaerophorus globosus, Usnea

filipendula, Usnea scabrata

Sub­regional distribution Ahtiana pallidula, Alectoria s armentosa, Bryoria f uscescens,

Hypogymnia enteromorpha, Nephroma bellum, Nodobryoria

oregana

Polluted Air Regional nitrophytes Candelaria concolor, Physcia a dscendens, Xanthoria polycarpa

* Includes only species with highest indicator value, used by Geiser a nd Neitlich (2007) to define air quality zones.

Source: Geiser a nd Neitlich (2007). pollution-tolerant and nitrophilous taxa (Fenn et al., 2003a; Geiser and Neitlich, 2007). Indicators of

clean sites and polluted sites (Table C-3) were used by Geiser and Neitlich (2007) to create six lichen

zones of air quality within the region, from worst (all sensitive species absent) to best (all sensitive

species present). Air pollution was associated with effects on community composition of lichens, rather

than species richness. The most widely observed effects included paucity of sensitive, endemic species,

and enhancement of nitrophilous and non-native species (Geiser and Neitlich, 2007). The strongest

relationship was with wet NH +

4 deposition, consistent with findings in California (Jovan and McCune,

2005) and Europe (van Dobben et al., 2001). The zone of worst air quality was associated with absence

of sensitive lichens, enhancement of nitrophyllous lichens, mean wet NH +

4 deposition > 0.06 mg N/L,

lichen tissue N and S c oncentrations > 0.6% and 0.07 %, and SO 2 levels harmful to sensitive lichens.

Jovan and McCune (2005) constructed a model based on non-metric multidimensional scaling

ordination to analyze lichen species distribution from 98 FIA plots in the greater Central Valley of

California. The model used epiphytic macrolichen community data to reflect air quality and climate in

forested areas. Some species respond negatively to NO X and SOx deposition (McCune, 1988; Gauslaa,

1995; van Haluwyn and van Herk, 2002). Other species respond positively to NH Y deposition (de

Bakker, 1989; van Dobben and de Bakker, 1996; van Herk, 1999, 2001; Jovan and McCune, 2005).

Similarly, Jovan and McCune (2006) developed a model of NH 3 exposure to epiphytic

macrolichens in the Sierra Nevada region. They found that lichens provide a relatively inexpensive tool

for estimating fine-scale distributions of NH 3 exposure to terrestrial ecosystems. Because NH 3 has a high

deposition velocity (Asman and van Jaarsveld, 1992), dry deposition of reduced N exhibits high spatial

variability. Monitoring of species composition of epiphytic lichen communities can therefore help

quantify spatially variable eutrophication risk to forest health in the Sierra Nevada region (Jovan and

McCune, 2006).

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Table C­4. Contribution of fens to support of plant species diversity in selected states

Number of Percent of State Number of Non­Vascular Percent of State Percent of Vascular Species Vascular Flora Uncommon & Rare Species Found Uncommon & Rare State Area Found in Fens (# Found in Fens (% Species Found in in Fens Species Found in Fens in Fens Native) Native) Fens Source: Moore et al. (1989).



### Figure C­3. Number of nationally rare species versus standing crop in each of 401 quadrants from

wetlands in Ontario, Quebec, and Nova Scotia. 

# C.3. Transitional Ecosystems

The sensitivity of wetlands is particularly important given that they contain a disproportionately

high number of rare plant species (Figure C-3) (Moore et al., 1989). EPA reported that, of the 130 plant

species from the conterminous U.S. that were listed as threatened or endangered in 1987, 14% occurred

principally in wetlands (U.S. Environmental Protection Agency, 1993). Bedford and Godwin (2003)

indicated that a disproportionately high number of rare plant species occur in fens relative to their percent

land cover (Table C-4) (Bedford and Godwin, 2003). For example, fens comprise only 0.01% of

northeastern Iowa but contain 12% of the region’s rare plant species and 17% of the listed endangered,

threatened, and species of concern (Table C-4).

August 2008 C­23 DRAFT—DO NOT QUOTE OR CITENumber of

Vascular Species

Found in Fens (#

Native) Percent of State

Vascular Flora

Found in Fens (%

Native) Non­Vascular

Species Found

in Fens Number of

Uncommon & Rare

Species Found in

Fens Percent of State

Uncommon & Rare

Species Found in Fens Percent of

State Area

in Fens

Colorado

Idaho

Iowa

Montana

New

Hampshire

Calcareous

fens

Open fens

New Jersey

Calcareous

fens

Acidic

seeps

New York

North

Carolina ~500

327

320

(307)

174

340

245

169

440

(397) ~14

18

(17.2)

17.2

13.8

(19.0) 20

60

91

13

77 20

35

134

40

52

96

60

36

55

77 3.3

12.0

12.0

13.7

13.5

7.0

11.0 0.08–0.15

0.01

0.0015

0.078

0.0058

0.0726

0.00733

0.0073

0.00003

0.07

0.0023

Source: Bedford and Godwin (2003).



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# C.4. Aquatic Ecosystems

Aquatic systems can be subdivided into major types based on hydrology. At the broadest level,

freshwater aquatic ecosystems can be classified as riverine, lacustrine, and palustrine systems. Riverine

systems can be identified at varying scales, including valley segment, river reach, and channel unit.

Lacustrine systems include deepwater habitats associated with lakes and reservoirs. Palustrine systems

include small, shallow, or intermittent water bodies, including ponds. Each type of aquatic ecosystem is

potentially sensitive to nutrient enrichment effects from N deposition. Nevertheless, available data

documenting such effects are limited.

The dose-response data for aquatic organisms such as those cited here are generally expressed in

concentration units, as mg/L or μmol/L of N, for example. Such exposure concentration data cannot be

directly related to ecosystem exposure, which is generally expressed in such units as kg/ha. This is

because a given N deposition exposure can result in widely varying concentrations of N compounds

(especially NO

− 3 ) in water. For convenience, a concentration of 1 mg/L of N (as, for example, in the case

of NO

− 3 -N or NH +

4 -N) is equal to 71.4 μmol/L or 71.4 μeq/L of NO − 3 or NH +

4

.

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# C.4.1. History of Evaluating N Enrichment in Freshwater Aquatic

Ecosystems

The role of N deposition in freshwater eutrophication and acidification processes has been

considered secondary to P and S, and only within the past 20 years have there been studies questioning

the established science and showing N-limitation in some fresh waters, N excess in some terrestrial

systems, and N-caused acidification in poorly buffered fresh waters. A number of things have conspired

to prevent extensive evaluation of the effects of atmospheric N deposition on aquatic organisms via

nutrient-enrichment pathways. These include assumptions, or prevailing paradigms, that have channeled

scientific thought in one direction and away from others.

First were the assumptions for many years that atmospheric deposition was caused primarily by

sulfur (S) emissions and that effects on aquatic ecosystems were primarily caused by acidification

processes. Only after S e missions began to decrease substantially in response to the Clean Air Act

amendments did the role of NO X , and still later, NH 3 , emissions become recognized as potential agents of

environmental change. And even then, that role was assumed to be restricted mainly to acidification from

NO

− 3 , a strong acid anion, not eutrophication (Reuss and Johnson, 1985). Second, because N is the

nutrient most limiting to primary production in most ecosystems, it was assumed until fairly recently that

N was tightly cycled in terrestrial systems, and that excess NO − 3 leaching rarely occurred in natural

environments (Vitousek and Howarth, 1991). Finally, the attention of aquatic biologists has been

strongly focused on the role of P i n eutrophication of freshwaters for the past 40 years, largely due to the

demonstrated role of P i n causing large increases in algal productivity worldwide (Schindler et al., 1971;

Schindler, 1974). P is an essential, and often limiting, nutrient to aquatic organisms. A large number of

highly influential studies in the 1960s and 1970s exposed the role of wastewater, in particular phosphate

detergents, in causing excessive algal production and anoxia in Lake Mendota (Wisconsin), Lake

Washington (Washington), Lake Erie, and many other locations (Hasler, 1947; Vollenweider, 1968;

Edmondson, 1969, 1991). Because of the emphasis on P a s a major cause of fresh water eutrophication,

Downing and McCauley (1992) wrote as recently as 1992: “opinions differ on the role of N as a limiting

nutrient in lakes.”



# C.4.2. Interactions between N and P loading

Results from surveys, paleolimnological reconstructions of past conditions, experimental results,

and meta-analyses of hundreds of studies all consistently show N-limitation to be common in fresh

waters, especially in remote areas, and there is a nearly universal eutrophication response to N-enrichment

in lakes and streams that are N-limited. Surveys of lake N concentrations and trophic status along

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35 gradients of N deposition show increased inorganic N and increased productivity to be strongly related to

atmospheric N deposition. Where N-enrichment has occurred, P limitation, N+P colimitation, and a few

instances of Si depletion have been reported. Paleolimnological records show increases in productivity

and changes in algal assemblages in the recent past (since 1950) that are correlated with increased societal

use of synthetic N fertilizers and human. The paleolimnological evidence is strongest in regions with the

highest N deposition, and is weaker where N deposition is lower (Wolfe et al., 2001, 2003, 2006; Saros

et al., 2003). In additions to changes in productivity, algal community reorganization has been observed

in the paleolimnological record, experiments, and observations of N-enriched lakes, especially those

where enrichment has come from N deposition. A summary of additional studies addressing N-limitation

is given in Table C-5.

It is generally believed that the Laurentian Great Lakes are P-Limited (Schelske, 1991; Downing

and McCauley, 1992; Rose and Axler, 1998). Water quality in the open waters of these lakes has been

improving in recent years in response to controls on point sources of P (Nicholls et al., 2001). Work by

Levine et al. (1997), however, suggested a more complicated pattern of response to nutrient addition for

Lake Champlain. They added nutrients to in situ enclosures and measured indicators of P status,

including alkaline phosphatase activity and orthophosphate turnover time. Although P appeared to be the

principal limiting nutrient during summer, N addition also resulted in algal growth stimulation. During

spring, phytoplankton growth was not limited by P, N, or silica (Si), but perhaps by light or temperature

(Levine et al., 1997).

Data from 28 Sierra Nevada lakes sampled in 1985 and again in 1999 suggested that NO 3 −

concentrations decreased during that period and total P concentrations increased in more than 70% of the

lakes sampled. Sickman et al. (2003a) concluded that lakes throughout the Sierra Nevada appear to be

experiencing measurable eutrophication in response to atmospheric deposition of nutrients, but N

deposition is only part of the process. Based on the evidence of increased P loading throughout the Sierra

Nevada, Sickman et al. (2003a) concluded that site-specific P sources were unlikely to be the cause of

observed trends. They proposed that atmospheric deposition and accelerated internal cycling of P in

response to changes in climatic factors were the most likely sources of increased P loading to the Sierra

Nevada Lakes, but it is not known why atmospheric deposition of P to these lakes has increased over

time. Possibilities include use of organo-phosphate pesticides and aeolian transport of soils and dust that

are high in P from the San Joaquin Valley to the Sierra Nevada Mountains (Bergametti et al., 1992;

Lesack and Melack, 1996; Sickman et al., 2003a).

Data from a survey of 44 lakes east and west of the Continental Divide in Colorado indicated that

lakes on the western side of the Continental Divide averaged 6.6 μeq/L of NO 3 − , whereas lakes on the

eastern side of the Continental Divide averaged 10.5 μeq/L of NO 3 − concentration. In the Colorado Front

Range, NO 3 − concentrations in lakes above15 μeq/L have commonly been measured, suggesting some

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16 Table C­5. Summary

ecosystems. of additional evidence for N limitation on productivity of freshwater

Region Endpoint Observation Ecosystem

Type Reference

Sweden

Rocky Mountains

of C olorado a nd

Wyoming

Texas

Rocky Mountains

of C olorado a nd

Wyoming N

limitation

N

limitation

N

limitation

N

limitation a consistent pattern of nutrient limitation showing N limitation for deposition below

approximately 2.5 kg N/ha/yr, co­limitation of N and P for deposition between ~2.5 and

5.0 kg N/ha/yr, and P limitation in areas with N deposition greater than 5.0 kg N/ha/yr.

Review: t he author c oncluded that t he effects of a tmospheric N deposition w ere

uncertain a nd that a w idespread s hift from N to P limitation had not been clearly

de monstrated.

some instances o f seasonsal N ­limitation, and other instances o f year­round N­limitation

R eview: Author s tated that r ecent studies di d suggest a c hange in diatom species

do minance in t he 1950s, bu t widespread species changes across lakes in t he region a nd

the r ole of N deposition in t hese changes n eeded confirmation. T hus, t he available data

w ere not clear at t hat t ime, but suggested that s ome changes had likely occurred in

s ome aquatic e cosystems. l akes

rivers B ergström et

al. (2005)

Burns (2004)

Stanley et a l.,

1990

B urns (2004) degree of N-saturation (Baron, 1992). A meta-analysis of 42 regions in Europe and North America

suggested that a majority of lakes in the northern hemisphere were limited by N in their natural state.

While many of these lakes now receive sufficient N from deposition that they are no longer N-limited,

some lakes in remote regions still maintain their original oligotrophic or ultra-oligotrophic status.



# C.4.3. Aquatic Species Affected

The following section contains studies in which the amount of N added was less than 10 mg NO − 3

N/L, or 714 μM, and most studies tested the effects of 5 mg NO

− 3 -N/L or less. Many of these studies are

summarized in Table C.6. Overall, several major effects were reported on biota treated with N

enrichment: (1) the effects on algae included growth stimulation, increased cell densities, decline or

stimulation of individual taxa, and decline in diversity; (2) the amount of N required to stimulate growth

in phytoplankton is extremely low: 3 μM or less; and (3) animal responses included no response,

decreased reproductive capability, declines in growth rate and biomass, mortality, and in one case,

increased fitness because NO − 3 was detrimental to a fungal parasite.



## C.4.3.1. Phytoplankton and Plants

Two species of diatom, Asterionella formosa and Fragilaria crotonensis

, now dominate the flora of

at least several alpine and montane Rocky Mountain lakes (Interlandi and Kilham, 1998; Baron et al.,

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34 2000; Wolfe et al., 2001, 2003; Saros, 2003; Saros, 2005). These species are opportunistic algae that have

been observed to respond rapidly to disturbance and slight nutrient enrichment in many parts of the world.

They were among the first diatoms to increase in abundance following watershed settlement and

agricultural development in European lake watersheds in the 12th and 13th centuries (Anderson et al.,

1995; Lotter, 1998), and North American settlements in the 18th and 19th centuries (Christie and Smol,

1993; Hall et al., 1999). In these studies, as well as in a Swedish lake influenced by acidic deposition,

these two diatom species expanded following initial disturbance, and were later replaced by other species

more tolerant of either acidification or eutrophication (Renberg et al., 1993; Hall et al., 1999). Moreover,

the growth of A. formosa has been stimulated with N amendments during in situ incubations, using

bioassays and mesocosms (6.4 to 1616 μM N/L; McKnight et al., 1990) (76 μM N/L; Lafrancois, 2004)

(18 μM N/L; Saros, 2005).

It may seem obvious that additions of N stimulate cell growth, but not all species of diatoms or

other algae are equally responsive to N supply. A. formosa and F. crotonensis have extremely low

resource requirements for P, enabling them to outcompete other algae for resources and such differences

in resource requirements allow some species to gain a competitive edge over others upon nutrient

addition, and as a consequence, shifts in assemblages have been observed (Wolfe et al., 2001, 2003;

Lafrancois, 2004; Saros, 2005). This is in keeping with findings of Interlandi and Kilham (2001), who

demonstrated that maximum species diversity was maintained when N levels were extremely low (<3 μM

N) in lakes in the Yellowstone National Park (Wyoming, Montana) region. The implication is that species

diversity declines with increasing availability of N, and this finding complements the results of terrestrial

studies that also showed a negative relationship between species diversity and N availability (Stevens,

2004; Suding et al., 2005; Gilliam, 2006).

P limitation and co-limitation of both N and P are reported for fresh waters in the literature,

particularly during summer (Morris and Lewis, 1988; Elser, 1990) Downing and McCauley, 1992;

Sickman et al., 2003b). Because diatoms in northern temperate freshwaters respond rapidly and favorably

to N enrichment and also have relatively high Si requirements, Si can be depleted, at least seasonally,

from waters that are relatively high in N and P.

Silica depletion due to nutrient enrichment has been reported for the Great Lakes (Conley et al.,

1993). Increased growth of silicate-utilizing diatoms as a result of NO 3 − and phosphate (PO 4 − )-induced

eutrophication, and subsequent removal of fixed biogenic Si via sedimentation has brought about changes

in the ratios of nutrient elements Si, N, and P. In turn, such changes can cause shifts from diatoms to nonsiliceous phytoplankton in large rivers and coastal marine regions (Ittekot, 2003). Reduction in dissolved

Si in lakewater corresponded to phytoplankton blooms under ice and large numbers of diatoms during

spring in Loch Vale Watershed, Rocky Mountain National Park (Campbell et al., 1995). This is a

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29 potential seasonal issue in water bodies underlain by aluminosilicate rocks because mineral weathering

can replenish the Si supply.



# C.4.4. Seasonal N Input and Cyanobacteria

Some ecosystems are seasonally enhanced with N from atmospheric deposition, either from

snowmelt flushing of accumulated N in winter snow, or from flushing during dormancy of terrestrial

vegetation (Stoddard, 1994). While many eutrophic and hypereutrophic freshwater ecosystems have

seasonal or perennial cynaobacteria that fix atmospheric N, obviating the need for an external source of N

(Wetzel, 2001), we found only one study of an oligotrophic lake with obligate N-fixing bacteria (Reuter

et al., 1985). N-fixation is energy expensive and sometimes limited by trace metal availability, so obligate

N-fixing cyanobacteria (formerly called blue-green algae) are rarely found in ultra-oligotrophic waters

(McKnight et al., 1990; Vitousek and Howarth, 1991). Because of this, oligotrophic and ultraoligotrophic

waters are extremely sensitive to even low inputs of N from atmospheric deposition. Anabaena circinalis

,

an obligate N-fixing cyanobacterium, was suppressed with additions of 500 μM/L N (Higley et al., 2001),

and DIN levels >~200 μM/L N completely inhibited N fixation in Castle Lake, CA (Reuter et al., 1985).



# C.4.5. Nitrate Toxicity: Invertebrates

Toxic responses to N exposure by aquatic invertebrates have been identified in a number of studies.

Toxic response thresholds are typically much higher than the levels of N in surface waters that could be

attributable to N deposition in the U.S. Safe Concentrations (SC), or threshold values of N, were

determined by Camargo and Ward (1995) for several aquatic insects at different life stages. Early instars

are generally more sensitive to N in solution than later or adult stages. The SC for late instars of

Hydropsyche occidentalis

, a caddis fly, was found to be 171 μM/L, and concentrations greater than this

value induced mortality. The SC was 100 μM/L for early instars of the same species (Camargo and Ward,

1995). Another caddis fly, Cheumatopsyche pettiti

, tolerated higher concentrations, with safe

concentrations of 171 and 250 μM N/L, respectively for early and late instars (Camargo and Ward, 1995).

Two species of amphipod did not survive after 120-hr exposure to NO − 3 concentrations of 200 μM N/L

for one species, and 314 μM N/L for the other (Camargo et al., 2005). No observable effect

concentrations above which Ceriodaphinia dubia exhibited reduced reproductive capability ranged

broadly in laboratory experiments, but some effects were seen at concentrations greater than 507 μM N/L

(Scott and Crunkilton, 2000). A decline in Daphnia spp. was observed in mesocosm nutrient enrichment

experiments where 75 μM N/L was added, but this was attributed to lower food quality of the algal

assemblage that replaced the original species as a result of fertilization (Lafrancois, 2004). Thus, toxic

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32 responses seem to occur at N concentrations that are much higher than the concentrations required to

elicit a response in competitive interactions.

A whole-ecosystem experiment at the Bear Brook watershed, ME simulated the effects of N and S

deposition by means of experimental (NH 4 ) 2 SO 4 addition over a period of 10 years. Researchers found

that elevated N inputs had minimal effect on stream detritus processing (Chadwick and Huryn, 2003).

They also found that N additions had no significant effect on stream macroinvertebrate secondary

production or varying production by functional feeding groups. They concluded that climate-related

variables such as flow duration and litter inputs controlled secondary production when N was not limiting

(Chadwick and Huryn, 2005).

Changes to aquatic food webs have not been as thoroughly explored as changes to algal

assemblages, but a few studies have shown declines in zooplankton biomass (Paul et al., 1995;

Lafrancois, 2004) in response to N-related shifts in phytoplankton biomass toward less palatable taxa with

higher C:P ratios (Elser et al., 2001). N enrichment of arctic streams not only increased periphyton

biomass and productivity, but also stimulated the entire ecosystem, increasing decomposition rates, fungal

biomass, and invertebrates (Benstead et al., 2005).



# C.4.6. Nitrate Toxicity: Amphibians and Fish

A summary of studies the effects of nitrate on amphibians and fish is given by Table C-6. It

appears that very high NO − 3 concentrations in surface water are required to elicit a toxic response in

amphibian populations. Concentrations that caused no observed effects and no observed adverse effects

ranged from 357 to 714 μM N/L for frogs, salamanders, and the American toad ( Bufo americanus )

(Hecnar, 1995; Laposata and Dunson, 1998; Johansson et al., 2001; Romansic et al., 2006). In one

experiment, the red-legged frog ( Rana aurora ) exhibited a decreased susceptibility to Saprolegnia mold

when exposed to elevated NO − 3 concentrations (Romansic et al., 2006).

According to one review, adverse direct effects of N deposition on fish due to nutrient enrichment

are probably minimal (Burns, 2004). N concentrations alone are not high enough to influence fish

metabolism, and the extent of eutrophication is insufficient (due to induced P limitation in oligotrophic

waters) to cause O 2 depletion.

Other research suggests that the eggs and fry of rainbow trout ( Oncorhyncus mykiss ; including

steelhead), cutthroat trout ( O. clarki ), and chinook salmon ( O. tshawytscha ) are susceptible to elevated

concentrations of NO

− 3 , with rainbow trout mortality occurring after 30 day incubations in concentrations

>79 μM N/L (Kincheloe et al., 1979). There were no observed effects reported below this concentration.

Chinook salmon and cutthroat trout eggs and fry responded to slightly higher concentrations; no observed

effects occurred below 164 μM N/L, but mortality occurred at higher concentrations (Kincheloe et al.,

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Table C­6. Summary of effects of N enrichment on aquatic biota in freshwater ecosystems.

Species Common Name Life

Stage N Concentration

­ (mg NO 3 ­N/L) Observed Effects Reference

Algae

Phytoplankton

Asterionella formosa

Asterionella formosa

Multiple species

Asterionella formosa

Fragilaria crotonensis

Multiple species

Fragilaria crotonensis

Fragilaria crotonensis

Staurosirella pinnata

Tetracyclus glans

Multiple species

not identified

not identified

not identified

Multiple species

Multiple species diatoms

diatoms

diatoms

diatoms

diatoms

diatoms

diatoms

diatoms

diatoms

benthic diatoms

phytoplankton

assemblages

phytoplankton

assemblages

phytoplankton

assemblages

phytoplankton

assemblages

crysophytes

epilimnetic algae 0.252 mg/L

6.4 umol/L

1.06 mg/L

5.7 × 10−4 (>0.041

uM) (high light)

0.252 mg/L

1.06 mg/L

3.5 × 10−4 (>0.028

uM) (high light)

8.4 × 10−6 (>0.006

uM) (med light)

8.4 × 10−6 (>0.006

uM) (med light)

1.7 × 10−4 (>0.012

uM) (low light)

3.0 uM

0.5 mg/L

0.3 uM

100 ug/L

1.21 mg/L

0.012 mg/L stimulated growth

stimulated growth

(low ambient N­deposition):increase in chlorophylla content and growth rate; no cell density effect

increased growth rate (measured at half the

maximum growth rate)

stimulated growth

(low ambient N deposition):increase in chlorophylla content and growth rate; no cell density effect

increased growth rate (measured at half the

maximum growth rate)

increased growth rate (measured at half the

maximum growth rate)

increased growth rate (measured at half the

maximum growth rate)

increased growth rate (measured at half the

maximum growth rate)

N saturation value for maximum diversity in WY

low N lakes

NO3 stimulated growth seasonally, while tributary

periphyton communities were P limited

NH4 additions more effective at stimulating growth

than NO3

stimulated NO3 uptake

(elevated ambient N deposition): no response to

NO3 additions; increased chlorophyll­a and cell

density when NO3 combined with acid and P

increased growth rate (measured at half the

maximum growth rate) Saros et al.

(Saros, 2005)

McKnight et al.

(1990)

Lafrancois et al.

(Lafrancois,

2004)

Michel et al.

(2006)

Saros et al.

(Saros, 2005)

Lafrancois et al.

(Lafrancois,

2004)

Michel et al.

(2006)

Michel et al.

(2006)

Michel et al.

(2006)

Michel et al.

(2006)

Interlandi et al.

(1999)

Stanley et al.

(1990)

Levine and

Whalen (2001)

Axler and Reuter

(1996)

Lafrancois et al.

(Lafrancois,

2004)

Priscu et al.

(1985) 1979). Lake whitefish ( Coregonus clupeaformis ) and lake trout ( Salvelinus namaycush ) embryos

displayed developmental delays at concentrations greater than 446 and 114 μM N/L, respectively

(McGurk et al., 2006). All of these toxic threshold concentrations are much higher than the concentrations

of NO − 3 in surface water that would routinely be expected to occur solely in response to atmospheric N

deposition in the U.S. Nevertheless, such high concentrations of streamwater NO − 3 have been measured in

the Great Smoky Mountains, NC (Cook, 1994) and in mixed conifer forests in southern California (Fenn

and Poth, 1999).

August 2008 C­31 DRAFT—DO NOT QUOTE OR CITESpecies Common Name Life

Stage N Concentration

­ (mg NO 3 ­N/L) Observed Effects Reference

Multiple species hypolimnetic 0.050 mg/L increased growth rate (measured at half the Priscu et al.

algae maximum growth rate) (1985)

Periphyton

attached benthic 0.5 M NaNO3 in biomass increased in response to N and N&P Smith and Lee

algae 2%agar additions during period of seasonal N­limitation (2006)

(July­August)

attached benthic 2.5 M NO3 stimulated stream algal growth during Bushong and

algae seasonal N­limitation Bachmann

(1989)

not identified attached benthic 0.5 M NaNO3 in NO3 alone stimulated stream algal growth during Wold and

algae 3%agar seasonal N­limitation, while N & P co­limited Hershey (1999)

growth in other times

not identified attached benthic 0.5 M NaNO3 in NO3 alone stimulated stream algal growth during Allen and

algae 3%agar seasonal N­limitation, while N & P co­limited Hershey (1996)

growth in other times

not identified attached benthic 0.036 mg/L stimulated NO3 uptake Axler and Reuter

algae (1996)

not identified attached benthic 0.5 M NaNO3 in no growth response Higley et al.

algae 2%agar (2001)

not identified epilithic ~700 ug NO3­N suppressed N2­fixation Reuter et al.

(1985)

not identified attached benthic 0.16 mg/L increased summer growth rate (measured at half Reuter et al.

algae the maximum growth rate) (1985)

not identified attached benthic 0.32 mg/L increased winter growth rate (measured at half Reuter et al.

algae the maximum growth rate) (1986)

not identified sublittoral epilithic 0.259 mg/L increased growth rate (measured at half the Reuter and Axler

algae maximum growth rate) (1992)

not identified eulittoral epilithic 0.126 mg/L increased growth rate (measured at half the Reuter and Axler

algae maximum growth rate) (1992)

not identified epipelic algae 0.713 mg/L increased growth rate (measured at half the Reuter and Axler

maximum growth rate) (1992)

Cyanobacteria

Anabaena circinalis N fixing 0.5 M NaNO3 in decreased abundance Higley et al.

cyanobacteria 2%agar (2001)

Microcycstis sp. non­N­fixing 0.28 mg/L increased growth rate; increased mycrocystin and Gobler et al.

cyanobacteria anatoxin­a concentrations (2007)

Invertebrates

Hydropsyche caddis fly early 1.4 (SC) mortality Camargo and

occidentalis instaar Ward (1995)

late 2.2 (SC) mortality Camargo and

instaar Ward (1995)

Cheumatopsyche pettiti caddis fly early 2.4 (SC) mortality Camargo and

instaar Ward (1995)

late 3.5 (SC) mortality Camargo and

instaar Ward (1995)

Echinogammarus amphipod adult 2.8 (120­h LC0.01) mortality Camargo et al.

echinosetosus (2005)

Eulimnogammarus amphipod adult 4.4 (120­h LC0.01) mortality Camargo et al.

toletanus (2005)

Ceriodaphnia dubia water adult 7.1­56.5 (7­d NOEC) decreased reproductive ability; fewer neonates Scott and

flea/cladoceran produced per female Crunkilton (2000)

Daphnia pulex Water flea adult 1.06 (low ambient N­deposition): decreased biomass in Lafrancois et al.

response to NO3 Lafrancois

August 2008 C­32 DRAFT—DO NOT QUOTE OR CITESpecies Common Name Life

Stage N Concentration

­ (mg NO 3 ­N/L) Observed Effects Reference

Daphnia schoedleri Water flea adult 1.06 (low ambient N­deposition): decreased biomass in

response to NO3 Lafrancois et al.

(Lafrancois,

2004)

Vertebrates

Amphibians

Rana temporaria

Rana sylvatica

Ambystoma

jeffersonianum

Ambystoma maculatum

Ambystoma gracile

Rana aurora

Hyla regilla

Pseudacris triseriata

Rana pipiens

Bufo americanus common frog

wood frog

Jefferson’s

salamander

spotted

salamander

northwestern

salamander

red­legged frog

Pacific tree frog

striped chorus

frog

northern leopard

frog

American toad larvae

fertilized

eggs

fertilized

eggs

fertilized

eggs

larvae

larvae

larvae

tadpole

tadpole

fertilized

eggs 5 (70­d NOEC)

9 (NOAEL)

9 (NOAEL)

9 (NOAEL)

5­20

5­20

5­20

10 (100­d LOEC)

10 (100­d LOEC)

9.0 (NOAEL) delayed development, lower growth rate and body

mass at metamorphosis

no effect of NO3 on survivorship

no effect of NO3 on survivorship

no effect of NO3 on survivorship

no effect of NO3 on survivorship

no effect of NO3 on survivorship; NO3 decreased

susceptibility to Saprolegnia mold

no effect of NO3 on survivorship

mortality

mortality

no effect of NO3 on survivorship Johansson et al.

(2001)

Laposata and

Dunson (1998)

Laposata and

Dunson (1998)

Laposata and

Dunson (1998)

Romansic et al.

(2006)

Romansic et al.

(2006)

Romansic et al.

(2006)

Hecnar (1995)

Hecnar (1995)

Laposata and

Dunson (1998)

Fish

Oncorynchus mykiss

(anadromous)

Oncorynchus mykiss

(nonanadromous)

Oncorynchus mykiss

(nonanadromous)

Oncorynchus

tshawytscha

Salmo clarki

Salmo clarki

Coregonus

clupeaformis steelhead

rainbow trout

rainbow trout

chinook salmon

cutthroat trout

(Lahontan)

cutthroat trout

(Lahontan)

lake whitefish eggs

eggs

fry

fry

eggs

fry

embryo 1.1 (30­d NOEC)

1.1 (30­d NOEC)

1.1 (30­d NOEC)

2.3 (30­d NOEC)

2.3 (30­d NOEC)

4.5 (30­d NOEC)

6.25 (~120­d NOEC) mortality occurred above this value

mortality occurred above this value

mortality occurred above this value

mortality occurred above this value

mortality occurred above this value

mortality occurred above this value

hatching and developmental delays Kincheloe et al.

(1979)

Kincheloe et al.

(1979)

Kincheloe et al.

(1979)

Kincheloe et al.

(1979)

Kincheloe et al.

(1979)

Kincheloe et al.

(1979)

McGurk et al.

(2006)

Miscellaneous

Saprolegnia spp. pathogenic water

mold 5­20 mg NO3/L decreased ability to infect and kill the larvae of the

red­legged frog, Rana aurora Romansic et al.

(2006)

NOEC = No­observed­effect concentration; NOAEL = No­observed­adverse­effect level; SC = Safe concentration



### 1

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# C.5. Estuary and Coastal Ecosystems

There are a variety of factors that govern the sensitivity of estuaries and near-coastal marine waters

to eutrophication from atmospheric N deposition. Of critical importance is the total N input from all

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32 � Physiography (geomorphology, dominant biological communities, biogeographic province);

� Type of primary production base (i.e., seagrasses, phytoplankton, coral, attached intertidal

algae, etc.);

� Stratification and extent to which phytoplankton occupy the nutrient-rich photic zone; and

� Allochthonous inputs of organic matter. s ources, including bo th atmospheric and non-atmospheric sources. Other key elements include the

dilution capacity of the watershed, which reflects the volume of water available to dilute added N, and

flushing rate, which reflects the time required for inflowing water to replace estuary volume, (Bricker

et al., 1999; NRC, 2000). Other potentially important factors can include the following (NRC, 2000):

A n umber o f factors control the N loading rates to estuaries and the potential effects of N

deposition on nutrient loading and algal blooms. Estuaries communicate with fresh water on the upstream

side and with the ocean on the downstream side. The flushing of fresh river water through the system and

the movement and mixing of salt water from the ocean are complicated and are always changing in

response to weather and tidal cycles. The surface area, volume, and depth of the estuary are also critical

factors governing the sensitivity of an estuary to N inputs. Decreases in grazer, filter-feeder, and higher

trophic level populations of fish and shellfish exacerbate problems associated with nutrient overenrichment (Jackson et al., 2001).

At the upstream end of an estuary, the water is primarily fresh much of the time. Discharge of N

from the land surface, only a part of which is of atmospheric origin (mainly as deposition to the land that

subsequently leached to the river water), dominates new N inputs. Further downstream within the estuary,

where fresh water is more thoroughly mixed with saltwater, much of the terrestrial N load is assimilated

by phytoplankton and benthic flora or removed by microbes in the process of denitrification (Paerl, 2002).

The importance of atmospheric N as a contributor to the total N load beyond this zone probably increases,

but there are no data to evaluate that.

The principal watershed features that control the amount of increased N flux to estuaries in the U.S.

include human population, agricultural production, and the size of the estuary relative to its drainage

basin (Peierls et al., 1991; Caddy, 1993; Fisher et al., 2006). Dense human populations generate large

volumes of nutrient-rich wastewater. Tertiary sewage treatment can reduce effluent N concentrations to

less than 35 μM, but these technologies have not been promoted as aggressively in the U.S. as elsewhere

(Conley et al., 2002; EPA, 2003). Agricultural production is heavily dependent on fertilizer application to

generate high yields from small areas. Fertilizer application has dramatically increased NO − 3

concentrations in ground water in many agricultural areas (Bohlke and Denver, 1995), which can leach to

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28 surface waters. Large terrestrial drainage basins that drain into small estuaries tend to have high nutrient

flux if the land is heavily populated or used for agriculture.

In addition to estuaries, coastal marine ecosystems are highly susceptible to nutrient enrichment,

especially from N. Land clearing, agricultural land use, sewage treatment discharge, and atmospheric

deposition can all result in high loadings of N to the coastal zone. Excessive N inputs contribute to a

range of impacts, including enhanced algal blooms, decreased distribution of seagrasses, and decreased

dissolved oxygen (DO) concentration (Valiela et al., 1992; Nixon, 1995; Borum, 1996; Bricker, 1999).

Because of human population growth and the great popularity of coastal areas, there is substantial

potential for increased N loading to coastal ecosystems from both atmospheric and non-atmospheric

sources.



# C.5.1. Interacting Factors with Productivity

Results of empirical observations and short-term (3 weeks) marine mesocosm experiments suggest

that there can be wide variation in the response of autotroph biomass to nutrient addition (Cloern, 2001;

Olsen et al., 2006). Such variation may be attributable to the time scale of the observations, rate of water

exchange, grazing pressure, and other environmental factors (Olsen et al., 2006).



# C.5.2. Hydrology Interactions with Phytoplankton Biomass

River discharge has a huge influence on the hydrology and nutrient cycling of estuaries. For

example, discharge from the large watershed of the Susquehanna River is important to the seasonal and

interannual variability in the hydrology of Chesapeake Bay (Fisher et al., 1988; Malone et al., 1988).

When discharge from the Susquehanna River is low, summer phytoplankton biomass in Chesapeake Bay

tends to be low compared to spring conditions, and the phytoplankton community is dominated by small

and flagellated forms (Marshall and Lacouture, 1986). Under higher river flows, summer phytoplankton

biomass in the bay is higher, and has an increased prevalence of diatoms (Paerl et al., 2006).

Hydrologic variation interacts with nutrient supply to control phytoplankton seasonal patterns in

Chesapeake Bay. High biomass during the spring diatom bloom leads to consequent sedimentation of

organic material out of the photic zone during the transition to summer (Malone et al., 1996; Harding

et al., 2002). Microbial decomposition of this material then fuels the pattern of summer anoxia in bottom

waters (Paerl et al., 2006). N loading to Chesapeake Bay and its tributaries during spring high runoff

periods contributes to periods of P limitation and co-limitation (Boynton et al., 1995). The ecosystem then

returns to N limitation during low flow summer months (Paerl, 2002).

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# C.6. Effects on Watersheds and Landscapes

C.6.1. Interactions among Terrestrial, Transitional, and Aquatic

Ecosystems

Streams, and to a lesser extent lakes, can serve as indicators of regional environmental change

(Seastedt et al., 2004), partly because they integrate conditions within their watersheds including

atmospheric, edaphic, geologic, and hydrologic conditions. Streams reflect the terrestrial environment

most closely during high flow when much of the stream water enters the channel from the upper soil

where most of the biological activity occurs. The terrestrial signal can be less clear in lakes because they

have the capacity to store water and modify water chemistry through internal processes to a greater degree

than streams (Lawrence et al., in review).

Young and Sanzone (2002) provided a checklist of ecological attributes that should be considered

when evaluating the effects of an environmental stressor on the integrity of ecological systems (Table C7). The Essential Ecological Attributes (EEAs) listed in the table represent groups of related ecological

characteristics (Harwell et al., 1999), including landscape condition, biotic condition, chemical and

physical characteristics, ecological processes, hydrology and geomorphology, and natural disturbance

regimes. The first three ecological attributes listed in Table C-7 can be classified primarily as “patterns,”

whereas the last three are “processes” (cf. Bormann and Likens, 1979). They can be affected by a variety

of environmental stressors (Figure C-4).

Of concern in this annex are relationships between NO X atmospheric deposition, derived from

anthropogenic sources, and one or more of the EEAs. The ranges of likely changes in ecosystem patterns

and processes associated with changes in deposition are discussed in the subsections that follow.

The following discussion assesses and characterizes the overall condition or integrity of

ecosystems within the U.S. that are affected by the deposition of atmospheric N and its role as a nutrient.

The six EEAs

– landscape condition, biotic condition, chemical/physical characteristics, ecological

processes, hydrology/geomorphology, and natural disturbance regimes (Table C-7)

– provide a

hierarchical framework for assessing ecosystem status. Characteristics related to structure, composition,

or functioning of ecological systems may be determined by the use of endpoints or ecological indicators

of condition that are measureable and significant either ecologically or to society (Harwell et al., 1999).

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### Table C­7. Essential ecological attributes and reporting categories

Landscape Condition Ecological Processes

Extent of Ecological System/Habitat Types

Landscape Composition

Landscape Pattern and Structure

Biotic Condition

Ecosystems and Communities

Community Extent

Community Composition

Trophic Structure

Community Dynamics

Physical Structure

Species and Populations

Population Size

Genetic Diversity

Population Structure

Population Dynamics

Habitat Suitability

Organism Condition

Physiological Status

Symptoms of Disease or Trauma

Signs of Disease

Chemical and Physical Characteristics

(Water, Air, Soil, and Sediment)

Nutrient Concentrations

N

P

Other Nutrients

Trace Inorganic and Organic Chemicals

Metals

Other Trace Elements

Organic Compounds

Other Chemical Parameters

pH

Dissolved O 2

Salinity

Organic Matter

Other

Physical Parameters Energy Flow

Primary Production

Net Ecosystem Production

Growth Efficiency

Material Flow

Organic Carbon Cycling

N and P Cycling

Other Nutrient Cycling

Hydrology and Geomorphology

Surface and Groundwater flows

Pattern of Surface flows

Hydrodynamics

Pattern of Groundwater flows

Salinity Patterns

Water Storage

Dynamic Structural Characteristics

Channel/Shoreline Morphology, Complexity

Extent/Distribution of Connected Floodplain

Aquatic Physical Habitat Complexity

Sediment and Material Transport

Sediment Supply/Movement

Particle Size Distribution Patterns

Other Material Flux

Natural Disturbance Regimes

Frequency

Intensity

Extent

Duration

Source: Young and Sanzone (2002).



### 1

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### 1

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5

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7 Figure C-4. Sample stressors and the essential ecological attributes they affect.

Source: Young and Sanzone (2002).

The relationships among the EEAs are complex because all are interrelated. Changes in one EEA

may affect, directly or indirectly, every other EEA. Ecological processes create and maintain

environmental patterns, and these patterns affect how the processes are expressed (Young and Sanzone,

2002). Changes in patterns or processes can result in changes in the status and functioning of an

ecosystem.

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32

33 Changes in the biodiversity, composition, and structure of ecosystems relate directly to functional

integrity. Changes in biodiversity are of particular significance in altering ecosystem function. The energy

obtained by plants (producers) from sunlight during photosynthesis and the chemical nutrients taken up

by those plants from the soil and the atmosphere are transferred to other species (consumers) within the

ecosystem through food webs. The movement of chemical nutrients through an ecosystem is cyclic, as the

nutrients are used or stored and eventually returned to the soil by microorganisms and fungi

(decomposers). Energy is transferred among organisms through the food webs and eventually is

dissipated into the environment as heat. The flows of energy and cycling of nutrients provide the

interconnectedness among the elements of the ecosystem and transform the community from a random

collection of numerous species into an integrated whole.

Human existence and welfare on this planet depend on life-support services provided by the

interaction of the EEAs. Both ecosystem structure and function play essential roles in providing goods

and services (Table C-8) (Daily, 1997). Ecosystem processes provide diverse benefits including

absorption and breakdown of pollutants, cycling of nutrients, binding of soil, degradation of organic

waste, maintenance of a balance of gases in the air, regulation of radiation balance and climate, and

fixation of solar energy (Westman, 1977; Daily, 1997; World Resources Institute, 2000). These ecological

benefits, in turn, provide economic benefits and values to society (Costanza et al., 1997; Pimentel et al.,

1997). Goods such as food crops, timber, livestock, fish, and drinking water have market value. The

values of ecosystem services such as flood-control, wildlife habitat, cycling of nutrients, and removal of

air pollutants are more difficult to measure (Goulder and Kennedy, 1997). See discussion in Annex F.

Biodiversity is an important consideration at all levels of biological organization, including species,

individuals, populations, and ecosystems. Human-induced changes in biotic diversity and alterations in

the structure and functioning of ecosystems are the two most dramatic ecological trends of the past

century (Vitousek, 1997b; EPA, 2004). The deposition of nutrient N from the atmosphere has the

potential to alter ecosystem structure and function by altering nutrient cycling and changing biodiversity.

It is important to understand how ecosystems respond to stress to determine the extent to which

anthropogenic stresses, including N deposition, affect ecosystem services and products (Table C-8).

Particular concern has developed within the past decade regarding the consequences of decreasing

biological diversity (Hooper and Vitousek, 1997; Chapin et al., 1998; Ayensu et al., 1999; Wall, 1999;

Tilman, 2000). Human activities that decrease biodiversity also alter the complexity and stability of

ecosystems, and change ecological processes. In response, ecosystem structure, composition and function

can be affected (Figure C-5) (Pimm, 1984; Tilman and Downing, 1994; Tilman, 1996; Chapin et al.,

1998; Levlin, 1998; Peterson et al., 1998; Daily and Ehrlich, 1999; Wall, 1999).

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### Table C­8. Primary goods and services provided by ecosystems.

Ecosystem Goods Services

Agroecosystems

Coastal Ecosystems

Forest Ecosystems

Freshwater

Grassland

Ecosystems Food crops

Fiber crops

Crop genetic resources

Fish and shellfish

Fishmeat (animal feed)

Seaweeds (for food and industrial use)

Salt

Genetic resources

Timber

Fuelwood

Drinking and irrigation water

Fodder

Nontimber products (vines, bamboos, leaves,

etc.)

Food (honey, mushrooms, fruit, and other edible

plants; game)

Genetic resources

Drinking and irrigation water

Fish

Hydroelectricity

Genetic resources

Livestock (food, game, hides, and fiber)

Drinking and irrigation water

Genetic resources Maintain limited watershed functions (infiltration, flow control, and partial soil

protection)

Provide habitat for birds, pollinators, and soil organisms important to

agriculture

Sequester atmospheric carbon

Provide employment

Moderate storm impacts (mangroves, barrier islands)

Provide wildlife (marine and terrestrial (habitat and breeding

areas/hatcheries/nurseries

Maintain biodiversity

Dilute and treat wastes

Provide harbors and transportations routes

Provide human and wildlife habitat

Provide employment

Contribute aesthetic beauty and provide recreations

Remove air pollutants, emit O 2

Cycle nutrients

Maintain array of watershed functions (infiltration, purification, flow control,

soil stabilization)

Maintain biodiversity

Sequester atmospheric carbon

Moderate weather extremes and impacts

Generate soil

Provide employment

Provide human and wildlife habitat

Contribute aesthetic beauty and provide recreation

Buffer water flow (control timing and volume)

Dilute and carry away wastes

Cycle nutrients

Maintain biodiversity

Provide aquatic habitat

Provide transportation corridor

Provide employment

Contribute aesthetic beauty and provide recreation

Maintain array of watershed functions (infiltration, purification, flow control,

and soil stabilization)

Cycle nutrients

Remove air pollutants and emit O 2

Maintain biodiversity

Generate soil

Sequester Atmospheric carbon

Provide human and wildlife habitat

Provide employment

Contribute aesthetic beauty and provide recreation

Source: World Resources Institute (2000).



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7

8

9

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12 Source: Modified from Ayensu et al. (1999).



### Figure C­5 Linkages among various ecosystem goods and services (food, water, biodiversity, forest

products) and other driving forces (climate change).



# C.6.2. Interactions with Land Use and Disturbance

Scientific understanding of N cycling in forested watersheds is complicated by ecosystem response

to climatic variation, human land use, and various kinds of landscape disturbance, including insect

infestation, wind storm, fire, and disease (Aber and Driscoll, 1997; Goodale, 2000) Mitchell et al., 2006).

N dynamics in watersheds of mixed land use (i.e., agriculture, urban, forest) are even more complicated.

It is clear that disturbances have major impacts on nutrient enrichment from N deposition, and that these

effects can be long-lasting. Nevertheless, the scientific community is only in the early stages of learning

how to quantify these interactions.

Changes in land use can affect nutrient heterogeneity in the mineral soil of forest stands. For

example, Fraterrigo et al. (2005) found that patterns of variance in soil C, N, and Ca 2+ concentration

increased with the extent of intensive past land use in western North Carolina. Land use might alter the

local patchiness of soil nutrients by decoupling interactions among microclimate, topography, vegetation,

and soil biota. In particular, mechanical soil mixing and maintenance of agricultural monocultures can

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32 homogenize soils in cultivated systems (Robertson et al., 1993; Paz-Gonzalez and Taboada, 2000). Such

effects may be important if the land use is changed to forest. Similarly, changes in species composition

can alter the spatial distribution of nutrients in litter inputs (Dijkstra and Smits, 2002; Fraterrigo et al.,

2005).

In the northeastern U.S., concentrations of N in streams of upland forested watersheds tend to be

considerably lower than in streams draining watersheds with other land uses. In a comparison of small

watersheds in eastern New York, concentrations of N were highest and most variable in a stream draining

a watershed where the predominant land use was row crop production. Total dissolved N concentration in

streams in sewered suburban and urban watersheds were somewhat lower and less variable than in

streams draining the agricultural watershed. Streams in urban and suburban watersheds may also

experience high episodic N loading caused by combined sewer overflows (Driscoll et al., 2003c).



# C.6.3. Timber Harvest and Fire

Timber harvest contributes to nutrient removal from the ecosystem via biomass export and

acceleration of leaching losses (Bormann et al., 1968; Mann et al., 1988). In particular, logging

contributes to loss of N and Ca 2+ from the soil (Tritton et al., 1987; Latty, 2004). The extent of nutrient

loss is determined, at least in part, by the intensity of the logging and whether or not it is accompanied by

fire (Latty, 2004). The species composition of the regrowth vegetation also has important effects on

nutrient cycling. Fire is sometimes followed by establishment of N-fixing vegetation that provides

substantial sources of N

r (Johnson, 1995; Johnson et al., 2004). Tree species also vary dramatically in

their N cycling properties, especially in their influence on litter mass and quality (Finzi et al., 1998;

Ferrari, 1999; Ollinger, 2002). Thus, over time, the extent of effect of logging and fire on nutrient cycling

can increase, depending largely on shifts in tree species composition and the degree to which C and N

pools are altered in the mineral soil and the forest floor.

Dissolved N exports have been clearly shown to increase substantially after major watershed

disturbance, often reaching peak concentrations in streamwater within 1 to 3 years of disturbance, and

then returning to background concentrations after about 5 to 10 years (Likens et al., 1978; Bormann and

Likens, 1979; Eshleman et al., 2000). Such transient NO − 3 leakage has been shown to occur subsequent to

both logging (Martin et al., 1984; Dahlgren and Driscoll, 1994; Yeakley et al., 2003) and insect

infestation (Eshleman et al., 1998, 2004).

The extent to which timber harvesting influences leaching of NO − 3 and base cations from soils to

drainage waters depends on changes in primary productivity, nutrient uptake by plants and

microorganisms within the terrestrial ecosystem, and hydrological pathways for transferring nutrients to

drainage water (Hazlett et al., 2007). Because of the variety of responses and interactions of

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35 environmental and forest litter and soil conditions, it is difficult to generalize about the influence of

harvesting on N cycling (Grenon et al., 2004; Hazlett et al., 2007).

We do know, however, that land use history constitutes a major influence on N leaching from

forested watersheds that receive moderate to high levels of atmospheric N deposition (Pardo, 1995; Aber

and Driscoll, 1997; Goodale, 2000; Lovett et al., 2000). The severity of effect and length of the recovery

period probably vary according to the nature of the past disturbance. Extensive past logging appears to

have considerable and long-lasting effects on nutrient cycling (Goodale and Aber, 2001; Fisk et al.,

2002). Latty et al. (2004) compared soil nutrient pools and N cycling among three forest stands in the

Adirondack Mountains: old growth, selectively logged, and selectively logged and then burned. The

logging and fire had occurred about 100 years previously. Results suggested that even relatively light

logging, plus burning, may influence the extent of subsequent N limitation over time scales of decades to

centuries (Latty, 2004). Models of forest ecosystem response to disturbance incorporate such long-lasting

effects of land use on C and N storage, cycling, and release (Aber et al., 1997).

Chen and Driscoll (2004) simulated the response of five forested watersheds in the Adirondack and

Catskill regions of New York to changes in atmospheric deposition and land disturbance. Simulation

results suggested that forest harvesting caused increased leaching of base cations and NO 3 − from the

watersheds. These changes also affected model projections of future pH and acid neutralizing capacity

(ANC) of lake water. Model results suggested that lakewater pH and ANC were lower in response to

forest cutting as compared with undisturbed conditions.

Nitrification rates at old growth sites in the White Mountains of New Hampshire (63 ± 4.3 kg

N/ha/yr) were approximately double those at previously burned (34 ± 4.4 kg N/ha/yr) and previously

logged (29 ± 4.7 kg N/ha/yr) sites (Goodale, 2001). Fire and logging disturbances had occurred about 100

years previously on these study sites. Nitrification increased as forest floor C:N ratio decreased, resulting

in higher NO 3 − concentrations in streamwater. These results suggest that forest disturbance can have longlasting effects on N cycling and the potential for N saturation.

Thus, disturbance can affect N cycling and the response of forest ecosystems to N deposition. In

addition, it also appears that vegetative changes stimulated by N deposition may affect the frequency and

severity of disturbance. Excess N

r deposition is thought to be impacting essential ecological attributes

associated with terrestrial ecosystems and how they respond to disturbance. Effects of N

r deposition

influence habitat suitability, genetic diversity, community dynamics and composition, nutrient status,

energy and nutrient cycling, and frequency and intensity of natural fire disturbance regimes. For example,

several lines of evidence suggest that N

r deposition may be contributing to greater fuel loads and thus

altering the fire cycle in a variety of ecosystem types (Fenn et al., 2003a). Invasive grasses, which can be

favored by high N deposition, promote a rapid fire cycle in many locations (D’Antonio and Vitousek,

1992). The increased productivity of flammable understory grasses increases the spread of fire and has

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32 been hypothesized as one mechanism for the recent conversion of coastal sage shrub (CSS) to grassland

in California (Minnich and Dezzani, 1998).

High grass biomass has also been associated with increased fire frequency in the Mojave Desert

(Brooks, 1999; Brooks and Esque, 2002; Brooks et al., 2004). This effect is most pronounced at higher

elevation, probably because the increased precipitation at higher elevation contributes to greater grass

productivity. Increased N supply at lower elevation in arid lands can only increase productivity to the

point at which moisture limitation prevents additional growth. Fire was relatively rare in the Mojave

Desert until the past two decades, but now fire occurs frequently in areas that have experienced invasion

of exotic grasses (Brooks, 1999).



# C.6.4. Insect Infestation and Disease

Insect infestation and plant disease, via atmospheric N deposition, can alter the effects of nutrient

enrichment on forest ecosystems. Such disturbances alter the pool of N

r in the forest floor with short-term

impacts on NO − 3 and base cation leaching. Positive influences of N deposition on root and seed biomass

of an annual plant, common ragweed, were suppressed by herbivory, which increased with higher

available plant shoot N (Throop, 2005).

Eshleman et al. (2004) applied a regional lithology-based unit N export response function model to

simulate NO − 3 export to streams in the Chesapeake Bay watershed. The model considered the geographic

distribution of bedrock classes and the timing and extent of defoliation by gypsy moth ( Lymantria dispar )

larvae. Modeling results suggested that the regional annual NO

− 3 -N export increased during the year

following peak insect defoliation by about 1500%, from an initial rate of 0.1 kg N/ha/yr to nearly 1.5 kg

N/ha/yr.

Between the mid-1980s and the early 1990s, the southward expanding range of the gypsy moth

traversed Shenandoah National Park, VA (Webb, 1999). Some areas of the park were heavily defoliated 2

to 3 years in a row. The White Oak Run watershed, for example, was more than 90% defoliated in both

1991 and 1992. The gypsy moth population in White Oak Run then collapsed due to pathogen outbreak.

This insect infestation of the forest ecosystem resulted in substantial effects on streamwater chemistry.

The most notable effects of the defoliation on park streams were dramatic increases in the concentration

and export of N and base cations in streamwater. Figure C-6 shows the increase in NO − 3 export that

occurred in White Oak Run. Following defoliation, NO − 3 export increased to previously unobserved

levels and remained high for over 6 years before returning to predefoliation levels. The very low baseline

levels of NO − 3 export in park streams were consistent with expectations for N-limited, regenerating

forests (e.g., Aber, 1989; Stoddard, 1994). Release of NO − 3 to surface waters following defoliation was

likewise consistent with previous observations of increased N export due to forest disturbance (e.g.,

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18 Likens et al., 1970; Swank, 1988). The exact mechanisms have not been determined, but it is evident that

the repeated consumption and processing of foliage by the gypsy moth larva disrupted the ordinarily tight

cycling of N in the forests within this park.



### Figure C­6. Effect of watershed

defoliation by the gypsy moth

caterpillar on NO −

3 

### flux in

streamwater.

White Oak Run was heavily

defoliated for three consecutive

years. The watershed area

defoliated was 46.5% in 1990, 92.9%

in 1991, and 90.4% in 1992. In 1993,

the gypsy moth population

collapsed and there was no further

defoliation.

Source: Sullivan et al. (2003).

The elevated concentrations of NO − 3 following defoliation did not appear to contribute to baseflow

acidification in White Oak Run. This was due to a concurrent increase in concentrations of base cations in

streamwater (Webb et al., 1995). Both NO − 3 and base cation concentrations also increased during highrunoff conditions, although the increase in base cations did not fully compensate for the episodic increase

in NO

− 3

. As a consequence, episodic acidification became more frequent and more extreme (Webb et al.,

1995).

Large trees in old growth forests may resorb less N from foliage than do younger trees on

previously logged or burned sites (cf. Killingbeck, 1996). This process would be expected to contribute to

an alleviation of N limitation on plant processes in old-growth forests (Latty, 2004). This effect might

also extend to herbivores, which are often N-limited (Mattson, 1980). Latty et al. (Latty, 2004) attributed

the high severity of beech bark disease in old growth forests to such a mechanism. Beech bark disease is

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29 Source: Sullivan et al. (2003). caused by an introduced scale insect ( Crytococcus fagisuga ), which has high N requirements (Wargo,

1988; Houston, 1994). The N-rich foliage in the old growth forest may improve insect fitness,

contributing to a higher rate of infestation in the old growth stands (Latty, 2004).



# C.6.5. Urbanization

Perhaps the most noteworthy impact of urban land use on processes of nutrient enrichment from N

deposition concerns the transport of Nr to N-limited estuarine and near-coastal waters. In agricultural, and

especially in forested areas, it is generally expected that most atmospherically deposited N is taken up by

terrestrial vegetation. Relatively little of the deposited N is available for transport to downstream

receiving waters. This is not the case for urban land use. Urbanization often involves substantial clearing

of vegetation and compaction of soil (Poff et al., 1997; Burges et al., 1998; Jones et al., 2000; Trombulak

and Frissell, 2000; Alberti et al., 2007). Due to the relatively large impervious surface area in the urban

landscape (buildings, roads, parking lots, etc.), a higher percentage of precipitation is routed directly to

surface waters, with less opportunity for vegetative uptake of deposited N (Arnold and Gibbons, 1996;

Montgomery and Buffington, 1998). Therefore, atmospheric N deposition contributes proportionately

more NO − 3 to surface waters in urban settings than it does with other land uses. The reduction in riparian

and wetland coverage and functionality also diminishes the ability of the urban watershed to filter

contaminants from runoff, including atmospherically deposited N (Peterjohn and Correll, 1984). Because

many large urban areas are both located close to the coastline and expected to receive relatively high NO X

deposition, they can constitute sizeable sources of body contribution to estuarine and marine waters.



# C.6.6. Agriculture

Agricultural ecosystems are not sensitive to levels of N deposition typically found in the U.S.

Rather, such ecosystems often act as net sources of NH 3 emissions rather than as sinks (Grünhage et al.,

1992; Krupa, 2003). Atmospheric N deposition can contribute a quantitatively important component of

the N

r requirements of pastures and croplands. In such settings, atmospheric N deposition provides an

additional chronic source of N fertilizer. This may be viewed as a beneficial outcome. Nevertheless, some

of the N that is atmospherically deposited on agricultural land may eventually leach to drainage waters

and contribute to eutrophication, especially in estuarine and near-coastal marine environments. Industrial

livestock operations also contribute substantial amounts of NH Y to the atmosphere, some of which is

deposited on coastal waters.

Agriculture also experiences indirect effects of NO X emissions through the formation of groundlevel O 3

. Such effects are not considered in this review.

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# C.6.7. Other Disturbances

In some ecosystems, chronic additions of atmospherically derived N may have had irreversible

consequences that involve interactions with invasive, non-native plants. For example, California has

many plant species that occur in shrub, forb, and grasslands that receive high N deposition. There are up

to 200 sensitive plant species in southern California coastal sage scrub (CSS) communities alone (Skinner

and Pavlik, 1994). About 25 plant species are thought to be extinct in California, most of them forbs that

occurred in sites that have experienced conversion to annual grassland (EPA, 2005). As CSS v egetation

continues to convert to grassland dominated by invasive species, loss of additional rare plant species may

occur. Invasive plant species are often identified as a major threat to rare native plant species. However,

the occurrence of invasive species may combine with other stress factors, including N deposition, to

promote increased productivity of invasive species at the expense of native species.

As sensitive vegetation is lost, wildlife species that depend on these plants can also be adversely

affected. There are several threatened or endangered wildlife species listed by the U.S. Fish and Wildlife

Service, including the desert tortoise ( Gopherus agassizii ) and checkerspot butterfly that are native to

plant communities in California thought to be sensitive to atmospheric N input. A native to the San

Francisco Bay area, the bay checkerspot butterfly has declined in association with invasion of exotic

grasses that replaced the native forbs on which the butterfly depends. In particular, the larval stage of the

butterfly is dependent on Plantago erecta

, which is increasingly being outcompeted by exotic grasses

(EPA, 2005).

Decline in the population of the desert tortoise may be due to a number of co-occurring stresses,

including grazing, habitat destruction, drought, disease, and a declining food base. In the desert shrub

inter-spaces, sites where native forbs once flourished, invasive grasses now dominate, reducing the

nutritional quality of foods available to the tortoise (Nagy et al., 1998; Fenn, 2003a). N deposition

contributes to the productivity and density of grasses at the expense of native forbs (Brooks, 2003).



# C.6.8. Multiple Stress Response

Ecosystems are often subjected to multiple stressors, of which nutrient enrichment from

atmospheric deposition of N is only one. Additional stressors are also important, including O 3 exposure,

climatic variation, natural and human disturbance, the occurrence of invasive non-native plants, native

and non-native insect pests, and disease. Atmospheric N deposition interacts with these other stressors to

affect ecosystem patterns and processes in ways that we are only beginning to understand.

For example, terrestrial ecosystems at many locations are subjected to high N deposition and high

exposure to O 3

. This is especially true in portions of southern California and the Appalachian Mountains.

Mixed conifer forests in the San Bernardino and San Gabriel mountains in southern California are

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Table C­9. Ecological effects of N deposition described for study sites in the Western U.S.

Ecological or

Environmental Impact Location Level of Uncertainty Possibility of Broader

Occurrence

(at other sites) Reference

Effects in Aquatic Systems

− Elevated NO 3 in runoff;

most severe in southern

California and in chaparral

catchments in the

southwestern Sierra

Nevada

N enrichment and shifts in

diatom communities in

alpine lakes

Reduced lake water clarity

and increased algal

growth Transverse ranges of

southern California; lowelevation catchments in the

Sierra Nevada; high­elevation

catchments in the Colorado

Front Range

Colorado Front Range; Lake

Tahoe (California/Nevada

border)

Lake Tahoe

(California/Nevada border);

high­elevation lakes

throughout central and

southern Sierra Nevada Well­documented

response

Documented for two

lakes east of the

Continental Divide and

Lake Tahoe

Well­documented

response; N and P

deposition believed to

be important factors It is unclear how

widespread this

phenomenon is outside the

ecosystems listed, because

there is littler information

from low­elevation systems

in the Sierra Nevada and

elsewhere.

These effects seem likely in

other N­enriched lakes but

have not been investigated.

Lake Tahoe is an unusual

case because of its

renowned lake clarity;

extent of occurrence

elsewhere in northern Sierra

Nevada is unknown. Williams et al. (1996),

Fenn and Poth (1999),

Fenn et al. (Fenn, 2003)

Baron et al. (2000),

Wolfe et al. (2001),

Goldman (1988)

Jassby et al. (1994)

Sickman et al. (2003a) exposed to high levels of atmospheric O 3 and receive atmospheric N deposition in the range of about 5 to

over 30 kg/ha/yr (Takemoto et al., 2001). Spatial variability in N deposition is high due to the patchy

characteristics of these forests and associated canopy effects on dry deposition processes. The forest

ecosystems have reached N-saturation, as evidenced by high NO − 3 concentrations in stream water.

However, evaluation of N effects on vegetation is complicated by the concurrent effects of O 3 , which has

damaged sensitive plant species, especially ponderosa and Jeffrey pine. Bytnerowicz (Bytnerowicz, 2002)

summarized N/O 3 interactions and consequent effects.

Peak diurnal concentrations of atmospheric O 3 and NO 2 co-occur at Tanbork Flat in the San

Bernardino Mountains (Bytnerowicz et al., 1987). They can have counteracting effects, with O 3 reducing

growth and N deposition enhancing growth of pine trees (Grulke and Balduman, 1999).

Jeffrey and ponderosa pine are the most sensitive western coniferous tree species to injury from O 3

pollution (Miller et al., 1983; Duriscoe and Stolte, 1989). In some areas of the western Sierra Nevada

Mountains, O 3 concentrations have been high enough to cause visible foliar injury to these species and

reduced needle retention (Bytnerowicz, 2002). Reduced radial growth has also been observed (Peterson

et al., 1987, 1991). In the San Bernardino Mountains, trees of these species that exhibit severe foliar

injury from O 3 do not show growth reductions (Arbaugh et al., 1999), and this has been attributed to the

fertilizing effects of high N deposition (Takemoto et al., 2001; Bytnerowicz, 2002). This may be an

example of counteracting effects from O 3 and N air pollution. It is also possible that N deposition in the

western Sierra Nevada Mountains may increase growth of pines, especially on nutritionally poor granitic

soils (Takemoto et al., 2001).

August 2008 C­48 DRAFT—DO NOT QUOTE OR CITEPossibility of Broader Ecological or Location Level of Uncertainty Occurrence Reference Environmental Impact (at other sites)

Increased NO 3 − Several regions, mainly Fairly well established  Evidence suggests that Sickman et al. (2002)

concentrations in high­ downwind of urban centers from lake surveys, but urban plumes and

elevation lakes more data needed for agricultural emissions affect

improved definition of lake NO3− levels. There is

frequency and severity also evidence of impacts on

low­elevation lakes.

Effects in Terrestrial Systems

Enhanced growth of Costal sage scrub, southern N deposition, It is not known if this effect Weiss (1999), Allen et al. (in

invasive species California; San Francisco Bay fertilization studies, occurs elsewhere, but it is press)

area and plant community expected that nitrophilous

data supportive, but species will be selected for if

moderate uncertainty N accumulates in soil.

remains

Lichen community Parts of the Pacific Northwest; Well­established Because of the sensitivity of Nash and Sigal (Nash, 1999)

changes many areas in California; response; a highly many lichen species, it is

north and central Colorado sensitive air pollution likely that this effect occurs

indicator elsewhere.

Deleterious effects on San Francisco Bay area; Supportive evidence, There is a high likelihood of Weiss (1999), Brooks (2003)

threatened and southern California but high degree of effects in some habitats

endangered species uncertainty about the where N accumulates in

precise role of N soils

deposition

Altered fire cycle Coastal sage scrub in Hypothesis based on Because it has not been Allen et al. (in press)

southern California observations, studied elsewhere, it is

fertilization studies, uncertain whether this

and N deposition and effect occurs in other areas.

N cycling data; high

level of uncertainty

Altered forest C cycling San Bernardino Mountains Documented response It is uncertain whether this Grulke and Balduman (1999)

and fuel accumulation effect occurs in other areas.

Physiological perturbation San Bernardino Mountains Documented response This effect has not been Grulke et al. (1998),

of overstory species widely studied but is Grulke and Balduman (1999),

expected for sensitive plant Takemoto et al. (2001)

species exposed to O 3 and

adapted to N limitation but

growing in N­enriched soils.

Forest expansion into Great Plains of western Supportive evidence It is not know whether this Köchy and Wilson (2001)

grasslands Canada found, but high degree effect occurs in other areas.

of uncertainty as to

the role of N

deposition

N emissions as a major National forests and parks Well­established This is known to occur in Fenn et al. (2003c),

contributor to regional throughout California, the effect; contribution areas far removed from IMPROVE data (4 March 2003;

haze problem Pacific Northwest, and some from Nous pollutants emissions sources because http://vista.circa.colostate.edu/

sites in the Interior West has been quantified of long­range transport. improve)

NO X emissions as Southern California; Sierra Well­established effect Significant O 3 injury to Miller and McBride (1999),

precursors for phytotoxic Nevada vegetation has not been Carroll et al. (2003)

levels of O 3 , leading to O 3 reported from other sites

injury to sensitive plant downwind of urban centers

species but cannot be ruled out as

urban regions expand.

Note: Summary includes the degree of uncertainty regarding the role of N deposition in each effect and the likelihood that these effects may occur

elsewhere in the West.

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# ANNEX C

­ References

Aber, J. D. (1992) Nitrogen cycling and nitrogen saturation in temperate forest ecosystems.

Trends Ecol. Evol. 7: 220-224.

Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N d eposition of N

cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11: 639­

648.

Aber, J. D.; Goodale, C. L.; Ollinger, S. V.; Smith, M.-L.; Magill, A. H.; Martin, M. E.; Hall, R.

A.; Stoddard, J. L. (2003) Is nitrogen deposition altering the nitrogen status of

northeastern forests? Bioscience 53: 375-389.

Aber, J. D.; Nadelhoffer, K. J.; Steudler, P.; Melillo, J. M. (1989) Nitrogen saturation in northern

forest ecosystems: excess nitrogen from fossil fuel combustion may stress the biosphere.

BioScience 39: 378-386.

Aber, J. D.; Ollinger, S. V.; Driscoll, C. T.; Likens, G. E.; Holmes, R. T.; Freuder, R. J.;

Goodale, C. L. (2002) Inorganic nitrogen losses from a forested ecosystem in response to

physical, chemical, biotic, and climatic perturbations. Ecosystems 5: 648-658.

Aber, J. D.; Ollinger, S. V.; Federer, C. A.; Driscoll, C. (1997) Modeling nitrogen saturation in

forest ecosystems in response to land use and atmospheric deposition. Ecol. Model. 101:

61-78.

Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.; McNulty, S.;

Currie, W.; Rustad, L.; Fernandez, I. (1998) Nitrogen saturation in temperate forest

ecosystems. BioScience 48: 921-934.

Adams, S. M.; Greeley, M. S.; Law, J. M.; Noga, E. J.; Zelikoff, J. T. (2003) Application of

multiple sublethal stress indicators to assess the health of fish in Pamlico Sound

following extensive flooding. Estuaries 26: 1365-1382.

Adamsen, A. P. S. and G. M. King. 1993. Methane consumption in temperate and sub-arctic

forest soils

- rates, vertical zonation, and responses to water and nitrogen. Applied and

Environmental Microbiology 59:485-490.

Aerts, R. (1990) Nutrient use efficiency in evergreen and deciduous species from heathland.

Oecologia 84: 391-397.

Aerts, R. and H. de Caluwe. 1999. Nitrogen deposition effects on carbon dioxide and methane

emissions from temperate peatland soils. Oikos 84:44-54.

Aerts, R. and S. Toet. 1997. Nutritional controls on carbon dioxide and methane emission from

Carex-dominated peat soils. Soil Biology & Biochemistry 29:1683-1690.

Aerts, R.; Berendse, F. (1988) The effect of increased nutrient availability on vegetation

dynamics in wet heathlands. Vegetatio 76: 63-69.

Aerts, R.; Berendse, F.; De Caluwe, H.; Schmits, M. (1990) Competition in heathland along an experimental gradient of nutrient availability. Oikos 57: 310-318.

Aerts, R.; Verhoeven, J. T. A.; Whigham, D. F. (1999) Plant-mediated controls on nutrient

cycling in temperate fens and bogs. Ecology 80: 2170-2181.

August 2008 C­50 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 A eschlimann, U., J. Nosberger, P. J. Edwards, M. K. Schneider, M. Richter, and H. Blum. 2005.

Responses of net ecosystem CO 2 

### exchange in managed grassland to long-term CO 2 

### enrichment, N fertilization and plant species. Plant Cell and Environment 28:823-833.

Alberti, M.; Booth, D.; Hill, K.; Coburn, B.; Avolio, C.; Coe, S.; Spirandelli, D. (2007) The

impact of urban patterns on aquatic ecosystems: an empirical analysis in Puget lowland

sub-basins. Landscape Urban Plan. 80: 345-361.

Alexander, R. B.; Boyer, E. W.; Smith, R. A.; Schwarz, G. E.; Moore, R. B. (2007) The role of

headwater streams in downstream water quality. J. Am. Water Resour. Assoc. 43: 41-59.

Alexander, R. B.; Johnes, R. J.; Boyer, E. W.; Smith, R. A. (2002) A comparison of models for

estimating the riverine export of nitrogen from large watersheds. Biogeochemistry 57/58:

295-339.

Alexander, R. B.; Smith, R. A.; Schwarz, G. E.; Preston, S. D.; Brakebill, J. W.; Srinivasan, R.;

Pacheco, P. A. (2001) Atmospheric nitrogen flux from the watersheds of major estuaries

of the United States: An application of the SPARROW watershed model. In: Valigura,

R.; Alexander, R.; Castro, M.; Meyers, T.; Paerl, H.; Stacey, P.; Turner, R. E., eds.

Nitrogen loading in coastal water bodies: an atmospheric perspective. Washington, DC:

American Geophysical Union; pp. 119-170.

Allen, E. B.; Rao, L. E.; Steers, R. J. (2008) Impacts of atmospheric nitrogen deposition on

vegetation and soils at Joshua Tree National Park. In: Webb, R. H.; Fenstermaker, J. S.;

Heaton, J. S.; Hughson, D. L.; McDonald, E. V.; Miller, D. M., eds. The Mojave Desert:

ecosystem processes and sustainability. Las Vegas, NV: University of Nevada Press (in

press).

Allen, N. S.; Hershey, A. E. (1996) Seasonal changes in chlorophyll a response to nutrient

amendments in a North Shore tributary of Lake Superior. J. N. Am. Benthol. Soc. 15:

170-178.

Ambus, P. and G. P. Robertson. 1999. Fluxes of CH 4 

### and N 2 

### O in aspen stands grown under

ambient and twice-ambient CO 2 

### . Plant and Soil 209:1-8.

Ambus, P. and G. P. Robertson. 2006. The effect of increased n deposition on nitrous oxide,

methane and carbon dioxide fluxes from unmanaged forest and grassland communities in

Michigan. Biogeochemistry 79:315-337.

Ambus, P. and S. Christensen. 1995. Spatial and seasonal nitrous-oxide and methane fluxes in

danish forest-ecosystems, grassland-ecosystems, and agroecosystems. Journal of

Environmental Quality 24:993-1001.

Ambus, P., S. Zechmeister-Boltenstern, and K. Butterbach-Bahl. 2006. Sources of nitrous oxide

emitted from European forest soils. Biogeosciences 3:135-145.

Ambus, P.; Robertson, G. P. (2006) The effect of increased N deposition on nitrous oxide,

methane, and carbon dioxide fluxes from unmanaged forest and grassland communities in

Michigan. Biogeochemistry 79: 315-337.

Anderson, N. J.; Renberg, I.; Segerstrom, U. (1995) Diatom production responses to the

development of early agriculture in a boreal forest lake-catchment (Kassjon, Northern

Sweden). J. Ecol. 83: 809-822.

August 2008 C­51 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Arbaugh, M. J.; Peterson, D. L.; Miller, P. R. (1999) Air pollution effects on growth of

ponderosa pine, Jeffrey pine, and bigcone Douglas fir. In: Miller, P. R.; McBride, J. R.,

eds. Oxidant air pollution impacts in the montane forests of southern California. New

York: Springer-Verlag; pp. 179-207.

Arheimer, B.; Wittgren, H. B. (1994) Modelling the effects of wetlands on regional nitrogen

transport. Ambio 23: 378-386.

Arnebrandt, K.; Bååth, E.; Söderström, B. (1990) Changes in microfungal community structure

after fertilization of scots pine forest soils with ammonium nitrate or urea. Soil Biol.

Biogeochem. 22: 309-312.

Arnold, C. L.; Gibbons, C. J. (1996) Impervious surface coverage: emergence of a key

environmental indicator. J. Am. Planning Assoc. 62: 243-258.

Arrigo, K. R. (2005) Marine microorganisms and global nutrient cycles. Nature 437: 349-355.

Ashby, J. A.; Bowden, W. B.; Murdoch, P. S. (1998) Controls on denitrification in riparian soils

in headwater catchments of a hardwood forest in the Catskill Mountains, U.S.A. Soil

Biol. Biogeochem. 30: 853-864.

Asman, W. A. H.; Van Jaarsveld, J. A. (1992) A variable-resolution transport model applied for

NH

x 

### for Europe. Atmos. Environ. 26: 445-464.

Avis, P. G.; McLaughlin, D. J.; Dentinger, B. C.; Reich, P. B. (2003) Long-term increases in

nitrogen supply alters above and belowground ectomycorrhizal communities and

increases dominance of Russula species. New P hytol. 160: 239-253.

Axler, R. P.; Reuter, J. E. (1996) Nitrate uptake by phytoplankton and periphyton: whole-lake

enrichments and mesocosm-N-15 experiments in an oligotrophic lake. Limnol. Oceanogr.

41: 659-671.

Ayensu, E.; Van R. Claasen, D.; Collins, M.; Dearing, A.; Fresco, L.; Gadgil, M.; Gitay, H.;

Glaser, G.; Juma, C.; Krebs, J.; Lenton, R.; Lubchenco, J.; McNeely, J. A.; Mooney, H.

A.; Pinstrup-Andersen, P.; Ramos, M.; Raven, P.; Reid, W. V.; Samper, C.; Sarukhan, J.;

Schei, P.; Tundisi, J. G.; Watson, R. T.; Guanhua, X.; Zakri, A. H. (1999) International

ecosystem assessment. Science (Washington, DC) 286: 685-686.

Baddeley, J. A.; Thompson, D. B. A.; Lee, J. A. (1994) Regional and historic variation in the

nitrogen content of Racomitrium lanuginosum in Britain in relation to atmospheric

nitrogen deposition. Environ. Pollut. 84: 189-196.

Baez, S.; Fargione, J.; Moore, D. I.; Collins, S. L.; Gosz, J. R. (2007) Atmospheric nitrogen

deposition in the northern Chihuahuan desert: temporal trends and potential

consequences. J. Arid Environ. 68: 640-651.

Baggs, E. M. and H. Blum. 2004. CH 4 

### oxidation and emissions of CH 4 

### and N 2 

### O f rom Lolium

perenne swards under elevated atmospheric CO 2



### . Soil Biology & Biochemistry 36:713­

723.

Baggs, E. M., M. Richter, G. Cadisch, and U. A. Hartwig. 2003. Denitrification in grass swards

is increased under elevated atmospheric CO 2



### . Soil Biology & Biochemistry 35:729-732.

Bajwa, R.; Read, D. J. (1985) The biology of the mycorrhizae in the Ericaceae. IX. Peptides as nitrogen sources for the ericoid endophyte and for mycorrhizal and non-mycorhizzal

plants. New P hytol. 101: 459-467.

August 2008 C­52 DRAFT—DO NOT QUOTE OR CITE

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21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Baker, L. A. (1991) Regional estimates of atmospheric dry deposition. In: Charles, D. F., ed.

Acidic deposition and aquatic ecosystems: regional case studies. New Y ork, NY:

Springer-Verlag; pp. 645-652.

Barker, D. H.; Vanier, C.; Naumburg, E.; Charlet, T. N.; Nielsen, K. M.; Newingham, B. A.;

Smith, S. D. (2006) Enhanced monsoon precipitation and nitrogen deposition affect leaf

traits and photosynthesis differently in spring and summer in the desert shrub Larrea

tridentata. New P hytol. 169: 799-808.

Baron, J. (1992) Surface waters. In: Baron, J.; Arthur, M. A.; Denning, S.; Harris, M. A.; Mast,

M. A.; Mcknight, D. M.; Mclaughlin, P.; Rosenlund, B. D.; Spaulding, S. A.; Walthall, P.

M., eds. Biogeochemistry of a subalpine ecosystem. Loch Vale watershed. New Y ork:

Springer-Verlag; pp. 142-186.

Baron, J. S. (2006) Hindcasting nitrogen deposition to determine ecological critical load. Ecol.

Appl. 16: 433-439.

Baron, J. S.; Rueth, H. M.; Wolfe, A. M.; Nydick, K. R.; Allstott, E. J.; Minear, J. T.; Moraska,

B. (2000) Ecosystem responses to nitrogen deposition in the Colorado Front Range.

Ecosystems 3: 352-368.

Basiliko, N., T. R. Moore, R. Jeannotte, and J. L. Bubier. 2006. Nutrient input and carbon and

microbial dynamics in an ombrotrophic bog. Geomicrobiology Journal 23:531-543.

Bauer, G. A.; Bazaaz, F. A.; Minocha, R.; Long, S.; Magill, A. H.; Aber, J. D.; Berntson, G. M.

(2004) Effects of chronic N a dditions on tissue chemistry, photosynthetic capacity, and

carbon sequestration potential of a red pine (Pinus resinosa Ait.) stand in the NE United

States. For. Ecol. Manage. 196: 173-186.

Bedford, B. L.; Godwin, K. S. (2003) Fens of the United States: distribution, characteristics, and

scientific connection versus legal isolation. Wetlands 23: 608-629.

Bedford, B. L.; Walbridge, M. R.; Aldous, A. (1999) Patterns in nutrient availability and plant

diversity of temperature North American wetlands. Ecol. Soc. Am. 80: 2151-2169.

Benstead, J. P.; Deegan, L. A.; Peterson, B. J.; Huryn, A. D.; Bowden, W. B.; Suberkropp, K.;

Buzby, K. M.; Green, A. C.; Vacca, J. A. (2005) Responses of a beaded Arctic stream to

short-term N a nd P fertilisation. Freshwater Biol. 50: 277-290.

Berendse, F.; Breemen, N. V.; Rydin, H.; Buttler, A.; Heijmans, M.; Hoosbeek, M. R.; Lee, J.

A.; Mitchell, A.; Saarinen, T.; Vassander, H.; Wallen, B. (2001) Raised atmospheric CO 2



### levels and increased N d eposition cause shifts in plant species composition and

production in Sphagnum bogs. Glob. Change Biol. 7: 591-598.

Bergametti, G.; Remoudaki, E.; Losno, R.; Steiner, E.; Chatenet, B. (1992) Source, transport and

deposition of atmospheric phosphorus over the northwestern Mediterranean. J. Atmos.

Chem. 14: 501-513.

Bergström, A.; Blomqvist, P.; Jansson, M. (2005) Effects of atmospheric nitrogen deposition on

nutrient limitation and phytoplankton biomass in unproductive Swedish lakes. Limnol.

Oceanogr. 50: 987-994.

Bergström, A.-K.; Jansson, M. (2006) Atmospheric nitrogen deposition has caused nitrogen

enrichment and eutrophication of lakes in the northern hemisphere. Glob. Change Biol.

12: 635-643.

August 2008 C­53 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Bernhardt, E. S.; Likens, G. E. (2002) Dissolved organic carbon enrichment alters nitrogen

dynamics in a forest stream. Ecology 83: 1689-1700.

Bernot, M. J.; Dodds, W. K.; Gardner, W. S.; McCarthy, M. J.; Sobolev, D.; Tank, J. L. (2003)

Comparing denitrification estimates for a Texas estuary by using acetylene inhibition and

membrane inlet mass spectrometry. Appl. Environ. Microbiol. 69: 5950-5956.

Bert, D.; Leavitt, S. W.; Dupouey, J.-L. (1997) Variations in wood 13 

### δ C and water-use efficiency

of Abies alba during the last century. Ecology 78: 1588-1595.

Bloom, A. J.; Chapin, F. S., III; Mooney, H. A. (1985) Resource limitation in plants—an

economic analogy. Annu. Rev. Ecol. Syst. 16: 363-392.

Bobbink, R. (1998) Impacts of tropospheric ozone and airborne nitrogenous pollutants on natural

and semi-natural ecosystems: a commentary. New Phytol. 139: 161-168.

Bobbink, R.; Boxman, D.; Fremstad, E.; Heil, G.; Houdijk, A.; Roelofs, J. (1992) Critical loads

for nitrogen eutrophication of terrestrial and wetland ecosystems based upon changes in

vegetation and fauna. In: Grennfelt, P.; Thörnelöf, E., eds. Critical loads for nitrogen.

Nord 92:41. Nordic Council of Ministers, Copenhagen, pp. 111-159.

Bobbink, R.; Hornung, M.; Roelofs, J. G. M. (1998) The effects of air-borne nitrogen pollutants

on species diversity in natural and semi-natural European vegetation. J. Ecol. 86: 717­

738.

Bodelier, P. L. E.; Roslev, P.; Heckel, T.; Frenzel, P. (2000) Stimulation by ammonium-based

fertilizers of methane oxidation in soil around rice roots. Nature 403: 421-424.

Bohlke, J. K.; Denver, J. M. (1995) Combined use of ground-water dating, chemical and isotopic

analyses to resolve the history and fate of nitrate contamincation in 2 agricultural

watersheds, Atlantic coastal-plain, Maryland. Water Resour. Res. 31: 2319-2339.

Borken, W. and F. Beese. 2005. Control of nitrous oxide emissions in European beech, Norway

spruce and Scots pine forests. Biogeochemistry 76:141-159.

Borken, W., F. Beese, R. Brumme, and N. Lamersdorf. 2002. Long-term reduction in nitrogen

and proton inputs did not affect atmospheric methane uptake and nitrous oxide emission

from a German spruce forest soil. Soil Biology & Biochemistry 34:1815-1819.

Bormann, F. H.; Likens, G. E. (1979) Pattern and process in a forested ecosystem. New Y ork,

NY: Springer-Verlag.

Bormann, F. H.; Likens, G. E.; Fisher, D. W.; Pierce, R. S. (1968) Nutrient loss accelerated by

clear-cutting of a forest ecosystem. Science (Washington, DC) 159: 882-884.

Bormann, F. H.; Likens, G. E.; Melillo, J. M. (1977) Nitrogen budget for an aggrading northern

hardwood forest ecosystem. Science (Washington, DC) 196: 981-983.

Borum, J. (1996) Shallow w aters and land/sea boundaries. In: Jorgensen, B. B.; Richardson, K.,

eds. Eutrophication in coastal marine ecosystems. Washington DC: American

Geophysical Union; pp. 179-203.

Bouwman, A. F.; Van Drecht, G.; Knoop, J. M.; Beusen, A. H. W.; Meinardi, C. R. (2005)

Exploring changes in river nitrogen export the world's oceans. Glob. Biogeochem. Cycles

19(GB1002): 10.1029/2004GB002314.

August 2008 C­54 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 B owden, R. D., G. Rullo, G. R. Stevens, and P. A. Steudler. 2000. Soil fluxes of carbon dioxide,

nitrous oxide, and methane at a productive temperate deciduous forest. Journal of

Environmental Quality 29:268-276.

Bowden, R. D., J. M. Melillo, P. A. Steudler, and J. D. Aber. 1991. Effects of nitrogen additions

on annual nitrous-oxide fluxes from temperate forest soils in the northeastern unitedstates. Journal of Geophysical Research-Atmospheres 96:9321-9328.

Bowman, W. D. (1992) Inputs and storage of nitrogen in winter snowpack in an alpine

ecosystem. Arct. Alp. Res. 24: 211-215.

Bowman, W. D. (1994) Accumulation and use of nitrogen and phosphorus following fertilization

in two alpine tundra communities. Oikos 70: 261-270.

Bowman, W. D. (2000) Biotic controls over ecosystem response to environmental change in

alpine tundra of the Rocky Mountains. Ambio 29: 396-400.

Bowman, W. D.; Fisk, M. C. (2001) Primary production. In: Bowman, W. D.; Seastedt, T. R.,

eds. Structure and function of an alpine ecosystem: Niwot Ridge, Colorado. Oxford, UK:

Oxford University Press; pp. 177-197.

Bowman, W. D.; Gartner, J. R.; Holland, K.; Wiedermann, M. (2006) Nitrogen critical loads for

alpine vegetation and terrestrial ecosystem response: are we there yet? Ecol. Appl. 16:

1183-1193.

Bowman, W. D.; Steltzer, H. (1998) Positive feedbacks to anthropogenic nitrogen deposition in

Rocky Mountain alpine tundra. Ambio 27: 514-517.

Bowman, W. D.; Theodose, T. A.; Fisk, M. C. (1995) Physiological and production responses of

plant growth forms to increases in limiting resources in alpine tundra: implications for

differential community response to environmental change. Oecologia 101: 217-227.

Bowman, W. D.; Theodose, T. A.; Schardt, J. C.; Conant, R. T. (1993) Constraints of nutrient

availability on primary production in two alpine tundra communities. Ecology 74: 2085­

2097.

Boxman, A. W.; Blanck, K.; Brandrud, T.-E.; Emmett, B. A.; Gundersen, P.; Hogervorst, R. F.;

Kjonass, O. J.; Persson, H.; Timmermann, V. (1998a) Vegetation and soil biota response

to experimentally-changed nitrogen inputs in coniferous forest ecosystems of the

NITREX project. For. Ecol. Manage. 101: 65-79.

Boxman, A. W.; van der Ven, P. J. M.; Roelofs, J. G. M. (1998b) Ecosystem recovery after a

decrease in nitrogen input to a Scots pine stand at Ysselsteyn, the Netherlands. For. Ecol.

Manage. 101: 155-163.

Boyer, E. W.; Goodale, C. L.; Jaworski, N. A.; Howarth, R. W. (2002) Anthropogenic nitrogen

sources and relationships to riverine nitrogen export in the northeastern U.S.A.

Biogeochemistry 57/58: 137-169.

Boyer, E.; Howarth, R. W.; Galloway, J. N.; Dentener, F. J.; Green, P. A.; Vörösmarty, C. J.

(2006) Riverine nitrogen export from the continents to the coasts. Glob. Biogeochem.

Cycles 20.

Boynton, W. R.; Garber, J. H.; Summers, R.; Kemp, W. M. (1995) Inputs, transformations, and

transport of nitrogen and phosphorus in Chesapeake Bay and selected tributaries.

Estuaries 18: 285-314.

August 2008 C­55 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Bradford, M. A., P. Ineson, P. A. Wookey, and H. M. Lappin-Scott. 2001. The effects of acid

nitrogen and acid sulphur deposition on CH 4 

### oxidation in a forest soil: a laboratory study.

Soil Biology & Biochemistry 33:1695-1702.

Bredemeier, M.; Blanck, K.; Xu, Y. J.; Tietema, A.; Boxman, A. W.; Emmett, B.; Moldan, F.;

Gundersen, P.; Schleppi, P.; Wright, R. F. (1998) Input-output budgets at the NITREX

sites. For. Ecol. Manage. 101: 57-64.

Bricker, S. B.; Clement, C. G.; Pirhalla, D. E.; Orlando, S. P.; Farrow, D. G. G. (1999) National

estuarine eutrophication assessment: effects of nutrient enrichment in the nation's

estuaries. Silver Spring, MD: Special Projects Office and the National Centers for Coastal

Ocean Science, National Ocean Service, National Oceanic and Atmospheric

Administration.

Broderick, S. J.; Cullen, P.; Maher, W. (1988) Denitrification in a natural wetland receiving

secondary treated effluent. Water Res. 4: 431-439.

Brooks, M. L. (1999) Alien annual grasses and fire in the Mojave Desert. Madroño 46: 13-19.

Brooks, M. L. (2003) Effects of increased soil nitrogen on the dominance of alien annual plants

in the Mojave Desert. J. Appl. Ecol. 40: 344-353.

Brooks, M. L.; D'Antonio, C. M.; Richardson, D. M.; Grace, J. B.; Keeley, J. E.; DiTomaso, J.

M.; Hobbs, R. J.; Pellant, M.; Pyke, D. (2004) Effects of invasive alien plants on fire

regimes. BioScience 54: 677-688.

Brooks, M. L.; Esque, T. C. (2002) Alien annual plants and wildfire in desert tortoise habitat:

status, ecological effects, and management. Chelonian Conserv. Biol. 4: 330-340.

Brumme, R. and F. Beese. 1992. Effects of liming and nitrogen-fertilization on emissions of CO 2



### and N 2 

### O f rom a temporate forest. Journal of Geophysical Research-Atmospheres

97:12851-12858.

Brumme, R., W. Borken, and S. Finke. 1999. Hierarchical control on nitrous oxide emission in

forest ecosystems. Global Biogeochemical Cycles 13:1137-1148.

Brunet, J.; Diekmann, M.; Falkengren-Grerup, U. (1998) Effects of nitrogen deposition on field

layer vegetation in south Swedish oak forests. Environ. Pollut. (Oxford, U.K.) 102(suppl.

1): 35-40.

Bubier, J. L., T. R. Moore, and L. A. Bledzki. 2007. Effects of nutrient addition on vegetation

and carbon cycling in an ombrotrophic bog. Global Change Biology 13:1168-1186.

Burges, S. J.; Wigmosta, M. S.; Meena, J. M. (1998) Hydrological effects of landuse change in a

zero-order catchment. J. Hydro. Engr. 3: 86-97.

Burkholder, J. M.; Dickey, D. A.; Kinder, C. A.; Reed, R. E.; Mallin, M. A.; McIver, M. R.;

Cahoon, L. B.; Melia, G.; Brownie, C.; Smith, J.; Deamer, N.; Springer, J.; Glasgow, H.

B.; Toms, D. (2006) Comprehensive trend analysis of nutrients and related variables in a large eutrophic estuary: a decadal study of anthropogenic and climatic influences.

Limnol. Oceanogr. 51(1, pt. 2): 463-487.

Burns, D. A. (2004) The effects of atmospheric nitrogen deposition in the Rocky Mountains of

Colorado and southern Wyoming, USA—a critical review. Environ. Pollut. 127: 257­

269.

August 2008 C­56 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Burns, D. A.; Kendall, C. (2002) Analysis of 15 

### N a nd 18 

### δ δ O t o differentiate NO

- 3 

### sources in

runoff at two watersheds in the Catskill Mountains of New Y ork. Water Resour. Res. 38:

1051.

Burton, A. J., K. S. Pregitzer, and R. L. Hendrick. 2000. Relationships between fine root

dynamics and nitrogen availability in Michigan northern hardwood forests. Oecologia

125:389-399.

Bushong, S. J.; Bachmann, R. W. (1989) In situ nutrient enrichment experiments with periphyton

in agricultural streams. Hydrobiologia 178: 1-10.

Butterbach-Bahl, K., L. Breuer, R. Gasche, G. Willibald, and H. Papen. 2002. Exchange of trace

gases between soils and the atmosphere in Scots pine forest ecosystems of the

northeastern German lowlands 1. Fluxes of N 2 

### O, NO/NO 2 

### and CH 4 

### at forest sites with

different N-deposition. Forest Ecology and Management 167:123-134.

Butterbach-Bahl, K.; Breuer, L.; Gasche, R.; Willibald, G.; Papen, H. (2002a) Exchange of trace

gases between soils and the atmosphere in Scots pine forest ecosystems of the

northeastern German lowlands. 1. Fluxes of N 2 

### O, NO/NO 2 

### and CH 4 

### at forest sites with

different N-deposition. For. Ecol. Manage. 167: 123-134.

Butterbach-Bahl, K.; Gasche, R.; Willibald, G.; Papen, H. (2002b) Exchange of N-gases at the

Hoglwald forest: a summary. Plant Soil 240: 117-123.

Bytnerowicz, A. (2002) Physiological/ecological interactions between ozone and nitrogen

deposition in forest ecosystems. Phyton 42: 13-28.

Bytnerowicz, A.; Fenn, M. E. (1996) Nitrogen deposition in California forests: a review.

Environ. Pollut. 92: 127-146.

Bytnerowicz, A.; Miller, P. R.; Olszyk, D. M. (1987) Dry deposition of nitrate, ammonium and

sulfate to a Ceanothus crassifolius canopy and surrogate surfaces. Atmos. Environ. 21:

1749-1757

Caddy, J. F. (1993) Toward a comparative evaluation of human impacts on fishery ecosystems of

enclosed and semi-enclosed seas. Rev. Fish. Sci. 1: 57-95.

Camargo, J. A.; Alonso, A.; Salamanca, A. (2005) Nitrate toxicity to aquatic animals: a review

with new d ata for freshwater invertebrates. Chemosphere 58: 1255-1267.

Camargo, J. A.; Ward, J. V. (1995) Nitrate (NO 3 

### -N) toxicity to aquatic life: a proposal of safe

concentrations for two species of nearctic freshwater invertebrates. Chemosphere 31:

3211-3216.

Campbell, D. H.; Clow, D. W.; Ingersoll, G. P.; Mast, M. A.; Spahr, N. E.; Turk, J. T. (1995)

Processes controlling the chemistry of two snowmelt-dominated streams in the Rocky

Mountains. Water Resour. Res. 31: 2811-2821.

Campbell, D. H.; Kendall, C.; Chang, C. C. Y.; Silva, S. R.; Tonnessen, K. A. (2002) Pathways

for nitrate release from an alpine watershed: determination using 15 

### N a nd 18 

### δ δ O. Water

Resour. Res. 31: 2811-2821.

Campbell, J. L.; Hornbeck, J. W.; McDowell, W. H.; Buso, D. C.; Shanley, J. B.; Likens, G. E.

(2000) Dissolved organic nitrogen budgets for upland, forested ecosystems in New

England. Biogeochemistry 49: 123-142.

August 2008 C­57 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Canary, J. D., R. B. Harrison, J. E. Compton, and H. N. Chappell. 2000. Additional carbon

sequestration following repeated urea fertilization of second-growth Douglas-fir stands in

western Washington. Forest Ecology and Management 138:225-232.

Cape, J. N.; Leith, I. D.; Fowler, D.; Murray, M. B.; Sheppard, L. J. Eamus, D.; Wilson, R. H. F.

(1991) Sulphate and ammonium in mist impair the frost hardening of red spruce seedlings. New P hytol. 118: 119-126.

Carreiro, M. M.; Sinsabaugh, R. L.; Repert, D. A.; Parkhurst, D. F. (2000) Microbial enzyme

shifts explain litter decay responses to simulated nitrogen deposition. Ecology 81: 2359­

2365.

Carroll, J. J.; Miller, P. R.; Pronos, J. (2003) Historical perspectives on ambient ozone and its

effects on the Sierra Nevada. In: Bytnerowicz, A.; Arbaugh, M. J.; Alonso, R., eds.

Ozone air pollution in the Sierra Nevada: distribution and effects on forests; pp. 33-54.

New Y ork, NY: Elsevier. (Developments in Environmental Science: v. 2).

Caspersen, J. P.; Pacala, S. W.; Jenkins, J. C.; Hurtt, G. C.; Moorcroft, P. R.; Birdsey, R. A.

(2000) Contributions of land-use history to carbon accumulation in U.S. forests. Science

290: 1148-1151.

Castaldi, S. and K. A. Smith. 1998. The effect of different N s ubstrates on biological N 2 

### O

production from forest and agricultural light textured soils. Plant and Soil 199:229-238.

Castro, M. S., P. A. Steudler, J. M. Melillo, J. D. Aber, and S. Millham. 1992. Exchange of N 2 

### O

and CH 4 

### between the atmosphere and soils in spruce-fir forests in the northeastern unitedstates. Biogeochemistry 18:119-135.

Castro, M. S., W. T. Peterjohn, J. M. Melillo, P. A. Steudler, H. L. Gholz, and D. Lewis. 1994.

Effects of nitrogen-fertilization on the fluxes of N 2 

### O, CH 4 

### , and CO 2 

### from soils in a florida

slash pine plantation. Canadian Journal of Forest Research-Revue Canadienne De

Recherche Forestiere 24:9-13.

Castro, M. S.; Driscoll, C. T. (2002) Atmospheric nitrogen deposition has caused nitrogen

enrichment and eutrophication of lakes in the northern hemisphere. Environ. Sci.

Technol. 36: 3242-3249.

Castro, M. S.; Driscoll, C. T.; Jordan, T. E.; Reay, W. G.; Boynton, W. R.; Seitzinger, S. P.;

Styles, R. V.; Cable, J. E. (2001) Contribution of atmospheric depostition to the total

nitrogen loads to thirty-four estuaries on the Atlantic and Gulf Coasts of the United

States. In: Valigura, R. A.; Alexander, R. B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.;

Stacey, P. E.; Turner, R. E., eds. Nitrogen loading in coastal water bodies: an atmospheric

perspective. Washington, DC: American Geophysical Union; pp. 77-106.

Castro, M. S.; Driscoll, C. T.; Jordan, T. E.; Reay, W. G.; Boynton, W. R. (2003) Sources of

nitrogen to estuaries in the United States. Estuaries 26: 803-814.

Chadwick, M. A.; Huryn, A. D. (2003) Effect of a whole-catchment N a ddition on stream

detritus processing. J. North Am. Benthol. Soc. 22: 194-206.

Chadwick, M. A.; Huryn, A. D. (2005) Response of stream macroinvertebrate production to

atmospheric N d eposition and channel drying. Limnol. Oceanogr. 50: 228-236.

August 2008 C­58 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Chan, A. S. K., P. A. Steudler, R. D. Bowden, J. Gulledge, and C. M. Cavanaugh. 2005.

Consequences of nitrogen fertilization on soil methane consumption in a productive

temperate deciduous forest. Biology and Fertility of Soils 41:182-189.

Chapin, F. S,. III.; Moilanen, L.; Kielland, K. (1993) Preferential use of organic nitrogen for

growth by a non-mycorrhizal arctic sedge. Nature 361: 150-153.

Chapin, F. S., III. (1980) The mineral nutrition of wild plants. Annu. Rev. Ecol. Syst. 11: 233­

260.

Chapin, F. S., III. (1991) Integrated responses of plants to stress: a centralized system of

physiological responses. BioScience 41: 29-36.

Chapin, F. S., III; Bloom, A. J.; Field, C. B.; Waring, R. H. (1987) Plant responses to multiple

environmental factors. BioScience 37: 49-57.

Chapin, F. S., III; Sala, O. E.; Burke, I. C.; Grime, J. P.; Hooper, D. U.; Lauenroth, W. K.;

Lombard, A.; Mooney, H. A.; Mosier, A. R.; Naeem, S.; Pacala, S. W.; Roy, J.; Steffen,

W. L.; Tilman, D. (1998) Ecosystem consequences of changing biodiversity. BioScience

48: 45-52.

Chappelka, A. H.; Samuelson, L. J. (1998) Ambient ozone effects on forest trees of the eastern

United States: a review. New P hytol. 139: 91-108.

Charbonneau, R.; Kondolf, G. M. (1993) Land use change in California: nonpoint source water

quality impacts. Environ. Manage. 17: 453-460.

Chen, L.; Driscoll, C. T. (2004) Modeling the response of soil and surface waters in the

Adirondack and Catskill regions of New Y ork to changes in atmospheric deposition and

historical land disturbance. Atmos. Environ. 38: 4099-4109.

Christensen, T. R., A. Michelsen, S. Jonasson, and I. K. Schmidt. 1997. Carbon dioxide and

methane exchange of a subarctic heath in response to climate change related

environmental manipulations. Oikos 79:34-44.

Christie, C. E.; Smol, J. P. (1993) Diatom assemblages as indicators of lake rophic status in

southeastern Ontario lakes. J. Phycol. 29: 575-586.

Cione, N. K.; Padgett, P. E.; Allen, E. B. (2002) Restoration of a native shrubland impacted by

exotic grasses, frequent fire and nitrogen deposition in southern California. Restor. Ecol.

10: 376-384.

Cloern, J. E. (2001) Our evolving conceptual model of the coastal eutrophication problem. Mar.

Ecol. Prog. Ser. 210: 223-253.

Cole, D. W.; Rapp, M. (1981) Elemental cycling in forest ecosystems. In: Reichle, D. E., ed.

Dynamic properties of forest ecosystems. Cambridge, United Kingdom: Cambridge

University Press; pp. 341-409. (International Biological Programme, 23).

Compton, J. E.; Watrud, L. S.; Porteous, A.; DeGrood, S. (2004) Response of soil microbial

biomass and community composition to chronic nitrogen additions at Harvard forest. For.

Ecol. Manage. 196: 143-158.

Conley, D. J.; Markager, S.; Andersen, J.; Ellermann, T.; Svendsen, L. M. (2002) Coastal

eutrophication and the Danish National Aquatic Monitoring and Assessment Program.

Estuaries 25: 848-861.

August 2008 C­59 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 C onley, D. J.; Schelske, C. L.; Stoermer, E. F. (1993) Modification of the biogeochemical cycle

of silica with eutrophication. Mar. Ecol. Prog. Ser. 101: 179-192.

Cook, R. B.; Elwood, J. W.; Turner, R. R.; Bogle, M. A.; Mulholland, P. J.; Palumbo, A. V.

(1994) Acid-base chemistry of high-elevation streams in the Great Smoky Mountains.

Water Air Soil Pollut. 72: 331-356.

Cooper, A. B. (1990) Nitrate depletion in the riparian zone and stream channel of a small

headwater catchment. Hydrobiologia 202: 13-26.

Costanza, R.; d'Arge, R.; De Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.;

Naeem, S.; O'Neill, R. V.; Paruelo, J.; Raskin, R. G.; Sutton, P.; Van Den Belt, M. (1997)

The value of the world's ecosystem services and natural capital. Nature (London) 387:

253-259.

Crill, P. M., P. J. Martikainen, H. Nykanen, and J. Silvola. 1994. Temperature and n-fertilization

effects on methane oxidation in a drained peatland soil. Soil Biology & Biochemistry

26:1331-1339.

Curtis, C. J., B. A. Emmett, B. Reynolds, and J. Shilland. 2006. How important is N 2 

### O

production in removing atmospherically deposited nitrogen from UK moorland

catchments? Soil Biology & Biochemistry 38:2081-2091.

Dahlgren, R. A.; Driscoll, C. T. (1994) The effects of whole-tree clear cutting on soil processes

at Hubbard Brook Experimental Forest, New Hampshire, USA. Plant Soil 58: 239-262.

Daily, G. C. (1997) Introduction: what are ecosystem services? In: Daily, G. C., ed. Nature's

services: societal dependence on natural ecosystems. Washington, DC: Island Press; pp.

1-10.

Daily, G. C.; Ehrlich, P. R. (1999) Managing Earth's ecosystems: an interdisciplinary challenge.

Ecosystems 2: 277-280.

Dalsgaard, T.; Tharndrup, B.; Canfield, D. E. (2005) Anaerobic ammonium oxidation

(anammox) in the marine environment. Res. Microbiol. 156: 457-464.

D'Antonio, C. M.; Vitousek, P. M. (1992) Biological invasions by exotic grasses, the grass/fire

cycle, and global change. Annu. Rev. Ecol. Syst. 23: 63-87.

Das, B.; Vinebrooke, R. D.; Sanchez-Azofeifa, A.; Rivard, B.; Wolfe, A. P. (2005) Inferring

sedimentary chlorophyll concentrations with reflectance spectroscopy: a novel approach

to reconstructing historical changes in the trophic status of mountain lakes. Can. J. Fish.

Aquat. Sci. 62: 1067-1078.

Davidson, E. A.; Keller, M.; Erickson, H. E.; Verchot, L. V.; Veldkamp, E. (2000) Testing a

conceptual model of soil emissions of nitrous and nitric oxides. BioScience 50: 667-680.

Davidson, E. A.; Seitzinger, S. (2006) The enigma of progress in denitrification research. Ecol.

Appl. 16: 2057-2063.

De Bakker, A. J. (1989) Effects of ammonia emission on epiphytic lichen vegetation. Acta Bot.

Neerl. 38: 337-342.

Del Giorgio, P. A.; Cole, J. J.; Cimberlic, A. (1997) Respiration rates in bacteria exceed

phytoplankton production in unproductive aquatic system. Nature 385: 148-151.

August 2008 C­60 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Delaune, R. D., C. W. Lindau, E. Sulaeman, and A. Jugsujinda. 1998. Nitrification and

denitrification estimates in a Louisiana swamp forest soil as assessed BY N -15 isotope

dilution and direct gaseous measurements. Water Air and Soil Pollution 106:149-161.

Delgado, J. A., A. R. Mosier, R. H. Follett, R. F. Follett, D. G. Westfall, L. K. Klemedtsson, and

J. Vermeulen. 1996. Effects of N m anagement on N O a n CH 2 

### d 15

4 

### fluxes and N Recovery

in an irrigated mountain meadow. Nutrient Cycling in Agroecosystems 46:127-134.

D'Elia, C. F.; Sanders, J. G.; Boynton, W. R. (1986) Nutrient enrichment studies in a coastal

plain estuary: phytoplankton growth in large-scale, continuous cultures. Can. J. Fish.

Aquat. Sci. 43: 397-406.

Devito, K. J.; Dillon, P. J.; LaZerte, B. D. (1989) Phosphorus and nitrogen retention in five

Precambrian shield wetlands. Biogeochemistry 8: 185-204.

Devol, A. (2003) Solution to a marine mystery. Nature 422: 575-576.

DeWalle, D. R.; Kochenderfer, J. N.; Adams, M. B.; Miller, G. W.; Gilliam, F. S.; Wood, F.;

Odenwald-Clemens, S. S.; Sharpe, W. E. (2006) Vegetation and acidification. In: Adams,

M. B.; DeWalle, D. R.; Hom, J. L., eds. The Fernow w atershed acidification study.

Dordrecht, The Netherlands: Springer; pp. 137-188.

Diemer, M. 1997. Effects of elevated CO 2 

### on gas exchange characteristics of alpine grassland.

Acta Oecologica-International Journal of Ecology 18:177-182.

Dierberg, F. E.; Brezonik, P. L. (1983) Nitrogen and phosphorus mass balances in natural and

sewage-enriched cypress domes. J. Appl. Ecol. 20: 323-337.

Dijkstra, F. A.; Smits, M. M. (2002) Tree species effects on calcium cycling: the role of calcium

uptake in deep soils. Ecosystems 5: 385-398.

Dise, N. B.; Wright, R. F. (1995) Nitrogen leaching from European forests in relation to nitrogen

deposition. For. Ecol. Manage. 71: 153-161.

Dodds, W. K. (2006) Eutrophication and trophic state in rivers and streams. Limnol. Oceanogr.

51(1 pt. 2): 671-680.

Dortch, Q.; Whitledge, T. E. (1992) Does nitrogen or silicon limit phytoplankton production in

the Mississippi River plume and nearby regions? Cont. Shelf. Res. 12: 1293-1309.

Downing, J. A. (1997) Marine nitrogen: phosphorus stoichiometry and the global N:P cycle.

Biogeochemistry 37: 237-252.

Downing, J. A.; McCauley, E. (1992) The nitrogen-phosphorus relationship in lakes. Limnol.

Oceanogr. 37: 936-945.

Drenovsky, R. H.; Richards, J. H. (2005) Nitrogen addition increases fecundity in the desert

shrub Sarcobatus vermiculatus. Oecologia 143: 349-356.

Driscoll, C. T.; Driscoll, K. M.; Roy, K. M.; Mitchell, M. J. (2003b) Chemical response of lakes

in the Adirondack region of New Y ork to declines in acidic deposition. Environ. Sci.

Technol. 37: 2036-2042.

Driscoll, C. T.; Van Dreason, R. (1993) Seasonal and long-term temporal patterns in the

chemistry of Adirondack lakes. Water Air Soil Pollut. 67: 319-344.

Driscoll, C. T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C. L.;

Groffman, P.; Hopkinson, C.; lambert, K.; Lawrence, G.; Ollinger, S. (2003c) Nitrogen

August 2008 C­61 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 p ollution in the northeastern United States: sources, effects, and management options.

BioScience 53: 357-374.

Driscoll, C.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C.; Groffman, P.;

Hopkinson, C.; Lambert, K.; Lawrence, G.; Ollinger, S. (2003a) Nitrogen pollution:

sources and consequences in the U.S. Northeast. Environment 45: 8-22.

Dueck, T. A.; Dorèl, F. G.; Ter Horst, R.; Van der Eerden, L. J. M. (1990) Effects of ammonia,

ammonium sulphate and sulphur dioxide on the frost sensitivity of Scots pine (Pinus

sylvestris L.). Water Air Soil Pollut. 54: 35-49.

Dueck, T. A.; Van der Eerden, L. J. M.; Breemsterboer, B.; Elderson, J. (1991) Nitrogen uptake

and allocation by Calluna vulgaris (L.) Hull and Deschampsia flexuosa (L.) Trin. exposed

to 15 

### NH 3 

### . Acta Bot. Neerl. 40: 257-267.

Dumont, E.; Harrison, J. A.; Kroeze, C.; Bakker, E. J.; Seitzinger, S. P. (2005) Global

distribution and sources of dissolved inorganic nitrogen export to the coastal zone: results

from a spatially explicit, global model. Glob. Biogeochem. Cycles 19.

Duriscoe, D. M.; Stolte, K. W. (1989) Photochemical oxidant injury to ponderosa pine (Pinus

ponderosa Laws.) and Jeffrey pine (Pinus jeffreyi Grev. and Balf.) in the national parks

of the Sierra Nevada of California. In: Olson, R. K.; Lefohn, A. S., eds. Effects of air

pollution on western forests [an A&WMA symposium; June; Anaheim, CA]. Pittsburgh,

PA: Air and Waste Management Asssociation; pp. 261-278. (APCA transactions series:

no. 16).

Eadie, B. J.; McKee, B. A.; Lansing, M. B.; Robbins, J. A.; Metz, S.; Trefry, J. H. (1994)

Records of nutrient-enhanced coastal productivity in sediments from the Louisiana

continental shelf. Estuaries 17: 754-765.

Edmondson, W. T. (1969) Eutrophication in North America. Washington, D.C.: National

Academy of Sciences, Printing and Publishing Office.

Edmondson, W. T. (1991) The uses of ecology: Lake Washington and beyond. Seattle, WA:

University of Washington Press.

Edwards, P. J.; Wood, F.; Kochenderfer, J. N. (2002) Baseflow and peakflow chemical responses

to experimental applications of ammonium sulphate to forested watersheds in northcentral West Virginia, USA. Hydrol. Process. 16: 2287-2310.

Egerton-Warburton, L. M.; Allen, E. B. (2000) Shifts in arbuscular mycorrhizal communities

along an anthropogenic nitrogen deposition gradient. Ecol. Appl. 10: 484-496.

Egerton-Warburton, L. M.; Graham, R. C.; Allen, E. B.; Allen, M. F. (2001) Reconstruction of

the historical changes in mycorrhizal fungal communities under anthropogenic nitrogen

deposition. Proc. R. Soc. London B 268: 2479-2484.

Eilers, J. M.; Kanciruk, P.; McCord, R. A.; Overton, W. S.; Hook, L.; Blick, D. J.; Brakke, D. F.;

Kellar, P. E.; DeHaan, M. S.; Silverstein, M. E.; Landers, D. H. (1987) Western lake

survey phase I: characteristics of lakes in the western United States, volume II: data

compendium for selected physical and chemical variables. Washington, DC: U.S.

Environmental Protection Agency, Office of Acid Deposition, Environmental Monitoring

and Quality Assurance; EPA report no. EPA/600/3-86/054B. Available from: NTIS,

Springfield, VA; PB88-146832/REB.

August 2008 C­62 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Eliason, S. A.; Allen, E. B. (1997) Exotic grass competition in suppressing native shrubland reestablishment. Restor. Ecol. 5: 245-255.

Ellenberg, H. (1987) Floristic changes due to eutrophication. In: Asman, W. A. H.; Diederen, S.

M. A., eds. Ammonia and acidification: proceedings of a symposium of the European

Association for the Science of Air Pollution (EURASAP); April; Bilthoven, The

Netherlands. European Association for the Science of Air Pollution; pp. 301-308.

Elser, J. J.; Bracken, M. E. S.; Cleland, E. E.; Gruner, D. S.; Harpole, W. S.; Hillebrand, H., II;

Ngai, J. T.; Seabloom, E. W.; Shurin, J. B.; Smith, J. E. (2007) Global analysis of

nitrogen and phosphorus limitation of primary producers in freshwater, marine, and

terrestrial ecosystems. Ecol. Lett.: submitted.

Elser, J. J.; Hayakawa, K.; Urabe, J. (2001) Nutrient limitation reduces food quality for

zooplankton: Daphnia response to seston phosphorus enrichment. Ecology 82: 898-903.

Elser, J. J.; Marzolf, E. R.; Goldman, C. R. (1990) Phosphorus and nitrogen limitation of

phytoplankton growth in the freshwaters of North America: a review a nd critique of

experimental enrichments. Can. J. Fish. Aquat. Sci. 47: 1468-1477.

Elvir, J. A., G. B. Wiersma, M. E. Day, M. S. Greenwood, and I. J. Fernandez. 2006. Effects of

enhanced nitrogen deposition on foliar chemistry and physiological processes of forest

trees at the Bear Brook Watershed in Maine. Forest Ecology and Management 221:207­

214.

Elvir, J. A.; Wiersma, G. B.; White, A. S.; Fernandez, I. J. (2003) Effects of chronic ammonium

sulfate treatment on basal area increment in red spruce and sugar maple at the Bear Brook

Watershed in Maine. Can. J. Forest. Res. 33: 862-869.

Emmett, B. A. (1999) The impact of nitrogen on forest soils and feedbacks on tree growth. Water

Air Soil Pollut. 116: 65-74.

Emmett, B. A.; Boxman, D.; Bredemeier, M.; Gunderson, P.; Kjønaas, O. J.; Moldan, F.;

Schleppi, P.; Tietema, A.; Wright, R. F. (1998) Predicting the effects of atmospheric

nitrogen deposition in conifer stands: evidence from the NITREX e cosystem-scale

experiments. Ecosystems 1: 352-360.

Engstrom, P.; Dalsgaard, T.; Hulth, S.; Aller, R. C. (2005) Anaerobic ammonium oxidation by

nitrate (anammox): implications for N 2 

### production in coastal marine sediments. Geochim.

Cosmochim. Acta 69: 2057-2065.

Erickson, H., M. Keller, and E. A. Davidson. 2001. Nitrogen oxide fluxes and nitrogen cycling

during postagricultural succession and forest fertilization in the humid tropics.

Ecosystems 4:67-84.

Eshleman, K. N.; Fiscus, D. A.; Castro, N. M.; Webb, J. R.; Herlihy, A. T. (2004)

Regionalization of disturbance-induced nitrogen leakage from mid-Appalachian forests

using a linear systems model. Hydrol. Process. 18: 2713-2725.

Eshleman, K. N.; Gardner, R. H.; Seagle, S. W.; Castro, N. M.; Fiscus, D. A.; Webb, J. R.;

Galloway, J. N.; Deviney, F. A.; Herlihy, A. T. (2000) Effects of disturbance on nitrogen

export from forested lands of the Chesapeake Bay watershed. Environ. Monit. Assess. 63:

187-197.

August 2008 C­63 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Eshleman, K. N.; Morgan II, R. P.; Webb, J. R.; Deviney, F. A.; Galloway, J. N. (1998)

Temporal patterns of nitrogen leakage from mid-Appalachian forested watersheds: role of

insect defoliation. Water Resour. Res. 34: 2005-2116.

Everett, K. R.; Brown, J. (1982) Some recent trends in the physical and chemical

characterization and mapping of tundra soils, arctic slope of Alaska. Soil Sci. 133: 264­

280.

Falkengren-Grerup, U. (1986) Soil acidification and vegetation changes in deciduous forest in

southern Sweden. Oecologia 70: 339-347.

Falkengren-Grerup, U. (1989) Soil acidification and its impact on ground vegetation. Ambio 18:

179-183.

Falkengren-Grerup, U. (1998) Nitrogen response of herbs and graminoids in experiments with

simulated acid soil solution. Environ. Pollut. 102(suppl. 1): 93-99.

Fangmeier, A.; Hadwiger-Fangmeier, A.; Van der Eerden, L.; Jäger, H.-J. (1994) Effects of

atmospheric ammonia on vegetation—a review. Environ. Pollut. 86: 43-82.

Fenn, M. E.; Baron, J. S.; Allen, E. B.; Rueth, H. M.; Nydick, K. R.; Geiser, L.; Bowman, W. D.;

Sickman, J. O.; Meixner, T.; Johnson, D. W.; Neitlich, P. (2003a) Ecological effects of

nitrogen deposition in the western United States. BioScience 53: 404-420.

Fenn, M. E.; Bytnerowicz, A. (1997) Summer throughfall and winter deposition in the San

Bernardino Mountains in southern California. Atmos. Environ. 31: 673-683.

Fenn, M. E.; De Bauer, L. I.; Hernández-Tejeda, T. (2002) Urban air pollution and forests:

resources at risk in the Mexico City air basin. New York, NY: Springer-Verlag.

(Ecological studies: v. 156).

Fenn, M. E.; Haeuber, R.; Tonnesen, G. S.; Baron, J. S.; Grossman-Clarke, S.; Hope, D.; Jaffe,

D. A.; Copeland, S.; Geiser, L.; Rueth, H. M.; Sickman, J. O. (2003c) Nitrogen

emissions, deposition, and monitoring in the western United States. BioScience 53: 391­

403.

Fenn, M. E.; Poth, M. A. (1999) Temporal and spatial trends in streamwater nitrate

concentrations in the San Bernardino Mountains, southern California. J. Environ. Qual.

28: 822-836.

Fenn, M. E.; Poth, M. A.; Aber, J. D.; Baron, J. S.; Bormann, B. T.; Johnson, D. W.; Lemly, A.

D.; McNulty, S. G.; Ryan, D. F.; Stottlemyer, R. (1998) Nitrogen excess in North

American ecosystems: predisposing factors, ecosystem responses, and management

strategies. Ecol. Appl. 8: 706-733.

Fenn, M. E.; Poth, M. A.; Bytnerowicz, A.; Sickman, J. O.; Takemoto, B. K. (2003b) Effects of

ozone, nitrogen deposition, and other stressors on montane ecosystems in the Sierra

Nevada. In: Bytnerowicz, A.; Arbaugh, M. J.; Alonso, R., eds. Ozone air pollution in the

Sierra Nevada: distribution and effects on forests; pp. 111-155. New Y ork, NY: Elsevier.

(Developments in Environmental Science: v. 2).

Fenn, M. E.; Poth, M. A.; Johnson, D. W. (1996) Evidence for nitrogen saturation in the San

Bernardino Mountains in southern California. For. Ecol. Manage. 82: 211-230.

Ferrari, J. B. (1999) Fine-scale patterns of leaf litterfall and nitrogen cycling in an old-growth

forest. Can. J. Forest. Res. 29: 291-302.

August 2008 C­64 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 F ield, C. B.; Mooney, H. A. (1986) The photosynthesis-nitrogen relationship in wild plants. In:

Givnish, T. J., ed. On the economy of plant form and function. Cambridge, UK:

Cambridge University Press.

Findlay, D. L.; Hecky, R. E.; Kasian, S. E. M.; Stainton, M. P.; Hendzel, L. L.; Schindler, E. U.

(1999) Effects on phytoplankton of nutrients added in conjunction with acidification.

Freshwater Biol. 41: 131-145.

Finzi, A. C.; Van Breemen, N.; Canham, C. D. (1998) Canopy tree-soil interactions within

temperate forests: species effects on soil carbon and nitrogen. Ecol. Appl. 8: 440-446.

Fisher, D. C.; Oppenheimer, M. (1991) Atmospheric nitrogen deposition and the Chesapeake

Bay estuary. Ambio 20: 102-108.

Fisher, T. R.; Hagy, J. D., III; Boynton, W. R.; Williams, M. R. (2006) Cultural eutrophication in

the Choptank and Patuxent estuaries of Chesapeake Bay. Limnol. Oceanogr. 51(1 pt. 2):

435-447.

Fisher, T. R.; Harding, L. W., Jr.; Stanley, D. W.; Ward, L. G. (1988) Phytoplankton, nutrients,

and turbidity in the Chesapeake, Delaware, and Hudson estuaries. Estuarine Coastal Shelf

Sci. 27: 61-93.

Fisher, T. R.; Lee, K.-Y.; Berndy, H.; Benitez, J. A.; Norton, M. M. (1998) Hydrology and

chemistry of the Choptank River basin in the Chesapeake Bay drainage. Water Air Soil

Pollut. 105: 387-397.

Fisk, M. C.; Schmidt, S. K. (1996) Microbial responses to nitrogen additions in alpine tundra

soil. Soil Biol. Biogeochem. 28: 751-755.

Fisk, M. C.; Schmidt, S. K.; Seastedt, T. R. (1998) Topographic patterns of above- and

belowground production and nitrogen cycling in Alpine tundra. Ecology 79: 2253-2266.

Fisk, M. C.; Zak, D. R.; Crow, T. R. (2002) Nitrogen storage and cycling in old- and secondgrowth northern hardwood forests. Ecology 83: 73-87.

Fore, L. S.; Karr, J. R.; Wisseman, R. W. (1996) Assessing invertebrate responses to human

activities: evaluating alternative approaches. J. North Am. Benthol. Soc. 15: 212-231.

Forkner, R. E.; Hunter, M. D. (2000) What goes up must come down? Nutrient addition and

predation pressure on oak herbivores. Ecology 81: 1588-1600.

Fraterrigo, J. M.; Turner, M. G.; Pearson, S. M.; Dixon, P. (2005) Effects of past land use on

spatial heterogeneity of soil nutrients in southern Appalachian forests. Ecol. Monogr. 75:

215-230.

Frey, S. D.; Knorr, M.; Parrent, J. L.; Simpson, R. T. (2004) Chronic nitrogen enrichment affects

the structure and function of the soil microbial community in temperate hardwood and

pine forests. For. Ecol. Manage. 196: 159-171.

Frost, P. C.; Stelzer, R. S.; Lamberti, G. A.; Elser, J. J. (2002) Ecological stoichiometry of

trophic interactions in the benthos: understanding the role of C:N:P ratios in lentic and

lotic habitats. J. N. Am. Benthol. Soc. 21: 515-528.

Galloway, J. N. (1998) The global nitrogen cycle: changes and consequences. Environ. Pollut.

102(suppl. 1): 15-24.

August 2008 C­65 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Galloway, J. N.; Aber, J. D.; Erisman, J. W.; Seitzinger, S. P.; Howarth, R. W.; Cowling, E. B.;

Cosby, B. J. (2003) The nitrogen cascade. BioScience 53: 341-356.

Galloway, J. N.; Cowling, E. B. (2002) Reactive nitrogen and the world: 200 years of change.

Ambio 31: 64-71.

Galloway, J. N.; Schlesinger, W. H.; Levy, H.; Michaels, A. F.; Schnoor, J. L. Nitrogenfixation: anthropogenic enhancement, environmental response. Glob. Biogeochem.

Cycles 9: 235-252. (1995) Nitrogen-fixation: anthropogenic enhancement,

environmental response. Glob. Biogeochem. Cycles 9: 235-252.

Garner, J. H. B. (1994) Nitrogen oxides, plant metabolism, and forest ecosystem response. In:

Alscher, R. G.; Wellburn, A. R., eds. Plant responses to the gaseous environment:

molecular, metabolic and physiological aspects, [3rd international symposium on air

pollutants and plant metabolism]; June 1992; Blacksburg, VA. London, United Kingdom:

Chapman & Hall; pp. 301-314.

Gauslaa, Y. (1995) Lobarion, an epiphytic community of ancient forests, threatened by acid rain.

Lichenologist 27: 59-76.

Gebauer, R. L. E.; Reynolds, J. F.; Tenhunen, J. D. (1995) Growth and allocation of the arctic

sedges Eriophorum angustifolium and E. vaginatum: effects of variable soil oxygen and

nutrient availability. Oecologia 104: 330-339.

Geiser, L.; Neitlich, P. (2007) Air pollution and climate gradients in western Oregon and

Washington indicated by epiphytic macrolichens. Environ. Pollut. 145: 203-218.

Gilliam, F. S. (2006) Response of the herbaceous layer of forest ecosystems to excess nitrogen

deposition. J. Ecol. 94: 1176-1191.

Gilliam, F. S.; Hockenberry, A. W.; Adams, M. B. (2006) Effects of atmospheric deposition on

the herbaceous layer of a central Appalachian hardwood forest. J. Torrey Bot. Soc. 133:

240-254.

Gobler, C. J.; Davis, T. W.; Coyne, K. J.; Boyer, G. L. (2007) Interactive influences of nutrient

loading, zooplankton grazing, and microcystin synthetase gene expression on cyanobacterial bloom dynamics in a eutrophic New Y ork lake. Harmful Algae 6: 119­

133.

Goldman, C. R. (1988) Primary productivity, nutrients, and transparency during the early onset

of eutrophication in ultra-oligotrophic Lake Tahoe, California-Nevada. Limnol.

Oceanogr. 33: 1321-1333.

Goodale, C. L.; Aber, J. D. (2001) The long-term effects of land-use history on nitrogen cycling

in northern hardwood forests. Ecol. Appl. 11: 253-267.

Goodale, C. L.; Aber, J. D.; McDowell, W. H. (2000) The long-term effects of disturbance on organic and inorganic nitrogen export in the White Mountains, New H ampshire.

Ecosystems 3: 433-450.

Goodale, C. L.; Aber, J. D.; Vitousek, P. M. (2003) An unexpected nitrate decline in New

Hampshire streams. Ecosystems 6: 75-86.

Goodale, C. L.; Aber, J. D.; Vitousek, P. M.; McDowell, W. H. (2005) Long-term decreases in

stream nitrate: successional causes unlikely; possible links to DOC? Ecosystems 8: 334­

337.

August 2008 C­66 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Goodroad, L. L. and D. R. Keeney. 1984. Nitrous-oxide emission from forest, marsh, and prairie

ecosystems. Journal of Environmental Quality 13:448-452.

Gotelli, N. J.; Ellison, A. M. (2002) Nitrogen deposition and extinction risk in the northern

pitcher plant, Sarracenia purpurea. Ecology 83: 2758-2765.

Goulder, L. H.; Kennedy, D. (1997) Valuing ecosystem services: philosophical bases and

empirical methods. In: Daily, G. C., ed. Nature's services: societal dependence on natural

ecosystems. Washington, DC: Island Press; pp. 23-47.

Granberg, G.; Sundh, I.; Svensson, B. H.; Nilsson, M. (2001) Effects of temperature and nitrogen

and sulfur deposition, on methane emission from a boreal mire. Ecology 82: 1982-1998.

Green, P. A.; Vörösmarty, C. J.; Meybeck, M.; Galloway, J. N.; Peterson, B. J.; Boyer, E. W.

(2004) Pre-industrial and contemporary fluxes of nitrogen through rivers: a global

assessment based on typology. Biogeochemistry 68: 71-105.

Grenon, F.; Bradley, R. L.; Joanisse, G.; Titus, B. D.; Prescott, C. E. (2004) Mineral N

availability for conifer growth following clearcutting: responsive versus non-responsive

ecosystems. For. Ecol. Manage. 188: 305-316.

Grimm, N. B.; Fisher, S. G. (1986) Nitrogen limitation in a Sonoran Desert stream. J. North Am.

Benthol. Soc. 5: 2-15.

Groffman, P. M. (1994) Denitrification in freshwater wetlands. Curr. Topics Wetland

Biogeochem. 1: 15-35.

Groffman, P. M.; Altabet, A. M.; Böhlke, J. K.; Butterbach-Bahl, K.; David, M. B.; Firestone,

M. K.; Giblin, A. E.; Kana, T. M.; Nielsen, L. P.; Voytek, M. A. (2006) Methods for

measuring denitrification: diverse approaches to a difficult problem. Ecol. Appl. 16:

2091-2122.

Grulke, N. E.; Andersen, C. P.; Fenn, M. E.; Miller, P. R. (1998) Ozone exposure and nitrogen

deposition lowers root biomass of ponderosa pine in the San Bernardino Mountains,

California. Environ. Pollut. 103: 63-73.

Grulke, N. E.; Balduman, L. (1999) Deciduous conifers: high N d eposition and O 3 

### exposure

effects on growth and biomass allocation in ponderosa pine. Water Air Soil Pollut. 116:

235-248.

Grünhage, L.; Dämmgen, U.; Haenel, H. D.; Jäger, H. J. (1992) Vertikale flüsse von spurengasen

in der bodennahen atmosphäre. Landbauforsch. Voelkenrode 128: 201-245.

Gulledge, J. and J. P. Schimel. 2000. Controls on soil carbon dioxide and methane fluxes in a variety of taiga forest stands in interior Alaska. Ecosystems 3:269-282.

Gulledge, J.; Doyle, A. P.; Schimel, J. P. (1997) Different NH +

4 

### -inhibition patterns of soil CH 4



### ­ oxidizer populations across sites. Soil Biol. Biochem 29: 13-21.

Gundersen, P.; Callesen, I.; De Vries, W. (1998) Nitrate leaching in forest ecosystems is related

to forest floor C/N r atios. Environ. Pollut. 102(suppl. 1): 403-407.

Gundhardt-Goerg, M. S.; McQuattie, C. J.; Maurer, S.; Frey, B. (2000) Visible and microscopic

injury in leaves of five deciduous tree species related to current critical ozone levels.

Environ. Pollut. 109: 489-500.

August 2008 C­67 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Hachmoller, B.; Matthews, R. A.; Brakke, D. F. (1991) Effects of riparian community structure,

sediment size, and water-quality on the macroinvertebrate communities in a small,

suburban stream. Northwest Sci. 65: 125-132.

Hagy, J. D.; Boynton, W. R.; Keefe, C. W.; Wood, K. V. (2004) Hypoxia in Chesapeake Bay,

1950-2001: long-term change in relation to nutrient loading and river flow. Estuaries 27:

634-658.

Hall, R. I.; Leavit, P. R.; Quinlan, R.; Dixit, A. S.; Smol, J. P. (1999) Effects of agriculture,

urbanization, and climate on water quality in the northern Great Plains. Limnol.

Oceanogr. 44: 739-756.

Hall, S. J. and P. A. Matson. 1999. Nitrogen oxide emissions after nitrogen additions in tropical

forests. Nature 400:152-155.

Hallingbäck, T. (1991) Blue-green algae and cyanophilic lichens threatened by air pollution and

fertilization. Sven. Bot. Tidskr. 85: 87-104.

Harding, L. W. J.; Mallonee, M. E.; Perry, E. S. (2002) Toward a predictive understanding of

primary productivity in a temperate, partially stratified estuary. Estuar. Coast. Shelf Sci.

55: 437-463.

Harpole, W. S., D. L. Potts, and K. N. Suding. 2007. Ecosystem responses to water and nitrogen

amendment in a California grassland. Global Change Biology 13:2341-2348.

Harwell, M. A.; Myers, V.; Young, T.; Bartuska, A.; Gassman, N.; Gentile, J. H.; Harwell, C. C.;

Appelbaum, S.; Barko, J.; Causey, B.; Johnson, C.; McLean, A.; Smola, R.; Templet, P.;

Tosini, S. (1999) A f ramework for an ecosystem integrity report card. BioScience 49:

543-556.

Hasler, A. D. (1947) Eutrophication of lakes by domestic drainage. Ecology 28: 383-395.

Hayden, M. J.; Ross, D. S. (2005) Denitrification as a nitrogen removal mechanism in a Vermont

peatland. J. Environ. Qual. 34: 2052-2061.

Hazlett, P. W.; Gordon, A. M.; Voroney, R. P.; Sibley, P. K. (2007) Impact of harvesting and

logging slash on nitrogen and carbon dynamics in soils from upland spruce forests in

northeastern Ontario. Soil Biol. Biogeochem. 39: 43-57.

Hecnar, S. J. (1995) Acute and chronic toxicity of ammonium-nitrate fertilizer to amphibians

from southern Ontario. Environ. Toxicol. Chem. 14: 2131-2137.

Hedin, L. O.; Von Fischer, J. C.; Ostrom, N. E.; Kennedy, B. P.; Brown, M. G.; Robertson, G. P.

(1998) Thermodynamic constraints on nitrogen transformations and other

biogeochemical processes at soil-stream interfaces. Ecology 79: 684-703.

Hefting, M. M.; Bobbink, R.; De Caluwe, H. (2003) Nitrous oxide emission and denitrification

in chronically nitrate-loaded riparian buffer zones. J. Environ. Qual. 32: 1194-1203.

Heil, G. W.; Bruggink, M. (1987) Competition for nutrients between Calluna vulgaris (L.) Hull

and Molinia caerulea (L.) Moench. Oecologia 73: 105-108.

Heil, G. W.; Diemont, W. H. (1983) Raised nutrient levels change heathland in grassland.

Vegetation 53: 113-120.

August 2008 C­68 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Hicke, J. A.; Asner, G. A.; Randerson, J. T.; Tucker, C. J.; Los, S.; Birdsey, R. A.; Jenkins, J. C.;

Field, C. B. (2002) Trends in North American net primary productivity derived from

satellite observations, 1982-1998. Glob. Biogeochem. Cycles 16(1018):

10.1029/2001GB001550.

Higley, B.; Carrick, H. J.; Brett, M. T.; Luecke, C.; Goldman, C. R. (2001) The effects of

ultraviolet radiation and nutrient additions on periphyton biomass and composition in a sub-alpine lake (Castle Lake, USA). Int. Rev. Hydrobiol. 86: 147-163.

Hill, A. R. (1996) Nitrate removal in stream riparian zones. J. Environ. Qual. 25: 743-755.

Hill, B. H.; Elonen, C. M.; Jicha, T. M.; Cotter, A. M.; Trebitz, A. S.; Danz, N. P. (2006)

Sediment microbial enzyme activity as an indicator of nutrient limitation in Great Lakes

coastal wetlands. Freshwater Biol. 51: 1670-1683.

Hill, W. R.; Knight, A. W. (1988) Nutrient and light limitation of algae in 2 northern California

streams. J. Phycol. 24: 125-132.

Hinga, K. R.; Keller, A. A.; Ovlatt, C. A. (1991) Atmospheric deposition and nitrogen inputs to

coastal waters. Ambio 20: 256-260.

Högberg, P.; Fan, H.; Quist, M.; Binkleys, D.; Oloftamm, C. (2006) Tree growth and soil

acidification in response to 30 years of experimental nitrogen loading on boreal forest.

Glob. Change Biol. 12: 489-499.

Holland, E. A.; Braswell, B. H.; Lamarque, J. F.; Townsend, A.; Sulzman, J.; Muller, J. F.;

Dentener, F.; Brasseur, G.; Levy, H.; Penner, J. E.; Roelofs, G. J. (1997) Variations in the

predicted spatial distribution of atmospheric nitrogen deposition and their impact on

carbon uptake by terrestrial ecosystems. J. Geophys. Res. 102: 15849-15866.

Holland, E. A.; Dentener, F. J.; Braswell, B. H.; Sulzman, J. M. (1999) Contemporary and preindustrial global reactive nitrogen budgets. Biogeochemistry 46: 7-43.

Hooper, D. U.; Vitousek, P. M. (1997) The effects of plant composition and diversity on

ecosystem processes. Science (Washington, DC) 277: 1302-1305.

Horvath, L., E. Fuhrer, and K. Lajtha. 2006. Nitric oxide and nitrous oxide emission from

Hungarian forest soils; linked with atmospheric N-deposition. Atmospheric Environment

40: 7786-7795.

Houdijk, A.; Verbeek, P. J. M.; Van Dijk, H. F. G.; Roelofs, J. G. M. (1993) Distribution and

decline of endangered herbaceous heathland species in relation to the chemical

composition of the soil. Plant Soil 148: 137-143.

Houston, D. R. (1994) Major new t ree epidemics: beech bark disease. Annu. Rev. Phytopathol.

32: 75-87.

Howarth, R. W.; Billen, G.; Swaney, D.; Twonsend, A.; Jaworski, N.; Lajtha, K.; Downing, J.

A.; Elmgren, R.; Caraco, N.; Jordan, T.; Ferendse, F.; Freney, J.; Kudeyarov, V.;

Murdoch, P.; Zhu, Z.-L. (1996) Regional nitrogen budgets and riverine N & P fluxes for

the drainages to the North Atlantic Ocean: natural and human influences.

Biogeochemistry 35: 75-139.

Howarth, R. W.; Boyer, E. W.; Pabich, W. J.; Galloway, J. N. (2002) Nitrogen use in the United

States from 1961-2000 and potential future trends. Ambio 31: 88-96.

August 2008 C­69 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Howarth, R. W.; Marino, R. (2006) Nitrogen as the limiting nutrient for eutrophication in coastal

marine ecosystems: evolving views over three decades. Limnol. Oceanogr. 51: 364-376.

Huberty, L. E.; Gross, K. L.; Miller, C. J. (1998) Effects of nitrogen addition on successional

dynamics and species diversity in Michigan old-fields. J. Ecol. 86: 794-803.

Hungate, B. A., C. P. Lund, H. L. Pearson, and F. S. Chapin. 1997. Elevated CO 2 

### and nutrient

addition alter soil N c ycling and N t race gas fluxes with early season wet-up in a

California annual grassland. Biogeochemistry 37:89-109.

Hyvarinen, M.; Crittenden, P. D. (1998) Relationships between atmospheric nitrogen inputs and

the vertical nitrogen and phosphorus concentration gradients in the lichen Cladonia

portentosa. New P hytol. 140: 519-530.

Ineson, P., P. Coward, D. G. Benham, and S. M. C. Robertson. 1998. Coniferous forests as "secondary agricultural" sources of nitrous oxide. Atmospheric Environment 32:3321­

3330.

Inouye, R. S. (2006) Effects of shrub removal and nitrogen addition on soil moisture in

sagebrush steppe. J. Arid Environ. 65: 604-618.

Interlandi, S. J.; Kilham, S. S. (1998) Assessing the effects of nitrogen deposition on mountain

waters: a study of phytoplankton community dynamics. Water Sci. Technol. 38: 139-146.

Interlandi, S. J.; Kilham, S. S. (2001) Limiting resources and the regulation of diversity in

phytoplankton communities. Ecology 82: 1270-1282.

Interlandi, S. J.; Kilham, S. S.; Theriot, E. C. (1999) Responses of phytoplankton to varied

resource availability in large lakes of the Greater Yellowstone ecosystem. Limnol.

Oceanogr. 44: 668-682.

Ittekot, V. (2003) Carbon-silicon interactions. In: Melillo, J. M.; Field, C. B.; Moldan, B., eds.

Interactions of the major biogeochemical cycles: global change and human impacts.

Washington, D.C.: Island Press; pp. 311-322.

Jackson, J. B. C.; Kirby, M. X.; Berger, W. H.; Bjorndal, K. A.; Botsford, L. W.; Bourque, B. J.;

Bradbury, R. H.; Cooke, R.; Erlandson, J.; Estes, J. A.; Huges, T. P.; Kidwell, S.; Lange,

C. B.; Lenihan, H. S.; Pandolfi, J. M.; Peterson, C. H.; Steneck, R. S.; Tegner, M. J.;

Warner, R. R. (2001) Historical overfishing and recent collapse of coastal ecosystems.

Science 293: 629-637.

Jansson, M.; Bergstrom, A. K.; Drakare, S.; Blomqvist, P. (2001) Nutrient limitation of

bacterioplankton and phytoplankton in humic lakes in northern Sweden. Freshwater Biol.

46: 653-666.

Jassby, A. D.; Reuter, J. E.; Axler, R. P.; Goldman, C. R.; Hackley, S. H. (1994) Atmospheric

deposition of N a nd phosphorus in the annual nutrient load of Lake Tahoe (CaliforniaNevada). Water Resour. Res. 30: 2207-2216.

Jaworski, N. A.; Howarth, R. W.; Hetling, L. J. (1997) Atmospheric deposition of nitrogen

oxides onto the landscape contributes to coastal eutrophication in the northeast United

States. Environ. Sci. Technol. 31: 1995-2004.

Jetten, M. S. M.; Cirpus, I.; Kartal, B.; van Niftrik, L.; van de Pas-Schoonen, K. T.; Sliekers, O.;

Haaijer, S. C. M.; Van der Star, W. R. L.; Schmid, M.; Van de Vossenberg, J.; Schmidt,

I.; Harhangi, H. R.; van Loosdrecht, M.; Gijs Kuenen, J.; Op den Camp, H.; Strous, M. 1

August 2008 C­70 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39 ( 2005) 1994-2004: 10 years of research on the anaerobic oxidation of ammonium.

Biochem. Soc. Trans. 33: 119-123.

Jetten, M. S. M.; Sliekers, O.; Kuypers, M. M. M.; Dalsgaard, T.; Van Niftrik, L.; Cirpus, I.; Van

de Pas-Schoonen, K. T.; Lavik, G.; Thamdrup, B.; Le Paslier, D.; Op Den Camp, H. J.

M.; Hulth, S.; Nielsen, L. P.; Abma, W.; Third, K.; Engstrom, P.; Kuenen, J. G.;

Jorgensen, B. B.; Canfield, D. E.; Damste, J. S. S.; Revsbech, N. P.; Fuerst, J.;

Weissenbach, J.; Wagner, M.; Schmidt, I.; Schmid, M.; Strous, M. (2003) Anaerobic

ammonium oxidation by marine and freshwater planctomycete-like bacteria. Appl.

Microbiol. Biotechnol. 63: 107-114.

Johansson, M.; Rasanen, K.; Merila, J. (2001) Comparison of nitrate tolerance between different

populations of the common frog, Rana temporaria. Aquat. Toxicol. 54: 1-14.

Johnson, C. E. (1995) Soil nitrogen status 8 years after whole-tree clear-cutting. Can. J. Forest.

Res. 25: 1346-1355.

Johnson, D. W., A. M. Hoylman, J. T. Ball, and R. F. Walker. 2006. Ponderosa pine responses to

elevated CO 2 

### and nitrogen fertilization. Biogeochemistry 77:157-175.

Johnson, D. W.; Susfalk, R. B.; Caldwell, T. G.; Murphy, J. D.; Miller, W. W.; Walker, R. F.

(2004) Fire effects on carbon and nitrogen budgets in forests. Water Air Soil Pollut:

Focus 4: 263-275.

Johnson, L. B.; Richards, C.; Host, G. E.; Arthur, J. W. (1997) Landscape influences on water

chemistry in Midwestern stream ecosystems. Freshwater Biol. 37: 193-208.

Jones, J. A.; Swanson, F. J.; Wemple, B. C.; Snyder, K. U. (2000) Effects of roads on hydrology,

geomorphology, and disturbances patches in stream networks. Conserv. Biol. 14: 76-85.

Joos, F.; Prentice, I. C.; House, J. I. (2002) Growth enhancement due to global atmospheric

change as predicted by terrestrial ecosystem models: consistent with US forest inventory

data. Glob. Change Biol. 8: 299-303.

Jovan, S.; McCune, B. (2005) Air-quality bioindication in the greater central valley of California,

with epiphytic macrolichen communities. Ecol. Appl. 15: 1712-1726.

Jovan, S.; McCune, B. (2006) Using epiphytic macrolichen communities for biomonitoring

ammonia in forests of the greater Sierra Nevada, California. Water Air Soil Pollut. 170:

69-93.

Justic, D.; Rabalais, N. N.; Turner, R. E. (1995a) Stoichiometric nutrient balance and origin of

coastal eutrophication. Mar. Pollut. Bull. 30: 41-46.

Justic, D.; Rabalais, N. N.; Turner, R. E. (1997) Impacts of climate change on net productivity of

coastal waters: implications for carbon budget and hypoxia. Clim. Res. 8: 225-237.

Justic, D.; Rabalais, N. N.; Turner, R. E.; Dortch, Q. (1995b) Changes in nutrient structure of

river-dominated coastal waters: Stoichiometric nutrient balance and its conseqeuences.

Estuar. Coast. Shelf Sci. 40: 339-356.

Justic, D.; Rabalais, N. N.; Turner, R. E.; Wiseman Jr., W. J. (1993) Seasonal coupling between

riverborne nutrients, net productivity and hypoxia. Mar. Pollut. Bull. 26: 184-189.

August 2008 C­71 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Kahl, J.; Norton, S.; Frenandez, I.; Rustad, L.; Handley, M. (1999) Nitrogen and sulfur inputoutput budgets in the experimental and reference watersheds, Bear Brook Watershed in

Maine (BBWM). Environ. Monit. Assess. 55: 113-131.

Kana, T. M.; Sullivan, M. B.; Cornwell, J. C.; Groszkowski. (1998) Denitrification in estuarine

sediments determined by membrane inlet mass spectrometry. Limnol. Oceanogr. 43: 334­

339.

Kauppi, P. E.; Mielikaeinen, K.; Kuusela, K. (1992) Biomass and carbon budget of European

forests, 1971 to 1990. Science (Washington, DC) 256: 70-74.

Keller, J. K., S. D. Bridgham, C. T. Chapin, and C. M. Iversen. 2005. Limited effects of six years

of fertilization on carbon mineralization dynamics in a Minnesota fen. Soil Biology &

Biochemistry 37:1197-1204.

Keller, M., W. A. Kaplan, S. C. Wofsy, and J. M. Dacosta. 1988. Emissions of N 2 

### O f rom tropical

forest soils

- response to fertilization with NH +

4 

### , NO

-



### , and PO 3-

3 4



### . Journal of Geophysical

Research-Atmospheres 93:1600-1604.

Kemp, W. M.; Sampou, P.; Caffrey, J.; Mayer, M.; Henriksen, K.; Boynton, W. R. (1990)

Ammonium recycling versus denitrification in Cheasapeake Bay sediments. Limnol.

Oceanogr. 35: 1545-1563.

Kenk, G.; Fischer, H. (1988) Evidence from nitrogen fertilisation in the forests of Germany.

Environ. Pollut. 54: 199-218.

Kielland, K. (1994) Amino acid absorption by Arctic plants: implications for plant nutrition and

nitrogen cycling. Ecology 75: 2373-2383.

Kielland, K. (1995) Landscape patterns of free amino acids ina rctic tundra soils.

Biogeochemistry 31: 85-98.

Killingbeck, K. T. (1996) Nutrients in senesced leaves: keys to the search for potential resorption

and resorption proficiency. Ecology 77: 1716-1727.

Kincheloe, J. W.; Wedemeyer, G. A.; Koch, D. L. (1979) Tolerance of developing salmonid eggs

and fry to nitrate exposure. Bull. Environ. Contam. Toxicol. 23: 575-578.

King, G. M.; Schnell, S. (1994) Ammonium and nitrate inhibition of methane oxidation by

Methylobacter albus BG8 and Methylosinus trichosporium OB3b atlow m ethane

concentrations. Appl. Environ. Microbiol. 60: 3508-3513.

Kirschbaum, M. U. F. (1994) The temperature dependence of soil organic matter decomposition,

and the effect of global warming on soil organic C storage. Soil. Biol. Biochem. 27: 753­

760.

Kitzler, B., S. Zechmeister-Boltenstern, C. Holtermann, U. Skiba, and K. Butterbach-Bah. 2006.

Controls over N 2 

### O, NOx and CO 2 

### fluxes in a calcareous mountain forest soil.

Biogeosciences 3:383-395.

Kitzler, B., S. Zechmeister-Boltenstern, C. Holtermann, U. Skiba, and K. Butterbach-Bahl. 2006.

Nitrogen oxides emission from two beech forests subjected to different nitrogen loads.

Biogeosciences 3:293-310.

Kleb, H. R.; Wilson, S. D. (1997) Vegetation effects on soil resource heterogeneity in prairie and

forest. American Naturalist 150: 283-298.

August 2008 C­72 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 K lemedtsson, L., A. K. Klemedtsson, F. Moldan, and P. Weslien. 1997. Nitrous oxide emission

from Swedish forest soils in relation to liming and simulated increased N-deposition.

Biology and Fertility of Soils 25:290-295.

Klopatek, J. M.; Barry, M. J.; Johnson, D. W. (2006) Potential canopy interception of nitrogen in

the Pacific Northwest, USA. For. Ecol. Manage. 234: 344-354.

Kochy, M.; Wilson, S. D. (2001) Nitrogen deposition and forest expansion in the Northern Great

Plains. J. Ecol. 89: 807-817.

Koerselman, W.; Van Kerkhoven, M. B.; Verhoeven, J. T. A. (1993) Release of inorganic N, P,

and K in peat soils; effect of temperature, water chemistry and water level.

Biogeochemistry 20: 63-81.

Kooijman, A. M.; Bakker, C. (1994) The acidification capacity of wetland bryophytes as

influenced by simulated clean and polluted rain. Aquat. Bot. 48: 133-144.

Korb, J. E.; Ranker, T. A. (2001) Changes in stand composition and structure between 1981 and

1996 in four Front Range plant communities in Colorado. Plant Ecol. 157: 1-11.

Kristensen, H. L.; Gundersen, P.; Callesen, I.; Reinds, G. J. (2004) Throughfall nitrogen

deposition has different impacts on soil solution nitrate concentration in European

coniferous and deciduous forests. Ecosystems 7: 180-192.

Krupa, S. V. (2003) Effects of atmospheric ammonia (NH 3 

### ) on terrestrial vegetation: a review.

Environ. Pollut. 124: 179-221.

Kuypers, M. M. M.; Lavik, G.; Woebken, D.; Schmid, M.; Fuchs, B.; Amann, R.; Jorgensen, B.

B.; Jetten, M. S. M. (2005) Massive nitrogen loss from the Benguela upwelling system

through anaerobic ammonium oxidation. Proc. Natl. Acad. Sci. 102: 6478-3483.

Lafrancois, B. M.; Nydick, K. R.; Caruso, B. (2003) Influence of nitrogen on phytoplankton

biomass and community composition in fifteen Snowy Range lakes (Wyoming, U.S.A.).

Arct. Anarct. Alp. Res. 35: 499-508.

Lafrancois, B. M.; Nydick, K. R.; Johnson, B. M.; Baron, J. S. (2004) Cumulative effects of

nutrients and pH on the plankton of two mountain lakes. Can. J. Fish. Aquat. Sci. 61:

1153-1165.

Lane, D. R.; BassiriRad, H. (2002) Differential responses of tallgrass prairie species to nitrogen

- 

### loading and varying ratios of NO 3 

### to NH 4 + 

### . Funct. Plant Biol. 29: 1227-1235.

Laposata, M. M.; Dunson, W. A. (1998) Effects of boron and nitrate on hatching success of

amphibian eggs. Arch. Environ. Contam. Toxicol. 35: 615-619.

Larson, G. L.; Moore, S. E. (1985) Encroachment of exotic rainbow trout into stream populations

of native brook trout in the Southern Appalachian Mountains. Trans. Am. Fish. Soc. 114:

195-203.

Latty, E. F.; Canham, C. D.; Marks, P. L. (2004) The effects of land-use history on soil

properties and nutrient dynamics in northern hardwood forests of the Adirondack

Mountains. Ecosystems 7: 193-207.

Laurence, J. A.; Amundson, R. G.; Friend, A. L.; Pell, E. J.; Temple, P. J. (1994) Allocation of

carbon in plants under stress: an analysis of the ROPIS experiments. J. Environ. Qual. 23:

412-417.

August 2008 C­73 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Lawrence, G. B.; David, M. B.; Lovett, G. M.; Murdoch, P. S.; Burns, D. A.; Stoddard, J. L.;

Baldigo, B. P.; Porter, J. H.; Thompson, A. W. (1999) Soil calcium status and the

response of stream chemistry to changing acidic deposition rates. Ecol. Appl. 9: 1059­

1072.

Leggett, Z. H. and D. L. Kelting. 2006. Fertilization effects on carbon pools in loblolly pine

plantations on two upland sites. Soil Science Society of America Journal 70:279-286.

Lesack, L. F. W.; Melack, J. M. (1996) Mass balance of major solutes in a rainforest catchment

in the central Amazon: implications for nutrient budgets in tropical rainforests.

Biogeochemistry 32: 115-142.

Levine, M. A.; Whalen, S. C. (2001) Nutrient limitation of phytoplankton production in Alaskan

arctic foothill lakes. Hydrobiologia 455: 189-201.

Levine, S. N.; Shambaugh, A. D.; Pomeroy, S. E.; Braner, M. (1997) Phosphorus, nitrogen, and

silica as controls on phytoplankton biomass and species composition in Lake Champlain

(USA-Canada). Intern. Assoc. for Great Lakes Res. 23: 131-148.

Levlin, S. A. (1998) Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1:

431-436.

Likens, G. E.; Bormann, F. H.; Johnson, N. M.; Fisher, D. W.; Pierce, R. S. (1970) Effects of

forest cutting and herbicide treatment on nutrient budgets in the Hubbard Brook

watershed-ecosystem. Ecol. Monogr. 40: 23-47.

Likens, G. E.; Bormann, F. H.; Pierce, R. S.; Reiners, W. A. (1978) Recovery of a deforested

ecosystem. Science 199: 492-496.

Lilleskov, E. A.; Fahey, T. J.; Horton, T. R.; Lovett, G. M. (2002) Belowground ectomycorrhizal

fungal community change over a nitrogen deposition gradient in Alaska. Ecology 83:

104-115.

Lilleskov, E. A.; Fahey, T. J.; Lovett, G. M. (2001) Ectomycorrhizal fungal aboveground

community change over an atmospheric nitrogen deposition gradient. Ecol. Appl. 11:

397-410.

Lindau, C. W., R. D. Delaune, and J. H. Pardue. 1994. Inorganic nitrogen processing and

assimilation in a forested wetland. Hydrobiologia 277:171-178.

Lohman, K.; Jones, J. R.; Baysinger-Daniel, C. (1991) Experimental evidence for nitrogen

limitation in a northern Ozark stream. J. North Am. Benthol. Soc. 10: 14-23.

Lohman, K.; Priscu, J. C. (1992) Physiological indicators of nutrient deficiency in cladophora

(chlorophyta) in the Clark Fork of the Columbia River, Montana. J. Phycol. 28: 443-448.

Lohrenz, S. E.; Fahnenstiel, G. L.; Redakje, D. G.; Lang, G. A.; Chen, X.; Dagg, M. J. (1997)

Variations in primary production of northern Gulf of Mexico continental shelf waters

linked to nutrient inputs from the Mississippi River. Mar. Ecol. Prog. Ser. 155: 445-454.

Lotter, A. F. (1998) The recent eutrophication of Baldeggersee (Switzerland) as assessed by

fossil diatom assemblages. Holocene 8: 395-405.

Lovett, G. M. (1992) Atmospheric deposition and canopy interactions of nitrogen. In: Johnson,

D. W.; Lindberg, S. E., eds. Atmospheric deposition and forest nutrient cycling: a synthesis of the integrated forest study. New Y ork, NY: Springer-Verlag, Inc.; pp. 152­

August 2008 C­74 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 166. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds.

Ecological studies: analysis and synthesis: v. 91).

Lovett, G. M.; Weathers, K. C.; Arthur, M. A. (2002) Control of nitrogen loss from forested

watersheds by carbon: nitrogen ratio and tree species composition. Ecosystems 5: 712­

718.

Lovett, G. M.; Weathers, K. C.; Sobczak, W. V. (2000) Nitrogen saturation and retention in

forested watersheds of the Catskill Mountains, New Y ork. Ecol. Appl. 10: 73-84.

Lowe, P. N.; Lauenroth, W. K.; Burke, I. C. (2002) Effects of nitrogen availability on the growth

of native grasses and exotic weeds. J. Range Manage. 55: 94-98.

Lowrance, R. (1992) Groundwater nitrate and denitrification in a coastal plain riparian forest. J.

Environ. Qual. 21: 401-405.

Macdonald, J. A., U. Skiba, L. J. Sheppard, B. Ball, J. D. Roberts, K. A. Smith, and D. Fowler.

1997. The effect of nitrogen deposition and seasonal variability on methane oxidation and

nitrous oxide emission rates in an upland spruce plantation and moorland. Atmospheric

Environment 31:3693-3706.

MacDonald, J. A.; Dise, N. B.; Matzner, E.; Arbruster, M.; Gundersen, P.; Forsius, M. (2002)

Nitrogen input together with ecosystem nitrogen enrichment predict nitrate leaching from

European forests. Glob. Change Biol. 8: 1028-1033.

Mack, M. C., E. A. G. Schuur, M. S. Bret-Harte, G. R. Shaver, and F. S. Chapin. 2004.

Ecosystem carbon storage in arctic tundra reduced by long-term nutrient fertilization.

Nature 431:440-443.

Magill, A. H., J. D. Aber, J. J. Hendricks, R. D. Bowden, J. M. Melillo, and P. A. Steudler. 1997.

Biogeochemical response of forest ecosystems to simulated chronic nitrogen deposition.

Ecological Applications 7:402-415.

Magill, A. H., J. D. Aber, W. S. Currie, K. J. Nadelhoffer, M. E. Martin, W. H. McDowell, J. M.

Melillo, and P. Steudler. 2004. Ecosystem response to 15 years of chronic nitrogen

additions at the Harvard Forest LTER, Massachusetts, USA. Forest Ecology and

Management 196:7-28.

Magill, A. H.; Aber, J. D.; Berntson, G. M.; McDowell, W. H.; Nadelhoffer, K. J.; Melillo, J. M.;

Steudler, P. (2000) Long-term nitrogen additions and nitrogen saturation in two

temperate forests. Ecosystems 3: 238-253.

Magill, A. H.; Aber, J. D.; Currie, W. S.; Nadelhoffer, K. J.; Martin, M. E.; McDowell, W. H.;

Melillo, J. M.; Steudler, P. (2004) Ecosystem response to 15 years of chronic nitrogen

additions at the Harvard Forest LTER, Massachusetts, USA. For. Ecol. Manage. 196: 7­

28.

Makipää, R.; Karjalainen, T.; Pussinen, A.; Kellomäki, S. (1999) Effects of climate change and

nitrogen deposition on the carbon sequestration of a forest ecosystem in the boreal zone.

Can. J. Forest. Res. 29: 1490-1501.

Maljanen, M., H. Jokinen, A. Saari, R. Strommer, and P. J. Martikainen. 2006. Methane and

nitrous oxide fluxes, and carbon dioxide production in boreal forest soil fertilized with

wood ash and nitrogen. Soil Use and Management 22:151-157.

August 2008 C­75 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Malone, D. J.; Conley, T. R.; Fisher, P. M.; Gilbert, L. W.; Harding, J.; Dellner, K. G. (1996)

Scales of nutrient-limited phytoplankton productivity in Chesapeake Bay. Estuaries 19:

371-385.

Malone, T. C.; Crocker, L. H.; Pike, S. E.; Wendler, B. W. (1988) Influence of river flow o n the

dynamics of phytoplankton in a partially stratified estuary. Mar. Ecol. Progr. Ser. 48:

235-249.

Mann, L. K.; Johnson, D. W.; West, D. C.; Cole, D. W.; Hornbeck, J. W.; Martin, C. W.;

Riekerk, H.; Smith, C. T.; Swank, W. T.; Tritton, L. M.; Van Lear, D. H. (1988) Effects

on whole-tree and stem-only clearcutting on postharvest hydrologic losses, nutrient

capital, and regrowth. For. Sci. 34: 412-428.

Marler, R. J.; Stromberg, J. C.; Pattern, D. T. (2001) Growth response of Populus fremontii,

Salix gooddingii, and Tamarix ramosissimi seedlings under different nitrogen and

phosphorus concentrations. J. Arid Environ. 49: 133-146.

Marshall, H. G.; Lacouture, R. (1986) Seasonal patterns of growth and composition of

phytoplankton in the lower Chesapeake Bay and vicinity. Estuar. Coast. Shelf Sci. 23:

115-130.

Martin, C. W.; Noel, D. S.; Federer, C. A. (1984) Effects of forest clearcutting in New England

on stream chemistry. J. Environ. Qual. 13: 204-210.

Mattson, W. J., Jr. (1980) Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Syst.

11: 119-161.

McClain, M. E.; Boyer, E. W.; Dent, C. L.; Gergel, S. E.; Grimm, N. B.; Groffman, P. M.; Hart,

S. C.; Harvey, J. W.; Johnston, C. A.; Mayorga, E.; McDowell, W. H.; Pinay, G. (2003)

Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic

ecosystems. Ecosystems 6: 301-312.

McCune, B. (1988) Lichen communities along O 3 

### and SO 2 

### gradients in Indianapolis. Bryologist

91: 223-228.

McCune, B.; Geiser, L. (1997) Macrolichens of the Pacific Northwest. Corvallis, OR: Oregon

State University Press.

McDowell, W. H.; Magill, A. H.; Aitkenhead-Peterson, J. A.; Aber, J. D.; Merriam, J. L.;

Kaushal, S. S. (2004) Effects of chronic nitrogen amendment on dissolved organic matter

and inorganic nitrogen in soil solution. For. Ecol. Manage. 196: 29-41.

McGurk, M. D.; Landry, F.; Tang, A.; Hanks, C. C. (2006) Acute and chronic toxicity of nitrate

to early life stages of lake trout (Salvelinus namaycush) and lake whitefish (Coregonus

clupeaformis). Environ. Toxicol. Chem. 25: 2187-2196.

McHale, M. R.; Cirmo, C. P.; Mitchell, M. J.; McDonnell, J. J. (2004) Wetland nitrogen

dynamics in an Adirondack forested watershed. Hydrol. Process. 18: 1853-1870.

McKane, R. B.; Johnson, L. C.; Shaver, G. R.; Nadelhoffer, K. J.; Rastetter, E. B.; Fry, B.;

Giblin, A. E.; Kielland, K.; Kwiatkowski, B. L.; Laundre, J. A.; Murray, G. (2002)

Resource-based niches provide a basis for plant species diversity and dominance in arctic

tundra. Nature 415: 68-72.

McKnight, D. M.; Smith, R. L.; Bradbury, J. P.; Baron, J. S.; Spaulding, S. (1990) Phytoplankton

dynamics in 3 Rocky Mountain Lakes, Colorado USA. Arct. Alp. Res. 22: 264-274.

August 2008 C­76 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 M cNeil, B. E.; Read, J. M.; Sullivan, T. J.; McDonnell, T. C.; Fernandez, I. J.; Driscoll, C. T.

(2008) The spatial pattern of nitrogen cycling in the Adirondack Park, New York. Ecol.

Appl.: in press.

McNulty, S. G.; Aber, J. D.; Newman, S. D. (1996) Nitrogen saturation in a high elevation New

England spruce-fir stand. For. Ecol. Manage. 84: 109-121.

McNulty, S. G.; Boggs, J.; Aber, J. D.; Rustad, L.; Magill, A. (2005) Red spruce ecosystem level

changes following 14 years of chronic N fertilization. For. Ecol. Manage. 219: 279-291.

Meyers, T.; Sickles, J.; Dennis, R.; Russell, K.; Galloway, J.; Church, T. (2001) Atmospheric

nitrogen deposition to coastal estuaries and their watersheds. In: Valigura, R. A.;

Alexander, R. B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; Stacey, P. E.; Turner, R. E.,

eds. Nitrogen Loading in Coastal Water Bodies: an Atmospheric Perspective.

Washington, DC: AGU Press. (Coastal and Estuarine Studies, v.57).

Michel, T. J.; Saros, J. E.; Interlandi, S. J.; Wolfe, A. P. (2006) Resource requirements of four

freshwater diatom taxa determined by in situ growth bioassays using natural populations

from alpine lakes. Hydrobiologia 568: 235-243.

Milberg, P.; Lamont, B. B.; Pérez-Fernández, M. A. (1999) Survival and growth of native and

exotic composites in response to a nutrient gradient. Plant Ecol. 145: 125-132.

Milchunas, D. G.; Lauenroth, W. K. (1995) Inertia in plant community structure: state changes

after cessation of nutrient-enrichment stress. Ecol. Appl. 5: 452-458.

Miller, A. E.; Bowman, W. D. (2002) Variation in nitrogen-15 natural abundance and nitrogen

uptake traits among co-occuring alpine species: do species partition by nitrogen form?

Oecologia 130: 609-616.

Miller, P. R.; Longbotham, G. J.; Longbotham, C. R. (1983) Sensitivity of selected western

conifers to ozone. Plant Dis. 67: 1113-1115.

Miller, P. R.; McBride, J. R., eds. (1999) Oxidant air pollution impacts in the Montane forests of

southern California: a case study of the San Bernardino Mountains. New York, NY:

Springer-Verlag. (Ecological Studies: v. 134.)

Minnich, R. A.; Barbour, M. G.; Burk, J. H.; Fernau, R. F. (1995) Sixty years of change in

California conifer forests of the San Bernardino mountains. Conserv. Biol. 9: 902-914.

Minnich, R. A.; Dezzani, R. J. (1998) Historical decline of coastal sage scrub in the RiversidePerris Plain, California. Western Birds 29: 366-391.

Mitchell, M. J.; Driscoll, C. T.; Kahl, J. S.; Likens, G. E.; Murdoch, P. S.; Pardo, L. H. (1996)

Climatic control of nitrate loss from forested watersheds in the northeast United States.

Environ. Sci. Technol. 30: 2609-2612.

Mitchell, M. J.; Piatek, K. B.; Christopher, S.; Mayer, B.; Kendall, C.; McHale, P. J. (2006)

Solute sources in stream water during consecutive fall storms in a northern hardwood

forest watershed: a combined hydrological, chemical and isotopic approach.

Biogeochemistry 78: 217-246.

Mitchell, R. J.; Truscot, A. M.; Leith, I. D.; Cape, J. N.; Dijk, N. V.; Tang, Y. S.; Fowler, D.;

Sutton, M. A. (2005) A study of the epiphytic communities of Atlantic oak woods along

an atmospheric nitrogen deposition gradient. J. Ecol. 93: 482-492.

August 2008 C­77 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Mohn, J., A. Schurmann, F. Hagedorn, P. Schleppi, and R. Bachofen. 2000. Increased rates of

denitrification in nitrogen-treated forest soils. Forest Ecology and Management 137:113­

119.

Montgomery, D. R.; Buffington, J. M. (1998) Channel processes, classification, and response. In:

Naiman, R. J.; Bilby, R., eds. River ecology and management: lessons from the Pacific

coastal ecoregion. New York: Springer-Verlag; pp. 13-42.

Moore, D. R. J.; Keddy, P. A.; Gaudet, C. L.; Wisheu, I. C. (1989) Conservation of wetlands: do

infertile wetlands deserve a higher priority? Biol. Conserv. 47: 203-217.

Morley, S. A.; Karr, J. R. (2002) Assessing and restoring the health of urban streams in the Puget

Sound Basin. Conserv. Biol. 16: 1498-1509.

Morris, D. P.; Lewis, W. M., Jr. (1988) Phytoplankton nutrient limitation in Colorado mountain

lakes. Freshwater Biol. 20: 315-327.

Morris, J. T. (1991) Effects of nitrogen loading on wetland ecosystems with particular reference

to atmospheric deposition. Annu. Rev. Ecol. Syst. 22: 257-279.

Mosier, A., D. Schimel, D. Valentine, K. Bronson, and W. Parton. 1991. Methane and nitrousoxide fluxes in native, fertilized and cultivated grasslands. Nature 350:330-332.

Mulholland, P. J.; Valett, H. M.; Webster, J. R.; Thomas, S. A.; Cooper, L. W.; Hamilton, S. K.;

Peterson, B. (2004) Stream denitrification and total nitrate uptake rates measured using a

field 15 

### N t racer addition approach. Limnol. Oceanogr. 49: 809-820.

Munoz-Hincapie, M., J. M. Morell, and J. E. Corredor. 2002. Increase of nitrous oxide flux to the

atmosphere upon nitrogen addition to red mangroves sediments. Marine Pollution

Bulletin 44:992-996.

Nadelhoffer, K. J.; Colman, B. P.; Currie, W. S.; Magill, A. H.; Aber, J. D. (2004) Decadal-scale

fates of 15 

### N t racers to oak and pine stands under ambient and elevated N i nputs at the

Harvard Forest (USA). For. Ecol. Manage. 196: 89-107.

Nadelhoffer, K.; Downs, M.; Fry, B.; Magill, A.; Aber, J. (1999) Controls on N r etention and

exports in a forested watershed. Environ. Monit. Assess. 55: 187-210.

Nagy, K. A.; Henen, B. T.; Vyas, D. B. (1998) Nutritional quality of native and introduced food

plants of wild desert tortoises. J. Herpetol. 32: 260-267.

Nash, T. H., III; Sigal, L. L. (1999) Epiphytic lichens in the San Bernardino mountains in

relation to oxidant gradients. In: Miller, P.R.; McBride, J.R. ,eds. Oxidant air pollution

impacts in the montane forests of southern California: a case study of the San Bernardino

Mountains. New Y ork, NY: Springer-Verlag; pp 223-234. (Ecological studies: v. 134).

Näsholm, T.; Ekblad, A.; Nordin, A.; Giesler, R.; Hogberg, M.; Högberg, P. (1998) Boreal forest

plants take up organic nitrogen. Nature (London) 392: 914-916.

National Atmospheric Deposition Program/National Trends Network. (2006) Isopleth maps. Champaign, IL: Program Office, Illinois State Water Survey. Available:

http://nadp.sws.uiuc.edu/isopleths/annualmaps.asp [21 November, 2007].

National Research Council (NRC). (1991) Rethinking the ozone problem in urban and regional

air pollution. Washington, DC: National Academy Press. Available:

http://www.nap.edu/openbook.php?isbn=0309046319 [21 November, 2007].

August 2008 C­78 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 National Research Council (NRC). (2000) Clean coastal waters: understanding and reducing the

effects of nutrient pollution. Washington, DC: National Academy Press. Available:

http://www.nap.edu/openbook.php?isbn=0309069483 [21 November, 2007].

Neff, J. S.; Bowman, W. D.; Holland, E. A.; Fisk, M. C.; Schmidt, S. K. (1994) Fluxes of nitrous

oxide and methane from nitrogen-amended soils in a Colorado alpine ecosystem.

Biogeochemistry 27: 23-33.

Nellemann, C.; Thomsen, M. G. (2001) Long-term changes in forest growth: potential effects of

nitrogen deposition and acidification. Water Air Soil Pollut. 128: 197-205.

Nicholls, K. H.; Hopkins, G. J.; Standke, S. J.; Nakamoto, L. (2001) Trends in total phosphorus

in Canadian nearshore waters of Laurentian Great Lakes: 1976-1999. Great Lakes Res.

27: 402-422.

Nixon, S. W. (1995) Coastal marine eutrophication: a definition, social causes, and future

concerns. Ophelia 41: 199-219.

Nordin, A.; Näsholm, T.; Ericson, L. (1998) Effects of simulated N d eposition on understorey

vegetation of a boreal coniferous forest. Funct. Ecol. 12: 691-699.

Nordin, A.; Strengbom, J.; Witzell, J.; Nasholm, T.; Ericson, L. (2005) Nitrogen depositon and

the biodiversity of boreal forests: implications for the nitrogen critical load. Ambio 34:

20-24.

Norton, S.; Kahl, J.; Fernandez, I. (1999) Altered soil-soil water interactions inferred from

stream water chemistry at an artificially acidified watershed at Bear Brook Watershed,

Maine USA. Environ. Monit. Assess. 55: 97-111.

Nydick, K. R.; Lafrancois, B. M.; Baron, J. S. (2004a) NO 3 

### uptake in shallow, oligotrophic,

mountain lakes: the influence of elevated NO 3 

### concentrations. J. North Am. Benthol. Soc.

23: 397-415.

Nydick, K. R.; Lafrancois, B. M.; Baron, J. S.; Johnson, B. M. (2003) Lake-specific responses to

elevated atmospheric nitrogen deposition in the Colorado Rocky Mountains, U.S.A.

Hydrobiologia 510: 103-114.

Nydick, K. R.; Lafrancois, B. M.; Baron, J. S.; Johnson, B. M. (2004b) Nitrogen regulation of

algal biomass, productivity, and composition in shallow m ountain lakes, Snowy Range,

Wyoming, USA. Can. J. Fish. Aquat. Sci. 61: 1256-1268.

Nykanen, H., H. Vasander, J. T. Huttunen, and P. J. Martikainen. 2002. Effect of experimental

nitrogen load on methane and nitrous oxide fluxes on ombrotrophic boreal peatland. Plant

and Soil 242:147-155.

Officer, C. B.; Biggs, R. B.; Taft, J. L.; Cronin, L. E.; Tyler, M. A.; Boynton, W. R. (1984)

Chesapeake Bay anoxia: origin, development, and significance. Science (Washington,

DC) 223: 22-27.

Officer, C. B.; Ryther, J. H. (1980) The possible importance of silicon in marine eutrophication.

Mar. Ecol. Prog. Ser. 3: 83-91.

Ollinger, S. V.; Aber, J. D.; Lovett, G. M.; Millham, S. E.; Lathrop, R. G.; Ellis, J. M. (1993) A

spatial model of atmospheric deposition for the northeastern U.S. Ecol. Appl. 3: 459-472.

August 2008 C­79 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 O llinger, S. V.; Aber, J. D.; Reich, P. B. (1997) Simulating ozone effects on forest productivity:

interactions among leaf-, canopy-, and stand-level processes. Ecol. Appl. 7: 1237-1251.

Ollinger, S. V.; Smith, M. L.; Martin, M. E.; Hallett, R. A.; Goodale, C. L.; Aber, J. D. (2002)

Regional variation in foliar chemistry and N cycling among forests of diverse history and

composition. Ecology 83: 339-355.

Olsen, Y.; Agustí, S.; Andersen, T.; Duarte, C. M.; Gasol, J. M.; Gismervik, I.; Heiskanen, A.-S.;

Hoell, E.; Kuuppo, P.; Lignell, R.; Reinertsen, H.; Sommer, U.; Stibor, H.; Tamminen,

T.; Vadstein, O.; Vaqué, D.; Vidal, M. (2006) A comparative study of responses in

planktonic food web structure and function in contrasting European coastal waters

exposed to experimental nutrient addition. Limnol. Oceanogr. 51(1 pt 2): 488-503.

Op Den Camp, H. J. M.; Kartal, B.; Guven, D.; Van Niftrik, L. A. M. P.; Haaijer, S. C. M.; Van

Der Star, W. R. L.; Pas-Schoonen, K. T. V. D.; Cabezas, A.; Ying, Z.; Schmid, M. C.;

Kuypers, M. M. M.; Van de Vossenberg, J.; Harhangi, H. R.; Picioreanu, C.; Van

Loosdrecht, M. C. M.; Kuenen, J. G.; Strous, M.; Jetten, M. S. M. (2006) Global impact

and application of the anaerobic ammonium-oxidizing (anammox) bacteria. Biochem.

Soc. Trans. 34: 174-178.

Oura, N., J. Shindo, T. Fumoto, H. Toda, and H. Kawashima. 2001. Effects of nitrogen

deposition on nitrous oxide emissions from the forest floor. Water Air and Soil Pollution

130:673-678.

Padgett, P. E.; Allen, E. B.; Bytnerowicz, A.; Minich, R. A. (1999) Changes in soil inorganic

nitrogen as related to atmospheric nitrogenous pollutants in southern California. Atmos.

Environ. 33: 769-781.

Padgett, P.; Allen, E. B. (1999) Differential responses to nitrogen fertilization in native shrubs

and exotic annuals common to Mediterranean coastal sage scrub of California. Plant

Ecol. 144: 93-101.

Paerl, H. (1995) Coastal eutrophication in relation to atmospheric nitrogen deposition: current

perspectives. Ophelia 41: 237-259.

Paerl, H. (1997) Coastal eutrophication and harmful algal blooms: importance of atmospheric

deposition and groundwater as "new" nitrogen and other nutrient sources. Limnol.

Oceanogr. 42: 1154-1162.

Paerl, H. W. (1985) Enhancement of marine primary production by nitrogen-enriched acid rain.

Nature (London) 315: 747-749.

Paerl, H. W. (2002) Connecting atmospheric nitrogen deposition to coastal eutrophication.

Environ. Sci. Technol. 36: 323A-326A.

Paerl, H. W.; Boynton, W. R.; Dennis, R. L.; Driscoll, C. T.; Greening, H. S.; Kremer, J. N.;

Rabalais, N. N.; Seitzinger, S. P. (2001) Atmospheric deposition of nitrogen in coastal

waters: biogeochemical and ecological implications. In: Valigura, R. W.; Alexander, R.

B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; Stacey, P. E.; Turner, R. E., eds. Nitrogen

loading in coastal water bodies: an atmospheric perspective. Washington, DC: American

Geophysical Union; pp. 11-52.

Paerl, H. W.; Dennis, R. L.; Whitall, D. R. (2002) Atmospheric deposition of nitrogen:

implications for nutrient over-enrichment of coastal waters. Estuaries 25: 677-693.

August 2008 C­80 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 P aerl, H. W.; Valdes, L. M.; Peierls, B. L.; Adolf, J. E.; Harding, L. W. (2006) Anthropogenic

and climatic influences on the eutrophication of large estuarine ecosystems. Limnol.

Oceanogr. 51(1pt 2): 448-462.

Paerl, H.; Pinckney, J.; Fear, J.; Peierls, B. (1998) Ecosystem responses to internal and watershed

organic matter loading: Consequences for hypoxia in the eutrophying Neuse River

Estuary, NC, USA. Mar. Ecol. Prog. Ser. 166: 17-25.

Papen, H. and K. Butterbach-Bahl. 1999. A 3-year continuous record of nitrogen trace gas fluxes

from untreated and limed soil of a N-saturated spruce and beech forest ecosystem in

Germany - 1. N 2 

### O emissions. Journal of Geophysical Research-Atmospheres 104:18487­

18503.

Papen, H., M. Daum, R. Steinkamp, and K. Butterbach-Bahl. 2001. N 2 

### O and CH 4 

### -fluxes from

soils of a N-limited and N-fertilized spruce forest ecosystem of the temperate zone.

Journal of Applied Botany-Angewandte Botanik 75:159-163.

Pardo, L. H.; Driscoll, C. T.; Likens, G. E. (1995) Patterns of nitrate loss from a chronosequence

of clear-cut watersheds. Water Air Soil Pollut. 85: 1659-1664.

Pardo, L. H.; Kendall, C.; Pett-Ridge, J. P.; Change, C. C. Y. (2004) Evaluating the source of

streamwater nitrate using δ 15 

### N and δ 18 

### O in nitrate in two watersheds in New Hampshire,

USA. Hydrol. Process. 18: 2699-2712.

Parker, J. L., I. J. Fernandez, L. E. Rustad, and S. A. Norton. 2001. Effects of nitrogen

enrichment, wildfire, and harvesting on forest-soil carbon and nitrogen. Soil Science

Society of America Journal 65:1248-1255.

Paschke, M. W.; McLendon, T.; Redente, E. F. (2000) Nitrogen availability and old-field

succession in a shortgrass steppe. Ecosystems 3: 144-158.

Paul, A. J.; Leavitt, P. R.; Schindler, D. W.; Hardie, A. K. (1995) Direct and indirect effects of

predation by a calanoid copepod (subgenus: Hesperodiaptomus) and of nutrients in a

fishless alpine lake. Can. J. Fish. Aquat. Sci. 52: 2628-2638.

Paz-Gonzalez, A.; Taboada, M. T. (2000) Nutrient variability from point sampling on 2 meter

grid in cultivated and adjacent forest land. Comm. Soil Sci. Plant Anal. 31: 2135-2146.

Pearcy, R. W.; Björkman, O.; Caldwell, M. M.; Keeley, J. E.; Monson, R. K.; Strain, B. R.

(1987) Carbon gain by plants in natural environments. BioScience 37: 21-29.

Pearson, J.; Stewart, R. G. (1993) Atmospheric ammonia deposition and its effects on plants.

Tansley Review No. 56. New Phytol. 125: 283-305.

Peierls, B. L.; Caraco, N. F.; Pace, M. L.; Cole, J. J. (1991) Human influence on river nitrogen.

Nature 350: 386-387.

Peterjohn, W. T.; Adams, M. B.; Gilliam, F. S. (1996) Symptoms of nitrogen saturation in two

central Appalachian hardwood forest ecosystems. Biogeochemistry 35: 507-522.

Peterjohn, W. T.; Correll, D. L. (1984) Nutrient dynamics in an agricultural watershed:

observations on the role of a riparian forest. Ecology 65: 1466-1475.

Peterson, B. J.; Wollheim, W. M.; Mulholland, P. J.; Webster, J. W.; Meyer, J. L.; Tank, J. L.;

Marti, E.; Bowden, W. B.; Valett, H. M.; Hershey, A. E.; McDowell, W. H.; Dodds, W.

August 2008 C­81 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 K.; Hamilton, S. K.; Gregory, S. V.; Morrall, D. D. (2001) Control of nitrogen export

from watersheds by headwater streams. Science 292: 86-89.

Peterson, D. L.; Arbaugh, M. J.; Robinson, L. J. (1991) Regional growth changes in ozonestressed ponderosa pine (Pinus ponderosa) in the Sierra Nevada, California, USA.

Holocene 1: 50-61.

Peterson, D. L.; Arbaugh, M. J.; Wakefield, V. A.; Miller, P. R. (1987) Evidence of growth

reduction in ozone-injured Jeffrey pine (Pinus jeffreyi Grev. and Balf.) in Sequoia and

Kings Canyon National Parks. JAPCA 3 7: 906-912.

Peterson, G.; Allen, C. R.; Holling, C. S. (1998) Ecological resilience, biodiversity, and scale.

Ecosystems 1: 6-18.

Phillips, R. L., S. C. Whalen, and W. H. Schlesinger. 2001. Influence of atmospheric CO 2



### enrichment on nitrous oxide flux in a temperate forest ecosystem. Global Biogeochemical

Cycles 15:741-752.

Pike, L. H. (1978) The importance of epiphytic lichens in mineral cycling. Bryologist 81: 247­

257.

Pilcher, H. (2005) Pipe Dreams. Nature 437: 1227-1228.

Pilegaard, K., U. Skiba, P. Ambus, C. Beier, N. Bruggemann, K. Butterbach-Bahl, J. Dick, J.

Dorsey, J. Duyzer, M. Gallagher, R. Gasche, L. Horvath, B. Kitzler, A. Leip, M. K.

Pihlatie, P. Rosenkranz, G. Seufert, T. Vesala, H. Westrate, and S. ZechmeisterBoltenstern. 2006. Factors controlling regional differences in forest soil emission of

nitrogen oxides (NO and N 2 

### O). Biogeosciences 3:651-661.

Pimentel, D.; Wilson, C.; McCullum, C.; Huang, R.; Dwen, P.; Flack, J.; Tran, Q.; Saltman, T.;

Cliff, B. (1997) Economic and environmental benefits of biodiversity. BioScience 47:

747-757.

Pimm, S. L. (1984) The complexity and stability of ecosystems. Nature (London) 307: 321-326.

Pinay, G.; Black, V. J.; Planty-Tabacchi, A. M.; Gumiero, B.; Décamps, H. (2000) Geomorphic

control of denitrification in large river floodplain soils. Biogeochemistry 50: 163-182.

Pinay, G.; Roques, L.; Fabre, A. (1993) Spatial and temporal patterns of denitrification in a riparian forest. J. Appl. Ecol. 30: 581-591.

Pitcairn, C. E. R.; Fowler, D.; Leith, I. D.; Sheppard, L. J.; Sutton, M. A.; Kennedy, V.; Okello,

E. (2003) Bioindicators of enhanced nitrogen depositon. Environ. Pollut. 126: 353-361.

Poff, N. L.; Allan, J. D.; Bain, M. B.; Karr, J. R.; Prestegaard, K. L.; Richter, B. D.; Sparks, R.

E.; Stromberg, J. C. (1997) The natural flow r egime: a paradigm for river conservation

and restoration. BioScience 47: 769-784.

Ponnamperuma, F. N. (1972) The chemistry of submerged soils. Adv. Agron. 24: 29-96.

Pregitzer, K. S., A. J. Burton, D. R. Zak, and A. F. Talhelm. 2008. Simulated chronic nitrogen

deposition increases carbon storage in Northern Temperate forests. Global Change

Biology 14:142-153.

Priscu, J. C.; Axler, R. P.; Goldman, C. R. (1985) Nitrogen metabolism of the shallow a nd deep

water phytoplankton in a subalpine lake. Oikos 45: 137-147.

August 2008 C­82 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39 Providoli, I.; Bugmann, H.; Siegwolf, R.; Buchmann, N.; Schleppi, P. (2005) Flow o f deposited

inorganic N i n two Gleysol-dominated mountain catchments traced with 15 

### NO

- 3 

### and

15 

### NH +

4



### . Biogeochemistry 76: 453-475.

Purbopuspito, J., E. Veldkamp, R. Brumme, and D. Murdiyarso. 2006. Trace gas fluxes and

nitrogen cycling along an elevation sequence of tropical montane forests in Central

Sulawesi, Indonesia. Global Biogeochemical Cycles 20:11.

Quist, M. E.; Nasholm, T.; Lindeberg, J.; Johannisson, C.; Hogbom, L.; Hogberg, P. (1999)

Responses of a nitrogen-saturated forest to a sharp decrease in nitrogen input. J. Environ.

Qual. 28: 1970-1977.

Rabalais, N. N. (1998) Oxygen depletion in coastal waters. NOAA s tate of the coast report.

Silver Spring, MD: National Oceanic and Atmospheric Administration. Available:

http://oceanservice.noaa.gov/websites/retiredsites/sotc_pdf/HYP.PDF [21 November,

2007].

Rabalais, N. N. (2002) Nitrogen in aquatic ecosystems. Ambio 31: 102-112.

Rabalais, N. N.; Turner, R. E.; Justic, D.; Dortch, Q.; Wiseman, W. J., Jr.; Sen Gupta, B. K.

(1996) Nutrient changes in the Mississippi River and system responses on the adjacent

continental shelf. Estuaries 19: 386-407.

Redbo-Torstensson, P. (1994) The demographic consequences of nitrogen fertilization of a population of sundew, Drosera rotundifolia. Acta Bot. Neerl. 43: 175-188.

Redfield, A. C. (1958) The biological control of chemical factors in the environment. Am. Sci.

46: 205-221.

Regina, K., H. Nykanen, M. Maljanen, J. Silvola, and P. J. Martikainen. 1998. Emissions of N 2 

### O

and NO a nd net nitrogen mineralization in a boreal forested peatland treated with

different nitrogen compounds. Canadian Journal of Forest Research-Revue Canadienne

De Recherche Forestiere 28:132-140.

Reich, P. B. (1987) Quantifying plant response to ozone: a unifying theory. Tree Physiol. 3: 63­

91.

Reich, P. B.; Grigal, D. F.; Aber, J. D.; Gower, S. T. (1997a) Nitrogen mineralization and

productivity in 50 hardwood and conifer stands on diverse soils. Ecology 78: 335-347.

Reich, P. B.; Walters, M. B.; Ellsworth, D. S. (1997b) From tropics to tundra: global

convergence in plant functioning. Proc. Nat. Acad. Sci. 94: 13730-13734.

Reiners, W. A. (1986) Complementary models in ecology. Am. Nat. 27: 59-73.

Renberg, I.; Korsman, T.; Anderson, N. J. (1993) A t emporal perspective of lake acidification in

Sweden. Ambio 22: 264-271.

Reuss, J. O.; Johnson, D. W. (1985) Effect of soil processes on the acidification of water by acid

deposition. J. Environ. Qual. 14: 26-31.

Reuss, J. O.; Johnson, D. W. (1986) Acid deposition and the acidification of soils and waters.

New Y ork, NY: Springer-Verlag. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.;

Remmert, H., eds. Ecological studies: analysis and synthesis: v. 59).

August 2008 C­83 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 R euter, J. E.; Axler, R. P. (1992) Physiological characterisisics of inorganic nitrogen uptake by

spatially separate algal communities in a nitrogen-deficient lake. Freshwater Biol. 27:

227-236.

Reuter, J. E.; Loeb, S. L.; Axler, R. P.; Carlton, R. G.; Goldman, C. R. (1985) Transformation of

nitrogen following an epilimnetic nitrogen fertilization in Castle Lake, CA. 1. epilithic

periphyton responses. Arch. Hydrobiol. 102: 425-433.

Reuter, J. E.; Loeb, S. L.; Goldman, C. R. (1986) Inorganic nitrogen uptake by epilithic

periphyton in a N-deficient lake. Limnol. Oceanogr. 31: 149-160.

Riegman, R. (1992) Phaeocystis blooms and eutrophication of the continental coastal zones of

the North Sea. Mar. Biol. 112: 479-484.

Riggan, P. J.; Lockwood, R. N.; Lopez, E. N. (1985) Deposition and processing of airborne

nitrogen pollutants in Mediterranean-type ecosystems of southern California. Environ.

Sci. Technol. 19: 781-789.

Roberts, B. J.; Howarth, R. W. (2006) Nutrient and light availability regulate the relative

contribution of autotrophs and heterotrophs to respiration in freshwater pelagic

ecosystems. Limnol. Oceanogr. 51: 288-298.

Robertson, G. P.; Crum, J. R.; Ellis, B. G. (1993) The spatial variability of soil resources

following long-term disturbance. Oecologia 96: 451-456.

Roelofs, J. G. M. (1986) The effect of airborne sulphur and nitrogen deposition on aquatic and

terrestrial heathland vegetation. Experientia 42: 372-377.

Roem, W. J.; Berendse, F. (2000) Soil acidity and nutrient supply ratio as possible factors

determining changes in plant species diversity in grassland and heathland communities.

Biol. Cons. 92: 151-161.

Romansic, J. M.; Diez, K. A.; Higashi, E. M.; Blaustein, A. R. (2006) Effects of nitrate and the

pathogenic water mold Saprolegnia on survival of amphibian larvae. Dis. Aquat. Org. 68:

235-243.

Rose, C.; Axler, R. P. (1998) Uses of alkaline phosphatase activity in evaluating phytoplankton

community phosphorous deficiency. Hydrobiologia 361: 145-156.

Ross, D. S.; Lawrence, G. B.; Fredriksen, G. (2004) Mineralization and nitrification patterns at

eight northeastern USA forested research sites. For. Ecol. Manage. 188: 317-335.

Rudek, J.; Paerl, H. W.; Mallin, M. A.; Bates, P. W. (1991) Seasonal and hydrological control of

phytoplankton nutrient limitation in the lower Neuse River Estuary, North Carolina.

Mar. Ecol. Progr. Ser. 75: 133-142.

Rueth, H.; Baron, J. S. (2002) Differences in Englemann spruce forest biogeochemistry east and

west of the Continental Divide in Colorado, USA. Ecosystems 5: 45-57.

Ryerson, T. B.; Trainer, M.; Holloway, J. S.; Parrish, D. D.; Huey, L. G.; Sueper, D. T.; Frost, G.

J.; Donnelly, S. G.; Schauffler, S.; Atlas, E. L.; Kuster, W. C.; Goldan, P. D.; Hubler, G.;

Meagher, J. F.; Fehsenfeld, F. C. (2001) Observations of ozone formation in power plant

plumes and implications for ozone control strategies. Science (Washington, DC) 292:

719-723.

August 2008 C­84 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 R ysgaard, S.; Glud, R. N.; Risgaard-Petersen, N.; Dalsgaard, T. (2004) Denitrification and

anammox activity in Arctic marine sediments. Limnol. Oceanogr. 49: 1493-1502.

Rysgaard, S.; Risgaard-Petersen, N.; Sloth, N. P.; Jensen, K.; Nielsen, L. P. (1994) Oxygen

regulation of nitrification and denitrification in sediments. Limnol. Oceanogr. 39: 1643­

1652.

Saarnio, S. and J. Silvola. 1999. Effects of increased CO 2 

### and N on CH 4 

### efflux from a boreal

mire: a growth chamber experiment. Oecologia 119:349-356.

Saarnio, S.; Järviö, S.; Saarinen, T.; Vasander, H.; Silvola, J. (2003) Minor changes in vegetation

and carbon gas balance in a boreal mire under a raised CO 2 

### or NH 4 

### NO 3 

### supply.

Ecosystems 6: 46-60.

Saarnio, S.; Saarinen, T.; Vasander, H.; Silvola, J. (2000) A moderate increase in the annual CH 4 

### efflux by raised CO 2 

### or NH 4 

### NO 3 

### supply in a boreal oligotrophic mire. Glob. Change

Biol. 6: 137-144.

Saros, J. E.; Interlandi, S. J.; Wolfe, A. P.; Engstrom, D. R. (2003) Recent changes in the diatom

community structure of lakes in the Beartooth Mountain Range, USA. Arct. Anarct. Alp.

Res. 35: 18-23.

Saros, J. E.; Michel, T. J.; Interlandi, S. J.; Wolfe, A. P. (2005) Resource requirements of

Asterionella formosa and Fragilaria crotonensis in oligotrophic alpine lakes: implications

for recent phytoplankton community reorganizations. Can. J. Fish. Aquat. Sci. 62: 1681­

1689.

Schaeffer, S. M., S. A. Billings, and R. D. Evans. 2003. Responses of soil nitrogen dynamics in a

Mojave Desert ecosystem to manipulations in soil carbon and nitrogen availability.

Oecologia 134:547-553.

Schelske, C. L. (1991) Historical nutrient enrichment of Lake Ontario: paleolimnological

evidence. Can. J. Fish. Aquat. Sci. 48: 1529-1538.

Schimel, J. P.; Bennett, J. (2004) Nitrogen mineralization: challenges of a changing paradigm.

Ecology 85: 591-602.

Schindler, D. W. (1974) Eutrophication and recovery in experimental lakes: implications for lake

management. Science 184: 897-899.

Schindler, D. W. (1980) The effect of fertilization with phosphorous and nitrogen versus

phosphorus alone on eutrophication of experimental lakes. Limnol. Oceanogr. 25: 1149­

1152.

Schindler, D. W.; Armstrong, F. A. J.; Brunskill, G. J.; Holmgren, S. K. (1971) Eutrophication of

Lake 227, Experimental Lakes Area, northwestern Ontario, by addition of phosphate and

nitrate. J. Fish. Res. Board Can. 28: 1763-1782.

Schlesinger, W. H.; Ward, T. J.; Anderson, J. (2000) Nutrient losses in runoff from grassland and

shrubland habitats in southern New Mexico: II. Field plots. Biogeochemistry 49: 69-86.

Schwinning, S.; Starr, B. I.; Wojcik, N. J.; Miller, M. E.; Ehleringer, J. E.; Sanford, R. S. (2005)

Effects of nitrogen deposition on an arid grassland in the Colorado Plateau cold desert.

Rangeland Ecol. Manage. 58: 565-574.

August 2008 C­85 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 S cott, G.; Crunkilton, R. L. (2000) Acute and chronic toxicity of nitrate to fathead minnows

(Pimephales promelas), Ceriodaphnia dubia, and Daphnia magna. Environ. Toxicol.

Chem. 19: 2918-2922.

Scudlark, J. R.; Church, T. M. (1993) Atmospheric input of inorganic nitrogen to Delaware Bay.

Estuaries 16: 747-759.

Seastedt, T. R.; Bowman, W. D.; Caine, T. N.; McKnight, D.; Townsend, A.; Williams, M. W.

(2004) The landscape continuum: a model for high elevation ecosystems. BioScience 54:

111-121.

Seastedt, T. R.; Vaccaro, L. (2001) Plant species richness, productivity, and nitrogen and

phosphorus limitations across a snowpack gradient in alpine tundra, Colorado, USA.

Arct. Anarct. Alp. Res. 33: 100-106.

Seely, B.; Lajtha, K. (1997) Application of a 15 

### N tracer to simulate and track the fate of

atmospherically deposited N in the coastal forests of the Waquoit Bay watershed, Cape

Cod, Massachusetts. Oecologia 112: 393-402.

Seitzinger, S. P. (1988) Denitrification in freshwater and coastal marine ecosystems: ecological

and geochemical significance. Limnol. Oceanogr. 33: 702-724.

Seitzinger, S. P.; Nielsen, L. P.; Caffrey, J.; Christensen, P. B. (1993) Denitrification

measurements in aquatic sediments: a comparison of three methods. Biogeochemistry 23:

147-167.

Seitzinger, S. P.; Styles, R. V.; Boyer, E. W.; Alexander, R. B.; Billen, G.; Howarth, R. W.;

Mayer, B.; Van Breemen, N. (2002) Nitrogen retention in rivers: model development and

application to watersheds in the northeastern U.S.A. Biogeochemistry 57/58: 199-237.

Seitzinger, S.; Harrison, J. A.; Böhlke, J. K.; Bouwman, A. F.; Lowrance, R.; Peterson, B.;

Tobias, C.; Van Drecht, G. (2006) Denitrification across landscapes and waterscapes: a

synthesis. Ecol. Appl. 16: 2064-2090.

Shan, J. P., L. A. Morris, and R. L. Hendrick. 2001. The effects of management on soil and plant

carbon sequestration in slash pine plantations. Journal of Applied Ecology 38:932-941.

Shaver, G. R., L. C. Johnson, D. H. Cades, G. Murray, J. A. Laundre, E. B. Rastetter, K. J.

Nadelhoffer, and A. E. Giblin. 1998. Biomass and CO 2 

### flux in wet sedge tundras:

Responses to nutrients, temperature, and light. Ecological Monographs 68:75-97.

Shaver, G. R.; Billings, W. D. (1975) Root production and root turnover in a wet tundra

ecosystem, Barrow, Alaska. Ecology 56: 401-409.

Shaver, G. R.; Chapin, F. S. (1980) Response to fertilization by various plant growth forms in an

Alaskan tundra: nutrient accumulation and growth. Ecology 61: 662-675.

Sherrod, S. K.; Seastedt, T. R. (2001) Effects of the northern pocket gopher (Thomomys

talpoides) on alpine soil characteristics, Niwot Ridge, CO. Biogeochemistry 55: 195-218.

Sickman, J. O.; Leydecker, A.; Chang, C. C. Y.; Kendall, C.; Melack, J. M.; Lucero, D. M.;

Schimel, J. (2003b) Mechanisms underlying export of N from high-elevation catchments

during seasonal transitions. Biogeochemistry 64: 1-24.

Sickman, J. O.; Melack, J. M.; Clow, D. W. (2003a) Evidence for nutrient enrichment of highelevation lakes in the Sierra Nevada, California. Limnol. Oceanogr. 48: 1885-1892.

August 2008 C­86 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Sickman, J.; Melack, J.; Stoddard, J. (2002) Regional analysis of inorganic nitrogen yield and

retention in high-elevation ecosystems of the Sierra Nevada and Rocky Mountains.

Biogeochemistry 57/58: 341-374.

Sievering, H. (1999) Nitrogen deposition and carbon sequestration. Nature 400: 629-630.

Sievering, H.; Fernandez, I.; Lee, J.; Hom, J.; Rustad, L. (2000) Forest canopy uptake of

atmospheric nitrogen deposition at eastern U.S. conifer sites: carbon storage

implications? Glob. Biogeochem. Cycles 14: 1153-1159.

Silvola, J.; Saarnio, S.; Foot, J.; Sundh, I.; Greenup, A.; Heijmans, M.; Ekberg, A.; Mitchell, E.;

van Breeman, N. (2003) Effects of elevated CO 2 

### and N d eposition on CH 4 

### emissions

from European mires. Glob. Biogeochem. Cycles 17: 1068-1079.

Sitaula, B. K., L. R. Bakken, and G. Abrahamsen. 1995. Nutrient balance in Scots pine (Pinus

sylvestris L) forest .3. Fluxes of N 2 

### O f rom lysimeter as influenced by nitrogen input.

Water Air and Soil Pollution 85:1155-1159.

Skeffington, R. A.; Wilson, E. J. (1988) Excess nitrogen deposition: issues for consideration.

Environ. Pollut. 54: 159-184.

Skiba, U., L. Sheppard, C. E. R. Pitcairn, I. Leith, A. Crossley, S. van Dijk, V. H. Kennedy, and

D. Fowler. 1998. Soil nitrous oxide and nitric oxide emissions as indicators of elevated

atmospheric N d eposition rates in seminatural ecosystems. Environmental Pollution

102:457-461.

Skinner, M. W.; Pavlik, B. M., eds. (1994) California Native Plant Society's inventory of rare

and endangered vascular plants of California. Sacramento, CA: California Native Plant

Society.

Smith, G. F.; Nicholas, N. S. (1999) Post disturbance spruce-fir forest stand dynamics at seven disjunct sites. Castanea 64: 175-186.

Smith, L. K.; Voytek, M. A.; Böhlke, J. K.; Harvey, J. W. (2006) Denitrification in nitrate-rich

streams: application of N 2 

### :Ar and 15 

### N-tracer methods in intact cores. Ecol. Appl. 16:

2191-2207.

Smith, M.-L.; Ollinger, S. V.; Martin, M. E.; Aber, J. D.; Hallett, R. A.; Goodale, C. L. (2002)

Direct estimation of aboveground forest productivity through hyperspectral remote

sensing of canopy nitrogen. Ecol. Appl. 12: 1286-1302.

Smith, R. A.; Schwarz, G. E.; Alexander, R. B. (1997) Regional interpretation of water-quality

monitoring data. Water Resour. Res. 33: 2781-2798.

Smith, S. M.; Lee, K. D. (2006) Responses of periphyton to artificial nutrient enrichment in

freshwater kettle ponds of Cape Cod National Seashore. Hydrobiologia 571: 201-211.

Smith, V. H. (2006) Responses of estuarine and coastal marine phytoplankton to nitrogen and

phosphorus enrichment. Limnol. Oceanogr. 51(1 pt 2): 377-384.

Smith, V. H.; Tilman, G. D.; Nekola, J. C. (1999) Eutrophication: impacts of excess nutrient

inputs on freshwater, marine, and terrestrial ecosystems. Environ. Pollut. 100: 179-196.

Sobczak, W. V.; Findlay, S.; Dye, S. (2003) Relationship between DOC bioavailability and

nitrate removal in an upland stream: an experimental approach. Biogeochemistry 62: 309­

327.

August 2008 C­87 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43 Sollins, P.; Grier, C. C.; McCorison, F. M.; Cromack, K., Jr.; Fogel, R.; Fredriksen, R. L. (1980)

The internal element cycles of an old-growth Douglas-fir ecosystem in western Oregon.

Ecol. Monogr. 50: 261-285.

Soussana, J. F., V. Allard, K. Pilegaard, P. Ambus, C. Amman, C. Campbell, E. Ceschia, J.

Clifton-Brown, S. Czobel, R. Domingues, C. Flechard, J. Fuhrer, A. Hensen, L. Horvath,

M. Jones, G. Kasper, C. Martin, Z. Nagy, A. Neftel, A. Raschi, S. Baronti, R. M. Rees,

U. Skiba, P. Stefani, G. Manca, M. Sutton, Z. Tubaf, and R. Valentini. 2007. Full

accounting of the greenhouse gas (CO 2 

### , N 2 

### O, CH 4 

### ) budget of nine European grassland

sites. Agriculture Ecosystems & Environment 121:121-134.

Spiecker, H.; Meielikainen, K.; Kohl, M.; Skovsgaard, J. (1996) Growth trends in European

forests: studies from 12 countries. New Y ork, NY: Springer-Verlag. [European Forest

Research Institute Report 5].

Spoelstra, J.; Schiff, S. L.; Elgood, R. J.; Semkin, R. G.; Jeffries, D. S. (2001) Tracing the

sources of exported nitrate in the Turkey Lakes watershed using 15 

### N/ 14 

### N a nd 18 

### O/ 16 

### O

isotopic ratios. Ecosystems 4: 536-544.

Stacey, P. E.; Greening, J. S.; Kremer, J. N.; Peterson, D.; Tomasko, D. A. (2001) Contribution

of atmospheric nitrogen deposition to U.S. estuaries: summary and conclusions. In:

Valigura, R. W.; Alexander, R. B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; Stacey, P.

E.; Turner, R. E., eds. Nitrogen loading in coastal water bodies: An atmospheric

perspective. Washington, DC: American Geophysical Union; pp. 187-226.

Stanley, E. H.; Short, R. A.; Harrison, J. W.; Hall, R. I.; Wiedenfeld, R. C. (1990) Variation in

nutrient limitation of lotic and lentic algal communities in a Texas (USA) river.

Hydrobiologia 206: 61-71.

Steltzer, H.; Bowman, W. D. (1998) Differential influence of plant species on soil nitrogen

transformations within moist meadow a lpine tundra. Ecosystems 1: 464-474.

Sterner, R. W.; Elser, J. J. (2002) Ecological stoichiometry: the biology of elements from

molecules to the biosphere. Princeton, NJ: Princeton University Press.

Steudler, P. A.; Bowden, R. D.; Mellilo, J. M.; Aber, J. D. (1989) Influence of nitrogen

fertilization on methane uptake in temperate forest soil. Nature 341: 314-316.

Stevens, C. J.; Dise, N. B.; Mountford, J. O.; Gowing, D. J. (2004) Impact of nitrogen deposition

on the species richness of grasslands. Science 303: 1876-1879.

Stevens, R. J.; Laughlin, R. J.; Malone, J. P. (1997) Measuring the contributions of nitrification

and denitrification to the flux of nitrous oxide from soil. Soil Biol. Biogeochem. 29: 139­

151.

Stoddard, J. L. (1994) Long-term changes in watershed retention of nitrogen: its causes and

aquatic consequences. In: Baker, L. A., ed. Environmental chemistry of lakes and

reservoirs. Washington, DC: American Chemical Society; pp. 223-284. (Advances in

chemistry series no. 237).

Stoddard, J.; Kahl, J. S.; Deviney, F. A.; DeWalle, D. R.; Driscoll, C. T.; Herlihy, A. T.;

Kellogg, J. H.; Murdoch, P. S.; Webb, J. R.; Webster, K. E. (2003) Response of surface

water chemistry to the Clean Air Act Amendments of 1990. Research Triangle Park, NC:

U.S. Environmental Protection Agency, Office of Research and Development, National

Health and Environmental Effects Research Laboratory. EPA 6 20/R-03.001.

August 2008 C­88 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Stohlgren, T. J.; Binkley, D.; Chong, G. W.; Kalkhan, M. A.; Schell, L. D.; Bull, K. A.; Otsuki,

Y.; Newman, G.; Bashkin, M.; Son, Y. (1999) Exotic plant species invade hot spots of

native plant diversity. Ecol. Monogr. 69: 25-46.

Stolte, W.; McCollin, T.; Noordeloos, A.; Riegman, R. (1994) Effects of nitrogen source on the

size distribution within marine phytoplankton populations. J. Exp. Mar. Biol. Ecol. 184:

83-97.

Stribley, D. P.; Read, D. J. (1980) The biology of mycorrhiza in the Ericacae. VII. The

relationship between mycorrhizal infection and the capacity to utilize simple and

complex organic nitrogen sources. New P hytol. 86: 365-371.

Suding, K. N.; Collins, S. L.; Gough, L.; Clark, C.; Cleland, E. E.; Gross, K. L.; Milchunas, D.

G.; Pennings, S. (2005) Functional- and abundance-based mechanisms explain diversity

loss due to N f ertilization. Proc. Natl. Acad. Sci. U. S. A. 102: 4387-4392.

Suding, K. N.; Miller, A. E.; Bechtold, H.; Bowman, W. D. (2006) The consequence of species

loss on ecosystem nitrogen cycling depends on community compensation. Ocecologia

149: 141-149.

Sullivan, T. J. (2000) Aquatic effects of acidic deposition. Boca Raton, FL: Lewis Publishers.

Sullivan, T. J.; Cosby, B. J.; Laurence, J. A.; Dennis, R. L.; Savig, K.; Webb, J. R.; Bulger, A. J.;

Scruggs, M.; Gordon, C.; Ray, J.; Lee, H.; Hogsett, W. E.; Wayne, H.; Miller, D.; Kern,

J. S. (2003) Assessment of air quality and related values in Shenandoah National Park.

Philadelphia, PA: U.S. Department of the Interior, National Park Service, Northeast

Region; technical report NPS/NERCHAL/NRTR-03/090. Available:

http://www.nps.gov/nero/science/FINAL/shen_air_quality/shen_airquality.html [19 June,

2006].

Swank, W. T. (1988) Stream chemistry responses to disturbance. In: Swank, W. T.; Crossley, D.

S., eds. Forest hydrology and ecology at Coweeta. New York, NY: Springer-Verlag,

Inc.; pp. 339-358.

Takemoto, B. K.; Bytnerowicz, A.; Fenn, M. E. (2001) Current and future effects of ozone and

atmospheric nitrogen deposition on California's mixed conifer forests. For. Ecol. Manage.

144: 159-173.

Tank, J. L.; Dodds, W. K. (2003) Nutrient limitation of epilithic and epixylic biofilms in ten

North American streams. Freshwater Biol. 48: 1031-1049.

Taylor, G. E., Jr.; Hanson, P. J. (1992) Forest trees and tropospheric ozone: role of canopy deposition and leaf uptake in developing exposure-response relationships. Agric. Ecosyst.

Environ. 42: 255-273.

Templer, P. H.; Lovett, G. M.; Weathers, K. C.; Findlay, S. E.; Dawson, T. E. (2005) Influence

of tree species on forest nitrogen retention in the Catskill Mountains, New Y ork, USA.

Ecosystems 8: 1-16.

Tester, P. A.; Varnam, S. M.; Culver, M. E.; Eslinger, D. L.; Stumpf, R. P.; Swift, R. N.; Yungel,

J. K.; Black, M. D.; Litaker, R. W. (2003) Airborne detection of ecosystem responses to

an extreme event: phytoplankton displacement and abundance after hurricane induced

flooding in the Albemarle-Pamlico Sound System, North Carolina. Estuaries 26: 1353­

1364.

August 2008 C­89 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Theodose, T. A.; Bowman, W. D. (1997) Nutrient availability, plant abundance, and species

diversity in two alpine tundra communities. Ecology 78: 1861-1872.

Thorne, R. S. J.; Williams, W. P.; Gordon, C. (2000) The macroinvertebrates of a polluted

stream in Ghana. J. Freshwater Ecol. 15: 209-217.

Throop, H. L. (2005) Nitrogen deposition and herbivory affect biomass production and

allocation in an annual plant. Oikos 111: 91-100.

Throop, H. L.; Lerdau, M. T. (2004) Effects of nitrogen deposition on insect herbivory:

implications for community and ecosystem processes. Ecosystems 7: 109-133.

Tietema, A.; Emmett, B. A.; Gundersen, P.; Kjønaas, O. J.; Koopmans, C. J. (1998) The fate of

15 

### N-labelled nitrogen deposition in coniferous forest ecosystems. For. Ecol. Manage. 101:

19-27.

Tilman, D. (1981) Tests of resource competition theory using four species of Lake Michigan

algae. Ecology 62: 802-815.

Tilman, D. (1987) Secondary succession and the pattern of plant dominance along experimental

nitrogen gradients. Ecol. Monogr. 57: 189-214.

Tilman, D. (1996) Biodiversity: population versus ecosystem stability. Ecology 77: 350-363.

Tilman, D. (2000) Causes, consequences and ethics of biodiversity. Nature (London) 405: 208­

211.

Tilman, D.; Downing, J. A. (1994) Biodiversity and stability in grasslands. Nature (London) 367:

363-365.

Tilman, D.; Wedin, D. (1991) Dynamics of nitrogen competition between successional grasses. Ecology 72: 1038-1049.

Tomassen, H. B. M.; Smolders, A. J. P.; Lamers, L. P. M.; Roelofs, J. G. M. (2003) Stimulated

growth of Betula pubescens and Molinia caerulea on ombrotrophic bogs: role of high

levels of atmospheric nitrogen deposition. J. Ecol. 91: 357-370.

Tomassen, H. B. M.; Smolders, A. J. P.; Limpens, J.; Lamers, L. P. M.; Roelofs, J. G. M. (2004)

Expansion of invasive species on ombrotrophic bogs: dessication or high N deposition? J.

Appl. Ecol. 41: 139-150.

Townsend, A. R.; Braswell, B. H.; Holland, E. A.; Penner, J. E. (1996) Spatial and temporal

patterns in terrestrial carbon storage due to deposition of fossil fuel nitrogen. Ecol. Appl.

6: 806-814.

Townsend, A. R.; Howarth, R. W.; Bazzaz, F. A.; Booth, M. S.; Cleveland, C. C.; Collinge, S.

K.; Dobson, A. P.; Epstein, P. R.; Holland, E. A.; Keeney, D. R.; Mallin, M. A.; Rogers,

C. A.; Wayne, P.; Wolfe, A. H. (2003) Human health effects of a changing global

nitrogen cycle. Front. Ecol. Environ. 1: 240-246.

Treseder, K. K. (2004) The meta-analysis of mycorrhizal response to nitrogen, phosphorus, and

atmospheric CO 2 

### in field studies. New P hytol. 164: 347-355.

Tritton, L. M.; Martin, C. W.; Hornbeck, J. W.; Pierce, R. S. (1987) Biomass and nutrient

removals from commercial thinning and whole-tree clearcutting of central hardwoods.

Environ. Manage. 11: 659-666.

August 2008 C­90 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Trombulak, S. C.; Frissell, C. A. (2000) Review o f ecological effects of roads on terrestrial and

aquatic communities. Conserv. Biol. 14: 18-30.

Turner, R. E. (2002) Element ratios and aquatic food webs. Estuaries 25: 694-703.

Turner, R. E.; Qureshi, N.; Rabalais, N. N.; Dortch, Q.; Justic, D.; Shaw, R. F.; Cope, J. (1998)

Fluctuating silicate:nitrate ratios and coastal plankton food webs. Proc. Nat. Acad. Sci.

95: 13048-13051.

Turner, R. E.; Stanley, D.; Brock, D.; Pennock, J.; Rabalais, N. N. (2001) A c omparison of

independent N-loading estimates for U.S. estuaries. In: Valigura, R.; Alexander, R.;

Castro, M.; Meyers, T.; Paerl, H.; Stacey, P.; Turner, R. E., eds. Nitrogen loading in

coastal water bodies: an atmospheric perspective. Washington, DC.: American

Geophysical Union.

U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen.

Research Triangle Park, NC: Office of Health and Environmental Assessment,

Environmental Criteria and Assessment Office; report nos. EPA/600/8-91/049aF-cF. 3v.

Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517.

U.S. Environmental Protection Agency. (1999a) The benefits and costs of the Clean Air Act

1990 to 2010: EPA r eport to Congress. Washington, DC: Office of Air and Radiation;

EPA r eport no. 410-R-99-001. Available: http://www.epa.gov/air/sect812/1990­

2010/fullrept.pdf

U.S. Environmental Protection Agency. (1999b) Deposition of air pollutants to the great

waters—third report to Congress. Washington, DC: U.S. Government Printing Office.

http://www.epa.gov/air/oaqps/gr8water/3rdrpt/index.html

U.S. Environmental Protection Agency. (2000a) Nutrient Criteria Technical Guidance Manual:

Rivers and Streams. Washington, DC: U.S. Environmental Protection Agency. EPA-822­

B00-022. http://www.epa.gov/waterscience/criteria/nutrient/guidance/rivers/index.html

U.S. Environmental Protection Agency. (2000b) Nutrient Criteria Technical Guidance Manual:

Lakes and Reservoirs. Washington, DC: U.S. Environmental Protection Agency. EPA822-B00-001. http://www.epa.gov/waterscience/criteria/nutrient/guidance/lakes/lakes.pdf

U.S. Environmental Protection Agency. (2003) Economic analyses of nutrient and sediment

reduction actions to restore Chesapeake bay water quality. Annapolis, MD:

U.S. Environmental Protection Agency Region III, Chesapeake Bay Program Office.

http://www.chesapeakebay.net/ecoanalyses.htm.

U.S. Environmental Protection Agency. (2004) Air quality criteria for particulate matter.

Research Triangle Park, NC: National Center for Environmental Assessment; report no.

EPA/600/P-99/002aF-bF. 2v. http://cfpub.epa.gov/ncea/

U.S. Environmental Protection Agency. (2005) Review o f the national ambient air quality

standards for particulate matter: policy assessment of scientific and technical information.

OAQPS staff paper. Research Triangle Park, NC: Office of Air Quality Planning and

Standards; report no. EPA/452/R-05-005a.

http://www.epa.gov/ttn/naaqs/standards/pm/data/pmstaffpaper_20051221.pdf

August 2008 C­91 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Valiela, I.; Costa, J. E. (1988) Eutrophication of Buttermilk Bay, a Cape Cod coastal

embayment: concentrations of nutrients and watershed nutrient budgets. Environ.

Manage. 12: 539-553.

Valiela, I.; Costa, J. E.; Foreman, K. (1990) Transport of groundwater-borne nutrients from

watersheds and their effects on coastal waters. Biogeochemistry 10: 177-197.

Valiela, I.; Foreman, K.; LaMontagne, M.; Hersh, D.; Costa, J.; Peckol, P.; DeMeo-Andreson,

B.; D'Avanzo, C.; Babione, M.; Sham, C.; Brawley, J.; Lajtha, K. (1992) Couplings of

watersheds and coastal waters: sources and consequences of nutrient enrichment in

Waquoit Bay, Massachusetts. Estuaries 15: 443-457.

Valigura, R. A.; Alexander, R. B.; Brock, D. A.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.;

Stacey, P. E.; Stanley, D., eds. (2000) An assessment of nitrogen inputs to coastal areas

with an atmospheric perspective. In: Nitrogen loading in coastal water bodies: an atmospheric perspective. Washington, DC: American Geophysical Union. [AGU C oastal

Estuaries Series, no. 57].

Van Breemen, N.; Boyer, E. W.; Goodale, C. L.; Jaworski, N. A.; Paustian, K.; Seitzinger, S. P.;

Lajtha, K.; Mayer, B.; Van Dam, D.; Howarth, R. W.; Nadelhoffer, K. J.; Eve, M.; Billen,

G. (2002) Where did all the nitrogen go? Fate of nitrogen inputs to large watersheds in

the northeastern U.S.A. Biogeochemistry 57/58: 267-293.

Van Breemen, N.; Van Dijk, H. F. G. (1988) Ecosystem effects of atmospheric deposition of

nitrogen in The Netherlands. In: Dempster, J. P.; Manning, W. J., eds. Excess nitrogen

deposition. Environ. Pollut. 54: 249-274.

Van De Graaf, A.; Mulder, A.; De Brujin, P.; Jetten, M. S. M.; Robertson, L. A.; Kuenen, J. G.

(1995) Anaerobic oxidation of ammonium is a biologically mediated process. Appl.

Environ. Microbiol. 61: 1246-1251.

Van Dijk, H. F. G.; Roelofs, J. G. M. (1988) Effects of excessive ammonium deposition on the

nutritional status and condition of pine needles. Physiol. Plant. 73: 494-501.

Van Dobben, H. F.; de Bakker, A. J. (1996) Re-mapping of lichen biodiversity in The

Netherlands: effects of decreasing SO 2 

### and increasing NH 3



### . Acta Bot. Neerl. 45: 55-71.

Van Dobben, H. F.; Wolterbeek, T.; Wamelink, G. W. W.; ter Braak, C. J. F. (2001)

Relationship between epiphytic lichens, trace elements and gaseous atmospheric

pollutants. Environ. Pollut. 112: 163-169.

Van Drecht, G.; Bouwman, A. F.; Knoop, J. M.; Beusen, A. H. W.; Meinardi, C. R. (2003)

Global modeling of the fate of nitrogen from point and nonpoint sources in soils,

groundwater, and surface water. Global Biogeochem. Cycles 17(art. no. 1115):

10.1029/2003GB002060.

Van Egmond, K.; Bresser, T.; Bouwman, L. (2002) The European nitrogen case. Ambio 31: 72­

78.

Van Haluwyn, C.; van Herk, C. M. (2002) Bioindication: the community approach. In: Nimis, P.

L.; Scheidegger, C.; Wolseley, P. A., eds. Monitoring with lichens: monitoring lichens.

Dordrecht, The Netherlands: Kluwer Academic Publishers; pp. 39-64.

Van Herk, C. M. (1999) Mapping of ammonia pollution with epiphytic lichens in the

Netherlands. Lichenologist 31: 9-20.

August 2008 C­92 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 V an Herk, C. M. (2001) Bark pH and susceptibility to toxic air pollutants as independent causes

of changes in epiphytic lichen composition in space and time. Lichenologist 33: 419-441.

vanderNat, F., J. F. C. deBrouwer, J. J. Middelburg, and H. J. Laanbroek. 1997. Spatial

distribution and inhibition by ammonium of methane oxidation in intertidal freshwater

marshes. Applied and Environmental Microbiology 63:4734-4740.

Venterea, R. T.; Groffman, P. M.; Verchot, L. V.; Magill, A. H.; Aber, J. D.; Steudler, P. A.

(2003) Nitrogen oxide gas emissions from temperate forest soils receiving long-term

nitrogen inputs. Glob. Change Biol. 9: 346-357.

Venterea, R. T.; Groffman, P. M.; Verchot, L. V.; Magill, A. H.; Aber, J. D. (2004) Gross

nitrogen process rates in temperate forest soils exhibiting symptoms of nitrogen

saturation. For. Ecol. Manage. 196: 129-142.

Vitousek, P. M.; Aber, J. D.; Howarth, R. W.; Likens, G. E.; Matson, P. A.; Schindler, D. W.;

Schlesinger, W. H.; Tilman, D. G. (1997a) Human alteration of the global nitrogen cycle:

sources and consequences. Ecol. Appl. 7: 737-750.

Vitousek, P. M.; Howarth, R. W. (1991) Nitrogen limitation on land and in the sea: how can it

occur? Biogeochemistry 13: 87-115.

Vitousek, P. M.; Mooney, H. A.; Lubchenco, J.; Melillo, J. M. (1997b) Human domination of

Earth's ecosystems. Science (Washington, DC) 277: 494-499.

Vollenweider, R. A. (1968) Water management research: scientific fundamentals of the

eutrophication of lakes and flowing waters, with particular reference to nitrogen and

phosphorus as factors in eutrophication. Paris, France: Organisation for Economic Cooperation and Development; report DAS/CSI/68.27.

Wall, D. H. (1999) Biodiversity and ecosystem functioning. BioScience 49: 107-108.

Wall, D. H.; Moore, J. C. (1999) Interactions underground: soil biodiversity, mutualism, and

ecosystem processes. BioScience 49: 109-117.

Ward, B. B. (2003) Significance of anaerobic ammonium oxidation in the ocean. Trends

Microbiol. 11: 408-410.

Wargo, P. M. (1988) Amino nitrogen and phenolic constituents of bark of American beech,

Fagus grandifolia, and infestation by beech scale, Cryptococcus fagisuga. Eur. J. Forest

Pathol. 18: 279-290.

Watts, S. H.; Seitzinger, S. P. (2001) Denitrification rates in organic and mineral soils from

riparian sites: a comparison of N 2 

### flux and acetylene inhibition methods. Soil Biol.

Biogeochem. 22: 331-335.

Webb, J. R. (1999) Synoptic stream water chemistry. In: Bulger, A. J.; Cosby, B. J.; Dolloff, C.

A.; Eshleman, K. N.; Webb, J. R.; Galloway, J. N., eds. Shenandoah National Park: fish

in sensitive habitats (SNP:FISH); final report vol. II, Ch. 3. Prepared for: National Park

Service. Charlottesville, VA: University of Virginia, Department of Environmental

Sciences.

Webb, J. R.; Cosby, B. J.; Deviney, F. A., Jr.; Eshleman, K. N.; Galloway, J. N. (1995) Change

in the acid-base status of an Appalachian Mountain catchment following forest

defoliation by the gypsy moth. Water Air Soil Pollut. 85: 535-540.

August 2008 C­93 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Wedin, D. A.; Tilman, D. (1996) Influence of nitrogen loading and species composition on the

carbon balance of grasslands. Science (Washington, DC) 274: 1720-1723.

Weintraub, M. N.; Schimel, J. P. (2005) Nitrogen cycling and the spread of shrubs control

changes in the carbon balance of Arctic tundra ecosystems. BioScience 55: 408-415.

Weis, W.; Rotter, V.; Göttlein, A. (2006) Water and element fluxes during the regeneration of

Norway spruce with European beech: effects of shelterwood-cut and clear-cut. Water Air

Soil Pollut. 224: 304-317.

Weiss, S. B. (1999) Cars, cows, and checkerspot butterflies: nitrogen deposition and

management of nutrient-poor grasslands for a threatened species. Conserv. Biol. 13:

1476-1486.

Weitz, A. M., M. Keller, E. Linder, and P. M. Crill. 1999. Spatial and temporal variability of

nitrogen oxide and methane fluxes from a fertilized tree plantation in Costa Rica. Journal

of Geophysical Research-Atmospheres 104:30097-30107.

Westman, W. E. (1977) How m uch are nature's services worth? Measuring the social benefits of

ecosystem functioning is both controversial and illuminating. Science (Washington, DC)

197: 960-964.

Wetzel, R. G. (2001) Limnology: lake and river ecosystems. San Diego, CA: London: Academic

Press.

Wilcox, G.; Decosta, J. (1982) The effect of phosphorus and nitrogen addition on the algal

biomass and species composition of an acidic lake. Arch. Hydrobiol. 94: 393-424.

Williams, M. W.; Baron, J. S.; Caine, N.; Sommerfeld, R.; Sanford, R. (1996) Nitrogen

saturation in the Rocky Mountains. Environ. Sci. Technol. 30: 640-646.

Wilson, S. D.; Tillman, D. (1991) Components of plant competition along an experimental

gradient of nitrogen availability. Ecology 72: 1050-1065.

Wold, A. P.; Hershey, A. E. (1999) Spatial and temporal variability of nutrient limitation in 6

North Shore tributaries to Lake Superior. J. North Am. Benthol. Soc. 18: 2-14.

Wolfe, A. P.; Baron, J. S.; Cornett, R. J. (2001) Anthropogenic nitrogen deposition induces rapid

ecological changes in alpine lakes of the Colorado Front Range (USA). J. Paleolimnol.

25: 1-7.

Wolfe, A. P.; Cooke, C. A.; Hobbs, W. O. (2006) Are current rates of atmospheric nitrogen

deposition influencing lakes in the eastern Canadian Arctic? Arct. Anarct. Alp. Res. 38:

465-476.

Wolfe, A. P.; Van Gorpe, A. C.; Baron, J. S. (2003) Recent ecological and biogeochemical

changes in alpine lakes of Rocky Mountain National Park (Colorado, USA): a response to

anthropogenic nitrogen deposition. Geobiology 1: 153-168.

Wood, Y. A.; Meixner, T.; Shouse, P. J.; Allen, E. B. (2006) Altered ecohydrologic response

drives native shrub loss under conditions of elevated nitrogen deposition. J. Environ.

Qual. 35: 76-92.

Woodwell, G. M. (1970) Effects of pollution on the structure and physiology of ecosystems:

changes in natural ecosystems caused by many different types of disturbances are similar

and predictable. Science (Washington, DC) 168: 429-433.

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23 W orld Resources Institute. (2000) World resources 2000-2001: people and ecosystems: the

fraying web of life. Washington, DC: World Resources Institute.

Yeakley, J. A.; Coleman, D. C.; Haines, B. L.; Kloeppel, B. D.; Meyer, J. L.; Swank, W. T.;

Argo, B. W.; Deal, J. M.; Taylor, S. F. (2003) Hillslope nutrient dynamics following

upland riparian vegetation disturbance. Ecosystems 6: 154-167.

Ylimartimo, A. (1991) Effects of foliar nitrogen, potassium and magnesium concentrations on

the resistance of Scots pine seedlings to Scleroderris canker infection. Eur. J. Forest

Pathol. 21: 414-423.

Yoshida, L. C.; Allen, E. B. (2001) Response to ammonium and nitrate by a mycorrhizal annual

invasive grass and native shrub in southern California. Am. J. Bot. 88: 1430-1436.

Young, T. F.; Sanzone, S., eds. (2002) A framework for assessing and reporting on ecological

condition: an SAB report. Washington, DC: U.S. Environmental Protection Agency,

Science Advisory Board; report no. EPA-SAB-EPEC-02-009. Available:

http://www.epa.gov/sab/pdf/epec02009.pdf [9 December, 2003].

Zavaleta, E. S.; Shaw, M. R.; Chiariello, N. R.; Thomas, B. D.; Cleland, E. E.; Field, C. B.;

Mooney, H. A. (2003) Grassland responses to three years of elevated temperature, CO 2 

### , precipitation, and N deposition. Ecol. Monogr. 73: 585-604.

Zhang, L. H., C. C. Song, X. H. Zheng, D. X. Wang, and Y. Y. Wang. 2007. Effects of nitrogen

on the ecosystem respiration, CH 4 

### and N 2 

### O emissions to the atmosphere from the fresh

water marshes in northeast China. Environmental Geology 52:529-539.

Zhang, L.; Song, C.; Zheng, X.; Wang, D.; Wang, Y. (2007) Effects of nitrogen on the

ecosystem respiration, CH 4 

### and N 2 

### 0 emissions to the atmosphere from the freshwater

marshes in northeast China. Environ. Geol. 52: 529-539.

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# Annex D. Critical Loads

D.1. Background

Critical loads and critical levels are used to express how much deposition of an atmospheric

pollutant (a “load”) or how large a concentration of an airborne pollutant (a “level”) can be tolerated by

natural or artificial systems without significant harm or change occurring in those systems (see

Section D.2.1). The critical load and critical level approaches to quantifying the effects of pollutants

attempt to estimate the atmospheric deposition load or concentration that would be likely to cause

environmental harm. The expectation is that environmental harm can be avoided by keeping pollution

levels or loads below these critical values. This approach is commonly used to estimate loads or levels of

pollution required to protect lakes, streams, or forest soils from environmental harm. The basic principles

are, however, transferable to any sensitive receptor. Since the present evaluation deals primarily with the

effects of atmospheric deposition of S a nd N compounds, this chapter focuses on critical loads more than

critical levels.

Most critical load studies in North America have been undertaken in Canada. The critical load

approach has been used in Canada to design emission reduction programs (RMCC, 1990; Jeffries, 1993).

Modeling of critical loads for the 1997 Canadian Acid Rain Assessment (Jeffries, 1997) was conducted

for six regional clusters of lakes, four in eastern Canada, one in Alberta, but also the Adirondack

Mountains in New York. More recently, critical loads have been determined and mapped for waters

(Hindar, 2001; Henriksen, 2002; Aherne, 2004; Dupont, 2005; Watmough, 2005) and forest soils (Arp,

1996; Moayeri, 2001; Watmough, 2003; Ouimet, 2006), for a number of regions in eastern Canada. There

have also been a number of regional critical loads studies (cf. Henriksen, 2001; Ouimet, 2006) focused on

acid-sensitive lakes on the Canadian pre-Cambrian shield. Much of this work is summarized and

presented, along with steady state critical load maps for eastern Canada, in the 2004 Canadian Acid

Deposition Science Assessment (Jeffries, 2005).

At the regional, cross-border level, critical loads in northeastern North America have been

developed by a joint U.S.-Canadian cooperative. The Conference of New England Governors and Eastern

Canadian Premiers (NEG/ECP) has undertaken a program with the objective to “estimate sustainable

acidic deposition rates and exceedances for upland forests representative of the New England States and

the Eastern Canadian Provinces...” (NEG/ECP Forest Mapping Group, 2001). The Forest Mapping

Working Group within the NEG/ECP conducts regional assessments of the sensitivity of northeastern

North American forests to current and projected S a nd N emissions levels. The group is charged with

identifying specific forested areas most sensitive to continued S and N deposition and estimating

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34 deposition rates required to maintain forest health and productivity at large spatial scales (cf. Miller,

2006). The NEG/ECP has also provided estimates of critical loads for surface waters in northeastern

North America (Dupont, 2005).

Aside from the NEG/ECP studies, the use of critical loads to assess S and N deposition effects in

the U.S. has not been as geographically extensive as elsewhere in North America or Europe. Most critical

loads studies in the U.S. have focused on smaller sub-regional areas or individual sites. Critical loads

studies for forests in the U.S. have been centered in the northeast and have usually used a catchmentbased approach (Pardo, 1993; Pardo, 1996; cf. Aber, 2003; Driscoll, 2003). Critical load studies for

surface waters have been more extensive along the eastern seaboard. Critical loads have been estimated

for lakes in the Northeast (cf. Driscoll, 2001; Pembrook, 2004) and for streams in the Mid-Atlantic States

and central Appalachians (cf. Sverdrup et al., 1992; Sullivan et al., 2004). In the western U.S. there have

been a few studies of critical loads for acidification of surface waters (cf. Sullivan et al., 2004). The

primary concern in the West, however, has been the critical load of N deposition affecting both terrestrial

and aquatic resources through eutrophication and/or through N enrichment and its impact on community

structure (cf. Baron, 1994; Baron, 2000); Williams and Tonnessen, 2000; Fenn et al., 2003; Nydick et al.,

2003, 2004a; Wolfe et al., 2003; Burns, 2004; Stevens, 2004; Baron, 2006); Bowman et al., 2006).

The critical load approach has been used extensively in Europe for organizing information about

effects, and for specifying emissions reductions that would be required to protect ecosystems and other

sensitive receptors from the harmful effects of atmospheric S N deposition. During the 1970s, it was

recognized that transboundary air pollution in Europe had adverse ecological and economic

consequences. In response, the countries of the UN Economic Commission for Europe (UNECE)

developed the Convention on Long-range Transboundary Air Pollution (LRTAP), the first international

legally binding instrument to deal with problems of air pollution on a broad regional basis (see

http://www.unece.org/env/lrtap). Signed in 1979, it entered into force in 1983. The LRTAP Convention

requires that its Parties cooperate in research into the effects of S compounds and other major air

pollutants on the environment, including agriculture, forestry, natural vegetation, aquatic ecosystems, and

materials. To this end, the Executive Body for the Convention established a Working Group on Effects

(WGE) that is supported by a number of International Cooperative Programmes (ICPs). The ICP for

Mapping and Modeling generated maps of critical loads for all of Europe in 1995 (Posch et al. 1995).

Those maps are modified on a continuing basis (e.g. Posch et al. 2001). By comparing current or expected

future deposition to the critical loads maps, mapped estimates of exceedances have been generated. An

exceedance is the amount of S and N deposition that occurs at some specific time (past, current, or

future), above the critical load of deposition that would be required to protect against adverse effects on

the environment. The maps of estimated exceedances are used in negotiations to regulate pollutant

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32 emissions in Europe (for example, the 1999 Gothenberg Protocol via the UNECE Convention on

LRTAP).

Outside of North America and Europe, there is an increasing use of critical loads for assessment

purposes, and to inform policy development. Examples include studies in Siberia (Bashkin et al., 1995),

Thailand (Milindalekha et al., 2001), and South Africa (Van Tienhoven et al., 1995). In China, several

studies have been carried out to study the sensitivity of surface waters to acidification and the critical

loads of acid deposition (Duan et al., 2000a; Li et al., 2000; Ye et al., 2002; Hao et al., 2001), and to

calculate the critical loads of S a nd N acidity for soils at both the local and regional scales (Zhao and

Seip, 1991; Xie et al., 1995; Duan et al., 2000b, 2001).



# D.1.1. The Critical Load Process

The process of estimating critical loads is not a purely scientific enterprise. Management or policy

input to the process is needed to insure that the appropriate science is included and the appropriate

questions are addressed. The critical load process integrates knowledge of a multitude of physical,

chemical and biological mechanisms affected by ambient air quality, and presents the current scientific

understanding in a format that is most useful for assessing current or future management practices and

policy decisions regarding air quality, or the resources affected. The critical loads process provides

decision-making insight based on both scientific evidence and policy priorities.

Science and policy are closely coupled in the critical loads process. In the development of critical

load estimates, it is important to identify those elements that are essentially scientific in nature as opposed

to those elements that are driven by management or policy priorities. The scientific elements include tasks

such as: relating ambient air quality to pollutant deposition, quantifying the relationships between

pollutant deposition and resource responses, identifying the resources at risk to adverse effects,

understanding the temporal and spatial responses of resources to pollutant deposition, and more. The

policy-dependent elements include tasks such as: identifying the environmental resources to be protected,

establishing appropriate criteria for different land use areas (e.g., Class I areas, national parks, wildlife

refuges), defining significant harm to protected resources, and more. When all elements are integrated, it

is apparent that the critical load process provides a framework for alternate ways of examining and

understanding the cascade of effects from ambient air quality to resource effects, described in the

preceding four annexes. Changing scientific assumptions or understanding may result in different critical

load estimates for the same resources. Changing policy or management assumptions or priorities may also

result in different critical load estimates.

There is, therefore, no single “definitive” critical load for a natural resource. Critical load estimates

are explicitly linked to policy, but their reliability is conditioned on the soundness of the underlying

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31 science. As elements of the critical load process change, the critical load estimates will change to reflect

both the current state-of-knowledge and policy priorities. Changes in scientific understanding may

include: new dose-response relationships, better resource maps and inventories, larger survey datasets,

continuing time series monitoring, improved numerical models, etc. Changes in the policy elements may

include: new definitions of harm, new mandates for resource protection, focus on new pollutants, or

inclusion of perceived new threats that may exacerbate the pollutant effects (e.g., climate change).

The critical load process is thus an iterative process — as science changes, the content is updated;

as policy needs change, the content is re-directed. Being iterative, the process allows incremental

improvement in understanding resource responses to ambient air quality. Individual elements of the

process can be replaced as needed to reflect new science or policy. Continuing to update the process may

reduce uncertainty and risk, as new data or techniques allow refinement of existing pieces. The piecewise

nature of the process provides adaptability as new policy concerns arise, such as new pollutants or

mandates. As the critical load process advances, a “library” of critical load estimates will result.

Examining and comparing these accumulated results, and their underlying scientific and policy bases,

may produce a “weight of evidence” consensus, even if any single estimate entails substantial uncertainty.



# D.1.2. Organization of this Annex

This Annex chapter is intended as a review of the current state of critical loads science. It is not the

intention to address questions of management or policy other than to point out where these activities

influence the critical loads process. The material in Section D.2 presents necessary definitions and

describes the conceptual framework for a critical load analysis. This framework identifies those elements

that are primarily scientific in nature and those elements that require policy input. The framework also

describes the steps that are taken in deriving a critical load estimate for a given resource. It is not an

objective of this Annex to provide details of all critical loads studies that have been implemented in the

U.S. or elsewhere. The conceptual framework, however, provides a generalized summary of the steps

most critical loads studies have followed. Section D.3 discusses the time frame of responses for

implementation of a critical load. Time frames of resource response are often ignored, or assumed

implicitly, in defining a critical load analysis. The time required to implement the policy and technology

to achieve a critical load can also affect the responses of the resources at risk. The time frames of

response are important for selecting the data and models used to estimate critical loads. Section D-4

summarizes the tools (models and modeling approaches) commonly used in calculating critical loads. The

Annex concludes in Section 5 with a summary of the current agreement on critical loads uses in the U.S.

that was a product of the Multi-Agency Workshop on Critical Loads held in 2006. The workshop

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28 produced a series of recommendations for current and future activities related to critical loads analyses in

the U.S.



# D.2. Definitions and Conceptual Approach

D.2.1. Critical Load Definitions

Critical loads and critical levels are used to express how much deposition of an atmospheric

pollutant (a “load”) or how large a concentration of an airborne pollutant (a “level”) can be tolerated by

natural or artificial systems without significant harm or change occurring in those systems. The generally

accepted definition of a critical load or a critical level of atmospheric pollutants emerged from a pair of

international workshops held in the late 1980s (Nilsson, 1986; Nilsson, 1988). The workshop participants

defined a critical load or a critical level as “a quantitative estimate of an 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.”

This evaluation deals primarily with the effects of atmospheric deposition of S and N compounds.

This Annex, therefore, will deal exclusively with the concept of critical loads of S and N compounds from

atmospheric deposition. Critical levels of pollutant concentration will not be addressed. As discussed in

previous annexes, the deposition of both S and N has acidifying effects on receptors (Annex C), and the

deposition of oxidized and/or reduced N compounds can produce eutrophication or nutrient-enrichment

effects in receptors (Annex D). The following material, therefore, will focus on critical loads of S a nd N

for acidification effects, and on critical loads of N for nutrient effects.

In addition to the generic definition of a critical load/level presented above, the participants in the

second international workshop (the Skokloster Workshop) (Nilsson, 1988) developed a number of specific

definitions related to known atmospheric pollutants. Two of those definitions are relevant to this Annex.

Recognizing that both S and N compounds contribute to the acidity of deposition, the workshop

participants developed a definition for critical loads of S a nd N for acidification of an ecosystem: “the

highest deposition of acidifying compounds that will not cause chemical changes leading to long-term

harmful effects on ecosystem structure and function according to present knowledge.” Recognizing that N

in both oxidized (e.g. NO, NO , NO

− 2 3 ) and reduced (e.g. NH 3 , NH +

4 ) forms in deposition may influence

the eutrophication and nutrient balances of ecosystems, the workshop participants defined the critical load

of N for nutrient effects in an ecosystem as “the highest deposition of N as NH X and/or NO Y below which

harmful effects in ecosystem structure and function do not occur according to present knowledge.”

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27 All three definitions can be applied to different receptors in a number of different environments

(e.g., terrestrial ecosystems, transitional ecosystems, aquatic ecosystems, groundwater, agricultural crops,

etc.). A sensitive element can constitute a part of, or the whole of, an ecosystem. Harmful effects can

occur to individual organisms, to populations, or to entire communities within an ecosystem. Harmful

effects can also be defined at the level of the ecosystem itself as changes in ecosystem processes,

structure, and/or function.

While the concepts expressed in these definitions of critical loads and levels are easily understood

and intuitively satisfying, the application of the critical load concept requires careful consideration and

definition of a number of terms and procedures. It is apparent that there can be many different critical

load values for a given atmospheric pollutant depending on the receptor or sensitive element(s) being

considered. There can also be multiple different atmospheric pollutants that can produce the same harmful

effects in a given receptor. Therefore, the critical load of a given pollutant can potentially be dependent on

the deposition and/or atmospheric concentration of other pollutant species. Finally, the same atmospheric

pollutant can produce a variety of different disturbances in a sensitive ecosystem that might occur at

different pollutant loads. For example, N deposition produces both nutrient and acidification effects and

the critical load of N for each type of disturbance may be different.

Therefore, in order to derive a quantitative estimate of the critical load of an atmospheric pollutant,

a number of factors must be identified and defined Figure D-1. These include disturbance type, receptor,

sensitive elements, and definition of what constitutes significant harm. In addition, a numerical

relationship between pollutant deposition and the identified receptor response must be formulated,

generally based on either an empirical dose-response relationship or a steady state or dynamic numerical

model simulation. The next section outlines the steps (decisions) that must be taken to implement this

process.



# D.2.2. Critical Load Analysis Procedures

The development of a quantitative critical load estimate requires a number of steps. In this

discussion, Figure D-1 is used to illustrate the procedure. The Figure is simplified to facilitate general

discussion and does not represent the full complexity of the choices that must be made, or the scientific

understanding underlying those choices.

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### Table D­1. An example of the matrix of information that must be considered in the definition and

calculation of critical loads. Note that multiple alternative biological indicators, critical biological

responses, chemical indicators, and critical chemical limits 1

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1 1. Identify the ecosystem disturbance of concern (acidification, eutrophication, etc.). 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 subjected to the disturbance (forests, surface waters, crops,

etc.) Receptor sensitivity may vary locally and/or regionally, and the hierarchy of receptors

most sensitive to a particular type of disturbance may vary as well.

3. Identify the biological indicators within each receptor that are affected by atmospheric

deposition (individual organism, species, population, or community characteristics).

Indicators will vary geographically and perhaps locally within a given receptor type There are e ight g eneral steps that must be taken to define the basic critical load question in any

analysis.

August 2008 D­7 DRAFT—DO NOT QUOTE OR CITE4. Establish the critical biological responses that define “significant harm” to the biological

indicators (presence/absence, loss of condition, reduced productivity, species shifts, etc.).

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 (stream water pH, Al concentration, soil base

saturation, etc.). In some cases, the use of relatively easily measured chemical indicators

(e.g., surface water pH or 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%, Al

concentrations >100 μg/L, etc.). 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 calcium concentration mitigates the toxicity of Al to fish and plant roots).

7. Identify the atmospheric pollutants that control (affect) the pertinent chemical indicators

2− (deposition of SO 4 , NO 3 , NH 4 , HNO 3 , etc.). 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 (kg/ha/yr total deposition of S or NO 3 − N, etc.) 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 (as factors such as elevation or soil depth vary) and

may vary temporally at the same location (as accumulated deposition alters chemical

responses). 

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34 The definition of the critical load problem for a region or individual site generally requires that we

work down the Table from top to bottom (Table D-1) What is the disturbance? What receptors are

affected? What indicator organisms are, or were previously present and observable? What chemical

indicators are changing and can be measured? What atmospheric pollutant is driving the changes in the

chemical indicators?

The derivation of a quantitative estimate of a critical load generally requires that we work from the

bottom of the Table back towards the top, as indicated by the arrows in Table D-1. What is the maximum

load of a pollutant that will cause a shift in the chemical indicator to its critical limit such that a critical

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35 indicator response occurs, or does not occur? From this point of view, it can be seen that steps 8 and 6

require the development of dose-response functions for the components of the ecosystem being

considered (arrows in Table D-1). Step 8 describes the response of the chemical indicator as a function of

the pollutant load, and Step 6 describes the responses of the biological indicator as a function of the

chemical variable. As discussed in later sections, these response functions can be derived using empirical

(e.g., statistical) or process-based (e.g., mechanistic) models that are either time-invariant (static or steady

state) or time-variable (dynamic).

Each step in the development of the critical load, as summarized in Table D-1 can be classified as

either a predominantly scientific task or as a task benefiting from, or perhaps requiring, collaboration and

input from scientists, decision-makers, and other interested parties. For instance, tasks 1, 2, and 3 can be

viewed as predominantly scientific tasks that can be completed by asking questions of fact. At task 4,

however, questions of what defines “significant harm” entail subjective elements that cannot be

determined by scientific techniques alone. In anticipation of the ultimate use of the critical load definition

to set policy or establish management strategies, it is appropriate that political, socioeconomic, or perhaps

ethical considerations be brought to bear in defining “significant harm.” To define “harm” is to imply a

corrective action, the cost of which will have to be borne by someone. Having reached agreement on task

4, however, tasks 5, 6, 7, and 8 are again predominantly scientific in nature, requiring determination of the

causal links, represented as response functions or models, leading from the loading of the pollutant to the

defined “significant harm.”

This procedure will almost certainly result in calculation of multiple critical load values for a given

pollutant and analysis location. The multiple solutions derive from the nested sequence of disturbances,

receptors, and biological indicators that must be considered for a given pollutant. Multiple critical load

values may also arise from an inability to agree on a single definition of “significant harm” at step 4.

Finally, there is the inescapable heterogeneity of all natural environments. Consider soils for instance. The

high spatial variability of soils almost guarantees that for any reasonably sized soil-based “receptor” that

might be defined in a critical load analysis, there will be a continuum of critical load values for any

indicator chosen. The range of this continuum of values may be narrow enough to be ignored, but in any

critical load analysis there is nevertheless an a priori expectation of multiple values, or of a range of

values.

The existence of multiple estimates of critical loads for a given pollutant and receptor should

present no real problem. Examination of the range of critical loads derived may be deemed useful in

subsequent discussions of the analysis, and in the decision-making steps that may follow critical load

calculation. For instance, the lowest critical load of all those derived may be adopted as “the” critical

load, as is often done in Europe. This however, is a policy choice. The scientific task is the derivation of

the multiple values using best available information.

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# D.2.3. Target Load Definition

As seen in the previous section, it is expected that a potentially large number of critical load values

may be objectively determined for a given atmospheric pollutant and a given receptor. Like the definition

of “significant harm,” the choice of which critical load value to use for management or decision-making

is subjective, and should be driven by socioeconomic, political, and ethical considerations. The target load

concept was developed to address these issues. Target loads are deposition loads of a given pollutant,

based on critical load estimates for the pollutant, which incorporate policy and/or management decisions

about the amount of pollutant deposition, and therefore the amount of resource damage that is deemed

acceptable. Target loads can be set at, above, or below the various critical loads. If the target load is set

above some of the estimated critical loads, one accepts the inevitability that some of the ecosystem

components, generally the most sensitive, will be adversely affected. If the target load is set below all of

the estimated critical loads, a safety margin has been established to account for uncertainty inherent in the

process.

Given the spatial heterogeneity of natural systems, target loads might also be used to provide some

measure of “cumulative resource protection.” As discussed above, there typically exists a range of critical

loads for a particular “significant harm” in a particular receptor. Selecting a target load within the range

will provide protection for the fraction of the receptor with critical loads above the chosen target load,

whereas that fraction with critical loads below the chosen target load will be expected to suffer some harm

at that deposition level. In this way, it is possible to use the target load to define protection for some

cumulative proportion of the receptor (i.e., a target load for protection of 95% of the resource from

“significant harm”).

While most of the steps involved in estimating critical loads depend on sound, objective scientific

analysis, the selection of target loads is almost entirely a subjective judgment. The selection of target

loads must begin with reliable estimates of critical loads to set the constraints, and define the expected

consequences of the target load choices. Nevertheless, the final decisions of which indicators are the key

indicators, how much cumulative resource should be protected, how much sooner or later resource

protection will be implemented, cannot be answered scientifically. Political, socioeconomic, and ethical

considerations will form the basis of the final target load selections. Frequently, the legal mandates for

various public lands would have a determinant influence on the selection of target loads. For instance,

Federal Class I areas may be held to one standard of harm because of mandates to protect “natural

condition,” whereas Federal mixed-use lands may be held to a different standard of harm, and cropland to

yet another standard of harm.

It is also important to note that scientific understanding, modeling approaches, and the data used to

estimate critical loads are continually improving. Furthermore, the political, economic, and social

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32 environments surrounding selection of target loads are also constantly shifting. Therefore, the analysis

and estimation of critical and target loads must be an iterative process.



# D.3. Time Frame of Response

The critical load definitions and procedures discussed in the previous section do not explicitly

consider the time frame of ecosystem response. When is “significant harm” expected? How long will it be

before existing harm is reversed? When should critical loads be implemented? How long should a critical

load be maintained? The use of critical and target loads in resource management always has some time

frame of expected response, and some context of management priorities. For instance, it may be that a

target load well below the critical load would hasten the recovery of a receptor with existing harm. Or, it

may be that a receptor that has not yet been damaged can sustain a target load above the critical load for

some finite period before incurring “significant harm.” Such time frames can be very long (many decades

or centuries).

The time frame of response between implementation of a critical load and the corresponding

changes in biological or chemical indicators is a potentially important factor in establishing critical load

analysis procedures and in selecting the final target load. Analyses can be designed to provide estimates

of either “steady state critical loads” or “dynamic critical loads” depending on the perceived, or

mandated, importance of the time frame of response and the types of models (transfer functions) used.

Steady state critical loads analyses provide estimates of the long-term sustainable deposition of a

pollutant that will not cause “significant harm” to a receptor. This is the relevant information needed for

any receptor to provide protection from damage by the pollutant in perpetuity as the receptor comes into

equilibrium with the pollutant critical load (the implicit purpose of steady state analyses). However, no

information is given concerning the time to achieve the equilibrium or what may happen to the receptor

along the path to equilibrium. Estimated steady state critical loads for receptors that are currently

damaged provide no information concerning when the desired long-term sustainable protection will occur

and the existing “significant harm” will be mitigated. There exists the possibility that receptors with no

current damage could suffer “significant harm” while waiting for implementation of the critical load. The

possible occurrence, timing, and duration of such “interim periods of harm” are not the subject of steady

state analyses.

Dynamic critical loads analyses provide estimates of a specifically scheduled deposition load of a

pollutant that will not result in “significant harm” to a receptor at a specified time. This is the relevant

information needed for any receptor to provide protection from damage by the pollutant within a specified

time frame (the explicit purpose of dynamic analyses). However, care should be taken in interpreting the

results of dynamic analyses to ensure that “significant harm” to the receptor does not occur after the

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29 specific timetable has been completed. Many receptors can tolerate higher loads of a pollutant for a few

decades (a common length of specified schedules for dynamic analyses) than can be sustained over longer

periods. The use of dynamic critical load estimates in such cases may provide protection from harm

during a time frame of immediate interest, but ultimately fail to provide long-term protection, unless these

issues are considered.



# D.3.1. Steady State Critical Loads

If the time frame of response is not important, for instance, if the target load is to provide long-term

sustainable protection and the immediacy of the responses is not relevant, the use of static or steady state

models (response functions) is justified in the critical load analysis procedure. Using steady state models

to estimate critical loads and compare the estimated critical load to current or future deposition, only two

cases can be distinguished: (1) current or future deposition is below the critical load, or (2) current or

future deposition exceeds the critical load. In the first case, no problem is apparent, and no target load is

deemed necessary, unless increases in deposition are anticipated. In the second case, there is by definition

an increased risk of “significant harm” to the receptor and selection of a target load for resource

protection is indicated.

The lack of explicit consideration of time in a steady state critical load analysis can lead to

assumptions that are frequently not warranted. The critical load derived in a steady state analysis is an

estimate of the long-term, constant deposition that a receptor can tolerate with no significant harm after it

has equilibrated with the critical load deposition. However, biological and geochemical processes that

affect a receptor may delay the attainment of equilibrium (steady state condition) for years, decades, or

even centuries. By definition, steady state critical loads do not provide any information on these time

scales. As a result, it is often assumed that reducing deposition to, or below the steady state critical load

value will immediately eliminate or mitigate “significant harm.” That is, it is assumed that the chemical

indicator affected by the atmospheric pollutant immediately attains a non-critical value upon

implementation of the critical load, and that there is immediate biological recovery as well. As discussed

in the next section, these assumptions may not be valid.



# D.3.2. Dynamic Critical Loads

The time frame of receptor response is important if the establishment of target loads is tied to

defined schedules of deposition change or receptor responses. The use of time-dependent or dynamic

model response functions will be necessary if the critical load analysis considers the response time frame.

In the cascade of events that occur from changed deposition of an atmospheric pollutant to development

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30 � Stage 1: Pollutant deposition is below the critical load for either the critical chemical limit or

the critical biological response, and there is no “significant harm” to the receptor. As long as

deposition stays below the critical load, this is the ‘ideal’ situation.

� Stage 2: Pollutant deposition rises above the critical load, but chemical and biological

indicators still do not violate their respective criteria because there is a delay. No damage is

likely to occur at this stage, despite the exceedance of the critical load. The time between the

first exceedance of the CL and first violation of the biological criterion (first occurrence of

“significant harm”) is called the Damage Delay Time (DDT = t3–t1).

� Stage 3: Pollutant deposition is above the critical load and both the chemical and biological

criteria are violated. Measures to reduce emissions are taken to avoid further harm to the

receptor and pollutant deposition begins to decrease. of responses of key biological indicators, there are many processes in natural systems that are time and/or

resource dependent and therefore can introduce delays in the response pattern. In the decision-making

process leading to the adoption of target loads, there are likewise considerations of when deposition

changes can be initiated and completed and when biological indicator responses are desired. With

dynamic models, either empirical or process-based, a wide range of estimated critical loads can be

derived for comparison with current or future deposition depending on the temporal constraints imposed

on the critical load analysis. Temporal constraints that can be imposed on a given critical load analysis are

determined by: (1) the receptor responses—the characteristic time scales and inherent lags of the receptor

being analyzed (a function of hydrobiogeochemical processes in the receptor), and (2) the deposition

schedules—the years designated for beginning and completing the changes in deposition and for

evaluating the indicator responses (a function of political, socioeconomic, and management constraints).



# D.3.3. Receptor Responses

The general conceptual model of the linkages among pollutant deposition and the responses of

chemical and biological indicators can be characterized as a series of delays. In the causal chain from

deposition of pollutant to damage to key biological indicators there are two major links that can give rise

to delays. First, hydrological and biogeochemical processes in catchments can delay the responses of

chemical indicators. Second, biological processes and population dynamics can further delay the response

of biological indicators. The pattern of chemical and biological indicator responses can be represented

conceptually (Figure D-1) (adapted from Jenkins et al., 2003; Posch et al., 2003). Five stages in the

conceptual pattern can be distinguished (Figure D-1):

August 2008 D­13 DRAFT—DO NOT QUOTE OR CITE� Stage 4: Pollutant deposition has been reduced to a level below the critical load, but the

chemical and biological criteria are still violated, and thus “recovery” has not yet occurred.

The time between the first non-exceedance of the critical load and the subsequent nonviolation of both criteria can be called the Recovery Delay Time (RDT = t6–t4).

� Stage 5: This stage is similar to Stage 1. Pollutant deposition has been reduced to a level below

the critical load and neither the chemical nor biological criteria are violated. Only at this stage

can the receptor be considered to have recovered to an undamaged level. 

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22 Stages 2 and 4 can be further subdivided into two sub-stages each: chemical damage and recovery

delay times (DDTc = t2–t1 and RDTc = t5–t4; dark grey in Figure D-1 and (additional) biological damage

and recovery delay times (DDTb = t3–t2 and RDTb = t6–t5; light grey). Given opportunities for

“confounding effects” (i.e., mechanisms not related to acidic deposition but affecting biological

indicators, such as forest pest infestation or climate change) occurring during the “delay periods,” it is

clear that unambiguous short-term patterns of recovery of biological indicators are unlikely to be

observed, even in the presence of rather large declines in pollutant deposition. This has important

implications for recovery expectations.



# D.3.4. Deposition Schedules

Dynamic critical loads, by definition, must explicitly account for the receptor time scales and lags

described above. Therefore, the process of estimating dynamic critical load values for a given pollutant

must be based on a planned or assumed deposition schedule for changing the pollutant deposition and for

assessing the receptor responses. Three different time periods are specified, as illustrated in Figure D-3.

[The nomenclature used here for the three years specified in the deposition schedule conforms to that used

in European “dynamic target loads analyses” (Posch et al., 2003)].

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### Figure D­1. Conceptual patterns of pollutant deposition effects on a chemical indicator and a

corresponding biological indicator during increasing and decreasing deposition. Critical limits and

responses for the chemical and biological indicators are indicated as horizontal lines, along with

the critical load of deposition that produces these levels. The delays between the exceedance of the

critical load (t1), the violation of the critical chemical limit (t2), and the crossing of the critical

biological response (t3) are indicated in grey shades, highlighting the DDT. Similar delays in

chemical and biological recovery during deposition reductions (t4, t5, and t6) define the RDT of the

system.

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15 Figure D­2. Pollutant deposition patterns for defining the temporal parameters of dynamic critical

loads analyses. The deposition schedule requires that three years be specified: (1) the year in

which changes in pollutant deposition are begun, called the protocol year; 2) the year in which

changes in pollutant deposition are completed, called the implementation year; and (3) the year in

which the chemical or biological response indicator is evaluated, called the target year.

The first time period is the protocol year when deposition changes moving toward the critical load

are begun. It will be the case that voluntary or mandated changes in deposition will require a number of

years to get underway once a critical load has been calculated or target load has been selected. These

delays in moving toward the critical load will affect the dynamic responses of the chemical and biological

indicators and, therefore, must be included in the dynamic modeling of receptor response. Before the

protocol year, it must be assumed that pollutant deposition will be continuing along the pattern of recent

or historical deposition change or along the pattern dictated by future deposition scenarios already

planned and assumed to take effect.

The second time period is the implementation year when deposition changes are complete and

pollutant deposition has reached the desired critical or target load. It is likely that a number of years will

elapse between the time changes in deposition toward the critical load are initiated, and the time when

they are completed. During this transition period, pollutant deposition continues at a rate higher, or lower,

than the critical load. The effects of these years of deposition inputs above or below the critical load value

will affect the dynamic responses of the chemical and biological indicators, and must also be included in

the dynamic modeling of receptor response. It is assumed in dynamic critical loads analyses that the

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35 pollutant deposition to the receptor remains constant at the critical load for all years after the

implementation year.

The final time period is the target year when the biological indicators are evaluated. Recognizing

that there are inherent lags in receptor responses following changes in pollutant deposition, it a number of

years will frequently be allowed to elapse after the implementation year before the receptor responses are

assessed. It must also be recognized that receptor responses will continue to change over time. Thus,

selection of the target year will affect attainment of the critical limit.

The deposition schedule for a dynamic critical load analysis can be driven by a number of

considerations, and can be organized from protocol year to target year or vice versa. The selection of the

protocol and implementation years is often a matter of political will and economic possibility. Large-scale

pollution abatement programs take time to negotiate. Costs or engineering difficulties may delay the start

of the abatement program and affect the length of time it takes to complete the program once it is begun.

Once these constraints have been established, it is then possible to select a reasonable target year for

evaluation of the receptor responses. Alternately, resource management mandates might require that

“significant harm” to receptor indicators be mitigated or eliminated by a certain time. This establishes the

target year for the dynamic analysis and the protocol and implementation years must be selected to allow

time for any lags in the receptor responses to occur.

Clearly, there is tension between the two approaches when developing a deposition schedule. It is

possible, for instance, to defer the protocol and implementation years so far into the future that extensive

“significant harm” occurs to the receptor indicators in the intervening years. If that damage is especially

severe, the critical load, when the target year is finally reached, may not be achievable. Similarly, if a

receptor is currently suffering harm, it is possible to choose a target year for receptor response too close to

the present day to allow time for the receptor to recover, even if the pollutant deposition was reduced to

zero immediately.

Both of these hypothetical scenarios raise important points about dynamic critical load estimates.

Because time is explicitly incorporated, there are certain dynamic critical loads questions that have no

answer. Commonly called “you can’t get there from here” problems, these deposition schedules choose

protocol, implementation, or target years that are inconsistent with the time scales of receptor response.

For example, setting a target year 5 years in the future for achievement of no “significant harm” in a

receptor that is currently badly damaged, and has a history of high pollutant loading, may be asking the

impossible. Critical load estimates derived in this case would require having set the pollutant deposition

to zero some years in the past. In other words, the state of no significant harm cannot be reached within

the specified five years regardless of how deposition is changed within that five-year period (“you can’t

get there from here”). This problem is moot for steady state critical loads. Steady state critical loads

analyses will always provide some sensible estimate (zero or finite) of long-term sustainable pollutant

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32 deposition for every receptor because time is not a factor. Dynamic critical loads analyses, on the other

hand, may frequently provide non-quantitative results, but these results nonetheless convey useful

information concerning the current status of the receptor and point out the necessity to continue the

analysis with modified assumptions or expectations in order to develop realistic and achievable target

load values.



# D.3.5. Long­Term Implications

The explicit inclusion of time in critical loads estimation provides useful information for managers

or policymakers when deciding when and how much to alter pollutant emissions and deposition, but the

dynamic approach leads to implicit assumptions that must be recognized. Focus on the near-term aspects

of receptor responses (the years included in the deposition schedule) can be misleading. The implicit

assumption is that having attained the desired biological or chemical response in the target year, nothing

more will happen, or at least that further changes in the receptor, if they do occur, will not produce

“significant harm.” Available dynamic model critical load estimates suggest that this is not always true,

and the long-term implications of dynamic critical load estimates should be examined carefully.

The dynamic critical loads procedure assumes that pollutant deposition remains constant at the

critical load from the implementation year until the target year, and the assessment of receptor response.

Model simulations can be continued, assuming deposition at the constant critical load value, for a number

of years after the specified target year to ensure that lags in the receptor response will not result in

“significant harm” appearing at some later date, even though it was not present in the target year. Some

receptors have chemical or biological lags that are many decades long, or longer. Critical loads analyses

based on deposition schedules that cover only 20 to 30 years can produce the unwanted result of

estimating a dynamic critical load that avoids “significant harm” to the receptor in the target year, only to

have the receptor suffer damage some years later.

It is important to determine which receptor responses and which deposition schedules might lead to

such an unwanted result. There are some general guidelines concerning this potential problem. For any

receptor for which there is currently no “significant harm,” the estimated critical load provided by the

dynamic approach will be one that brings the biological or chemical indicators to the threshold of harm

without crossing it (the definition of the critical load). However, this dynamic critical load has then put

the biological or chemical indicator on a trajectory away from its currently good status and toward the

threshold of harm. In most cases, the trajectory toward harm will continue past the target year and

“significant harm” will occur in these receptors some time after the target year.

On the other hand, in any receptor for which there currently is “significant harm,” the estimated

critical load provided by the dynamic approach will be one that brings the biological and chemical

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30 indicators to the threshold of harm and crosses it, just to return to a state of no harm. This dynamic critical

load puts the biological or chemical indicator on a trajectory away from harm and towards good status. In

most cases, it is likely that the upward trajectory will continue past the target year and significant further

recovery will occur in these receptors after the target year.

These are, however, merely generalizations. Depending on the geochemical and biological process

affecting a receptor, there exist possibilities that upward trajectories could become downward trajectories

sometime after the target year and vice versa. The most straightforward procedure is to run the model(s)

used in the dynamic critical loads analyses for a sufficiently long time after the target year so that any

reasonable chance of delayed damage to the receptor or delayed recovery is either discovered or

discounted.



# D.3.6. Steady State and Dynamic Critical Loads

– Complementary

Information

There is no “correct” choice to be made between steady state and dynamic critical loads analyses.

Both provide estimates of pollutant loads that are intended to avoid “significant harm” to a receptor. Both

are valid scientific expressions of the receptor’s sensitivity to the pollutant. They differ primarily in the

time scales implicit in their use. Steady state analyses provide critical load estimates for long-term

sustainable protection, but ignore questions of near-term recovery and avoidance of interim harm.

Dynamic analyses provide critical load estimates that can be used to examine short-term or long-term

options for recovery of damaged systems and avoidance of interim harm, but may ignore the ultimate

long-term sustainability of the estimated deposition, which may evolve over centuries. Clearly, the two

approaches provide complementary information.

The complementary nature of the two critical loads approaches can be exploited in the selection of

a target load estimate for a receptor. Selecting the lower of the two critical load estimates for the receptor

(steady state or dynamic) should result in facilitation of recovery, or avoidance of harm, in the short-term,

as well as long-term sustainability once the receptor has reached equilibrium with the selected target load.

Multiple lines of evidence reflecting multiple critical load values can provide important information that

collectively provides the foundation for management decision-making.

The procedures, data requirements, and computational resources needed for each of the two critical

loads approaches may differ significantly depending on the models (response functions) adopted for the

analyses. Differences in the approaches may also depend on the disturbance, receptor, or indicator being

evaluated. The next two sections discuss the disturbances, receptors, and indicators relevant to deposition

of S a nd N, and the models used for calculation of critical load estimates by each approach.

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# D.4. Calculation of Critical Loads

The derivation of quantitative estimates of critical loads requires the development of dose-response

functions (models) for the components of the ecosystem being considered. Models are needed to describe

two different classes of dose-response function. Geochemical models describe the changes in the

chemical indicators that occur as functions of changes in the pollutant loads. Biological response models

describe the changes in the biological indicators as functions of changes in the chemical variables.

Models for either class of dose-response function can be developed using two general approaches.

Empirical models are based on direct observations of indicator response to pollutant deposition. They are

usually developed using statistical techniques and generally do not contain a mechanistic pathway linking

pollutant deposition to indicator response. Process-based models are based on conceptual representations

of chemical and biological mechanisms, and use mathematical equations to express the inter-relationships

among system components. Whereas process-based models frequently also use observations of receptor

responses to pollutant deposition for calibration and validation, they are fundamentally different from

empirical models in that mechanistic pathways from pollutant deposition to indicator response are

explicitly included in the model structures. In general, the geochemical models used to link S a nd N

deposition to chemical indicator response are mostly process-based, whereas biological responses to

acidification by S a nd N are mostly modeled using empirical approaches. Finally, both geochemical

models and biological response models, whether developed using either empirical or process-based

approaches, can be further classified as static or dynamic depending on whether or not time is included

among variables.



# D.4.1. Empirical Models

Empirical models can be constructed relating either chemical or biological indicators to pollutant

deposition. The empirical models currently in use for calculating critical loads employ steady state

approaches. This is not a necessary constraint, however, because even with no knowledge of the

underlying mechanisms, there exist many statistical techniques for relating the time-series of outputs and

inputs of ecosystems. The reason empirical critical loads models are usually based on a steady state

approach is primarily because time-series data of long enough duration to parameterize dynamic

empirical models are not generally available. In general, empirical models require less complex datasets,

are more straightforward to implement, and are easier to understand than process-based models. For some

receptors, the lack of conceptual understanding of the mechanisms of indicator response to pollutant

deposition renders the use of process-based models problematic, and the use of empirical models is then

the only viable critical load analysis approach.

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# D.4.2. Acidification Effects of Sulfur and N

Empirical models of critical loads for acidity assign critical loads to soils on the basis of soil

mineralogy and chemistry (UNECE, 2004). For example, at the Critical Loads Workshop at Skokloster

(Nilsson, 1988) soil forming materials were divided into five classes on the basis of the dominant

weatherable minerals. A critical load range, rather than a single value, was assigned to each of these

classes according to the amount of acidity that could be neutralized by the base cations produced by

mineral weathering. The classification of soil materials developed at Skokloster used a relatively small

range of primary silicate minerals and carbonates. A larger range of minerals was classified by Sverdrup

and Warfvinge (1988) and Sverdrup et al. (1990) for use in the PROFILE model (Warfvinge and

Sverdrup, 1992).



# D.4.3. Nutrient Effects of N

Empirical models of critical loads for nutrient N have been developed in Europe within LRTAP to

set critical loads for atmospheric N deposition (e.g., UNECE, 2004). Empirical critical loads of N for

natural and semi-natural terrestrial ecosystems and wetland ecosystems were first presented in a

background document for the 1992 workshop on critical loads held under the UNECE LRTAP

Convention at Lökeberg, Sweden (Bobbink et al., 1992). A number of European expert workshops have

taken place in order to reach agreement among specialists regarding the impacts of N for various

ecosystems and related critical loads (Nilsson, 1988; Bobbink, 1992)1996; Hornung, 1995; Achermann,

2003). Empirical relationships have also recently been developed in the U.S., particularly for western

ecosystems (e.g., (Baron, 1994; Baron, 2000) (Williams, 2000); Fenn, 2003) (Burns, 2004) (Nydick,

2004).



# D.4.4. Process­Based Models

A number of process-based models are currently in use for calculating critical loads using both

steady state and dynamic approaches. Developing a process-based modeling approach that includes all

appropriate chemical and biological indicators is a complex task. Some process models incorporate both

geochemical and biological response mechanisms in one program. An alternate approach is to chain

individual process-based models, for example taking the output of a geochemical model and passing it as

input to a biological-response model. In either approach, the level of process complexity varies a great

deal among the various available models. The choice of a particular process-based modeling approach to

be used in a critical load analysis (dynamic or steady state, all-in-one or chained, etc.) will depend on the

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29 scope of the analysis, the quality and quantity of available data, and the availability of resources (time and

money) for the analysis. The following is a brief overview of some of the process-based biogeochemical

models that are commonly used to calculate critical loads.



# D.4.5. Steady State Models

The Simple Mass Balance (SMB) model is the standard model for calculating critical loads for

terrestrial ecosystems under the LRTAP Convention (Sverdrup et al., 1990; Sverdrup and De Vries, 1994).

The SMB model is a single-layer model. There also exist multi-layer steady state models for calculating

critical loads in terrestrial ecosystems. Examples are the MACAL model (De Vries 1988) and the widely

used PROFILE model (Warfvinge and Sverdrup, 1992), which has at its core a model for calculating

weathering rates from total mineral analyses.

The Steady State Water Chemistry (SSWC) model (Sverdrup et al., 1990; Henriksen et al., 1992;

Henriksen and Posch, 2001) calculates critical loads of acidity for surface waters, based on the principle

that acid loads should not exceed the balance of non-marine, non-anthropogenic base cation sources and

sinks in a catchment, minus a buffer to protect selected biota from being damaged.

The First-order Acidity Balance (FAB) model for calculating critical loads for surface waters takes

into account sources and sinks within the lake and its terrestrial catchment. The original version of the

FAB model was developed and applied to Finland, Norway, and Sweden by Henriksen et al. (1992) and

Posch et al. (1997). A modified version was first reported in Hindar et al. (2000, 2001) and is described in

more detail by Henriksen and Posch (2001).



# D.4.6. Dynamic Models

MAGIC is a lumped-parameter model of intermediate complexity, developed to predict the longterm effects of acidic deposition on surface water chemistry (Cosby, 2001)Cosby et al., 1985a,b, 2001).

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 sub-model in

which the concentrations of major ions are assumed to be governed by simultaneous reactions involving

SO 4 2– adsorption, cation exchange, dissolution-precipitation- speciation of Al, and dissolution-speciation

of inorganic carbon (C) and (2) a mass balance sub-model in which the flux of major ions to and from the

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

biomass, and losses to runoff. At the heart of MAGIC is the size of the pool of exchangeable base cations

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

deposition, the chemical equilibria between soil and soil solution shift to give changes in surface water

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35 chemistry. The degree and rate of change of surface water acidity thus depend both on flux factors and the

inherent characteristics of the affected soils. MAGIC is described in more detail in Annex C.

PnET-BGC is an integrated dynamic biogeochemical model that simulates chemical

transformations of vegetation, soil, and drainage water. It was formulated by adding the sub-model BGC

(biogeochemistry) to PnET-CN, a model of C, water, and N balances (Aber and Federer, 1992; Aber and

Driscoll, 1997; Aber et al., 1997), in order to expand the model to include vegetation and organic matter

interactions of major elements (i.e., Ca 2 + , Mg2 + , K + , Na + , Si, S, P, Al 3 + , Cl

− , F

− ), abiotic soil processes,

solution speciation, and surface water processes (Gbondo-Tugbawa et al., 2001). The model was initially

developed for, and applied to, the northern hardwood forest ecosystem. It was tested extensively at the

Hubbard Brook Experimental Forest, New Hampshire, including a detailed sensitivity analysis of

parameter values. The model has subsequently been applied to intensively-studied watersheds in the

Adirondack and Catskill regions of New York and applied regionally to the Adirondacks (Chen and

Driscoll, 2005b) and northern New England (Chen and Driscoll, 2005a,c). See additional description in

Annex C.

Simulation Model for Acidification’s Regional Trends (SMART2) is a soil acidification and

nutrient cycling model and is an extension of the dynamic soil acidification model SMART (Kros et al.,

1995). The original model was a relatively simple simulation of the response of soil and soil water quality

to atmospheric inputs. Improvements in SMART2 include processes of canopy interactions, litter fall, root

decay, mineralization, and root uptake of nutrients. SMART2 has been used primarily in European critical

loads studies.

The Soil Acidification in Forest Ecosystems model (SAFE) was developed at the University of

Lund in Sweden (Warfvinge et al., 1993; Alveteg and Sverdrup, 2002). The main differences between the

SAFE and MAGIC models are: (a) weathering of base cations is not calibrated for SAFE, but it is

modeled with the PROFILE sub-model, using soil mineralogy as input (Warfvinge and Sverdrup, 1992);

b) SAFE is oriented to soil profiles in which water is assumed to move vertically through several soil

layers, (c) cation exchange between Al, H, and (divalent) base cations is modeled in SAFE with Gapon

exchange reactions rather than Gaines-Thomas reactions, and the exchange between the soil matrix and

soil solution is diffusion-limited. The standard version of SAFE does not include S adsorption although a

version, in which S adsorption is dependent on SO 4 2– concentration and pH of soil solution, has recently

been developed (Martinson et al., 2003.

ForSAFE is a mechanistic model that simulates N and C cycling and soil chemistry. Climatic

drivers within the model include temperature, precipitation, radiation, and deposition. ForSAFE combines

three established models (SAFE, PnET-CN, and DECOMP). SAFE simulates soil chemistry (e.g.,

chemical weathering, cation exchange, leaching, and solution equilibrium reactions). PnET-CN (Aber

et al., 1997) is used to predict forest growth within ForSAFE, through the simulation of C fixation,

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26 litterfall, and C and nutrient allocation. DECOMP (Walse et al., 1998) is a dynamic, multi-layered

process-oriented decomposition model that incorporates the influences of temperature, moisture, pH, and

Al. Very Simple Dynamic soil acidification model (VSD) only includes a few key processes, such as

cation exchange and N immobilisation, and a mass balance for cations and N (Posch et al., 2003). VSD

does not consider seasonal variations, as the time step in the model is one year. The VSD model is based

on mass balance equations that describe soil input-output fluxes and equations describing the rate-limited

(e.g., uptake and silicate weathering) and equilibrium (e.g., cation exchange) soil processes. Soil solution

chemistry is based solely on the net element input from the atmosphere (i.e., deposition minus net uptake

minus net immobilization) and geochemical interactions in the soil (i.e., CO 2 equilibria, weathering of

carbonates and silicates, and cation exchange). VSD simulates a single soil layer with a constant density

and a fixed depth. The concentration of the soil water leaving the compartment is assumed to be equal to

the annual precipitation excess.



# D.5. Use of Critical Loads in the U.S.

– Current Status

At the Multi-Agency Critical Loads Workshop for Sulfur and Nitrogen Deposition Effects on

Freshwater and Terrestrial Ecosystems, convened by the EPA, the U.S. Forest Service (USFS), the

National Park Service (NPS), and the USGS in May 2006, approximately 75 scientists, conservation

representatives, and state and federal agency officials gathered to share information, discuss scientific

advances, and develop a broad federal strategy for advancing critical loads in the U.S. (EPA, 2006). The

conclusions and recommendations of that workshop are presented below. These conclusions and

recommendations represent the current understanding of critical loads as scientific tool and policy

instrument in the U.S.

The conclusions and recommendations below were reached by the Federal Agencies sponsoring the

workshop. It is worth noting that some state agencies have pursued the use of critical loads independently

in order to link science and policy in addressing the management of natural resources. For instance, in the

State of Colorado, critical loads for N deposition that were developed for Rocky Mountain National Park

(Baron, 2006) are being used to develop goals for N emissions reductions by the State of Colorado, EPA,

and NPS. (See “Nitrogen Deposition Reduction Plan” at http://www.cdphe.state.co.us/ap/rmnp.html )

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21 � A critical load is defined as: a quantitative estimate of the 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 (Nilsson, 1988).

� Despite reductions in S and N emissions in the U.S., deposition rates still exceed preindustrial

levels and acidification and eutrophication effects remain widespread.

� Critical loads can be used to better understand impacts of atmospheric deposition, assess the

effectiveness of emissions programs, and guide natural resource management.

� The development of critical loads is a process that is subject to continued development and

improvement as knowledge advances.

� Adequate information exists to move forward with the development and limited application of

critical loads in some regions and ecosystems in the U.S.

� An intensive research and monitoring agenda should be pursued to support the development

and refinement of critical loads in the U.S.

� Critical loads should be based on a matrix of biological and chemical indicators for aquatic and

terrestrial ecosystems that account for acidification, N saturation, and eutrophication effects

and are relevant to the geographic area or ecosystem of concern.

� Adequate information exists to establish harmful effect thresholds for some indicators based on

specific protection and recovery objectives defined by policymakers and managers. 

# D.5.1. Current Recommendations on Critical Loads Uses in the

U.S.

The participants in the Multi-Agency Critical Loads Workshop developed a set of findings and

recommendations to help advance critical loads usage in the U.S. The “areas of agreement” published in

the workshop report (EPA, 2006) included the following:

August 2008 D­25 DRAFT—DO NOT QUOTE OR CITE� Dynamic models provide the most accurate site-specific information and account for timedependent processes, but are generally too data intensive to be applied across large geographic

areas at present. Simple mass balance models can be applied to current conditions in large

geographic areas, but in some instances do not adequately highlight some sensitive areas

becau se they tend to average conditions across the landscape. Hybrid approaches that link

observational datasets with dynamic and steady state models represent a useful approach for

regionalizing site-specific information. 

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22 � What are the appropriate applications of critical load estimates to policy and management

issues given current knowledge? For applications where buy-in to an incremental process does

not exist, greater investment in critical loads methods may be needed before this application

can be pursued.

� How strong is the relationship between specific indicators, thresholds, and biological

responses?

� What are the suitable interpretations and uses of existing databases for the development of

national simple mass balance critical load models?

� Update N and S emissions inventories on a state-by-state basis back to the 1900s to correspond

with methods used in current emissions inventories.

� Develop NH 3 emissions inventory. 

# D.5.2. Questions and Limitations Regarding Critical Loads Uses

in the U.S.

The participants in the Multi-Agency Critical Loads Workshop also developed a set of “Questions

Needing Further Discussion” (EPA, 2006):



# D.5.3. Critical Loads Research and Monitoring Needs

Finally, the participants in the Multi-Agency Critical Loads Workshop presented a list of “Critical

Loads Research and Monitoring Needs” (EPA, 2006), which are summarized below.



# D.5.4. Emissions and Deposition

August 2008 D­26 DRAFT—DO NOT QUOTE OR CITE� Improve dry deposition estimates for S and N.

� Improve total S and N deposition estimates.

� Measure gaseous NH 3 concentrations.

� Add NH 3 deposition measurements to current networks.

� Improve estimates of total deposition in complex terrain.

� Develop N and S deposition maps for North America. 

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20 � Improve spatial coverage and representativeness of soil chemistry databases, particularly in

sensitive terrain.

� Increase soil monitoring.

� Improve estimates of mineral weathering rates.

� Develop soil archiving and well characterized reference samples to promote cross-laboratory

comparisons.

� Expand research on the nature and size of soil nutrient pools.

� Conduct research on threshold values of soil quality for biologic responses.

� Determine N supply rates in different soil types.

� Investigate N soil accumulation rates in arid lands and implications for critical loads.

� Incorporate TIME and LTM surface water monitoring programs into a larger network with

better geographic coverage (e.g., the West and Southeast).

� Improve spatial coverage and representativeness of surface water chemistry databases,

particularly in sensitive and complex terrain. 

# D.5.5. Soils

D.5.6. S urface Waters

August 2008 D­27 DRAFT—DO NOT QUOTE OR CITE� Integrate fixed-site monitoring with regional probability monitoring design.

� Continue to monitor major drivers of acidity.

� Build critical loads considerations (e.g., validation, improvement, regionalization) into

monitoring from the start by combining chemistry, hydrology, deposition and biology, and

integrating site-specific models and measurements into regional contexts.

� Expand research to understand what is driving dissolved organic carbon (DOC) changes in the

East.

� Analyze the impact of groundwater transport on recovery times. 

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22 � Develop better understanding of the link between chemical indicators and biological response

(e.g., quantify the minimum N level at which plankton communities shift).

� Conduct additional research on the sequential impacts of N and relationship between N

deposition and ecosystem impacts.

� Integrate critical load estimates with biodiversity and climate change interactions.

� Undertake more research on biological change and “harmful effects” to help establish

appropriate critical loads thresholds (e.g., in arid lands, what level of productivity of exotic

invasive species will cause the reduction versus the extinction of native species?).

� Collect sediment cores from lakes that vary in rates of N deposition to track changes in diatom

assemblages.

� Improve representation of N dynamics in models.

� Expand models to include NH 3

.

� Improve explicit consideration of changing base cations and DOC.

� Conduct ground-truthing of forest sensitivity and other models. 

# D.5.7. Biological Effects

D.5.8. Critical Loads Models

August 2008 D­28 DRAFT—DO NOT QUOTE OR CITE� Integrate water flowpaths into nutrient cycling models since lateral and vertically upward

flowpaths are common.

� Understand and quantify uncertainties in models.

� Conduct site level model comparisons of dynamic and simple mass balance models.

� Integrate observational databases with steady state and dynamic models.

� Incorporate capacity to understand and evaluate climate change interactions. 

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Table

loads D­2. Biological indicators for the effects of elevated N deposition and related empirical

for major ecosystem types (according to the Eunis classification) occurring in Europe. critical

Ecosystem Type Biological Effect Indicators Empirical Critical

Load

(kg N/ha/yr)

Grasslands and tall forb habitats (E)

Sub­Atlantic semi­dry calcareous grassland

Non­Mediterranean dry acid and neutral

closed grassland

Inland dune grasslands

Low and medium elevation hay meadows

Mountain hay meadows

Moist and wet oligotrophic grasslands

Alpine and subalpine meadows

Moss and lichen dominated mountain

summits

Heathland habitats (F)

Northern wet heaths

Dry heaths

Arctic, alpine, and subalpine scrub habitats

Coastal habitat (B)

Shifting coastal dunes

Coastal stable dune grasslands

Coastal dune heaths

Moist to wet dune slacks

Mire, bog, and fen habitats (D)

Raised and blanket bogs

Poor fens

Rich fens

Mountain rich fens

Forest habitats (G)

Mycorrhizae

Ground vegetation

Lichens and algae Increased mineralization, nitrification and N leaching; increased tall grasses; decreased diversity

Increase in nitrophilous graminoids, decline of typical species

Decrease in lichens, increase in biomass, accelerated succession

Increased tall grasses, decreased diversity

Increase in nitrophilous graminoids, changes in diversity

Increase in tall graminoids, decreased diversity, decrease in bryophytes

Increase in nitrophilous graminoids, changes in diversity

Effects on bryophytes and lichens

Decreased heather dominance, transition heather to grass, decline in lichens and

mosses

Transition heather to grass, decline in lichens

Decline in lichens, mosses, and evergreen shrubs

Increased biomass, increased N leaching

Increase in tall grasses, decreased prostrate plants, increased N leaching

Increase in plant production, increased N leaching, accelerated succession

Increase in biomasss and tall graminoids

Changed species composition, N saturation of Spagnum

Increased sedges and vascular plant, negative effects on mosses

Increase in tall graminoids, decreased diversity, decrease of characteristic mosses

Increase in vascular plants, decrease in bryophytes

Reduced sporocarp production, reduced below ground species composition

Changed species composition, increased nitrophilous species; increased

susceptibility to parasites (insects, fungi, virus)

Increase in algae; decrease in lichens 15–25

10–20

10–20

20–30

10–20

10–25

10–15

5–10

10–20

10–20

5–15

10–20

10–20

10–20

10–25

5–10

10–20

15–35

15–25

10–20

10–15

10–15

Source: Adapted from Achermann and Bobbink (Achermann, 2003).

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# ANNEX D

­ References

Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N d eposition of N

cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11(4):

639-648.

Aber, J. D.; Federer, C. A. (1992) A generalized, lumped-parameter model of photosynthesis,

evapotranspiration and net primary production in temperate and boreal forest ecosystems.

Oecologia 92: 463-474.

Aber, J. D.; Nadelhoffer, K. J.; Steudler, P.; Melillo, J. M. (1989) Nitrogen saturation in northern

forest ecosystems: excess nitrogen from fossil fuel combustion may stress the biosphere.

BioScience 39: 378-386.

Aber, J. D.; Mellilo, J. M.; Nadelhoffer, K. J.; Pastor, J.; Boone, R. D. (1991) Factors controlling

nitrogen cycling and nitrogen saturation in northern temperate forest ecosystems. Ecol.

Appl. 1: 303-315.

Aber, J. D.; Ollinger, S. V.; Federer, C. A.; Driscoll, C. (1997) Modeling nitrogen saturation in

forest ecosystems in response to land use and atmospheric deposition. Ecol. Model. 101:

61-78.

Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.; McNulty, S.;

Currie, W.; Rustad, L.; Fernandez, I. (1998) Nitrogen saturation in temperate forest

ecosystems. BioScience 48: 921-934.

Aber, J. D.; Goodale, C. L.; Ollinger, S. V.; Smith, M.-L.; Magill, A. H.; Martin, M. E.; Hall, R.

A.; Stoddard, J. L. (2003) Is nitrogen deposition altering the nitrogen status of

northeastern forests? Bioscience 53: 375-389.

Achermann, B.; Bobbink, R. (2003) Empirical critical loads for nitrogen: proceedings of an

expert workshop held under the UNECE Convention on Long-range Transboundary Air

Pollution; November, 2002; Berne, Switzerland. Berne, Switzerland: Swiss Agency for

the Environment, Forests, and Landscape (SAEFL), Environmental Documentation No.

164, pp. 327.

Adams, M. B.; Burger, J. A.; Jenkins, A. B.; Zelazny, L. (2000) Impact of harvesting and

atmospheric pollution on nutrient depletion of eastern U.S. hardwood forests. For. Ecol.

Manage. 138: 301-319.

Aherne, J.; Larssen, T.; Dillon, P. J.; Cosby, B. J. (2004) Effects of climate events on elemental

fluxes from forested catchments in Ontario, Canada: modelling drought-induced redox

processes. Water Air Soil Pollut. Focus 4: 37-48.

Alveteg, M.; Sverdrup, H. (2002) Manual for regional assessments using the SAFE model

(draft). Lund, Sweden: Department of Chemical Engineering II, Lund University.

Arp, P. A.; Oja, T.; Marsh, M. (1996) Calculating critical S and N l oads and current exceedances

for upland forests in southern Ontario, Canada. Can. J. For. Res. 26: 696-709.

Baker, L. A. (1991) Appendix A: Ion enrichment analysis for the Regional Case Studies Project.

In: Charles, D. F., Christie, S., eds. Acidic deposition and aquatic ecosystems: regional

case studies. New Y ork, NY: Springer-Verlag; pp. 641-644.

August 2008 D­30 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

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12

13

14

15

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17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43 Baker, L. A.; Christensen, S. W. (1991) Effects of acidification on biological communities. In:

Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case studies. New

York, NY: Springer-Verlag; pp. 83-106.

Baker, L. S.; Kaufmann, P. R.; Herlihy, A. T.; Eilers, J. M. (1991) Current status of surface

water acid-base chemistry. In: Irving, P. M., ed. Acidic deposition: state of science and

technology, v. II, aquatic processes and effects. Washington, DC: National Acid

Precipitation Assessment Program; NAPAP State of Science/Technology report 9; pp. 9­

5

- 9-367, 9-A1, 9-B1, 9-C1, 9-D1, and 9-CP1.

Baker, J. P.; Van Sickle, J.; Gagen, C. J.; DeWalle, D. R.; Sharpe, W. E.; Carline, R. F.; Baldigo,

B. P.; Murdoch, P. S.; Bath, D. W.; Kretser, W. A.; Simonin, H. A.; Wigington, P. J., Jr.

(1996) Episodic acidification of small streams in the northeastern United States: effects

on fish populations. Ecol. Appl. 6: 423-437.

Baron, J. S. (2006) Hindcasting nitrogen deposition to determine ecological critical load. Ecol.

Appl. 16: 433-439.

Baron, J. S.; Ojima, D. S.; Holland, E. A.; Parton, W. J. (1994) Analysis of nitrogen saturation

potential in Rocky Mountain tundra and forest: implications for aquatic systems.

Biogeochemistry 27: 61-82.

Baron, J. S.; Rueth, H. M.; Wolfe, A. M.; Nydick, K. R.; Allstott, E. J.; Minear, J. T.; Moraska,

B. (2000) Ecosystem responses to nitrogen deposition in the Colorado Front Range.

Ecosystems 3: 352-368.

Bashkin, V. N.; Kozlov, M. Y.; Priputina, I. V.; Avrumuchev, A. V.; Dedkova, I. S. (1995)

Calculation and mapping of critical loads of S, N, and acidity of ecosystems of northern

Asia. Water Air Soil Pollut. 85: 2395-2400

Bobbink, R.; Boxman, D.; Fremstad, E.; Heil, G.; Houdijk, A.; Roelofs, J. (1992) Critical loads

for nitrogen eutrophication of terrestrial and wetland ecosystems based upon changes in

vegetation and fauna. In: Grennfelt, P.; Thörnelöf, E., eds. Critical loads for nitrogen.

Nord 92:41. Nordic Council of Ministers, Copenhagen, pp. 111-159.

Bobbink, R.; Hornung, M.; Roelofs, J. G. M. (1996) Empirical nitrogen critical loads for natural

and semi-natural ecosystems. Annex III. In: Manual on methodologies and criteria for

mapping critical levels/loads and geographical areas where they are exceeded. UNECE

Convention on Long-Range Transboundary Air Pollution. Umvelt Bundes Arnt, Berlin.

Bobbink, R.; Ashmore, M.; Braun, S.; Flückiger, W.; Van Den Wyngaert, I. J. J. (2003)

Empirical nitrogen critical loads for natural and semi-natural ecosystems: 2002 update.

In: Achermann, B.; Bobbink, R., eds. Empirical Critical Loads for Nitrogen. Berne:

Swiss Agency for Environment, Forest and Landscape SAEFL; pp. 43-170.

Bowman, W. D.; Gartner, J. R.; Holland, K.; Wiedermann, M. (2006) Nitrogen critical loads for

alpine vegetation and terrestrial ecosystem response: are we there yet? Ecol. Appl. 16:

1183-1193.

Brooks, M. L. (2003) Effects of increased soil nitrogen on the dominance of alien annual plants

in the Mojave Desert. J. Appl. Ecol. 40: 344-353.

Bulger, A. J.; Lien, L.; Cosby, B. J.; Henriksen, A. (1993) Trout status and chemistry from the

Norwegian thousand lake survey: statistical analysis. Can. J. Fish. Aquat. Sci. 50: 575­

585.

August 2008 D­31 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

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19

20

21

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28

29

30

31

32

33

34

35

36

37

38

39

40

41 Bulger, A. J.; Cosby, B. J.; Webb, J. R. (2000) Current, reconstructed past, and projected future

status of brook trout (Salvelinus fontinalis) streams in Virginia. Can. J. Fish. Aquat. Sci.

57: 1515-1523.

Burns, D. A. (2004) The effects of atmospheric nitrogen deposition in the Rocky Mountains of

Colorado and southern Wyoming, USA—a critical review. Environ. Pollut. 127: 257­

269.

Camargo, J. A.; Alonso, A.; Salamanca, A. (2005) Nitrate toxicity to aquatic animals: a review

with new d ata for freshwater invertebrates. Chemosphere 58: 1255-1267.

Campbell, D. H.; Baron, J. S.; Tonnessen, K. A.; Brooks, P. D.; Schuster, P. F. (2000) Controls

on nitrogen flux in alpine/subalpine watersheds. Water Resour. Res. 36: 37-48.

Campbell, D. H.; Muths, E.; Turk, J. T.; Corn, P. S. (2004) Sensitivity to acidification of

subalpine ponds and lakes in north-western Colorado. Hydrol. Processes 18: 2817-2834.

Centre for Ecology and Hydrology. (2003) Status of UK c ritical loads: critical loads methods,

data & maps. Monks Wood, UK: UK N ational Focal Centre.

Chen, L.; Driscoll, C. T. (2005a) A t wo-layer model to simulate variations in surface water

chemistry draining a northern forest watershed. Water Resour. Res. 41(W09425):

10.1029/2004WR003625.

Chen, L.; Driscoll, C. T. (2005b) Regional assessment of the response of the acid-base status of

lake-watersheds in the Adirondack region of New York to changes in atmospheric

deposition using PnET-BGC. Environ. Sci. Technol. 39: 787-794.

Chen, L.; Driscoll, C. T. (2005c) Regional application of an integrated biogeochemical model to

northern New En gland and Maine. Ecol. Appl. 15: 1783-1797.

Church, M. R.; Shaffer, P. W.; Thornton, K. W.; Cassell, D. L.; Liff, C. I.; Johnson, M. G.;

Lammers, D. A.; Lee, J. J.; Holdren, G. R.; Kern, J. S.; Liegel, L. H.; Pierson, S. M.;

Stevens, D. L.; Rochelle, B. P.; Turner, R. S. (1992) Direct/delayed response project:

future effects of long-term sulfur deposition on stream chemistry in the mid-Appalachian

region of the eastern United States. Corvallis, OR: U.S. Environmental Protection

Agency; report no. EPA/600/R-92/186.

Clean Air Act Advisory Committee. (2005) Recommendations to the Clear Air Act Advisory

Committee, Air Quality Management Work Group, phase I and next ateps, January 2005.

Washington, DC: Clean Air Act Advisory Committee.

Clow, D. W.; Sueker, J. K. (2000) Relations between basin characteristics and stream water

chemistry in alpine/subalpine basins in Rocky Mountain National Park, Colorado. Water

Resour. Res. 36: 49-62.

Clow, D. W.; Sickman, J. O.; Striegl, R. G.; Krabbenhoft, D. P.; Elliot, J. G.; Dornblaser, M.;

Roth, D. A.; Campbell, D. H. (2003) Changes in the chemistry of lakes and precipitation

in high-elevation national parks in the western United States, 1985-1999. Water Resour.

Res. 39(6) 1171: 10.1029/2002WR001533.

Cosby, B. J.; Hornberger, G. M.; Galloway, J. N.; Wright, R. F. (1985a) Modeling the effects of

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

chemistry. Water Resour. Res. 21: 51-63.

August 2008 D­32 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

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21

22

23

24

25

26

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28

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30

31

32

33

34

35

36

37

38

39

40

41

42 Cosby, B. J.; Wright, R. F.; Hornberger, G. M.; Galloway, J. N. (1985b) Modeling the effects of

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

catchment. Water Resour. Res. 21: 1591-1601.

Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Wright, R. F. (2001) Modelling the effects of acid

deposition: refinements, adjustments and inclusion of nitrogen dynamics in the MAGIC

model. Hydrol. Earth Syst. Sci. 5: 499-517.

Cosby, B. J.; Webb, J. R.; Galloway, J. N.; Deviney, F. A. (2006) Acidic deposition impacts on

natural resources in Shenandoah National Park. Philadelphia: U.S. Department of the

Interior, National Park Service, Northeast Region. Technical Report NPS/NER/NRTR-­

2006/066. Available:

http://www.nps.gov/nero/science/FINAL/SHEN_acid_dep/SHEN_acid_dep.htm (3

December, 2007).

Craig, B. W.; Friedland, A. J. (1991) Spatial patterns in forest composition and standing dead red

spruce in montane forests of the Adirondacks and northern Appalachians. Environ.

Monit. Assess. 18: 129-140.

Cronan, C. S.; Grigal, D. F. (1995) Use of calcium/aluminum ratios as indicators of stress in

forest ecosystems. J. Environ. Qual. 24: 209-226.

Cronan, C. S.; Schofield, C. L. (1990) Relationships between aqueous aluminum and acidic

deposition in forested watersheds of North America and northern Europe. Environ. Sci.

Tech. 24: 1100-1105.

De Vries, W. (1988) Critical deposition levels for nitrogen and sulphur on Dutch forest

ecosystems. Water Air Soil Pollut. 42: 221-239.

De Vries, W.; Reinds, G. J.; Vel, E. (2003) Intensive monitoring of forest ecosystems in Europe:

2. Atmospheric deposition and its impacts on soil solution chemistry. For. Ecol. Manage.

174: 97-115.

DeHayes, D. H.; Schaberg, P. G.; Hawley, G. J.; Strimbeck, G. R. (1999) Acid rain impacts on

calcium nutrition and forest health. BioScience 49: 789-800.

Dennis, T. E.; Bulger, A. J. (1995) Condition factor and whole-body sodium concentrations in a

freshwater fish: evidence for acidification stress and possible ionoregulatory overcompensation. Water Air Soil Pollut. 85: 377-382.

Driscoll, C. T.; Postek, K. M. (1995) The chemistry of aluminum in surface waters. In: Sposito,

G., ed. The environmental chemistry of aluminum. Chelsea, MI: Lewis Publishers; pp.

363-418.

Driscoll, C. T.; Lawrence, G. B.; Bulger, A. J.; Butler, T. J.; Cronan, C. S.; Eagar, C.; Lambert,

K. F.; Likens, G. E.; Stoddard, J. L.; Weathers, K. C. (2001) Acidic deposition in the

northeastern United States: sources and inputs, ecosystem effects, and management

strategies. BioScience 51: 180-198.

Driscoll, C.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C.; Groffman, P.;

Hopkinson, C.; Lambert, K.; Lawrence, G.; Ollinger, S. (2003) Nitrogen pollution:

sources and consequences in the U.S. Northeast. Environment 45: 8-22.

Drohan, P.; Stout, S.; Petersen, G. (1999) Spatial relationships between sugar maple (Acer

sacharum Marsh.), sugar maple decline, slope, aspect, and atmospheric deposition in

August 2008 D­33 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 northern Pennsylvania. In: Horsley, S. B.; Long, R. P., eds. Sugar Maple Ecology and

Health: Proceedings of an International Symposium; June, 1998; Warren, PA. Radnor,

PA: US Department of Agriculture, Forest Service, General Technical Report NE-261.

Duan, L.; Hao, J. M.; Xie, S. D.; Du, K. (2000a) Critical loads of acidity for surface waters in

China. Sci. Total Environ. 246: 1-10.

Duan, L.; Xie, S. D.; Zhou, Z. P.; Hao, J. M. (2000b) Critical loads of acid deposition on soil in

China. Water Air Soil Pollut. 118: 35-51.

Duan, L.; Xie, S. D.; Zhou, Z. P.; Ye, X. M.; Hao, J. M. (2001) Calculation and mapping of

critical loads for S, N and acidity in China. Water Air Soil Pollut. 130: 1199-1204.

Dupont, J.; Clair, T. A.; Gagnon, C.; Jeffries, D. S.; Kahl, J. S.; Nelson, S. J.; Peckenham, J. M.

(2005) Estimation of critical loads of acidity for lakes in northeastern United States and

eastern Canada. Environ. Monit. Assess. 109: 275-291.

Ellis, H.; Bowman, M. (1994) Critical loads and development of acid rain control options. J.

Environ. Eng. 120: 273-290.

Ellis, H.; Ringold, P. L.; Holdren, G. R., Jr. (1996) Emissions reductions and ecological

response: management models for acid rain control. Socioecon. Plann. Sci. 30: 15-26.

Federal/Provincial Research and Monitoring Coordinating Committee (RMCC). (1990) The

1990 Canadian long-range transport of air pollutants and acid deposition assessment

report, part 4: aquatic effects. Ottawa, Ontario, Canada: Federal/Provincial Research and

Monitoring Coordinating Committee.

Federer, C. A.; Hornbeck, J. W.; Tritton, L. M.; Martin, C. W.; Pierce, R. S. (1989) Long-term

depletion of calcium and other nutrients in eastern US forests. Environ. Manage. (N. Y.)

13: 593-601.

Fenn, M. E.; Dunn, P. H. (1989) Litter decomposition across an air-pollution gradient in the San

Bernardino Mountains. Soil Sci. Soc. Am. J. 53: 1560-1567.

Fenn, M. E.; Poth, M. A.; Aber, J. D.; Baron, J. S.; Bormann, B. T.; Johnson, D. W.; Lemly, A.

D.; McNulty, S. G.; Ryan, D. F.; Stottlemyer, R. (1998) Nitrogen excess in North

American ecosystems: predisposing factors, ecosystem responses, and management

strategies. Ecol. Appl. 8: 706-733.

Fenn, M. E.; Baron, J. S.; Allen, E. B.; Rueth, H. M.; Nydick, K. R.; Geiser, L.; Bowman, W. D.;

Sickman, J. O.; Meixner, T.; Johnson, D. W.; Neitlich, P. (2003) Ecological effects of

nitrogen deposition in the western United States. BioScience 53: 404-420.

Galloway, J. N.; Aber, J. D.; Erisman, J. W.; Seitzinger, S. P.; Howarth, R. W.; Cowling, E. B.;

Cosby, B. J. (2003) The nitrogen cascade. BioScience 53: 341-356.

Gbondo-Tugbawa, S. S.; Driscoll, C. T.; Aber, J. D.; Likens, G. E. (2001) Evaluation of an integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem.

Water Resour. Res. 37: 10578-1070.

Geiser, L.; Neitlich, P. (2007) Air pollution and climate gradients in western Oregon and

Washington indicated by epiphytic macrolichens. Environ. Pollut. 145: 203-218.

Groffman, P. M.; Altabet, A. M.; Böhlke, J. K.; Butterbach-Bahl, K.; David, M. B.; Firestone,

M. K.; Giblin, A. E.; Kana, T. M.; Nielsen, L. P.; Voytek, M. A. (2006) Methods for

August 2008 D­34 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 m easuring denitrification: diverse approaches to a difficult problem. Ecol. Appl. 16:

2091-2122.

Gundersen, P.; Callesen, I.; De Vries, W. (1998) Nitrate leaching in forest ecosystems is related

to forest floor C/N ratios. Environ. Pollut. 102(suppl. 1): 403-407.

Hao, J. M.; Ye, X. M.; Duan, L.; Zhou, Z. P. (2001) Calculating critical loads of sulfur

deposition for 100 surface waters in China using the MAGIC model. Water Air Soil

Pollut. 130: 1157-1162.

Henriksen, A.; Dillon, P. J. (2001) Critical load of acidity to surface waters in south-central

Ontario, Canada. I. Application of the Steady-State Water Chemistry SSWC model. Oslo,

Norway: Norwegian Institute for Water Research (NIVA). Acid Rain Research Report

2001:52.

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

surface waters. Water Air Soil Pollut. Focus 1: 375-398.

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

surface waters. Ambio 21: 356-363.

Henriksen, A.; Dillon, P. J.; Aherne, J. (2002) Critical loads of acidity for surface waters in

south-central Ontario, Canada: regional application of the Steady-State Water Chemistry

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

Hicks, B. B.; McMillen, R. T.; Turner, R. S.; Holdren, J.; Strickland, T. C. (1993) A national

critical loads framework for atmospheric deposition effects assessment: III Deposition

characterization. Environ. Manage. 17: 343-353.

Hindar, A.; Posch, M.; Henriksen, A.; Gunn, J.; Snucins, E. (2000) Application of the FAB

model to calculate critical loads of S and N for lakes in the Killarney Provincial Park

(Ontario, Canada). Oslo, Norway: Norwegian Institute for Water Research; NIVA report

SNO 4202-2000.

Hindar, A.; Posch, M.; Henriksen, A. (2001) Effects of in-lake retention of nitrogen on critical

load calculations. Water Air Soil Pollut. 130: 1403-1408.

Holdren, G. R., Jr.; Strickland, T. C.; Cosby, B. J.; Marmorek, D.; Bernard, D.; Santore, R.;

Driscoll, C. T.; Pardo, L.; Hunsaker, C.; Turner, R. S.; Aber, J. (1993) A national critical

loads framework for atmospheric deposition effects assessment: IV. Model selection,

applications and critical loads mapping. Environ. Manage. 17: 355-363.

Holdren, G. R.; Strickland, T. C.; Rosenbaum, B. J.; Turner, R. E.; Ryan, P. F.; McDowell, M.

K.; Bishop, G. D. (1993) Comparison of selected critical loads estimation approaches for

assessing the effects of sulfate deposition on lakes in the northeastern United States.

Washington, D.C.: U.S. Environmental Protection Agency; EPA/600/R93/074. Available

from: NTIS, Springfield, VA; PB92-119015.

Holdren, G. R.; Strickland, T. C.; Shaffer, P. W.; Ryan, P. F.; Ringold, P. L.; Turner, R. S.

(1993) Sensitivity of critical load estimates for surface waters to model selection and

regionalization schemes. J. Environ. Qual. 22: 279-289.

Hornung, M.; Bull, K. R.; Cresser, M.; Hall, J.; Langan, S. J.; Loveland, P.; Smith, C. (1995) An

empirical map of critical loads of acidity for soils in Great Britain. Environ. Pollut. 90:

301-310.

August 2008 D­35 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Horsley, S. B.; Long, R. P.; Bailey, S. W.; Hallett, R. A.; Hall, T. J. (2000) Factors associated

with the decline disease of sugar maple on the Allegheny Plateau. Can. J. Forest. Res. 30:

1365-1378.

Hunsaker, C.; Graham, R.; Turner, R. S.; Ringold, P. L.; Holdren, G. R., Jr.; Strickland, T. C.

(1993) A n ational critical loads framework for atmospheric deposition effects assessment:

II. Defining assessment end points, indicators, and functional subregions. Environ.

Manage. (N. Y.) 17: 335-341.

Interlandi, S. J.; Kilham, S. S. (1998) Assessing the effects of nitrogen deposition on mountain

waters: a study of phytoplankton community dynamics. Water Sci. Technol. 38: 139-146.

Jeffries, D. S., ed. (1997) Canadian acid rain assessment. Volume 3. The effects on Canada's

lakes, rivers and wetlands. Burlington, Ontario, Canada: Environment Canada.

Jeffries, D. S.; Lam, C. C. L. (1993) Assessment of the effect of acidic deposition on Canadian

lakes: determination of critical loads for sulphate deposition. Water Sci. Technol. 28:

183-187.

Jeffries, D. S.; Ouimet, R.; Aherne, J.; Arp, P. A.; Balland, V.; Demerchant, I.; Dupont, J.;

Franklyn, J.; Lam, C. C. L.; Norouzian, F.; Watmough, S. A.; Wong, I. (2005) Critical

load values and exceedances. In: 2004 Canadian acid deposition science assessment.

Ottawa: Meteorological Service of Canada. Environment Canada.

Jenkins, A.; Cosby, B. J.; Ferrier, R. C.; Larssen, T.; Posch, M. (2003) Assessing emission

reduction targets with dynamic models: deriving target load functions for use in

integrated assessment. Hydrol. Earth Syst. Sci. 7: 609-617.

Johnson, D. W.; Lindberg, S. E., eds. (1992) Atmospheric deposition and forest nutrient cycling:

a synthesis of the integrated forest study. New Y ork, NY: Springer-Verlag, Inc. (Billings,

W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. Ecological studies:

analysis and synthesis: v. 91).

Johnson, D. W.; Todd, D. E. (1990) Nutrient cycling in forests of Walker Branch Watershed,

Tennessee: roles of uptake and leaching in causing soil changes. J. Environ. Qual. 19: 97­

104.

Johnson, D. W.; Kelly, J. M.; Swank, W. T.; Cole, D. W.; Van Miegroet, H.; Hornbeck, J. W.;

Pierce, R. S.; Van Lear, D. (1988) The effects of leaching and whole-tree harvesting on

cation budgets of several forests. J. Environ. Qual. 17: 418-424.

Juggins, S.; Omerod, S. J.; Harriman, R. (1995) Relating critical loads to aquatic biota. In:

Critical loads of acid deposition for UK f reshwaters. London UK: Department of the

Environment; pp. 9-13.

Kauppi, P. E.; Mielikaeinen, K.; Kuusela, K. (1992) Biomass and carbon budget of European

forests, 1971 to 1990. Science (Washington, DC) 256: 70-74.

Kristensen, H. L.; Gundersen, P.; Callesen, I.; Reinds, G. J. (2004) Throughfall nitrogen

deposition has different impacts on soil solution nitrate concentration in European

coniferous and deciduous forests. Ecosystems 7: 180-192.

Kros, J.; Reinds, G. J.; de Vries, W.; Latour, J. B.; Bollen, M. J. S. (1995) Modelling abiotic site

factors in response to atmospheric deposition and upward seepage. In: Schoute, J. F. T.;

Finke, P. A.; Veeneklaas, F. R.; Wolfert, H. P., eds. Scenario studies for the rural

August 2008 D­36 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 environment: selected and edited proceedings of the symposium Scenario Studies for the

Rural Environment; September, 1994; Wageningen, the Netherlands. Dordrecht, The

Netherlands: Kluwer; pp. 445-448.

Lafrancois, B. M.; Nydick, K. R.; Caruso, B. (2003) Influence of nitrogen on phytoplankton

biomass and community composition in fifteen Snowy Range lakes (Wyoming, U.S.A.).

Arct. Anarct. Alp. Res. 35(4): 499-508.

Lafrancois, B. M.; Nydick, K. R.; Johnson, B. M.; Baron, J. S. (2004) Cumulative effects of

nutrients and pH o n the plankton of two mountain lakes. Can. J. Fish. Aquat. Sci. 61:

1153-1165.

Li, J. H.; Tang, H. X.; Bai, Q. Z.; Nie, Y. F.; Luan, Z. K. (2000) Aquatic acidification sensitivity

for regional environment: a multi-indicator evaluation approach. Water Air Soil Pollut.

11: 251-261.

Lien, L.; Raddum, G. G.; Fjellheim, A. (1992) Critical loads for surface waters: invertebrates and

fish. Oslo, Norway: Norwegian Institute for Water Research; acid rain research report no.

21.

Lien, L.; Raddum, G. G.; Fjellheim, A.; Henriksen, A. (1996) A c ritical limit for acid

neutralizing capacity in Norwegian surface waters, based on new analyses of fish and

invertebrate responses. Sci. Total Environ. 177: 173-193.

MacAvoy, S. W.; Bulger, A. J. (1995) Survival of brook trout (Salvelinus fontinalis) embryos

and fry in streams of different acid sensitivity in Shenandoah National Park, USA. Water

Air Soil Pollut. 85: 445-450.

Marco, A.; Blaustein, A. R. (1999) The effects of nitrite on behavior and metamorphosis in

cascades frogs (Rana cascadae). Environ. Toxicol. Chem. 18: 949-949.

Martinson, L.; Alveteg, M.; Warfvinge, P. (2003) Parameterization and evaluation of sulfate

adsorption in a dynamic soil chemistry model. Environ. Pollut. 124: 119-125.

McLaughlin, S. B.; Wimmer, R. (1999) Tansley review n o.104: Calcium physiology and

terrestrial ecosystem processes. New P hytol. 142: 373-417.

Milindalekha, J.; Vashkin, V. N.; Towpprayoon, S. (2001) Calculation and mapping of sulfur

critical loads for terrestrial ecosystems of Thailand. Water Air Soil Pollut. 130: 1265­

1270.

Miller, E. K. (2006) Assessment of forest sensitivity to nitrogen and sulfur deposition in Maine.

Conference of New En gland Governors and Eastern Canadian Premiers, Forest Mapping

Group. Augusta, ME: Maine Department of Environmental Protection, Bureau of Air

Quality. Available: http://www.maine.gov/dep/air/acidrain/ME-Forest-Mapping-Report2007-01-26.pdf [3 December, 2007].

Moayeri, M.; Meng, F. R.; Arp, P. A.; Foster, N. W. (2001) Evaluating critical soil acidification

loads and exceedances for a deciduous forest at the Turkey Lakes watershed. Ecosystems

4: 555-567.

Munson, R. K.; Gherini, S. A. (1991) Processes influencing the acid-base chemistry of surface

waters. In: Charles, D. F.; Christie, S., eds. Acidic deposition and aquatic ecosystems:

regional case studies. New Y ork: Springer-Verlag; pp. 9-34.

August 2008 D­37 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 NEG/ECP Forest Mapping Group. (2001) Protocol for assessment and mapping of forest

sensitivity to atmospheric S and N d eposition. Boston, MA: New En gland

Governors/Eastern Canadian Premiers: acid rain action plan. Action item 4: forest

mapping research project. Available: http://www.ecosystemsresearch.com/fmi/Protocol.pdf [3 December, 2007].

National Acid Precipitation Assessment Program. (1991) National Acid Precipitation

Assessment Program 1990 integrated assessment report. Washington, DC: National Acid

Precipitation Assessment Program.

National Research Council. (2004) Air quality management in the United States. Washington,

DC: The National Academies Press.

Nilsson, J., ed. (1986) Critical loads of nitrogen and sulphur. Copenhagen, Denmark: Council of

Ministers; environmental report 1986:11.

Nilsson, J.; Grennfelt, P., eds. (1988) Critical loads for sulphur and nitrogen: report from a workshop; March; Skokloster, Sweden. Copenhagen, Denmark: Nordic Council of

Ministers; Miljørapport 1988:15.

National Park Service (NPS). (2000) Federal land manager's air quality related values workgroup

(FLAG) phase I report. Lakewood, CO: U.S. Department of the Interior, National Park

Service, Air Resources Division. Available: http://199.128.173.141/Flag2000.pdf [8

January, 2008].

National Park Service (NPS). (2004) Federal land manager's critical loads for sulfur and nitrogen

workshop. Denver, CO: National Park Service Air Resources Division; U.S. Fish and

Wildlife Service Air Quality Branch; U.S. Forest Service, Air Resource Management

Program. Available:

http://www2.nature.nps.gov/air/Pubs/pdf/FinalCL_WorkshopSummary092904.pdf [8

January, 2008].

Nydick, K. R.; Lafrancois, B. M.; Baron, J. S.; Johnson, B. M. (2003) Lake-specific responses to

elevated atmospheric nitrogen deposition in the Colorado Rocky Mountains, U.S.A.

Hydrobiologia 510: 103-114.

Nydick, K. R.; Lafrancois, B. M.; Baron, J. S. (2004a) NO 3 

### uptake in shallow, oligotrophic,

mountain lakes: the influence of elevated NO 3 

### concentrations. J. North Am. Benthol. Soc.

23: 397-415.

Nydick, K. R.; Lafrancois, B. M.; Baron, J. S.; Johnson, B. M. (2004b) Nitrogen regulation of

algal biomass, productivity, and composition in shallow m ountain lakes, Snowy Range,

Wyoming, USA. Can. J. Fish. Aquat. Sci. 61: 1256-1268.

Ouimet, R.; Arp, P. A.; Watmough, S. A.; Aherne, J.; Demerchant, I. (2006) Determination and

mapping critical loads of acidity and exceedances for upland forest soils in eastern

Canada. Water Air Soil Pollut. 172: 57-66.

Pardo, L. H.; Driscoll, C. T. (1993) A c ritical review o f mass balance methods for calculating

critical loads of nitrogen for forested ecosystems. Environ. Rev. 1: 145-156.

Pardo, L. H.; Driscoll, C. T. (1996) Critical loads for nitrogen deposition: case studies at two

northern hardwood forests. Water Air Soil Pollut. 89: 105-128.

August 2008 D­38 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43 Pardo, L. H.; Duarte, N. (2007) Assessment of effects of acidic deposition on forested

ecosystems in Great Smoky Mountains National Park using critical loads for sulfur and

nitrogen. Lakewood, CO: U.S. Department of the Interior, National Park Service, Air

Resources Division. Available:

http://www2.nature.nps.gov/air/Pubs/pdf/GSMN_CL_Report_080830.pdf [3 December,

2007].

Pembrook, H. (2004) 2004 update: calculating critical loads of acidity and exceedances for acidimpaired lakes in Vermont using the steady-state water chemistry (SSWC) model.

Porter, E.; Blett, T.; Potter, D. U.; Huber, C. (2003) Protecting resources on federal lands:

implications of critical loads for atmospheric deposition of nitrogen and sulfur.

Bioscience 55: 603-612.

Posch, M.; De Smet, P. A. M.; Hettelingh, J.-P.; Downing, R. J., eds. (1995) Calculation and

mapping of critical thresholds in Europe. Status report 1995. Bilthoven, The Netherlands:

National Institute of Public Health and the Environment (RIVM), Coordination Center

for Effects; RIVM report no. 259101004. Available:

http://www.mnp.nl/bibliotheek/rapporten/259101004.pdf [4 December, 2007].

Posch, M.; Kämäri, J.; Forsius, J.; Henriksen, A.; Wilander, A. (1997) Environmental auditing:

exceedance of critical loads for lakes in Finland, Norway and Sweden: reduction

requirements for acidifying nitrogen and sulfur deposition. Environ. Manage. 21: 291­

304.

Posch, M.; DeSmet, P. A. M.; Hettelingh, J. P.; Downing, R. J. (2001) Calculation and mapping

of critical thresholds in Europe. Status report 2001. Bilthoven, The Netherlands:

National Institute of Public Health and the Environment (RIVM), Coordination Center

for Effects; RIVM report no. 259101010. Available:

http://www.mnp.nl/cce/Images/SR01_tcm42-16441.pdf [4 December, 2007].

Posch, M.; Hettelingh, J.-P.; Slootweg, J., eds. (2003) Manual for dynamic modelling of soil

response to atmospheric deposition. Bilthoven, The Netherlands: National Institute for

Public Health and the Environment (RIVM), Working Group on Effects of the

Convention on Long-range Transboundary Air Pollution; report 259101012. Available:

http://www.mnp.nl/cce/Images/ModMan1_tcm42-16497.pdf [4 December, 2007].

Raddum, G. G.; Fjellheim, A. (1984) Acidification and early warning organisms in freshwater in

western Norway. Verh.

- Int. Ver. Theor. Angew. Limnol. 22: 1973-1980.

Raddum, G. G.; Skjelvåle, B. L. (2001) Critical limit of acidifying compounds to invertebrates in

different regions of Europe. Water Air Soil Pollut. 130: 825-830.

Reuss, J. O.; Johnson, D. W. (1986) Acid deposition and the acidification of soils and waters.

New Y ork, NY: Springer-Verlag. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.;

Remmert, H., eds. Ecological studies: analysis and synthesis: v. 59).

Rochelle, B. P.; Church, M. R. (1987) Regional patterns of sulphur retention in watersheds of the

eastern U.S. Water Air Soil Pollut. 36(1-2): 61-73.

Rouse, J. D., Bishop, C. A., Struger, J. (1999) Nitrogen pollution: an assessment of its threat to

amphibian survival. Environ. Health Perspect. 107: 799-803.

Rowe, E. C.; Moldan, F.; Emmett, B. A.; Evans, C. D.; Hellsten, S. (2005) Model chains for

assessing impacts of nitrogen on soils, waters and biodiversity: a review. Brighton, UK:

August 2008 D­39 DRAFT—DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 Centre for Ecology and Hydrology, Natural Environment Research Council; report

project C02887.

Rueth, H. M.; Baron, J. S. (2001) Englemann spruce nitrogen dynamics across a nitrogen

deposition gradient in Colorado, USA. Ekologia (Bratislava, Slovakia) 20: 43-49.

Rueth, H.; Baron, J. S. (2002) Differences in Englemann spruce forest biogeochemistry east and

west of the Continental Divide in Colorado, USA. Ecosystems 5: 45-57.

Saros, J. E.; Interlandi, S. J.; Wolfe, A. P.; Engstrom, D. R. (2003) Recent changes in the diatom

community structure of lakes in the Beartooth Mountain Range, USA. Arct. Anarct. Alp.

Res. 35: 18-23.

Schaberg, P. G.; DeHayes, D. H.; Hawley, G. J.; Strimbeck, G. R.; Cumming, J. R.; Murakami,

P. F.; Borer, C. H. (2000) Acid mist, soil Ca and Al alter the mineral nutrition and

physiology of red spruce. Tree Physiol. (Victoria, BC, Can.) 20: 73-85.

Schaberg, P. G.; DeHayes, D. H.; Hawley, G. J.; Murakami, P. F.; Strimbeck, G. R.; McNulty, S.

G. (2002) Effects of chronic N f ertilization on foliar membranes, cold tolerance, and

carbon storage in montane red spruce. Can. J. Forest Res. 32: 1351-1359.

Shortle, W. C.; Smith, K. T. (1988) Aluminum-induced calcium deficiency syndrome in

declining red spruce. Science (Washington, DC) 240: 1017-1018.

Skjelkvåle, B. L.; Andersen, T.; Halvorsen, G. A.; Raddum, G. G.; Heegaard, E.; Stoddard, J.;

Wright, R. F. (2000) The 12-year report: acidification of surface water in Europe and

North America; trends, biological recovery and heavy metals. Oslo, Norway: Norwegian

Institute for Water Research; ICP Waters report 52/2000.

http://www.niva.no/symfoni/RappArkiv5.nsf/URL/C125730900463888C1256FB80053D

51D/$FILE/4208_200dpi.pdf

Skjelkvåle, B. L.; Evans, C. D.; Larssen, T.; Hindar, A.; Raddum, G. G. (2003) Recovery from

acidification in European surface waters: a view t o the future. Ambio 30: 170-175.

Stevens, C. J.; Dise, N. B.; Mountford, J. O.; Gowing, D. J. (2004) Impact of nitrogen deposition

on the species richness of grasslands. Science 303: 1876-1879.

Stoddard, J. L. (1994) Long-term changes in watershed retention of nitrogen: its causes and

aquatic consequences. In: Baker, L. A., ed. Environmental chemistry of lakes and

reservoirs. Washington, DC: American Chemical Society; pp. 223-284. (Advances in

chemistry series no. 237).

Strickland, T. C.; Holdren, G. R., Jr.; Ringold, P. L.; Bernard, D.; Smythe, K.; Fallon, W. (1993)

A n ational critical loads framework for atmospheric deposition effects assessment: I.

Method summary. Environ. Manage. (N. Y.) 17: 329-334.

Sullivan, T. J. (2000) Aquatic effects of acidic deposition. Boca Raton, FL: Lewis Publishers.

Sullivan, T. J.; Cosby, B. J.; Webb, J. R.; Snyder, K. U.; Herlihy, A. T.; Bulger, A. J.; Gilbert, E.

H.; Moore, D. (2002) Assessment of the effects of acidic deposition on aquatic resources

in the Southern Appalachian Mountains. Report prepared for the Southern Appalachian

Mountains Initiative (SAMI). Corvallis, OR: E&S Environmental Chemistry, Inc.

Sullivan, T. J.; Cosby, B. J.; Laurence, J. A.; Dennis, R. L.; Savig, K.; Webb, J. R.; Bulger, A. J.;

Scruggs, M.; Gordon, C.; Ray, J.; Lee, H.; Hogsett, W. E.; Wayne, H.; Miller, D.; Kern,

J. S. (2003) Assessment of air quality and related values in Shenandoah National Park.

August 2008 D­40 DRAFT—DO NOT QUOTE OR CITE

### 1

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3

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31

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33

34

35

36

37

38

39

40

41

42

43 Philadelphia, PA: U.S. Department of the Interior, National Park Service, Northeast

Region; technical report NPS/NERCHAL/NRTR-03/090. Available:

http://www.nps.gov/nero/science/FINAL/shen_air_quality/shen_airquality.html [19 June,

2006].

Sullivan, T. J.; Cosby, B. J.; Herlihy, A. T.; Webb, J. R.; Bulger, A. J.; Snyder, K. U.; Brewer, P.

F.; Gilbert, E. H.; Moore, D. L. (2004) Regional model projections of future effects of

sulfur and nitrogen deposition on streams in the southern Appalachian Mountains. Water

Resour. Res. 40(W02101): 10.1029/2003WR001998.

Sullivan, T. J.; Driscoll, C. T.; Cosby, B. J.; Fernandez, I. J.; Herlihy, A. T.; Zhai, J.; Stemberger,

R.; Snyder, K. U.; Sutherland, J. W.; Nierzwicki-Bauer, S. A.; Boylen, C. W.;

McDonnell, T. C.; Nowicki, N. A. (2006) Assessment of the extent to which intensivelystudied lakes are representative of the Adirondack Mountain region. Final report. Albany,

NY: New Y ork State Energy Research and Development Authority (NYSERDA); report

06-17. Available:

http://nysl.nysed.gov/uhtbin/cgisirsi/Qcwd6NzFby/NYSL/138650099/8/4298474 [1

November, 2007].

Sverdrup, H.; De Vries, W. (1994) Calculating critical loads for acidity with the simple mass

balance method. Water Air Soil Pollut. 72: 143-162.

Sverdrup, H.; Warfvinge, P. (1988) Weathering of primary silicate minerals in the natural soil

environment in relation to a chemical weathering model. Water Air Soil Pollut. 38: 387­

408.

Sverdrup, H.; Warfvinge, P. (1993) The effect of soil acidification on the growth of trees, grass

and herbs as expressed by the (Ca + 

### Mg + 

### K)/Al ratio. Lund, Sweden: Lund University,

Department of Chemical Engineering (Reports in ecology and environmental engineering

2).

Sverdrup, H.; De Vries, W.; Henriksen, A. (1990) Mapping critical loads: a guidance to the

criteria, calculations, data collection and mapping of critical loads. Copenhagen,

Denmark: Nordic Council of Ministers; environmental report 1990:14; NORD 1 990:98.

Sverdrup, H.; Warfvinge, P.; Rabenhorst, M.; Janicki, A.; Morgan, R.; Bowman, M. (1992)

Critical loads and steady-state chemistry for streams in the state of Maryland. Environ.

Pollut. 77: 195-203.

Takemoto, B. K.; Bytnerowicz, A.; Fenn, M. E. (2001) Current and future effects of ozone and

atmospheric nitrogen deposition on California's mixed conifer forests. For. Ecol. Manage.

144: 159-173.

Turner, R. S.; Cook, R. B.; Van Miegroet, H.; Johnson, D. W.; Elwood, J. W.; Bricker, O. P.;

Lindberg, S. E.; Hornberger, G. M. (1991) Watershed and lake processes affecting

surface water acid-base chemistry. In: Irving, P. M., ed. Acidic deposition: state of

science and technology, volume II, aquatic processes and effects. Washington, DC: The

U.S. National Acid Precipitation Assessment Program. (Acidic deposition: state of

science and technology report 10).

U.S. Environmental Protection Agency. (1995) Acid deposition standard feasibility study. Report

to Congress. Washington, DC: Office of Air and Radiation; report no. EPA 4 30-R-95­

001a.

August 2008 D­41 DRAFT—DO NOT QUOTE OR CITE

### 1

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25

26

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28

29

30

31

32

33

34

35

36

37

38

39

40

41

42 U.S. Environmental Protection Agency. (2006) Multi-agency critical loads workshop: sulfur &

nitrogen deposition effects on freshwater and terrestrial ecosystems. Final report.

Washington, DC: Clear Air Markets Division; contract no. EPA 6 8-W-03-02. Available:

http://nadp.sws.uiuc.edu/cladws/finalreport.pdf [4 December, 2007].

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

and criteria for modeling and mapping critical loads and levels and air pollution effects,

risks, and trends. Geneva, Switzerland: Convention on Long-Range Transboundary Air

Pollution. Available: http://www.icpmapping.org [16 August, 2006].

Van Tienhoven, A. M.; Olbrich, K. A.; Skoroszewski, R.; Taljaard, J.; Zunckel, M. (1995)

Application of the critical loads approach in South Africa. Water Air Soil Pollut. 85:

2577-2582.

Waddell, E.; Greenwood, R. (2006) Pacific Northwest nitrogen and sulfur deposition critical

loads workshop; September; North Cascades Environmental Learning Center. Workshop

summary report. Co-sponsored by: Northwest Clean Air Agency; National Park Service;

U.S. Department of Agriculture, Forest Service; U.S. Geological Survey. Available:

http://nadp.sws.uiuc.edu/clad/PNWCL2006WS.pdf [4 December, 2007].

Walse, C.; Gerg, B.; Sverdrup, H. (1998) Review a nd synthesis of experimental data of organic

matter decomposition with respect to the effect of temperature, moisture and acidity.

Environ. Rev. 6: 25-40.

Warfvinge, P.; Sverdrup, H. (1992) Calculating critical loads of acid deposition with PROFILE

­

- a steady-state soil chemistry model. Water Air Soil Pollut. 63: 119-143.

Warfvinge, P.; Falkengren-Grerup, U.; Sverdrup, H.; Andersen, B. (1993) Modelling long-term

cation supply in acidified forest stands. Environ. Pollut. 80: 209-221.

Watmough, S. A.; Dillon, P. J. (2003) Calcium losses from a forested catchment in south-central

Ontario, Canada. Environ. Sci. Technol. 37: 3085-3089.

Watmough, S. A.; Aherne, J.; Dillon, P. J. (2005) Effect of declining lake base cation

concentration on freshwater critical load calculation. Environ. Sci. Technol. 39: 3255­

3260.

Williams, M.; Tonnessen, K. (1999) Critical loads for inorganic nitrogen deposition in the

Colorado Front Range, USA. Ecol. Appl. 10: 1648-1665.

Wilmot, T. R.; Ellsworth, D. S.; Tyree, M. T. (1995) Relationships among crown condition,

growth, and stand nutrition in seven northern Vermont sugarbushes. Can. J. For. Res.

386-397.

Wolfe, A. P.; Baron, J. S.; Cornett, R. J. (2001) Anthropogenic nitrogen deposition induces rapid

ecological changes in alpine lakes of the Colorado Front Range (USA). J. Paleolimnol.

25: 1-7.

Wolfe, A. P.; Van Gorpe, A. C.; Baron, J. S. (2003) Recent ecological and biogeochemical

changes in alpine lakes of Rocky Mountain National Park (Colorado, USA): a response to

anthropogenic nitrogen deposition. Geobiology 1(2): 153-168.

Woodward, D. F.; Farag, A. M.; Mueller, M. E.; Little, E. E.; Vertucci, F. A. (1989) Sensitivity

of endemic Snake River cutthroat trout to acidity and elevated aluminum. Trans. Am.

Fish. Soc. 118: 630-641.

August 2008 D­42 DRAFT—DO NOT QUOTE OR CITE

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6 X ie, S.; Hao, J.; Zhou, Z.; Qi, L.; Yin, H. (1995) Assessment of critical loads in Liuzhou, China

using static and dynamic models. Water Air Soil Pollut. 85: 2401-2406.

Ye, X. M.; Hao, J. M.; Duan, L.; Zhou, Z. P. (2002) Acidification sensitivity and critical loads of

acid deposition for surface waters in China. Sci. Total Environ. 289: 189-203.

Zhao, D. W.; Seip, H. M. (1991) Assessing effects of acid deposition in southwestern China

using the MAGIC model. Water Air Soil Pollut. 60: 83-97.

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# Annex E. Effects of NO

Y 

# , NH

X 

# , and SO

X 

# on

Structures and Materials

E.1. Introduction

The purpose of this chapter is to summarize the research published since the most recent AQCDs

on materials and structures damage caused by NO Y , and NH X , SO X

. Materials and structures exposed to

the environment are subject to damage from exposure to sunlight, moisture, salt, windblown dust, and

cycles of temperature and humidity, whether or not air pollutants are present. However, NO Y , NH X , and

SO X may cause such damage to be greater or occur more rapidly than with natural environmental factors

alone. Damage to materials and structures may be physical, potentially affecting the durability or

maintenance needs of a material or structure, or may be purely aesthetic, affecting only the outward

appearance of the material or structure. In the case of historical buildings, monuments, or artifacts,

aesthetic damage may be a relevant concern.

Note that very extensive work related to materials damage from acidic deposition related to S a nd

N was conducted in the 1980s as part of the National Acid Precipitation Assessment Program (NAPAP,

1991) and so are not discussed here. In compiling information for this chapter on NO Y /NH X /SO X effects,

the information presented in the 1993 AQCD for Oxides of Nitrogen (EPA, 1993) and the 2004 PM

AQCD (EPA, 2004) was updated by literature searches reaching back to approximately 1992. This update

was based on peer-reviewed literature, with a focus on studies that were conducted in the U.S., that

evaluated effects at realistic ambient air pollutant levels, and that treated NO Y /NH X /SO X as components

of a complex mixture of air pollutants. These latter two factors result in an emphasis on studies done with

exposures to ambient atmospheric pollution, rather than exposures at high levels, e.g., in test chambers.

The studies cited in this chapter were selected from those found in a broad literature search based on

criteria that they (1) address damage caused by exposure to atmospheric contaminants, (2) focus on S a nd

N containing species, (3) provide a clear link between pollutant concentrations and damage, and (4) give

complete information on methods and data analysis used.

Broadly speaking, the pace of research on NO Y , NH X , and SO X materials effects has slowed

considerably since the publication of the previous AQCDs. In particular, although the literature searches

conducted for this update emphasized studies conducted in the U.S., the great majority of the relevant

publications found originated in Europe or Asia. The relative scarcity of recent U.S. studies on structural

and materials damage from NO Y /NH X /SO X may be a natural fall-off in research in this area, following the

extensive efforts that were summarized in the previous AQCDs and in the NAPAP report. Certainly the

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26 greater number and age of aesthetically valuable buildings and archeological sites in Europe and Asia,

relative to the U.S., may be a driving force for current research in those geographic areas. In this chapter,

each discussion of the effects of NO Y /NH X /SO X on a material type begins with a brief summary of the

state of knowledge as represented in the previous AQCDs, and then continues with a description of recent

research on that type of material.



# E.2. Environmental Exposures of Materials

E.2.1. Mechanisms of Materials Damage

As noted in the introduction to this chapter, materials damage may occur by natural physical

processes without the involvement of NO Y /NH X /SO X air pollutants. When those pollutants are involved,

the destructive processes may be chemical, physical, or even biological. Chemical processes include

direct reactions with gaseous pollutants such as NO 2 , SO 2 , or nitric acid (HNO 3 ), reaction with

electrolytes (proton (H + ), ammonium (NH +

4 ), nitrate (NO

− ), SO 2−

3 4 , etc.) in water on material surfaces,

and reactions with chemicals in deposited particulate matter. An example of a physical process is the

deterioration of stone that occurs when gypsum (CaSO 4 · 2H 2 O) forms from reaction of SO 2 with the

calcium carbonate (CaCO 3 ) in the stone. The gypsum thus formed occupies a larger volume than the

original stone, causing the surface to deteriorate. Biological degradation can occur when deposited

pollutants are oxidized to acids by fungi or bacteria.

A key factor affecting damage to certain materials, primarily metals and stone, is the frequency and

duration of wetting of the surface. Liquid water on materials surfaces can dissolve deposited pollutants,

producing reactive electrolyte solutions, and can serve as a reaction medium in which S and N oxides are

converted to more damaging acids. Pollutants deposited on surfaces may contain or form hygroscopic

salts, which enhance the formation of liquid water and thereby increase materials damage. As Dubowski

et al. (2004) (2004) have shown, the deposition of HNO 3 onto surfaces can increase the extent of wetting

of surfaces, and promote the damaging effects of both HNO 3 and other pollutants.



# E.2.2. Deposition Processes

Air pollutants come into contact with surfaces through both dry and wet depositional processes.

Dry deposition occurs in the absence of precipitation and is governed by factors such as atmospheric

turbulence, the chemical and physical properties of the pollutant (e.g., water solubility and reactivity for

gases; size, density, and shape for particles), and surface properties (e.g., reactivity, roughness, moisture

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32 level, and pH). The deposition rate of a pollutant is proportional to the atmospheric concentration of that

pollutant. Dry deposition of gases depends primarily on the water solubility of the gas, the moisture level

on the surface, and the pH of the electrolyte formed on the surface of a material. Nitric acid and NH 3 are

deposited very efficiently to most surfaces regardless of the surface properties of the material. Particle

size plays an important role in determining the rate of deposition of particles to a surface. For very small

particles, Brownian diffusion is the dominant deposition mechanism. For larger particles, inertial

impaction and gravitational settling are important deposition processes. Particles between 0.05 and 2 μm,

which include most atmospheric particles containing NO

−

, 2−

3 SO 4 , and NH +

4 , may have long atmospheric

lifetimes in the absence of moisture.

Wet deposition occurs when gas or particle species come into contact with moisture (as rain, fog,

snow, or ice). Atmospheric species can be dissolved into moisture and then deposited as the moisture falls

to the ground. Solubility and the chemical reactions of the dissolved species determine the degree of wet

deposition. For acid gases, high dissolution is observed due to the dissociation of the dissolved species in

water. Wet deposition of pollutants occurs at a faster rate than dry deposition, but is only an important

mechanism when moisture is present.



# E.2.3. Chemical Interactions of N and S Oxide Species

N and S o xide species are subject to many atmospheric reactions in both the gaseous and

particulate phase. Emissions of S and N oxides are primarily in the form of gas phase SO 2 and NO X

. In

the atmosphere, these species can be oxidized by reaction with other atmospheric species to gas and

particle phase product species. On the surface of materials, the oxides are generally oxidized to their acid

forms (nitrous acid (HNO 2 ), HNO 3 , sulfurous acid (H 2 SO 3 ), and sulfuric acid (H 2 SO 4 )), which then

dissociate to form nitrite, nitrate, sulfite, and sulfate ions. These acids are the primary species responsible

for damage to materials by S a nd N pollutants. NH 3 , the primary gaseous basic compound in the

atmosphere, can partly or completely neutralize these acids in particulate matter or in the aqueous phase,

forming NH +

4 ions.

On the surfaces of materials, N and S species can react to form a variety of degradation products.

On metals and stone, the possible degradation products include nitrite, nitrate, sulfite, and sulfate species

as well as minerals that incorporate nitrate or sulfate into a more complex composition. These degradation

products may be more or less reactive to further degradation than the original material. Degradation

products that are more reactive, or those that are soluble in water, do not have long lifetimes on a material

surface. They undergo further chemical reactions and are transformed to other species, or they are washed

off the surface by precipitation. Products which are less reactive and less soluble in water than the original

material may form a protective layer on the surface of the material which inhibits or prevents further

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32 damage from atmospheric pollutants. Products that are more reactive or water-soluble than the original

material are readily removed, exposing the surface to more damage. The protectiveness of the products

formed depends on the complex mixture of species present and the physical/chemical properties of the

material.

Synergistic effects, which influence the rate of degradation of materials, are possible in

atmospheres containing a complex mixture of pollutants. NO X may enhance the oxidation of sulfite to

sulfate and lead to faster rates of corrosion. The deposition velocity of SO 2 and NO X may be influenced

by the presence of HNO 3 deposited to the surface due to the increased degree of surface wetting.



# E.2.4. Materials Damage Experimental Techniques

The NO Y /NH X /SO X air pollutants are comprised of numerous distinct chemical species, which may

exist in the gaseous and/or particulate phases in the atmosphere, as well as in dissolved form in

atmospheric precipitation and in condensed water on surfaces. In order to test the damaging effects of

NO Y /NH X /SO X species on man-made materials, it is often necessary to simplify the system by testing

under controlled laboratory conditions, typically with a very limited set of pollutants in a test chamber.

Such tests generally use pollutant concentrations that are greatly elevated relative to ambient atmospheric

levels, and may also use exaggerated temperature, humidity, or wetting, to accelerate the development of

materials damage so that it can be detected. Chamber tests may not accurately mimic the mass transfer of

pollutants in the atmosphere, and efforts in such tests to isolate the effects of one pollutant from the

complex mixture present in the atmosphere are unrealistic. As a result, chamber tests may provide

valuable information on potential effects and mechanisms involving ambient air pollutants, but cannot

accurately predict the corrosion rates or effects of such pollutants in real situations.

Exposing materials of interest to the ambient atmosphere for extended time periods can provide a

realistic look at the effects of air pollutants on materials. However, such ambient exposure tests are

limited by the occurrence of natural (i.e., non-air pollutant) materials damage, and by the complexity of

the NO Y /NH X /SO X system. While it is relatively easy to determine which materials suffer more or less

damage during equivalent exposures to ambient air pollution, it is extremely difficult to determine which

air pollutants are responsible for the observed damage. This is due to the co-occurrence of all air

pollutants simultaneously, complexities in accurately measuring the suite of NO Y /NH X /SO X species, and

interconversions among species (e.g. SO 2−

X and SO 4 , NO X and HNO 3 ) related to contact with materials or

with moisture. The amount of time that surfaces are wet is a key factor in the extent of materials damage,

and this factor may be difficult to determine in ambient exposures, because the presence of air pollutants

themselves may enhance surface wetness on the microscale beyond that expected based on

meteorological conditions (Dubowsky, 2004). Ambient exposure tests lead to retrospective analyses, in

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23 which meteorological and air pollutant data, surface analyses, and measurements of chemical and

physical properties are evaluated statistically to estimate the impacts of air pollutants on the exposed

materials.



# E.3. Effects on Dyes and Textiles

E.3.1. Fading of Dyes

The fading of dyes by N oxides has long been recognized, and dye manufacturers have worked to

produce products less susceptible to this effect, through both improved dye chemicals and the use of

inhibitors in dye formulations to minimize fading. Fading has been observed with both red and blue dyes,

both on natural fibers (e.g., cotton, silk, wool) and on synthetics (e.g., nylon, rayon, polyester). The fading

effect of NO 2 is generally reported to be greater than that of NO on various dyes with various fabrics. In

exposures of dyed fabrics to ambient air, test samples must be shielded from sunlight, to avoid the

substantial fading of dyes that results from sunlight exposure. Under such conditions, NO 2 and ozone (O 3 )

are often found to be about equally important in the fading of dyed fabrics.

and DeVries, 1993).



# E.3.2. Degradation of Textile Fibers

N oxides can degrade a variety of synthetic fibers, with the greatest effects seen with nylon. With

NO 2 , the damage to nylon occurs due to breaking of the polymer chain (i.e., chain-scissioning). Similar

weakening of nylon has been observed in tests with elevated concentrations of HNO 3

. A synergistic effect

was observed between mechanical stress and NO X in the degradation of oriented nylon-6 fibers (Smith

and DeVries, 1993).



# E.4. Effects on Plastics and Elastomers

The group of materials called plastics includes a wide variety of polymeric materials such as

polyethylene, polypropylene, polystyrene, polyurethanes, acrylic polymers, phenolics, and fluorocarbon

polymers, among others. Plastic materials may include other components such as hardeners or

plasticizers, and fillers that may impart properties such as physical strength. Elastomers are polymers that

can stretch to at least their twice their normal dimensions and then return to their original dimensions

when the stress is removed. Examples of elastomers include various rubber formulations and neoprene.

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29 Plastics and elastomers can be damaged by NO 2 , SO 2 , and O 3 , as well as by UV radiation in sunlight, and

some studies have been designed to separate the effects of these factors.

Chamber studies at relatively high pollutant concentrations with sunlight or UV light have

generally shown greater damage than from the pollutants alone. NO 2 is damaging to a variety of polymers

and elastomers, causing either chain-scissioning or cross-linking (formation of additional bonds between

polymer chains) depending on the polymer. Polypropylene is reported to be damaged more severely by

SO 2 than by NO 2

. Elastomers are damaged more severely than plastics. In tests where light and NO 2 have

been present simultaneously, much of the damage observed in chamber tests has been attributed to O 3 ,

produced by the interactions of the pollutants and UV light, rather than to NO 2 alone. In studies in which

the same pollutant concentrations are present both with and without light, the greater damage observed in

samples exposed to the light is often attributed to the light itself, when in fact chemical processes initiated

by light (such as the formation of O 3 ) undoubtedly also play a part.

Cellulose nitrate can break down through hydrolytic, thermal, and photochemical reactions.

Addition of plasticizer to cellulose nitrate slows the degradation substantially. NO 2 is of particular interest

with regard to cellulose nitrate because it is not only capable of causing damage, but is also produced as a

result of damage to the material (Shashoua, 2006). NO 2 is formed when N-O bonds connecting cellulose

rings are broken. The NO 2 formed will then further degrade cellulose nitrate, thus the degradation is an

autocatalytic process.



# E.5. Effects on Metals

Metals are considered to be the materials most subject to damage from the NO Y /NH X /SO X air

pollutants, and have been the subject of a great deal of research. The nature and concentration of the

pollutant, its rate of deposition, and especially the duration of wetting of the surface are key factors in the

corrosion of metals. Numerous studies have indicated corrosion rates of metal surfaces on the order of 1

to several micrometers per year (μm/yr) under real or simulated atmospheric conditions.

Table E-1 summarizes the materials tested, exposure conditions, and findings of recent studies

related to the effects of NO Y /NH X /SO X air pollutants on metals. The studies listed in Table E-1 are

discussed where applicable in the following sections.



# E.5.1. Role of NO

Y 

# , NH

X 

# , and SOX in the Corrosion Process

In the atmosphere the NO Y /NH X /SO X pollutants occur together, along with other pollutants such as

O 3 or chloride salts. While wetting of metals surfaces is the single greatest factor promoting corrosion, an

important observation is the enhanced damage that occurs due to interactions among this mixture of

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32 pollutants. It must be noted that in many studies the various NO Y /NH X /SO X species have not been

adequately separated or quantified, and this may be the cause of conflicting observations from some

studies. However, some generalizations can be made. Sulfur and chloride pollutants are generally more

important at causing metals corrosion than N pollutants, however NO X (or NO Y ) and SO 2 together have

been shown to be more damaging than SO 2 alone. The combination of NO 2 and SO 2 has been shown to

result in a synergistic effect where the total damage from the mixture is greater than the additive damage

from the two pollutants separately (Svensson and Johansson, 1993a). This effect may be due to enhanced

wetting of the surfaces caused by NO Y pollutants, resulting in corrosion at lower relative humidities than

would otherwise be the case. This enhancement has been attributed to the formation of hygroscopic

nitrate salts, but may also be caused directly by the deposition of gaseous HNO 3 onto the surface

(Dubowsky, 2004). The corrosion effect of HNO 3 on zinc, copper, and steel is larger than that of SO 2

alone or a mixture of SO 2 and NO 2 (Samie et al., 2007).

Although deposition of NO Y /NH X /SO X species in particulate matter can soil metal surfaces, such

deposition does not directly result in substantial metals damage. However, under wet conditions these

soluble species form an electrolytic solution that can cause corrosion. Corrosion of steel and zinc has been

found to depend on the surface electrolyte irrespective of the presence of particles (Askey et al., 1993).

Temperature has been found to have a complex effect on metals corrosion. Lower temperatures

tend to increase surface wetness, but decrease the diffusivity of gaseous pollutants, and may reduce the

rates of some reactions that convert SO 2 and NO X to sulfuric and nitric acids. Thus, the effect of

temperature changes on long-term corrosion rates can be hard to predict.



# E.5.2. Effect on Economically Important Metals

Steel is the most common and economically important structural metal, and is often used in

galvanized form (i.e., with a protective coating of zinc). SO 2 is generally reported to be more corrosive

than NO X , however for well-protected steel the effects of ambient air pollutants are usually a small

increment on top of the natural weathering process. The N pollutants can have an enhancing effect on the

corrosion caused by the S p ollutants. This is attributed to the increased wetting that can result from the

presence of hygroscopic NO 3 salts. Relative humidity has been shown to be very important in the

corrosion of steels by SO 2 with much slower corrosion rates observed when the relative humidity is below

70% (Dehri et al., 1994). The presence of SO 2 has been shown to reduce the corrosion pitting of iron

induced by sodium chloride (NaCl) but there may be an overall synergistic effect among SO 2 , NO 2 , and

NaCl (Weissenrieder et al., 2004). Steel corrosion rates have been shown to decrease over time (Almeida,

2000) (Damian and Fako, 2000) approaching steady state rates after approximately 4000 days (Damian

and Fako, 2000).

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35 Zinc corrosion has been shown to be inversely dependent on temperature (Svensson and Johansson,

1996). Corrosion products formed on zinc in polluted environments are less water soluble, and therefore

more protective against further corrosion, than corrosion products formed in clean environments (Vilche

et al., 1995). The combination of SO 2 and NO 2 showed synergistic (i.e., greater than simply additive)

corrosive effects on zinc (Svensson and Johansson, 1993a). SO 2 slowed the NaCl

− induced corrosion of

zinc while NO 2 accelerated that corrosion (Svensson and Johansson, 1993b).

Aluminum is naturally protected from corrosion by a formation of a durable surface film, but some

effects of the NO Y /NH X /SO X pollutants have been observed. Minimal damage is caused to Al by NO X

.

The mixture of SO 2 and NO X is variously said to be either more or less corrosive to Al than SO 2 alone.

The deposition rate of SO 2 to Al was shown to increase in the presence of O 3 , but no effect on SO 2

deposition rate was found for NO 2 (Blucher et al., 2005). Interaction between SO 2 and NaCl results in an

increased corrosion rate but decreased pitting of Al compared to NaCl alone (Blucher et al., 2005). Oesch

and Faller (1997) found that SO 2 is more corrosive to Al than NO 2 and that there is no difference in Al

corrosion rate when exposed to NO or clean air.

Mixtures of NO X and SO 2 are more corrosive to copper than either pollutant alone. When hydrogen

sulfide (H 2 S) and O 3 were also evaluated for damage to copper, they also were found to be more

damaging to copper than NO X . The corrosion rate of copper exposed to SO 2 or NO 2 has been shown to

slow over time (Oesch, 1997) (Oesch and Faller, 1997; Leuenberger-Minger et al., 2002). The corrosion

rate of copper in the presence of NO 2 is greater than in clean air. In the first 24 h of exposure to NO 2 , an

acidic electrolyte is formed on the surface that dissolves copper oxides and results in an increased

corrosion rate (Dante and Kelly, 1993). Synergistic effects have been seen between SO 2 and O 3 (strong)

and NO 2 (weak) (Aastrup et al., 2000). High corrosion rates were observed for field exposures of copper

at sites with a combination of SO 2 and O 3 . After four years of exposure, 90% of the corrosion products

formed on copper remained on the surface (Leuenberger-Minger et al., 2002). The copper hydroxy

sulfates brochantite and antlerite are stable copper corrosion products formed in the presence of SO 2 , O 3 ,

and NO 2 (Strandberg, 1998).

Nickel is also damaged more severely by SO 2 or chloride salts than by NO X . Nickel samples

deployed at urban, industrial, and rural sites showed that corrosion rates increase with SO 2

concentrations. Soluble hydrated nickel sulfates were the main corrosion products and are easily removed

from the surface by rainfall events, thereby exposing the underlying surface (Jouen et al., 2004).

Kim et al. (2004) conducted a study of the effects of ambient SO 2 and NO 2 on steel, bronze,

copper, and marble at sites in China, Korea, and Japan. Both sheltered and unsheltered samples were

exposed with the corrosion rates of the unsheltered samples higher in all cases. The corrosion rate of steel

was the highest, followed by marble, bronze, and copper. Higher corrosion rates (especially for

unsheltered samples) were found to be correlated with high SO 2 concentrations.

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# E.5.3. Effects on Electronics

The increasingly wide penetration of electronic devices into daily life offers greater opportunities

for environmental damage to sensitive components. The hardware of communication systems may be

exposed to pollutants in outdoor air, and the ubiquitous cell phones may be exposed in both indoor and

outdoor environments. Sulfur and N oxides have been shown to corrode the metallic contacts in electronic

equipment, which are often made of copper or brass coated with a precious metal such as gold, palladium,

or nickel. Such materials are corroded more by NO 2 than by SO 2 , but a mixture of these two pollutants is

more corrosive than either alone. The combination of SO 2 and H 2 S i s also less damaging than either NO 2

alone or a combination of NO 2 and these pollutants. NO 2 is also moderately corrosive to solder in

electronic components.



# E.6. Effects on Paints

Painted surfaces are extremely common as a means of preventing damage to other materials, and

may be categorized as architectural coatings (e.g., house paint), product coatings (e.g., automobile

finishes), and special-purpose coatings (e.g., bridge paint). Environmental damage to painted surfaces is

expected, and periodic repainting is normal, but any factor that causes more rapid degradation or

discoloration of paints will require more frequent repainting and thus result in higher costs. Paint

formulations may differ widely for different applications, so the extent of air pollution damage in a given

application cannot necessarily be predicted from published information. Previous work at elevated

pollutant concentrations has shown that oil-based house paint is readily damaged by SO 2 and moisture,

and is more subject to damage by SO 2 than by NO 2

. Sample weights increased with increasing NO 2 , but it

is not clear if this indicates direct reaction of NO 2 or an enhancement of the effects of SO 2 and moisture

by NO 2

. The effect of SO 2 may be due to reaction with CaCO 3 and zinc oxide (ZnO) present in the paint.

Tests with various paints showed that NO X becomes incorporated into the paint surface upon long

exposure, apparently by reaction with polymers that make up the cured paint. In other tests HNO 3 was

found to produce substantially more damage to paints containing both low and high levels of carbonate

(CO 3 −) than did an equal mixing ratio of NO 2

.

Grosjean et al. (1983) studied the fading of colorants on cellulose paper. Twelve colorants were

exposed to various atmospheres including purified air, NO 2 , SO 2 , and a mixture of oxidants (O 3 , NO 2 , and

peroxyacetyl nitrate (PAN)). Not all colorants were tested in each atmosphere. The mixture of oxidants

resulted in the largest color change for each tested colorant. Only one colorant was exposed to SO 2 , NO 2 ,

and the mixture. For that colorant, the color change induced by the mixture of oxidants was

approximately three times the color change with NO 2 alone; the color change with SO 2 was

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28 approximately 70% of the color change with NO 2

. An increase in relative humidity results in increase

fading of colorants (Grosjean et al., 1993, 1994). Of 35 colorants exposed to a mixture of O 3 , NO 2 , a

PAN, nine exhibited substantial color changes and three exhibited moderate color changes (Grosjean

et al., 1993).

Paint samples were exposed to UV light, NO X , SO 2 , and a combination of the treatments by

Colombini et al. (2002). The exposure conditions were chosen to produce accelerated aging of paint

samples. Non-pigmented paints were chosen to isolate degradation of the paint binder from synergisti

effects with pigments. Exposure to the combination of treatments resulted in increased cross-linking i

the paint binder as well as formation of organic acids. An examination of paint samples taken from

naturally aged paintings confirmed the presence of organic acids as degradation products. d

nd

c

n



# E.7. Effects on Stone and Concrete

The effects of NO Y /NH X /SO X air pollutants on stone and concrete are undoubtedly the most widely

studied because of their impact on historic buildings, monuments, and archeological treasures. Table E-2

summarizes studies of the effects of NO Y /NH X /SO X on stone, concrete, and mortars. Calcareous stone

(i.e., that consisting of CaCO 3 , such as marble, limestone, and cement) is most susceptible to damage.

Mortar used in stone construction is often more porous than calcareous stone, and therefore more subject

to damage. The damage to such materials is attributed primarily to the effect of SO 2 in forming gypsum

(CaSO

@ 4 2H 2 O):

The gypsum thus formed occupies a larger volume than the original carbonate, so the stone surface

becomes pitted and damaged. Gypsum is also more soluble than the carbonate, so it can be removed by

precipitation, exposing the surface to further reaction and damage. As a result, dry deposition of SO 2 to

the stone surface between rain events is important, as it causes continued damage. The reaction of SO 2

with calcareous stone is more energetically favorable than the reaction of N oxides, and thus SO 2 is the

primary cause of damage to stone, however the combination of SO 2 and NO X is more damaging than SO 2

alone. This effect may be due to enhanced wetting of the stone, to oxidation of SO 2 by the N oxides, or to

formation of calcium nitrate (Ca(NO 3 ) 2 ), which is much more soluble than CaCO 3 and is easily washed

off the stone surface by precipitation. Removal of the nitrate salts in this way may result in

underestimation of the role of N oxides in stone damage when surface layers are analyzed for chemical

composition.

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35 Concrete is more susceptible to damage from N oxides than are the calcareous stones, because

concrete contains calcium hydroxide (Ca(OH) 2 ), which can react to form calcium nitrate (Ca(NO 3 ) 2 ). This

product is soluble and can be washed out of the concrete, weakening the material.

Deposition of particulate matter onto stone primarily results in soiling of the stone, due to the

elemental carbon and organic compound content of the deposited particles. The orientation of the surfaces

and size of the particles affect deposition: vertical surfaces are more affected by deposition of fine

particles, whereas horizontal surfaces are more affected by large particles. The fine particles carry the

bulk of the carbon and organic material. Metal oxides present in deposited particles may enhance the

reaction of SO 2 to form gypsum.

Gypsum crusts form more readily in rain-sheltered environments than on rain-washed stone

surfaces (Zappia et al., 1998). The presence of fog water has been shown to increase the rate of gypsum

formation on surfaces sheltered from rain washing (Del Monte and Rossi, 1997). Gypsum crust formation

has been shown to proceed at a faster rate when the stone surface is sprinkled with fly-ash particles. In

addition, fly-ash (or other carbonaceous particles) can become entrained in the gypsum matrix and affixed

to the stone surface. Normally, gypsum crusts are gray in color, but when carbon containing particles are

entrained, they become black (Ausset et al., 1999). While gypsum crusts are composed primarily of

sulfates, they have been found to contain nitrate compounds as well (Marinoni et al., 2003; MartinezArkarazo et al., 2007). The inclusion of nitrates in gypsum crusts suggests that N oxides, as well as SO 2 ,

play a role in the degradation of stone exposed to the atmosphere. The presence of particulate matter, in

addition to SO 2 , has been shown to increase gypsum formation by 20% (Boke et al., 1999). Ambient SO 2

concentrations alone are not adequate to predict the degree of damage to stone samples (Torfs and van

Grieken, 1996).

Dolomite has been shown to be less sensitive to sulfation than calcite (Lan, 2005). Corrosion of

marble due to S species has been found to be of the same order of magnitude as that caused by N species.

Damage is caused not by the gas phase oxides (SO 2 , NO 2 , NO) but by acid (H 2 SO 4 , HNO 3 ) and salt

(SO 4 2− , NO 3 − ) species present in the electrolyte which forms on the marble surface (Sikiotis and Kirkitsos,

1995). The rate of marble surface recession by rain washing is faster than the rate of gypsum crust

formation due to dry deposition of S and N containing pollutants. Marble is damaged by rain washing

through two mechanisms, dissolution of the gypsum crust and dissolution of the underlying marble. The

gypsum crust is more soluble in water than marble and is rapidly dissolved in rain. The naturally

occurring acidity in rainwater from the dissolution of carbon dioxide (CO 2 ) is an important mechanism by

which stone samples are degraded, but additional acidity from the dissolution of SO 2 and NO 2 in

rainwater does not greatly increase the solubility of marble in rainwater (Yerrapragada, 1996).

While gypsum is the primary degradation product found on stone, mortar, and concrete samples,

other damage products do occur. Sulfite species have been found on mortars as intermediate damage

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31 products. On mortars, a secondary damage mechanism exists in which gypsum reacts with calcium Al

hydrates present in the mortar to produce ettringite (Ca 6 Al

@ 2 (SO) 4 (OH) 12 26H 2 O). Ettringite is an

insoluble sulfate that may cause damage by expansion and lead to cracking of mortars (Sabbioni,

2002)(Sabbioni et al., 2001, 2002).



# E.8. Effects of NO

X 

# on Paper and Archival Materials

The cellulose fibers that make up paper are reactive with NO 2 and other NO Y species, and storage

condition standards have been set regarding acceptable levels of NO X for archives, libraries, and

museums. Exposure of archival materials to NO Y species in such facilities can arise from normal outdoor

or indoor sources, but also from generation of such NO Y species from the materials themselves.

Specifically, stored materials that include cellulose nitrate, e.g., in the form of photographic film,

adhesives, or recording media, can slowly decompose to release NO X and product species such as HNO 3

.

These emissions can degrade archival materials, and even be a safety hazard if allowed to accumulate. In

terms of outdoor air pollutants, it is likely that HNO 3 is a key reactant in the degradation of paper

archives. The rapid deposition velocity of HNO 3 and the numerous surfaces in archival facilities provide

opportunity for attack by HNO 3 , and probably result in the effects of HNO 3 being underestimated,

relative to those of NO X , based on indoor air measurements. Artists’ pigments can also be damaged by

extended exposure to ambient atmospheric NO 2

.

The effects of SO 2 and O 3 on paper were studied by Johansson and Lennholm (2000). The

deposition rate of SO 2 to fresh paper was found to decrease rapidly with time and approached steady state

after ten hours. The deposition rate of SO 2 to fresh paper in the presence of O 3 was found to be elevated

compared to SO 2 alone. The deposition rates to aged paper were much lower and there was no effect on

the SO 2 deposition rate observed in the presence of O 3

. The decrease in deposition rate with time is

thought to be due to protonation of all available carboxylate ions to carboxylic acid.

Pigments in works of art can be degraded or discolored by atmospheric pollutants. H 2 S h as been

shown to react with both copper and lead pigments, but only lead white has been seen to darken over time

(Smith and Clark, 2002). A synergistic effect has been detected between NO 2 and both benzene and

toluene resulting in an increased rate of attack on pigment oxides (Agelakopoulou et al., 2007).

Deposition of S to the surface of paintings, either as SO 2 or ammonium sulfate ((NH 4 ) 2 SO 4 ) particles, can

damage, varnish, or cause discoloring of paint (Gysels et al., 2004). Paint models subjected to accelerated

aging in SO 2 (10 ppm) and NO X (10 ppm) as well as UV radiation for 15 days exhibited a variety of

damage markers. Both nitrate and sulfate damage mechanisms were observed with sulfation sometimes

masking other processes (Arbizzani et al., 2004).

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# E.9. Costs of Materials Damage from NO

Y 

# , NH

X 

# , and SOX

Materials exposed to the ambient atmosphere are degraded and damaged through a number of

mechanisms. Damage associated with air pollutants result in effects such as decreased usable lifetime,

increased maintenance frequency, and loss of aesthetic appeal. It is difficult to separate the costs

associated with air pollutants from costs associated with other damage mechanisms. Some estimates of

cost have been based on empirically derived dose-response functions for specific materials. Other

estimates have been developed using inspection of actual materials damage and maintenance guidelines

for the materials (Cowell and Apsimon, 1996). Estimation of costs over large geographic areas is subject

to considerable uncertainty due to unknown distribution of materials at risk and spatial variations in

pollutant concentrations. A cost estimate for material cost savings from SO 2 emission reductions in

Europe was performed by Cowell and Apsimon (1996). In this study, the cost savings of theoretical future

emission reductions across Europe was modeled using cost data extrapolated from a study conducted in

three Norwegian cities. Total theoretical SO 2 reductions of 15,904 kilotons per year resulted in modeled

annual cost savings of $9,504 million total for Europe (Cowell and Apsimon, 1996; Apsimon and Cowell,

1996).



# E.10. Summary

Many types of materials, including metals, electronics, plastics, paints, stone, and paper may be

damaged by atmospheric NO Y /NH X /SO X species. Damage occurs due to dry and/or wet deposition of the

pollutants onto the surface of a material and subsequent formation of an electrolytic solution in water

present on the surface. At low relative humidity, when little water is present on surfaces, damage rates

have been observed to be much lower, and in some cases, no damage has been observed. Both SO 2 and

NO 2 have been implicated in damage processes for different materials. In general, damage to materials by

SO 2 is greater than by NO 2

. Little work has been conducted to investigate the effects of NO on material

damage. What work has been conducted shows no damage, or very minor damage for NO containing

environments compared to clean air. Synergistic effects between SO 2 and NO 2 lead to increased damage

rates for the gases in combination. Other species such as O 3 , NaCl, organics, or particulate matter have

also been shown to have synergistic effects with SO 2 and NO 2

. The corrosive effects of nitric acid have

been found to be stronger than effects of other NO Y /NH X /SO X species. Costs associated with damage to

materials by atmospheric pollutants are difficult to estimate because of the many sources of uncertainty in

the estimation process. For heavily polluted environments, the cost savings due to decreased rates of

material degradation could offset a significant portion of the costs to reduce emissions. In general, for

polluted environments, reductions in SO 2 or HNO 3 concentrations will reduce damage rates more than

August 2008 E­13 DRAFT—DO NOT QUOTE OR CITE

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Table E­1. Studies on corrosive effects of NO

Y 

### /NH

3 

### /SO

X 

### effects on metals.

Materials Exposure Conditions Findings Reference

Strandberg

(1998)

Aastrup et al.

(2000) Zinc

Mild Steel

Copper

Zinc

Galvanized

Iron

Zinc

Aluminum

Zinc

Zinc

Copper

Zinc

Aluminum

Copper

Copper The corrosion rate of copper in the presence of NO 2 was much

greater than in clean air. The surface electrolyte was found to

contain predominantly nitrate with only trace levels of nitrite.

After 24 h, the electrolyte had become sufficiently acidic to

dissolve the copper oxide layer. Once the copper oxide was dissolved, corrosion proceeded at a significantly faster rate. Corrosion was found to depend on the surface electrolyte

irrespective of the presence of particles. Inert particles were

found to increase corrosion rates in relatively unpolluted

atmospheres. With higher pollution levels, species leached from

particulate matter contribute to the conductance of the surface

electrolyte and thus increase the corrosion rate.

SO 2 slowed the corrosion of zinc with moderate to high surface

concentrations of NaCl due to the formation of sodium zinc

hydroxychloride sulfate. NO 2 (which, alone, is unreactive toward

zinc) accelerated the corrosion of zinc in the presence of small

amounts of NaCl.

Copper sulfite and cuprous oxide formed on copper surfaces

exposed to SO 2

. With O 3 present, an increased rate of mass gain

was measured, and copper sulfite was converted to copper

sulfate. NO 2 increased the mass gain to a lesser extent than O 3 and resulted in the formation of copper nitrate in addition to

copper sulfate. Askey et al.

(1993)

Dante and Kelly

(1993)

Dehri et al.

(1994)

Svensson and

Johansson

(1996) Svensson and

Johansson

(1993b)

Vilche et al.

(1995)

Oesch and

Faller (1997) Samples were exposed to fly­ash in clean air and in

air with SO

2 and/or HCl (presentation rates of 27 ×

10

− 6 and 4.7 × 10

− 6 mg/cm 2 s, respectively). A

synthetic acid rain solution was used to model wet

deposition.

Copper samples were exposed to 264 ppb NO 2 in a laboratory setting. Exposures were limited to 72 h to

study the initial corrosion behavior.

Zinc samples were exposed to SO

2 (0.78 ppm)

and/or NO 2 (1.06 ppm) for 420 h. Some samples

were treated with NaCl prior to exposure.

Samples were exposed to SO 2 or NH 3 in the

laboratory. Air was supplied at 5 cm/s and the

pollutant gases at 3 cm/s. The concentrations of the

gases were very high to accelerate the tests.

Samples were exposed to ambient air for 4 yrs at 6

sites. SO 2 deposition rates ranged from

10 mg/m 2 day to non­detectable levels across the

sites. Time of wetness was also measured at each

site. Corrosion products that developed in rural environments were

found to be easily removed from the surface and thus result in

poor protectiveness. Corrosion products formed in more

aggressive environments were found to be more protective

against continuing corrosion.

Samples were exposed at temperatures of 4, 14, 22,

and 30 C with 95% relative humidity. SO 2 was supplied at 500 (± 5) ppb and 107 (± 2) ppb. SO 2 induced corrosion was found to be inversely dependent on

temperature. The maximum corrosion rate (at 107 ppb SO 2 ) of

11 mg/cm2d was observed at 4 C. The corrosion rate at 30 C

was 6.8 mg/cm2d.

Samples were exposed in the laboratory to SO 2 (1.5 ppb, 0.5 ppm, 10 ppm), NO 2 (10 ppm), NO (10

ppm), or O 3 (10 ppm). NO was found to have no effect on the corrosion of copper, zinc,

or aluminum. Copper in the presence of SO 2 (10 ppm) and NO 2 led to significant material loss initially with a slowing of the rate

with increasing time. O 3 was found to have the strongest

influence on the corrosion of copper. Only very slight mass gains

were observed for copper exposed to 0.5 ppm SO 2

. A small

effect on zinc was observed for NO 2 with SO 2 (10 ppm) resulting

in the largest weight gain. SO 2 at 0.5 ppm had a much larger

effect on zinc than on copper. For aluminum, O 3 had the largest

effect followed by SO 2 and NO 2

. Corrosion rates in SO 2 were found to be largely dependent on

relative humidity. No such humidity dependence was observed

for corrosion induced by NH 3

. Corrosion rates in both gases decreased sharply with time (approaching steady state values

after 30 h).

Powdered samples of copper patina compounds

(tenorite, cuprite, brochantite, antlerite, and

atacamite) were exposed to SO 2 (476 ppb) alone or

in combination with NO 2 (450 ppb) or O 3 (500 ppb).

Some samples were pretreated with carbon. Tenorite reacted rapidly with SO 2 to form brochantite and other

sulfate containing products. Cuprite reacted slowly with SO 2 alone but addition of O 3 formed antlerite and brochantite. NO 2 did not produce the same effect. For samples with carbon on the

surface, the oxidation reaction was greatly enhanced.

Brochantite and antlerite were found to be stable in atmospheres

with SO 2 in combination with O 3 or NO 2

.

Samples were exposed to atmospheres containing

approximately 200 ppb of SO 2 , SO 2 and O 3 , or SO 2 and NO 2

. O 3 and NO 2 were introduced at different

times in the exposure scenarios. reductions in NO 2 , NO, or NH 3

. In areas with low SO 2 concentrations, reductions in NO 2 , O 3 , or

particulate matter concentrations may reduce damage rates.

August 2008 E­14 DRAFT—DO NOT QUOTE OR CITEMaterials Exposure Conditions Findings Reference

Mild Steel

Steel

Copper

Zinc

Nickel

Iron

Aluminum

Copper

Zinc

Steel S teel samples were exposed to the atmosphere in

47 marine atmospheres with varying levels of

chloride and SO 2

. Atmospheres were separated for

data analysis based on chloride and SO 2 deposition

rates.

Two types of steel were exposed to urban­industrial

and rural atmospheres for 20 yrs. Avg SO 2

3 concentrations were 90 and <10 � g/m (34 and

<4 ppb) for urban and rural environments,

respectively.

Samples were exposed in the field at 8 sites. SO 2 , NO 2 , and O 3 concentrations were monitored over a

4–yr exposure duration at each of the sites.

Nickel samples were exposed in the field at 3 sites

(urban, industrial, and rural) for 1 yr. Concentrations

of NO (41.1, 9.7, and 2.9 � g/m 3 ) (33, 8, and 2 ppb),

NO 2 3 ) (50.1, 24.2, and 8.7 � g/m (26, 13, and

5 ppb), SO 2 3 ) (22.3, 29.0, and 12.2 � g/m (8, 11,

and 5 ppb), and O 3 3 ) (25.8, 47.1, and 60.1 � g/m

(13, 24, and 30 ppb) were measured at the urban,

industrial, and rural sites, respectively.

Samples were exposed to humidified air in the

laboratory. Samples were exposed to clean air, SO 2 (200 ppb), SO 2 and NO 2 (each 200 ppb), or SO 2 and

O 3 (each 200 ppb). The same exposure conditions

were used for iron samples with NaCl deposited on

the surface.

Aluminum samples were exposed to SO 2 (96 ppb)

either alone or in the presence of other pollutants

(NaCl, NO 2 , or O 3 ).

Samples were exposed to HNO (50–180 ppb) in a

3 laboratory exposure chamber. Tests were conducted

at 65% and 85% relative humidity. S amples exposed at sites with moderate SO 2 and chloride

deposition rates formed compact, rounded corrosion structures.

Samples at sites with high SO 2 and moderate chloride exhibited

cracking in the corrosion products. Samples at sites with high

chloride and moderate SO 2 exhibited the highest corrosion rates

of the mixed atmospheres. The 1 site with high chloride and high

SO 2 exhibited a lower corrosion rate than expected.

The corrosion rates of the two grades of steel were similar with

values of 0.1 and 0.08 mm/yr for the urban and rural

environments, respectively. The initial rate of corrosion was significantly faster and steady state values were approached

after 4000 days exposure. The similar corrosion rates measured

for the samples were thought to be due to similar time of

wetness at both sites.

The highest corrosion losses for copper were observed at the site

with the highest combination of SO 2 and O 3

. For zinc, the highest

corrosion losses were observed at the site with the highest SO 2

concentration. Both metals showed a decrease in corrosion rate

with time. Runoff rates from copper were much smaller than

from zinc. 90% of the corrosion products remained on the

copper surface after 4 yrs; only 40% of the zinc corrosion

products remained after 4 yrs.

Mass loss rates of 320, 570, and 200 � g/cm2y were determined

for urban, industrial, and rural environments, respectively. Mass

loss was found to increase with increasing SO 2 concentration.

Soluble corrosion products were formed on the surface and then

removed by rainfall events. Hydrated nickel sulfates were the

main corrosion products formed on the nickel surface.

No corrosion products were detected on samples exposed to

humidified air alone. The addition of SO 2 alone was not enough

to initiate a change in corrosion behavior of the samples. When

an oxidant (NO 2 or O 3 ) was added to the humidified air/SO 2 system, a significant increase in corrosion rate was observed.

SO 2 was found to inhibit the NaCl induced corrosion of iron, but

the combination of SO 2 and NO 2 was found to accelerate NaCl

induced corrosion.

SO 2 alone resulted in the loss of metallic luster. 50% of the

surface had developed corrosion products after 672 h. Samples

exposed to NaCl alone showed significantly larger mass gain than

samples exposed to only SO 2

. The combination of SO 2 and NaCl

resulted in the largest mass gain (fastest corrosion rate). While

the rate of mass gain was highest with a combination of SO 2 and

NaCl, the pitting observed in the presence of NaCl alone was significantly reduced. O 3 was found to slightly increase the

deposition rate of SO 2 , no effect on SO 2 deposition rate was

observed for NO 2

.

The corrosion effects of HNO on carbon steel were larger than

3

on zinc or copper. The corrosion effect of HNO was found to be

3 larger than corrosion from SO 2 alone or a mixture of SO 2 with O 3

or NO 2

. No increase in corrosion was observed at 85% relative

humidity compared to 65% relative humidity. A lmeida et al.

(2000)

Damian and

Fako (2000)

LeuenbergerMinger et al.

(2002)

Jouen et al.

(2004)

Weissenrieder

et al. (2004)

Blucher et al.

(2005)

Samie et al.

(2007)



### Table E­2. Studies on corrosive effects of NO /NH

3 

### /SO

X 

### on stone.

Y

Materials Exposure Conditions Comments Reference

August 2008 E­15 DRAFT—DO NOT QUOTE OR CITEAugust 2008 E­16 DRAFT—DO NOT QUOTE OR CITE Materials Exposure Conditions Comments Reference

Marble

Limestone

Marble

Marble

Brick

M arble

L imestone

Mortars

Jaumont

l i mestone

C a lcium

carbonate

Mortars

Mortars

Concrete

M a rble L aboratory exposure to HNO 3 ranging from 54 to

3 4174 � g/m (21 to 1603 ppb). Field exposures were

conducted in several stages in Greece. Avg

3 concentrations for field exposures were 1.41 � g/m

3 3 (0.5 ppb) HNO 3 , 2.39 � g/m (3 ppb) NH 3 , 4.84 � g/m

− 3 3 NO 3 , 14.61 � g/m SO 2–

, and 5.01 � g/m NH 4 +.

4

Samples of differing thickness were exposed in the

field and runoff water collected for 5 mos. The avg

3 SO 2 concentration was 60 � g/m (23 ppb).

Laboratory exposure to 10 ppm of SO 2 and NO 2

. Field

exposure to either dry deposition or dry and wet

deposition in Louisville, Kentucky. Avg concentrations

of 10 ppb SO 2 and 25 ppb NO 2 for field exposures.

Samples exposed in the field for 8 da ys with multiple

fog episodes. Over 3 m easurement campaigns, the

3 mean po llutant concentrations were 17.2 � g/m

3 (7 ppb) SO 2 , 265 � g/m (139 ppb as NO 2 ) NO X , and

3 131 � g/m suspended particles.

S amples were exposed to urban environments in both

sheltered and unsheltered configurations. Pollutant

concentrations were not reported.

3 Samples w ere exposed to 340 � g/m (125 ppb) SO 2

3 and 98 � g/m (50 ppb) NO 2 in the laboratory. Samples

were either exposed naked or s prinkled with fly­ash

particles. F ield exposure was conducted for 1 yr with

samples sheltered from r ainwater. Avg SO 2

concentration during the field exposure was 107

3 � g/m (40 ppb).

Powdered calcium c arbonate was exposed to 10 ppm

SO 2 and 90% relative humidity for 124 days.

A wide range of mortar and plaster samples were

collected from sites throughout Europe. Pollutant

concentrations over the life of the buildings were not

reported.

Plaster and mortar samples were collected from

buildings in the Old Venice Arsenal. Pollutant

concentrations over the life of the building were not

reported.

Samples were collected from the interior of a tunnel in

Italy. T he tunnel formerly held a railway and currently

houses a road with heavy automobile traffic. SO 2

3 levels h ave declined from 350 � g/m (132 ppb) in

3 1970 t o ~10 � g/m (4 ppb) in 2002. NO X

concentrations h ave remained relatively constant

3 around 100 � g/m (52 ppb as NO 2 ) over the same

time period.

S amples exposed to atmosphere and sheltered from

rain at 4 sites. SO 2 concentrations ranged from ~2 to

20 ppb across the sites. M arble was found to be a very good sink for HNO 3

. The extent

of corrosion by sulfates and nitrates were found to be of the

same order of magnitude. Corrosion was found to be caused by

acid and salt species (HNO 3 , H 2 SO 4 , etc.) on the surface rather

than the oxides (SO 2 , NO X ).

Damage functions w e re developed to t ry to determine the i onic

su lfate content in runoff water from t he a mbient SO 2

concentration. It was determined that the ambient S O 2

concentration alone does not determine the s ulfate

co ncentration in runoff water.

Gypsum crust thickness o f 1 .9 � m for rain sheltered samples

after 1 yr of exposure. SO 2 was f ound to be the dominant factor

in crust formation. Surface recession due to rain washing was

14.5 � m/yr and due to dissolution of the gypsum crust as well

as dissolution of the original marble.

For all samples, gypsum was the o nly stable mineral formed

following exposure t o fog water in a polluted environment.

Exposure t o fog water may be a significant cause of corrosion

for materials sheltered from r ainwater, but is of lesser

importance if a material is exposed to rain.

Sulfation was the pr imary damage mechanism and was more

intense on mortars than on stones due to higher porosity.

Higher concentrations of degradation products were found on

samples sheltered from rain than on samples exposed to rain.

Sulfation ( gypsum formation) was found to proceed with

greater intensity for samples sprinkled with fly­ash than for

n aked samples. The growth of gypsum crystals fixed the fly­ash

t o t he surface of the limestone. Fly­ash was found to be an

important factor in c rust formation by facilitating gypsum crystal f ormation. Fly­ash particles also darken gypsum crusts

from gr ay to black.

Reaction be tween calcium carbonate and SO 2 was found to take

place in a liquid film on the calcium carbonate surface. The

presence of several different types of airborne particles was

found to increase the extent of sulfation by 20%. The SO 2

concentration used is unrealistic for ambient conditions.

Sulfation was the pr imary damage mechanism observed. Sulfite

was found as an intermediate damage product in the sulfation

pr ocess. E ttringite was also found as a secondary damage

product du e to a reaction between gypsum and calcium

aluminum hydrates.

Gypsum was found to be the primary damage product on all of

the mortars sampled. A secondary damage m echanism was

found where gypsum reacts with calcium aluminum hydrates to

form ettringite, an insoluble sulfate. The presence of sulfur in

the damage products indicates SO 2 as t he most aggressive

atmospheric pollutant toward mortars.

The u rban mixture of pollutants (SO 2 , NO X , CO 2 , and particles)

results in f ormation of dendritic crusts on concrete. Nitrates

were f ound to be present in the largely gypsum crusts. Soot

particles w ere found embedded in the crusts as well. Low

quality starting materials provide a more porous media that is

m ore s usceptible to degradation by atmospheric pollutants. The

degradation of c oncrete is more similar to that of sandstone

than of limestone.

Sulfation was the pr imary damage mechanism observed. Marble

containing dolomite was less sensitive to SO 2 than calcite

m arble. For relative humidity greater than 72%, humidity was

an important factor in determining the sulfation rate. S ikiotis and

K irkitsos

(1995)

Torfs a nd van

G rieken (1996)

Yerrapragada

et a l. ( 1996)

Del M onte and

R ossi (1997)

Zappia et al.

(1998)

Ausset et al.

(1999)

B o ke et a l.

( 1999)

Sa bbioni et al.

(2001)

S a bbioni et al.

(2002)

Marinoni e t a l.

(2003)

Lan et al.

(2005)

Limestone

Sandstone Samples were collected from 3 facades of a historical

building in Spain. Samples were collected from the

su rface as w ell as 5 mm be low the surface to

determine degradation an d original compositions,

respectively. Pollutant concentrations o ver the life of

the building were not reported. Th e main decay products on the surface were f ound t o be

nitrate compounds. Samples with black crusts o n the surface

we re found to have pr edominantly gypsum and soot, but

ni trate compounds were i dentified in th e crusts as well.

Sa ndstone samples were m uch more damaged t han limestone

s amples due to their h igher porosity. Ma rtinezArkarazo

et al.(2007)

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37 

# ANNEX E

­ References

Aastrup, T.; Wadsak, M.; Leygraf, C.; Schreinerb, M. (2000) In situ studies of the initial

atmospheric corrosion of copper influence of humidity, sulfur dioxide, ozone, and

nitrogen dioxide. J. Electrochem. Soc. 147: 2543-2551.

Agelakopoulou, T.; Bassiotis, I.; Metax, E.; Roubani-Kalantzopoulou, F. (2007) Benzene and

toluene influence with or without nitrogen dioxide on inorganic pigments of works of

art—Part II. Atmos. Environ. 41: 2009-2018.

Almeida, E.; Morcillo, M.; Rosales, B. (2000) Atmospheric corrosion of mild steel. Part II

­ Marine atmospheres. Mater. Corros. 51: 865-874.

Apsimon, H. M.; Cowell, D. (1996) The benefits of reduced damage to buildings from abatement

of sulphur dioxide emissions. Energy Policy 24: 651-654.

Arbizzani, R.; Casellatoa, U.; Fiorina, E.; Nodarib, L.; Russob, U.; Vigato, P. A. (2004) Decay

markers for the preventative conservation and maintenance of paintings. J. Cult. Heritage

5: 167-182.

Askey, A.; Lyon, S. B.; Thompson, G. E.; Johnson, J. B.; Wood, G. C.; Sage, P. W.; Cooke, M.

J. (1993) The effect of fly-ash particulates on the atmospheric corrosion of zinc and mild

steel. Corros. Sci. 34: 1055-1081.

Ausset, P.; Del Monte, M.; Lefevre, R. A. (1999) Embryonic sulphated black crusts on carbonate

rocks in atmospheric simulation chamber and in the field: role of carbonaceous fly-ash.

Atmos. Environ. 33: 1525-1534.

Blucher, D. B.; Svensson, J. E.; Johansson, L. G. (2005) Influence of ppb levels of SO 2 

### on the

atmospheric corrosion of aluminum in the presence of NaCl. J. Electrochem. Soc. 152:

B397-B404.

Boke, H.; Gokturk, E. H.; Caner-Saltik, E. N.; Demirci, S. (1999) Effect of airborne particle on

SO 2 

### -calcite reaction. Appl. Surface Sci. 140: 70-82.

Colombini, M. P.; Modugno, F.; Fuoco, R.; Tognazzi; A. (2002) A G C-MS study on the

deterioration of lipidic paint binders. Microchem. J. 73: 175-185.

Cowell, D.; Apsimon, H. (1996) Estimating the cost of damage to buildings by acidifying

atmospheric pollution in Europe. Atmos. Environ. 30: 2959-2968.

Damian, L.; Fako, R. (2000) Weathering structural steels corrosion in atmospheres of various

degrees of pollution in Romania. Mater. Corros. 51: 574-578.

Dante, J. F.; Kelly, R. G. (1993) The evolution of the adsorbed solution layer during atmospheric

corrosion and its effect on the corrosion rate of copper. J. Electrochem. Soc. 140: 1890­

1897.

Dehri, I.; Yazici, B.; Erbil, M.; Galip, H. (1994) The effects of SO 2 

### and NH 3 

### on the atmospheric

corrosion of galvanized iron sheet. Corros. Sci. 36: 2181-2191.

Del Monte, M.; Rossi, P. (1997) Fog and gypsum crystals on building materials. Atmos.

Environ. 31: 1637-1646.

August 2008 E­17 DRAFT—DO NOT QUOTE OR CITE

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40 Dubowski, Y.; Sumner, A. L.; Menke, E. J.; Gaspar, D. J.; Newberg, J. T.; Hoffman, R. C.;

Penner, R. M.; Hemminger, J. C.; Finlayson-Pitts, B. J. (2004) Interactions of gaseous

nitric acid with surfaces of environmental interest. Phys. Chem. Chem. Phys. 6: 3879­

3888.

Grosjean, D.; Grosjean, E.; Williams, E. L., II. (1993) Fading of artists' colorants by a mixture of

photochemical oxidants. Atmos. Environ. Part A 2 7: 765-772.

Grosjean, D.; Grosjean, E.; Williams, E. L., II. (1994) Fading of colorants by atmospheric

pollutants: reflectance spectroscopy studies. Sci. Total Environ. 151: 213-226.

Gysels, K.; Delalieuxa, F.; Deutscha, F.; Grieken, R. V.; Camuffob, D.; Bernardib, A.; Sturarob,

G.; Bussec, H-J.; Wieserc, M. (2004) Indoor environment and conservation in the Royal

Museum of Fine Arts, Antwerp, Belgium. J. Cult. Heritage 5: 221-230.

Johansson, A.; Lennholm, H. (2000) Influences of SO 2 

### and O 3 

### on the ageing of paper investigated by in situ diffuse reflectance FTIR and time-resolved trace gas analysis.

Appl. Surface Sci. 161: 163-169.

Jouen, S.; Jean, M.; Hannoyer, B. (2004) Atmospheric corrosion of nickel in various outdoor

environments. Corros. Sci. 46: 499-514.

Kim, S.-T.; Maedab, Y.; Tsujino, Y. (2004) Assessment of the effect of air pollution on material

damages in northeast Asia. Atmos. Environ. 38: 37-48.

Lan, T. T. N.; Thoaa, N. T. P.; Nishimurab, R.; Tsujinoc, Y.; Yokoid, M.; Maeda, Y. (2005)

New m odel for the sulfation of marble by dry deposition sheltered marble—the indicator

of air pollution by sulfur dioxide. Atmos. Environ. 39: 913-920.

Leuenberger-Minger, A. U.; Faller, M.; Richner, P. (2002) Runoff of copper and zinc caused by

atmospheric corrosion. Mater. Corros. 53: 157-164.

Marinoni, N.; Birelli, M. P.; Rostagno, C.; Pavese, A. (2003) The effects of atmospheric

multipollutants on modern concrete. Atmos. Environ. 37: 4701-4712.

Martinez-Arkarazo, I.; Angulo, M.; Bartolomé, L.; Etxebarria, N.; Olazabal, M. A.; Madariaga,

J. M. (2007) An integrated analytical approach to diagnose the conservation state of

building materials of a palace house in the metropolitan Bilbao (Basque Country, north of

Spain). Anal. Chim. Acta 584: 350-359.

National Acid Precipitation Assessment Program. (1991) National Acid Precipitation

Assessment Program 1990 integrated assessment report. Washington, DC: National Acid

Precipitation Assessment Program.

Oesch, S.; Faller, M. (1997) Environmental effects on materials: the efect of the air pollutants

SO 2 

### , NO 2 

### , NO a nd O 3 

### on the corrosion of copper, zinc and aluminum. A s hort literature

survey and results of laboratory exposures. Corros. Sci. 39: 1505-1530.

Sabbioni, C.; Zappia, G.; Riontinoa, C.; Blanco-Varela, M. T.; Aguilerac, J.; Puertasc, F.; Van

Balen, K.; Toumbakari, E. E. (2001) Atmospheric deterioration of ancient and modern

hydraulic mortars. Atmos. Environ. 35: 539-548.

Sabbioni, C.; Bonazzaa, A.; Zappia, G. (2002) Damage on hydraulic mortars: the Venice

arsenal. J. Cult. Heritage 3: 83-88.

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39 Samie, F.; Tidblad, J.; Kucera, V.; Leygraf, C. (2007) Atmospheric corrosion effects of HNO 3



### ­ comparison of laboratory-exposed copper, zinc and carbon steel. Atmos. Environ. 41:

4888-4896.

Shashoua, Y. (2006) Inhibiting the inevitable; current approaches to slowing the deterioration of

plastics. Macromol. Symp. 238: 67-77.

Sikiotis, D.; Kirkitsos, P. (1995) The adverse effects of nitrates on stone monuments. Sci. Total

Environ. 171: 173-182.

Smith, G. D.; Clark, R. J. H. (2002) The role of H 2 

### S in pigment blackening. J. Cult. Heritage 3:

101-105.

Smith, L. V.; DeVries, K. L. (1993) Mechanical properties of polymeric fibres exposed to stress

in a NO

x 

### environment. Polymer 34: 546-550.

Strandberg, H. (1998) Reactions of copper patina compounds--I. Influence of some air

pollutants. Atmos. Environ. 32: 3511-3520.

Svensson, J.-E.; Johansson, L.-G. (1993a) A l aboratory study of the effect of ozone, nitrogen

dioxide, and sulfur dioxide on the atmospheric corrosion of zinc. J. Electrochem. Soc.

140: 2210-2216.

Svensson, J.-E.; Johansson, L.-G. (1993b) A l aboratory study of the initial stages of the

atmospheric corrosion of zinc in the presence of NaCl; influence of SO 2 

### and NO 2



### . Corros.

Sci. 34: 721-740.

Svensson, J.-E.; Johansson, L.-G. (1996) The temperature-dependence of the SO 2 

### -induced

atmospheric corrosion of zinc; a laboratory study. Corros. Sci. 38: 2225-2233.

Torfs, K.; Van Grieken, R. (1996) Effect of stone thickness on run-off water composition and

derived damage functions in ambient exposure experiments. Atmos. Environ. 30: 1-8.

U.S. Environmental Protection Agency. (1982) Air quality criteria for particulate matter and

sulfur oxides. Research Triangle Park, NC: Office of Health and Environmental

Assessment, Environmental Criteria and Assessment Office; EPA r eport no. EPA-600/8­

82-029aF-cF. 3v. Available from: NTIS, Springfield, VA; PB84-156777.

U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen.

Research Triangle Park, NC: Office of Health and Environmental Assessment,

Environmental Criteria and Assessment Office; report nos. EPA/600/8-91/049aF-cF. 3v.

Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517.

U.S. Environmental Protection Agency. (2004) Air quality criteria for particulate matter.

Research Triangle Park, NC: National Center for Environmental Assessment; report no.

EPA/600/P-99/002aF-bF. 2v. Available: http://cfpub.epa.gov/ncea/ [9 November, 2004].

Vilche, J. R.; Varela F. E.; Acuna G.; Codaro E. N.; Rosales B. M.; Fernandez A.; Moriena G.

(1995) A s urvey of Argentinean atmospheric corrosion: I. Aluminum and zinc samples.

Corros. Sci. 37: 941-961.

Weissenrieder, J.; Kleber, C.; Schreiner, M.; Leygrafa, M. (2004) In situ studies of sulfate nest

formation on iron. J. Electrochem. Soc. 151: B497-B504.

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5 Y errapragada, S. S.; Chirra, S. R.; Jaynes, J. H.; Bandyopadhyay, J. K.; Gauri, K. L. (1996)

Weathering rates of marble in laboratory and outdoor conditions. J. Environ. Eng. 122:

856-863.

Zappia, G.; Sabbioni, C.; Riontino, C.; Gobbi, G.; Favoni, O. (1998) Exposure tests of building

materials in urban atmosphere. Sci. Total Environ. 224: 235-244.

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# Annex F. Valuation of the Environmental

Effects of N and S (non­materials)

F.1. Introduction

The monetary valuation of ecological effects associated with NO X and SO X emissions starts with

natural science endpoints. These may be things that people value directly, such as loss of a particular

species, or some remote effect on a resource that is not clearly understood by the general public or not

valued by the public for itself, such as forest soils. Of course, damage to forest soils will affect the

terrestrial ecosystem in ways that may be valuable to humans, such as tree growth, habitat, and even the

aesthetics of the forest. This Annex is a review of the literature that estimates such values for various

ecosystem endpoints or that provide values for effects that can be reasonably inferred from what is

provided.

The purpose of this Annex is to provide an assessment of the economics literature on the effects of

NO X and SO X emissions on terrestrial, transitional, and aquatic ecosystems.



# F.1.1. Valuation in the Context of NO

X 

# and SO

X

Figure F-1 provides a schematic representation of how economic valuation is derived from changes

to NO X and SO X secondary standards. Starting at the upper left-hand side, the NO X and SO X standards

are set and emissions reductions occur to change the ambient concentrations of NO X and SO X

. Reading

down from “Change in Ambient Concentrations,” these reductions will lead to changes in a variety of

ecological endpoints (as identified in the ISA) in terrestrial, transitional, and aquatic ecosystems. The box

below, “Change in Economic Endpoints,” refers to physical endpoints that people care about, in which

changes can be valued (at least in principle) in monetary terms. Many times, these are referred to as

ecosystem services. In a few cases, such as agricultural crop growth and yield, ecological and economic

endpoints are nearly the same. Finally, at the bottom of this diagram is a box labeled “Valuation

Methods,” which notes alternative approaches for placing monetary values on these economic endpoints.

As endpoints are discussed in detail in the ISA, this Annex focuses solely on valuation.

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8 Figure F­1. Illustration chart of the assessment.



## F.1.1.1. Ecosystem Services

Broadly defined, ecosystem services are the benefits that people obtain from ecosystems

(Millennium Ecosystem Assessment, 2003). In the Millennium Ecosystem Assessment (MA), ecosystem

services are classified into provisioning, regulating, supporting, and cultural services. Provisioning

services denote the products people obtain from ecosystems; regulating services are associated with the

ecosystem functions that regulate climate, nutrient cycle, water filtration, and so forth; supporting services

are ecosystem functions, such as primary productivity and production of O 2 , that support the provision of

ecosystem services; and cultural services are the non-material benefits ecosystems provide to people

through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences.

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35 Ecosystems are productive systems in which various biological and physical factors, as well as

their interactions, serve various functions in the production of ecosystem services. However, economic

valuation of the environment has focused mostly on the contributions of individual goods and services to

human well-being. Alternately, ecosystem services valuation is based on the various benefits generated by

the ecosystem (Polasky et al., 2005). In this case, benefits include both marketed and non-marketed

services, and their valuation considers the environment as a natural capital asset that generates returns on

investment in ecosystem protection and management.

For example, wetlands constitute a form of natural capital. They serve as flood barriers, soaking up

excess water and slowing and preventing floodwaters from spreading uncontrollably. Wetlands help

replenish groundwater and improve both ground and surface water quality by slowing down the flow of

water, and absorbing and filtering out sediments and contaminants. They also provide spawning habitat

for fish, supporting the regeneration of fisheries. In addition, wetlands provide habitat for many wildlife

species and support commercial and sport fishing, as well as hunting and other forms of recreation.

Though different functions and processes of ecosystems, such as water filtration, may be

economically important, they need to be viewed as inputs of or mechanisms for the production of

economically valuable services, such as drinking water, timber, or recreational benefits. The end products,

not the elements of the production process, ultimately generate economic well-being. Along these lines,

Boyd and Banzhaf (2007) advocate defining ecosystem services as “components of nature, directly

enjoyed, consumed, or used to yield human well-being.” In other words, ecosystem services are the end

products of nature to which ecosystems contribute as intermediate inputs or production technologies.

Though this distinction may at first seem unimportant, it is crucial for the accurate valuation of ecosystem

services. Regarding the incorporation of ecosystem services into the measurements of national income

and the value of goods and services produced in an economy, such as gross domestic product (GDP)

accounts, Boyd and Banzhaf (2007) note that:

If intermediate and final goods are not distinguished, the value of intermediate goods is doublecounted because the value of intermediate goods is embodied in the value of final goods. For example,

clean drinking water, which is consumed directly by a household, is dependent on a range of intermediate

ecological goods, but these intermediate goods should not be counted in an ecosystem service welfare

account. Also important is that ecosystem services are attributed only the incremental value they

contribute to the production of valuable end products. Using the above example, the value of ecosystem

services associated with drinking water denotes the marginal contribution of ecosystems in the production

of drinking water, not the full value of the final product.

Given the complexity and variety of ecosystems and their services, their valuation poses several

challenges. According to the National Academy of Sciences’ Committee on the Valuation of Ecosystem

Services, the importance of ecosystem functions and services is often taken for granted and overlooked in

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32 environmental decisionmaking. Moreover, the key challenge in the valuation of ecosystem services lies in

the difficult integration of economic valuation and ecological production theory. This is no

straightforward task, because many ecosystem goods and services are not quantifiable using available

methods, and the application of economic valuation methods may be subject to judgment, uncertainty, and

bias (Heal, 2005).

A study by Costanza et al. (1997), seeking to determine the value of global ecosystem services,

exemplifies the problems and pitfalls in the valuation of ecosystem services. Deriving and summing value

estimates from the existing literature for a wide range of ecosystem attributes and services, this study

suggested that the total value of global ecosystem services likely ranges from $16 to $54 trillion annually,

or roughly one to three times global GDP. The study has been influential and widely quoted and used,

especially among scientists and environmentalists. Economists consider it fundamentally problematic

both conceptually and methodologically, preferring to focus on the value of changes to ecosystem

services, which is relevant for policy, or what is termed the marginal value of ecosystem services. More

profoundly, the entire concept of the value of global ecosystem services is problematic; without the global

ecosystem we would all perish. The estimate of the value of global ecosystem services by Costanza et al.

(1997) has therefore been characterized as a “serious underestimate of infinity” (cf. Toman, 1998; Smith,

2007).



## F.1.1.2. Use of the Valuation Literature to Define Adversity

A secondary standard, as defined in Section 109(b)(2) of the Clean Air Act, must “specify a level of

air quality the attainment and maintenance of which, in the judgment of the Administrator, based on such

criteria, is required to protect the public welfare from any known or anticipated adverse effects associated

with the presence of [the] pollutant in the ambient air.” One way to quantify adverse effects is through

monetary valuation.

Adversity is difficult to quantify and measure, and there are several challenges to using a monetary

valuation approach. A major effect that is geographically extensive might be considered to be more

adverse than a more severe effect limited to one geographic location. Another problem is aggregation.

Any change in pollution may have multiple effects (i.e. effects on many types of ecosystem services)

leading to difficulty in aggregating in a consistent way.

Monetary values on any service or resource degradation reflect human preferences about what is a

severe effect. Larger unit values correlate with more severe effects, other things equal. Also, more

extensive effects, will contribute to larger welfare loss (or gain). In addition, since monetary units can be

added, the aggregation issue can be addressed by “simply” summing the welfare losses (or gains).

Although this is not strictly true (e.g., values for improvements in water quality and fish populations may

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27 not be additive), in principle the differences in how people conceptualize ecosystem improvements can be

captured in the way resource improvements are valued in monetary terms.

Clearly, there are many practical problems associated with using monetary value as a way of

defining adversity. First, many resources and services have not been valued and efforts to credibly transfer

the results of valuation studies to other areas and resources have been minimal. Second, studies

addressing multiple effects are particularly difficult to transfer and few in number. Finally, even with the

first two problems addressed, a judgment would still need to be made on whether the air quality standard

was the contributing factor for eliminating adverse (highly monetarily valued) effects.



## F.1.1.3. Methods for Selecting Literature for this Assessment

Assessing the economics literature on the effects of NO X and SO X emissions on terrestrial,

transitional, and aquatic ecosystems requires identifying and reviewing relevant studies addressing these

effects. Multiple methods were used for this Annex: searching existing databases of this valuation

literature; conducting systematic searches of the economics literature; reviewing a large number of key

articles, reports, authors, and journals; and identifying studies based on the expertise and familiarity with

the relevant literature of lead researchers.

Two existing databases on environmental valuation studies

– the Environmental Valuation

Reference Inventory (EVRI) and the Beneficial Use Values Database (BUVD)

– were particularly useful

for this assessment. The EVRI database, which includes nearly 1,900 valuation articles/studies on

environmental and human health effects, was screened according to criteria regarding the potential

relevancy of geographical location (U.S.), types of environmental goods and services valued (ecological

functions, extractive uses, non-extractive uses, passive uses); and environmental stressor. This resulted in

over 200 articles/studies of interest. BUVD is a relatively small database (131 articles/studies), so it was

imported into the literature review database in its entirety, with unrelated articles/studies later excluded on

an individual basis.

A large number of additional journals and literature databases were identified that publish and

cover research potentially relevant to this assessment. The selected peer-reviewed journals 1 and library

databases 2 were then reviewed using search engines and a range of key words developed to find studies

addressing relevant ecological endpoints (aquatic, transitional, terrestrial) and their economic values. The

1 American Economic Review, American Journal of A gricultural Economics, Canadian Journal of E conomics, Canadian Journal of F orestry,

Contemporary Economic Policy, Ecological Economics, Environment and Development Economics, Environmental and Resource Economics,

Environmental Science and Technology, Forest Science, Forestry Chronicle, Journal of A gricultural and Applied Economics, Journal of A pplied

Econometrics, Journal of A gricultural and Resource Economics, Journal of Agricultural Economics, Journal of E nvironmental Economics and

Management, Journal of F orest Economics, Journal of F orestry, Journal of P olitical Economy, Journal of R isk and Uncertainty, Land

Economics, Marine Resource Economics, Resource and Energy Economics, Review of Agricultural Economics, Review of E conomics and

Statistics, Water R esources Research.

2 AgEcon Search, Agricola, BioOne, CSA I llumina, EconLit, GeoRef, Google Scholar, SciSearch/Science Citation Index (Web of S cience),

SCOPUS, Sportfishing Values Database, SSRN.

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29 � Does the study address an ecological endpoint sensitive to reductions in NO X andSO X

emissions?

� Does the study value quality changes in the ecological endpoint, which is actually or

potentially attributed to reductions in NO X and SO X emissions?

� Is the study peer-reviewed and preferably, published in an academic journal?

� Very few, if any studies fully satisfy all above criteria. For this reason, studies that at least

partially satisfy these criteria were deemed potentially relevant for this project. Finally,

reviews and meta-analyses were included in the assessment whenever they were available and

dealt with potentially relevant ecological endpoints. tables of contents of those journals that could not be searched electronically were reviewed in hard copy

and relevant articles were added to the literature review database. These searches were augmented by

reviews of the bibliographies of the following EPA reports: EPA Report to Congress: The Benefits and

Costs of the Clean Air Act 1990–2010 (November 1999); Air Quality Criteria for Ozone and Related

Photochemical Oxidants (February 2006); Air Quality Criteria for Particulate Matter (October 2004).

The results from these searches were checked for duplicates and clearly irrelevant studies, after

which over 500 potentially relevant articles/studies were identified for initial assessment. Relevancy of

each study for this assessment was determined according to the following key criteria.

In t he initial assessment, each record’s potential relevancy to the assessment was rated on a scale of

1 to 4, with 1 indicating that the record appears directly relevant to this assessment (the study addresses

quality change of an ecological endpoint, which is actually or potentially attributable to NO X /SO X ).

Records rated 2 only partially satisfied the “relevancy criteria,” but were considered important to be

referenced in this report. Records rated 3 were to be reviewed more closely to determine their usefulness,

and those rated 4 were found not relevant for the purposes of this assessment. Because our goal in the

initial assessment was to avoid missing potentially relevant studies, we classified borderline cases to the

lower number category.

All studies rated 1 through 3 were next reviewed using several attributes, including ecological

endpoints, valuation techniques, geographical area, use vs. non-use value category, and other details of

interest. Of those studies, about half addressed aquatic ecosystems (Figure F-2). The reviewed studies

addressed many different ecological endpoints, such as sport fishing, commercial fisheries, aquatic

recreation (e.g., swimming and boating), general water quality, ecosystems services provided by aquatic

ecosystems, and coral reefs (Figure F-3). Nearly one third of the studies addressed terrestrial ecosystems

(e.g., forestry/commercial timber, outdoor recreation, and agriculture); the rest dealt with transitional

ecosystems (e.g., ecosystems services provided by wetlands and wetlands recreation).

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11 Figure F­2. Reviewed studies by ecosystem addressed.



# F.2. Conceptual Framework

F.2.1. Taxonomy of Values for Environmental Goods and Services

The economic value derived by society from these ecological goods and services can be

categorized as either “use” or “non-use” values. Use values comprise values for those goods and services

that can be used either directly or indirectly. Non-use values denote the characteristics of the ecological

goods and services that are not used at all but still hold economic value. The schematic in Figure F-4

illustrates these divisions of values for environmental goods and services.

Direct use values are held for those goods and services which can be directly consumed or utilized

by individuals or society. Some direct use goods, such as fish caught or timber cut are sold in markets and

thus valued using market data. Other direct uses, such as recreational use of ecosystems for fishing,

hunting, and sightseeing, are usually not bought or sold through a market and are therefore more difficult

to value. Similarly, changes in their value are more difficult to quantify. Thus, direct use can be

subdivided into directly used market and non-market goods or services.

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### Figure F­3. Reviewed studies by ecological endpoint.

Source: EPA (2002).



### Figure F­4. Taxonomy of values for environmental goods and services.

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8 Examples of direct, market uses of ecological goods and services include commercially sold food

sources (fish, crops, other animals); building materials (wood, stone); fuel sources (wood, coal, oil);

drinking water (groundwater, surface water); chemicals; and minerals (EPA, 2002). Examples of direct,

non-market uses of ecological goods and services include recreational fishing and hunting; beach use

(sunbathing, swimming, and walking); boating; hiking; camping; wildlife watching; and sightseeing

(EPA, 2002).

Indirect use values capture those ecological services that are not used directly but still provide

benefits of economic value to society. These services include flood control, storm water treatment, ground

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32 water recharge, climate control, pollution mitigation, wave buffering, soil generation, nutrient cycling,

habitat value, and biodiversity (EPA, 2002).

Non-use values denote the characteristics of the ecological goods and services that are not used at

all but still hold economic value. These non-use values include what are known as existence and bequest

values–the value of simply knowing that certain ecosystems exist and of ensuring that they continue to

exist for future generations, respectively. These services are also not traded in a market and quantifying

their value is a challenge.

Importantly, empirical methods for addressing non-use value generally estimate the total value of a

resource. Distinguishing between the use and non-use values often is not possible, and the valuation

results including non-use values should therefore generally be considered total valuation rather than nonuse valuation studies. Examples of non-use values of ecological goods and services include existence

value, cultural/historical value, intrinsic value, bequest value, and altruistic value (EPA, 2002).

In this Annex, the focus is on the potential incremental benefit that might be realized from reducing

levels of SO X or NO X by a relatively small amount, rather than the total value of the affected ecosystems.

Therefore, the term “total value” in this Annex generally denotes total marginal benefits that contain both

use and non-use values, not the total value of the entire resource. This notation is consistent with the

principles of economic valuation of the environment, which generally focus on predicting damages or

benefits from marginal changes in environmental conditions.



# F.2.2. Welfare Economics

Any environmental goods or services that somehow contribute to human well-being are

economically valuable. Their economic value reflects the capacity of the environment to satisfy different

human needs, which is related to the direct consumption of different goods and services derived from the

environment. It also includes different indirect, passive, and non-use values for environmental goods and

services, such as recreational benefits, enjoyment of natural landscape, purity of air and water, and

provision of habitat for species other than humans.

Economic valuation is rooted in the basic principle of consumer sovereignty. Rather than judging

whether an individual’s choices are right or wrong, each person is considered able to make rational

choices that advance his or her well-being, given the possibilities available. The principle of consumer

sovereignty extends also to the valuation of environmental goods and services. Even in the absence of

markets for environmental benefits, each individual is considered able to assess the importance of

changes in environmental quality on personal well-being or, as economists commonly refer to it, utility.

Consumer (CS) and producer surpluses (PS) are the basic monetary measures of well-being (or

welfare) in economics. They denote the “excess utility” consumers and producers enjoy when consuming

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35 or producing specific goods or services after paying for them. Producer surplus is measured by profit, the

value of production after accounting for all costs. Consumer surplus, which is a more subtle concept, can

be thought of as the difference between the price and the maximum value that an individual holds for a

good or service. However, it generally is not an exact measure of changes in welfare because CS does not

fix the baseline level of utility, thereby ignoring the income effects of a changing baseline

CS has two exact (Hicksian) measures: willingness-to-pay (WTP) and willingness-to-accept

(WTA). In the context of environmental valuation, WTP denotes an individual’s maximum willingness to

pay for an environmental good or service. WTA, on the other hand, stands for the minimum compensation

an individual is willing to accept to forgo an environmental good or service. Though WTP and WTA both

are exact measures of CS, they generally are not equal, meaning that the CS in general does not have a

unique measure. WTP and WTA for usual marketed goods and services are similar and within narrow

bounds of income effects (Willig, 1976). Similar findings can be expected with environmental goods and

services only when they have close substitutes (Shogren, 1994; Hanemann, 1991).

Since many environmental goods and services cannot be easily substituted, WTP and WTA are

expected to differ, sometimes substantially. In fact, the WTP-WTA divergence can range from zero to

infinity, depending on the substitutability of an environmental good and other market or non-market

goods (Hanley et al., 1997). Using WTA to measure welfare changes might often be justified on the

grounds of economic theory and property rights, but many studies choose to estimate WTP for its

practicality. In addition, WTP generally is lower than WTA and therefore is conservative, providing

another justification for using WTP for valuing the environment. Finally, both WTP and WTA can be

difficult to measure, and valuation studies therefore sometimes estimate the ordinary CS. It approximates

WTP and WTA and is between these two exact measures of welfare. In the special case of no income

effects, all different measures of consumer welfare coincide.

Various methods have been developed to determine the value of different ecological goods and

services by estimating the change in social welfare or WTP for changes in the quantity or quality of a

given environmental resource (see Table F-1). Some valuation techniques obtain WTP from observing

people’s actions (revealed preferences or RP) while others rely on people’s responses to hypothetical

situations (stated preferences or SP). Yet another set of valuation methods relies on other studies, either by

transferring their estimates into another context (benefits transfers) or by conducting statistical metaanalyses of earlier studies to examine their systematic findings. See Section F.2.3 for details about these

approaches.

As noted by Kramer et al. (2003), several forest protection valuation studies also considered the

sensitivity of contingent valuation estimates to various preference elicitation methods, including

dichotomous choice, payment card, and open-ended techniques. Haefele et al. (1992) used payment card

and dichotomous choice techniques in a contingent valuation survey measuring the WTP of Southern

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31 Appalachian residents for protecting high-elevation spruce-fir trees from exotic insects and air pollution.

Estimates for mean WTP were $20.86 per year using a payment card method, and $99.57 per year using a

discrete choice method. Sample sizes for each method were relatively small (232 and 236 respondents,

respectively). Another limitation concerns the wide span between the two valuation statistics, which

despite a large difference in the mean estimates showed no overlap of the 95% confidence intervals.

Similar studies have been performed by Loomis et al. (1996) and Kramer and Mercer (1997).



# F.2.3. Benefit Estimation Approaches

Table F-1 introduces different valuation techniques and indicated which type of value (direct use,

indirect use, total value) each can be used to estimate. Studies addressing non-use values are referred to in

this Annex as total valuation studies. This is because non-use valuation methods in fact generally estimate

the total value of a resource, including both its use and non-use components.



### Travel cost (including site choice models) and hedonic pricing methods are perhaps the most

regularly applied revealed preference (RP) methods for valuing the environment, whereas contingent

valuation and choice experiments are the most popular stated preference (SP) methods. One of the key

differences between the RP and SP methods is that RP methods can only fully address direct and indirect

use values, whereas SP methods are required for the estimation of total values of environmental resources,

including their non-use value component.



### Travel cost studies predict use values for ecological resources, such as natural parks, by

examining individuals’ travel expenditures to utilize that resource (most often at a park or some other

recreational site), including the opportunity cost of work time missed while traveling to and utilizing the

resource. Travel cost studies commonly use a random utility framework, which infers individuals’ WTP

for an ecological resource (again usually a recreational site) by observing their choice from among one or

more alternatives. While the travel cost method uses changes in the quality of one resource to ascertain its

value, the random utility model uses individuals’ choices among various options of various qualities at

various prices to do the same.



### Hedonic price studies predict the value of ecological resources by examining their effect on

property values. Assuming that all the benefits from living in a specific location and house are capitalized

into the market value of the property, hedonic models estimate the independent effects of different

housing characteristics on housing prices. Controlling for all observable housing and location

characteristics, hedonic pricing models examine environmental values for, for example, proximity to

forests or particular watersheds by estimating the implicit incremental price people are willing to pay for

that proximity. Hedonic pricing relies on assumptions such as efficiently functioning housing market and

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35 perfect information and mobility by individuals. However, because WTP is not necessarily tied to

ecosystem changes, this Annex does not consider property values as a method of valuation.

Other market or RP-based approaches to valuing changes in ecological goods and services include

the alternative/replacement cost method; avoidance expenditures/averting behavior method; referendum

method; user fee method; market price and market simulation method; and a host of different variations of

these and other valuation approaches. See, for example, Freeman (2003) for the theory and applications of

different methods for valuing environmental quality.

The RP methods have the advantage of gleaning their value estimates from individuals’ real world

actions. However, because they do not include the non-use value of ecological resources, none of them

capture total value. This problem has given rise to the development of a variety of non-market valuation

methods that use surveys to elicit preferences for public goods. Because these methods are generally

based on eliciting “stated” rather than “revealed” preferences, they are broadly categorized as SP

methods.



### The contingent valuation method is the most common SP method. It involves developing and

administering surveys, in which respondents are presented with a scenario or a program with specified

environmental outcomes and costs. Each respondent is asked to indicate approval or disapproval of the

proposed environmental scenario and its monetary cost. Researchers vary the proposed costs across

different survey respondents and use their choices to estimate how much people on average are willing to

pay for different scenarios to improve the environment. Because some of the respondents may use a

public good also for direct enjoyment, say viewing an endangered bird, the surveys actually capture total

value for the improvements, rather than just their non-use value.



### Choice experiment methods (including contingent ranking, contingent choice, and conjoint

analysis) separate an environmental good into its constituent attributes, recombine those attributes into

different bundles, and elicit respondents’ preferences for those bundles. Often a monetary value can be

assigned to those attributes and thus the process allows researchers to determine WTP for the bundle and

each attribute. Conjoint analysis usually is performed by choosing the most preferred attribute bundle

from a group (choice experiment, contingent choice) or via ranking a series of attribute bundles

(contingent ranking). Using conjoint analysis, researchers may be able to simultaneously value various

relevant goods or services that an environmental resource provides. For example, improving a public

body of water provides improved recreational opportunities, drinking water, and support of aquatic

ecosystems.



### Valuing the environment as a factor of production monetizes the incremental benefits from using

the environment as an input to production. In other words, this method treats the environment as a

production input comparable to other raw materials and infrastructure, such as land, capital, labor, and so

forth. This method is appropriate for valuing environmental effects that have direct value as a factor of

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34 production. Examples of such cases include the effects of water quality on the productivity of commercial

fisheries, or the impact of soil characteristics on agricultural productivity. However, this method is limited

to market goods and services and can only address their role as part of the production process. This

method does not address non-market environmental goods and services or non-use values.



### Benefits transfer approaches translate the entire estimated demand function from one application

to another. Sometimes the function is adjusted to meet the specific criteria of the target site, and then new

WTP value estimates are generated for the environmental good/service at the new site using the demand

function. Using the transferred demand function, both changes in the level of use and the unit value

benefits for the new site can be estimated (EPA, 2002). There are four general types of benefits transfer

technique: mean unit value transfer/ adjusted unit value transfer, benefit/demand function or model

transfer, meta-function transfer, and structural benefits transfer. The first three methods dominate the

literature (Smith et al., 2006).



### Mean unit value transfer/adjusted unit value transfer entails taking the value of a specific

environmental good or service (such as recreational hunting), sometimes from a single study of the same

good and sometimes estimated by averaging a range of value estimates from various primary studies, and

transferring that value to the same good or service at a new site.



### Benefit/demand function or model transfer is the translation of the entire estimated demand

function from one site to another. Sometimes the function is adjusted to meet the specific criteria of the

target site, and then new WTP value estimates are generated for the environmental good/service at the

new site using the demand function. Using the transferred demand function, both changes in the level of

use and the unit value benefits for the new site can be estimated (EPA, 2002). Meta-function transfer

involves the use of meta-regressions to combine the results of numerous valuation studies and allows

researchers to account for influencing factors, thus enabling them to create value estimates for new policy

sites. Structural benefits transfer , also known as preference calibration, requires selection of a preference

model which can describe individual choices over a set of market and associated non-market goods to

maximize utility when faced with budget constraints (Rosenberger and Loomis, 2001; Smith et al., 2006).



### Meta­study review provides a useful way of summarizing the literature on the valuation of

ecological endpoints from reductions in NO X and SO X , though there are few studies exactly on this topic.

But there are studies summarizing monetary valuation efforts for particular sets of endpoints, such as the

economic valuation of fresh water ecosystem services. Where such studies are available, they are

summarized in the appropriate section of this Annex.

Even more valuable are 

### meta­analyses , which perform statistical analyses of the results of original

studies. Such studies explain variation in monetary value estimated for various endpoints using features of

the original studies' methodologies as well as the characteristics of the site being studied and other factors.

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35 Smith and Pattanayak (2002) defined meta-analysis as the practice of using a collection of formal and

informal statistical methods to synthesize the results found in a well-defined class of empirical studies.

In general, the three uses of meta-analysis are: (1) synthesize or “take stock” of the literature on a

particular valuation topic; 2) test hypotheses with respect to the effects of explanatory variables on the

value construct of interest; and (3) use the estimated meta-analysis model to predict estimates of the value

construct across time and space (Bergstrom and Taylor, 2006). Bergstrom and Taylor (2006) provided a

review of the techniques and theory behind the use of meta-analysis for benefits transfer. They noted that

to conduct a successful meta-analysis for benefits transfer, it is important to be as comprehensive as

possible in terms of the studies to be included. Excluding a study would be equivalent to applying a zero

weight to the information in that study. The authors list some additional criteria to be considered when

identifying studies for inclusion in a meta-analysis, including controlling for the valuation method and

estimated welfare measure, as well as addressing the temporal and spatial scales of the valued commodity.

Extensive evaluation of the relative merits of, and issues with, different environmental valuation

methods is not within the scope of this Annex. Vast amounts of research have been conducted to develop

and evaluate alternative environmental valuation methods. For example, the Handbook of Environmental

Economics recently dedicated an entire 1,100–page volume to addressing methods for valuing

environmental changes (Mäler and Vincent, 2005).

The validity of environmental valuation methods is sometimes questioned, in particular that of SP

methods. Because survey-based valuation methods are based on what people say rather than what they do,

there is a tendency to question the credibility of the results. For this reason, nonmarket valuation

researchers, following the lead of the National Oceanic and Atmospheric Administration (NOAA) Expert

Panel that reviewed the highly publicized studies valuing damages from the EXXXXXXXXXXon Valdez

oil spill, often build into their surveys a series of validity tests, such as testing for the sensitivity of WTP

to the scope of resource being valued (Federal Register, 1993). Additionally, SP surveys are vulnerable to

a variety of issues dealing with the design and administration of surveys, as well as analyzing their data

(e.g., Carson and Hanemann, 2005).

Though examining actual choices lends credence to RP methods, they are not free of problems. For

example, hedonic pricing studies of housing markets rest on the assumptions that the housing market is at

equilibrium and that housing choices accurately reflect the attributes of interest, such as air pollution or

environmental amenities associated with the residential location. Hedonic studies and other RP studies,

such as recreation trip demand analysis, are also susceptible to potential biases. These include the

omission of important variables, which may thwart the efforts to accurately value environmental quality.

A number of studies have compared SP analyses with RP analyses, such as hedonic property value

studies. Generally, these comparisons have suggested that when similar environmental values are

examined, RP methods generally yield somewhat higher value estimates than SP methods. For example,

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29 Carson et al. (1996) reviewed over 80 studies which included comparisons of SP and RP methods, and

concluded that SP methods are on average about 75% to 90% of corresponding RP values.



# F.3. Valuation of forests and terrestrial ecosystems

F.3.1. Use Values

The impacts of NO X and SO X can occur over many different terrestrial ecosystems and use-value

efforts must look at each of these ecosystems individually. One serious threat to agriculture from NO X

emissions comes from ambient ozone (O 3 ), which is a byproduct of atmospheric reactions between

Volatile Organic Compounds (VOCs) and NO X

. In commercial forests, air pollution effects have not been

addressed in economics beyond evaluating the potential effects of O 3

. Unlike in agriculture, the scientific

understanding of O 3 effects on trees is more limited and related mostly to visual injury to leaves in young

saplings, an indicator that is difficult to link to tree growth in mature forests. However, the valuation of O 3

has already been evaluated in detail in the Air Quality Criteria Document (AQCD) for Ozone (EPA,

2006), and will not be covered in this Annex or the ISA (see also Tables F-2 and F-3).

Some studies have attempted to specifically measure use values associated with forest quality,

without regard to the factor that is altering forest quality. For example, an early study by Leuschner and

Young (1978a) considered the effects of crown density changes due to insect infestation around 19

lakeside campgrounds in east Texas. A travel-cost model estimated the CS l osses from a 10% reduction in

crown density to fall between 0.69% and 6.5%, depending on the number of substitute recreation sites

available. More studies that look at how forest quality affects various uses are listed in Table F-4. Also,

Table F-5 lists value estimates from a meta-analysis but does not reflect the considerable variation of

value estimates across underlying studies.

Figure F-5 depicts the linkages between aesthetic welfare benefits and air pollution. One such

linkage that is shown is the visual quality of forests. Changes in woodland appearances are monitored

using scientific indicators of ecosystem change, such as crown condition, mortality, foliar damage,

vegetation structure, and plant diversity (McLaughlin and Percy, 1999). However, in comparison to

valuation of changes in commercial timber, measuring the economic value of aesthetic changes in forests

and natural ecosystems can be more directly based on scientific information regarding the effects of

pollution on forest health. Although the scientific linkages between air pollution and visual forest quality

include large degrees of uncertainty, historic examples of air pollutant effects on forest aesthetics facilitate

their empirical valuation. Figure F-6 shows the regions and species that have been identified as

historically affected by air pollutants.

August 2008 F­15 DRAFT­DO NOT QUOTE OR CITESource: Exhibit 1 in IEc (1999a).



### Figure F­5. Linkages from emissions to forest aesthetics.

August 2008 F­16 DRAFT­DO NOT QUOTE OR CITESource: Figure 2 in IEc (1999a).



### Figure F­6. Geographic distribution of forested areas historically affected by air pollution.

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12 When the aesthetic quality of forests is important to people, visible injuries to trees can affect

consumer welfare. Loss of or damage to foliage, changes in tree density, changes in species composition,

and changes in vegetation structure can all affect the enjoyment that people derive from forests, and are

therefore appropriate for economic assessment. Estimation of the aesthetic value of forest condition uses

RP or SP methods and often focuses on long-term outcome stressors, rather than current intermediate

situations. Nevertheless, there are few scientific studies that relate changes in pollution concentrations to

these endpoints, and that make a direct linkage between science and economics. A memorandum by

Industrial Economics, Inc. (IEc) (1999b) produced for Benefit-Cost Analysis of the Clean Air Act 1999

,

provided a comprehensive review of early forest aesthetics valuation literature. A recent literature review

by Kramer et al. (2003) also summarized several key studies described in this report. Table F-5 lists and

describes studies which have estimated monetary value for the types of non-consumptive use and non-use

values for forests and natural ecosystems which may be affected by N and sulfur emissions.

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# F.3.2. Total Values

Several studies were reviewed by Kramer et al. (2003), and measured the total WTP of the general

public for forest aesthetics and attempted to separate contributions of use values and non-use values. Most

of these studies indicated that in the aggregate, non-use values (including option, bequest, and existence

value) were significantly greater than use values (recreation).

Walsh et al. (1990) employed a contingent valuation study using an iterative bidding technique to

estimate WTP of Colorado residents for protecting ponderosa pine ( Pinus ponderosa ) forests from the

mountain pine beetle ( Dendroctonus ponderosae ). Presented with pictures of mid-level forest quality

representing current conditions (100 to 125 live trees measuring more than 6–inches in diameter per acre),

respondents were asked their WTP to prevent the lowest forest quality (0 to 50 trees per acre) and to attain

the highest forest quality (125 to 175 trees per acre). Overall mean WTP for changes in forest quality was

estimated at $47 per household. In addition, respondents decomposed total value into four categories of

value (recreational use, option, existence, and bequest). Results showed that use values accounted for

27.4% of total value, and non-use values accounted for 72.6% of total value. The study was limited by

small sample size (200 respondents) and possible bias from framing the scenario as “one of the most

important issues facing Colorado residents.”

A study by Haefele et al. (1992) that measured WTP to protect Southern Appalachian spruce-fir

forests, used a decomposition approach to determine dominate influences on total values. The authors

found that non-use values (bequest and existence values) overshadowed use values as reasons to protect

forests. Holmes and Kramer (1996) relied on results from Haefele et al. (1992) to compare WTP between

recreational forest users and members of the general public. Mean household estimates for forest-users

($36.22) were substantially larger than estimates for nonusers ($10.37).

Kramer et al. (2003) used a dichotomous-choice, contingent-valuation format to determine WTP of

Southern Appalachian residents for protecting spruce-fir forests from insects and air pollution. The study

measured incremental levels of forest protection using two scenarios: the first increment occurred along

road and trail corridors, a scenario that would appeal to people valuing the ecosystem primarily for

recreational use; the second level was for the entire ecosystem, a scenario that would appeal to people

valuing the continued existence of the entire threatened ecosystem. Using randomly assigned values

between $2 and $500 dollars, respondents were asked if they would pay a certain tax amount for each

scenario. Results suggested that preferences for forest ecosystem protection created a well-behaved

demand curve, with incremental WTP increasing at a decreasing rate.

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# F.3.3. National­Scale Valuation

An EPA-contracted valuation study by IEc (1999b) developed a benefits-transfer model using

results from Holmes and Kramer (1996), Peterson et al. (1987, and Walsh et al. (1990) to estimate

national benefits from the 1990 Clean Air Act Amendments (CAAA). Calculations applied WTP values

from these previous studies across all households in the affected states. (See Table F-6 for more

information on the underlying original studies, and Table F-7 for benefits transfer results.) Estimates for

the total value of avoiding air pollution-induced damage to forest aesthetics in the U.S. during the period

1990–2010 were found to range from $3 to $17 billion. Due to limitations in the original studies and the

simplicity of the benefits-transfer model, the authors warned that the estimates should only be used to

consider the general magnitude of benefits to forest aesthetics (in the range of billions of dollars) rather

than as precise values.



# F.3.4. Valuation of Degrees of Injury

A few studies—Hollenhorst et al. (1993, Ruddell et al. (1989, Hammitt et al. (1994, Buhyoff et al.

(1982) —measured general preferences for forest aesthetics without estimating changes in welfare.

Hollenhorst et al. (1993) considered the effect of tree mortality on perceived forest aesthetics. On a 1 to

10 scale for visual and recreational appeal, 400 respondents ranked pictures of 25 sites with tree mortality

ranging from 6% to 98% from gypsy moth ( Lymantria dispar ) damage. The results produced a hillshaped function, whereby site appeal increased with mortality at levels as high as 40% but then declined.

The authors speculated that respondents had a general distaste for tree mortality but valued light

penetration to the forest understory, which allows for the growth of wild flowers and lower-level

vegetation.

Other studies confirmed this visual preference for light penetration, including Ruddell et al. (1989)

and Hammitt et al. (1994, who observed positive responses to forest edges and open middle grounds

without light-obstructing tree canopies. Buhyoff et al. (1982) tested how awareness of environmental

damages affects perception of aesthetic losses. The study compared aesthetic rankings of photographs

between a control group and a group that is informed about the cause of forest damage prior to the

ranking session. Results show a heightened level of sensitivity to forest damage by the “informed” group

of subjects.

Several other studies investigated the notion of aesthetic thresholds, or discontinuous jumps in

aesthetic preferences across small changes in visual injuries. Of particular interest to early studies were

thresholds at the lower limit of visual perceptibility.

Contingent valuation studies by Vaux et al. (1984) and Flowers et al. (1985) assessed aesthetic

preferences at recreational areas with fire damage and found that small differences in site appearance can

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34 produce large changes in recreation preferences. This finding is generally consistent with the results from

two early studies by Buhyoff et al. (1979) and Buhyoff and Wellman (1980, which asked respondents to

rate various levels of insect damage. The authors found that preferences seemed to be most affected by

the presence or absence of insect damage, as opposed to the degree of damage.

Crocker (1985) surveyed 100 recreationists for their WTP for recreational experiences at forested

sites with slight injury, moderate injury, or severe injury. Mean WTP estimates for the environment with

slight injury were three times higher than WTP for environments with moderate and severe injury,

suggesting that people are willing to pay a premium for recreational access below the lower limit of

perceptibility. As a group, respondents did not indicate a clear preference ordering between the moderate

and severe injury environments.

Holmes et al. (2006) use a hedonic method to predict the impact of forest damage due to the

hemlock woolly adelgid ( Adelges tsugae ), an invasive insect, on the value of residential properties.

Examining 3,379 residential property sales in New Jersey between 1992 and 2002, the study analyzed

how the appearance/health of the forest on the home’s parcel and within 0.1, 0.5, and 1 km buffers around

it affects the housing market. Controlling for other relevant variables, the study estimated that hemlock

health status had a statistically significant effect on property values. The estimation results suggest, for

example, that a 1–point increase (e.g., from 10% to 11%) in the percentage of healthy hemlocks (less than

25% foliar damage) of all forests on the home’s parcel was associated with a 0.66% sales price increase.

Similar changes in the hemlock forests and their health status in the home’s near proximity are predicted

to be associated with yet larger increments in the sales price.

At least one study seems to provide evidence against the concept of thresholds at the lower limit of

perceptibility. A referendum-type contingent valuation survey by Jenkins et al. (2002) used two sample

populations to value a forest protection program. The initial forest condition was described as “pristine”

for one group and already “somewhat damaged” for another group. The degree of forest damage incurred

in the absence of a forest protection program was the same for both sample groups. Regressions for the

entire sample population showed no statistically significant difference in WTP for forest protection

between the two groups.

Further analysis of Jenkins et al. (2002) suggested that aesthetic preferences and thresholds differ

between recreational groups. Comparisons among recreational groups revealed that consumptive forest

users (hunters and anglers) held values that were sensitive to change in forest condition, while nonconsumptive forest users (hikers and campers) held values that were insensitive to the same amount of

change. Overall, however, non-consumptive forest users expressed higher values than consumptive forest

users. Aesthetic thresholds also seem apparent for recovering forests. Paquet and Belanger (1997) found

that the aesthetic effects of clear cutting were largely removed once re-growth reached a height of 4 m.

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32 Finally, there is substantial literature related to the amenity value of urban/suburban forests and

open space. McConnell and Walls (2005) recently reviewed this literature, evaluating more than 60

published articles that have attempted to estimate the value of different types of open space. Two lines of

research emerge in this literature: studies that estimate the hedonic value of open space proximity to

residential properties, and studies that use SP methods to value preservation of open space. Unfortunately,

neither line of research generally comprises studies that provide information on the WTP for quality

changes to open space that might be caused by changes in NO X and SO X emissions. In most cases, the

proximity or preservation of open space or urban/suburban forest is valued without reference to the

quality or even the type of open space or forest. Although this literature demonstrates the value of the

availability and preservation of open space, its relevancy for the purposes of this assessment is at best

limited.



# F.3.5. Limitations and Uncertainties

Shortcomings in the air pollution terrestrial valuation literature have been well documented in

recent reviews (Adams and Horst, 2003; EPA, 2006). In existing economics literature, the effects of O 3 on

terrestrial endpoints overshadow assessments of other effects of NO X and SO X pollution, such as acid

deposition and N fertilization. The latter effects have been well documented in the scientific literature, but

the lack of valuation studies related to them limits full assessments of terrestrial damages from NO X and

SO X pollution. Incomplete scientific understanding of the effects of air pollutants on ecosystems and

economic endpoints extends to many valuation studies. Even in the valuation of O 3 effects, which have

been relatively thoroughly studied, improvements in noneconomic input data could play an important role

in evaluating the magnitude of vegetation damages from O 3

.

Economic models should incorporate more specific temporal (dynamic) and spatial data regarding

terrestrial effects of air pollution to reflect more realistic situations. For example, the O 3 effects valuation

literature analyzes air pollution effects across a period of time using only two or three scenarios that

represent large, static changes. In the real world, however, these changes would occur gradually and

incrementally. Future studies should try to consider more dynamic models that can describe effects of

marginal air quality changes. From a spatial perspective, previous studies often assumed that producer

responses are similar across large geographic areas. However, regional factors may be important,

suggesting a possible need for finer-scale agricultural and forestry data that would allow models to

consider micro-level physical and economic factors (Adams and Horst, 2003).

With regards to the effects of air pollution on agricultural crops, most economics studies date from

the 1980s and many focus on O 3

. Therefore, there is a general need for updated valuation studies that

consider agricultural damages from air pollution, especially with regards to the roles played by NO X and

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31 SO X . Specific issues include the need to develop new exposure-response functions for sensitive crops and

the effects of air pollution on crop yields under actual farm conditions.

Assessments of welfare losses in commercial forestry from air pollution are mainly limited by

scientific uncertainty regarding the extent of ecological damage. Restrained by the large size and slow

growth of trees, scientific research has primarily considered O 3 effects on seedlings and has little

transferability to mature trees.

Existing literature on valuation of changes in forest aesthetics has focused primarily on historical

cases of acute air pollution or insect infestation and may be overly simplistic. Although these valuations

are useful, no existing assessments have examined the effects of reduced air pollution on forest aesthetics.

Understanding the effects of marginal changes in air pollution would require the collection of long-term

high-quality data about forest health, as well as improved casual linkages between air pollutants and

visible injuries to trees. Scientific advancements at this scale would require a sophisticated monitoring

network operating over several decades. Until the impacts of pollution on the foundational services of

terrestrial ecosystems are better understood, valuation assessments may be premature. Additionally, most

studies also fail to distinguish between marginal values of forest health and average values of forest

health, an oversight that may create bias in final estimates.

A major limitation of both non-economic and economic forest health assessments concerns the

limited extent of documented ecosystem-level changes. Most forest health surveys focus on average forest

conditions that allow for modeling of near-term trends in economic value, but fail to detect fundamental

changes in ecosystem processes that sustain natural capital in the long run (McLaughlin, 1999).

Quantifications of impacts on species effectively conform to existing valuation methods; however, these

effects may be overshadowed by long- term or irreversible reductions in ecosystem structure and function

(EPA, 1999). Assessing ecosystem-level changes presents new challenges to both science and economics

as they include a large degree of uncertainty in the scale and nature of effects. In particular, economic

assessments of ecosystem impacts may require regional- to national-scale modeling of numerous

ecosystem functions rather than analyses of specific service flows that directly contribute to human

welfare (EPA, 1999). In spite of these challenges, the relationship between air pollution and forest health

has enormous implications for policy development and should be addressed in future research.

More advanced methods to address uncertainty and irreversibility are essential to future modeling

of complex ecosystem-level impacts. Several alternative methods to address ecological uncertainties are

currently being developed and should play a central role in future models (EPA, 1999).

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# F.4. Valuation of Transitional Ecosystems

The economic analysis of the benefits on wetlands (transitional ecosystems) from reduced

emissions are limited in this Annex to three recent and comprehensive meta-analyses. Brander et al.

(2006) provided a comprehensive review and meta-analysis of the wetland valuation literature. This study

improved upon previous similar studies (Brouwer, 1999) (Brouwer et al., 1999; Woodward and Wui,

2001) by including tropical wetlands estimates from other valuation methodologies, other wetland

services, and estimates from more countries.

It is well known that wetlands serve a variety of potentially valuable ecological functions,

including flood and flow control, storm buffering, sediment retention, groundwater discharge, and habitat

for plant and animal species. Wetlands also contribute to climate stabilization, carbon sequestration, and

the overall quality of the natural environment. Brander et al. (2006) (Table F-8) described these functions

and their associated goods and services, as well as their values as determined by common valuation

methods.



# F.4.1. Use and Non­Use Values

The Brander et al. (2006) meta-analysis included 191 different wetland valuation studies from 25

different countries. Eighty of those studies, half of which were from North America, provided useful

empirical data for the meta-analysis. A total of 215 individual observations were gleaned from those

studies for use in meta-regressions. The study reviewed the literature, discussed valuation techniques,

calculated average wetland values using the results from the combined data set, and then performed a

meta-regression of the data to determine which explanatory variables had the greatest effect on wetland

value. Non-market use values of wetlands, as well as non-use/existence values, were also included in the

analysis. Because not all of the studies generated WTP value estimates, wetland size and population

statistics were used to convert estimates from the diverse valuation methodologies to 1995 dollar values

per hectare per year, following the example of Woodward and Wui (2001), which is described below.

The results of the Brander et al. (2006) meta-analysis showed an average value for wetlands of

$2800/ha/yr (1995 dollars), and median value of $150/ha/yr. Individual values were calculated by wetland

type, the wetland service provided, contingent, and valuation method used. The meta-regression also

revealed that studies using the contingent valuation method consistently returned the highest wetland

values. It is noted that this may have been due to the type of wetland values that this method was applied

to, rather than something intrinsic to the methodology.

To look at the value differences from a reduction specifically in N emissions, there is a focus on

Brander et al.’s (2006) analysis of wetland value changes that resulted from a small change in water

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35 quality or some other ecosystem service. These results are valuable because they may be related to

changes in acid deposition as a result of changes in the concentrations of NO X or SO X . In the metaregression results of Brander et al. (2006), the only such ecosystem service which was shown to have a

statistically significant effect on wetland value was hunting. Hunting had an unexpected negative

influence on wetland value. Although not statistically significant, water quality, habitat and nursery

service (specifically support for commercial fisheries and hunting), fishing, and biodiversity all had

positive effects on wetland value. The accuracy of the value transfer exercise in Brander et al. (2006)

seems to be in line with other similar efforts, with an average transfer error of 74%. Overall, the value

transfer function from Brander et al. (2006) produced higher-value transfer error for less valuable

wetlands (and systematically over-predicted their value) and lower value transfer error for more valuable

wetlands (and slightly under-predicted their value).

Woodward and Wui (2001) found 39 studies that contained sufficient data for inter-study

comparison, including peer-reviewed and gray literature. Similar to the approach of Brander et al. (2006),

Woodward and Wui (2001) assumed that a wetland’s value is a function of its ecological characteristics

and socio-economic environment, and that there exists a true public WTP at a given moment for a

particular wetland. With these assumptions, they attributed variability in wetland value to two principal

sources: differing characteristics of the wetland and error in the estimation of the true value (Woodward

and Wui, 2001).

Brouwer et al. (1999) analyzed the WTP of 30 CV studies addressing ecosystem functions of

wetlands, providing a total of 103 estimates. The studies were mostly conducted in the U.S., but were not

in any way comprehensive spatially. They ranged from 1981 to 1997 and covered a wide range of

commodity definitions, including preserving wetlands threatened by flooding, maintaining or improving

current catch levels and fish populations, saving the bald eagle ( Haliaeetus leucocephalus ),

improving/maintaining habitat, water quality ladder changes, preventing shellfish bed closures, increasing

the number of protected rivers, and explicitly preserving a measure of water quality. No attempt was made

to standardize the degree of change being valued. Instead, the authors attempted to address differences in

the studies through the use of dummy variables and some degree of sub-setting.

Based on all of the included studies, Brouwer et al. (1999) estimated that the average WTP for the

addressed ecosystem functions was about $90 per household per year. These studies covered types of

wetlands (primarily fresh water systems), ecosystem functions (water quality and biodiversity, and to a

lesser extent water quantity and flood control), and wetlands size. Cutting across types of systems,

location, and function enhanced, the WTP for salt and fresh water improvements was about the same.

Within fresh water wetland systems, riverine wetland improvements appeared to be more highly valued

than palustrine wetland improvements. Across ecosystem function, flood control was more highly valued

than biodiversity, water quality and water generation, in that order. Improvements to larger wetlands

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29 appeared to be somewhat more highly valued than to improvements to smaller wetlands. Improvements in

use values averaged slightly more than $100 per household per year, whereas improvements in non-use

values only showed values about half this size. Spatially, values were higher in California, followed by

Georgia and Louisiana.

The review of outdoor recreation valuation studies by Rosenberger and Loomis (2001) included 13

studies and 59 value estimates associated with waterfowl hunting, a popular activity in transitional

ecosystems. On average, these studies estimated that the value of waterfowl hunting is about $32 per

person per day (1996 dollars). However, different value estimates ranged from about $2 to over $160 per

person per day. In their meta-analysis, Rosenberger and Loomis (2001) predicted a $40 value per person

for a day for waterfowl hunting (Table XXXXXXXXXX). These results demonstrate potential benefits

from waterfowl hunting, but do not address changes in the quality of transitional ecosystems that may

possibly be associated with reduced NO X /SO X emissions.



# F.4.2. Limitations and Uncertainties

Meta-analyses of wetland value (and the underlying original studies) are not directly useful because

they do not measure changes in ecosystem services that would follow from some tightening or loosening

of the standard for NO X or SO X

. Nevertheless, they have indirect use by pointing out the importance of

various methodologies, the overall values commonly assigned to wetland improvement, and types of

ecosystem services that might be affected by air pollutants. For example, both Brander et al. (2006) and

Woodward and Wui (2001) found that CV studies generally yielded greater value per acre than travel cost

studies. This is not surprising, as the former would generally include non-use values. Furthermore,

Woodward and Wui’s (2001) meta-analysis showed that the availability of bird hunting, bird watching,

and amenity services affect wetland value. For example, bird watching (which could be affected by bird

populations which, in turn, could be affected by nutrient enrichment) contributes more value to an

ecosystem than any of the other ecosystem services. This is mainly due to the popularity of bird watching

and the large numbers of people who engage in this recreational activity.



# F.5. Valuation of Aquatic Ecosystems

As discussed in the ISA and the preceding annexes, acidification and eutrophication are the two

main effects of NO X and SO X on aquatic ecosystems. In economic valuation of the effects of NO X and

SO X deposition on aquatic ecosystems, these effects are reflected partly in the WTP for recreational

fishing (with effects on the catch rate and fishing quality in a particular aquatic ecosystem) and partly on

aesthetic and non-use (and total) values. Valuation methods used to assess the value of aquatic ecosystems

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35 include contingent valuation (CV) (open-ended, discrete choice, etc.), choice experiments, hedonicpricing valuation, travel cost (TC) (hedonic travel cost and traditional travel cost), cost-effectiveness

analysis, avoided-damage cost, replacement cost, market analysis, and so forth. The CV method has

gained popularity for total (use plus non-use) value estimation, whereas the travel cost method plays an

important role in deriving use values. Currently, there is also a tendency to use stated and RP techniques

together for the valuation of recreational activities. Supplemental to the below discussions about valuation

of acidification and eutrophication on aquatic ecosystems, Table F-9 describes different aquatic valuation

studies according to the ecological effect examined (acidification, eutrophication) and lists their relevant

details.

An extensive literature review by Wilson and Carpenter (1999) provides an excellent starting point

to the valuation literature on aquatic ecosystems. This paper examined 30 studies published between 1971

and 1997 that estimated the value of nonmarket ecosystem services provided by fresh water bodies in the

U.S. Sixteen were SP studies, with the rest split between travel cost and hedonic pricing approaches. The

travel cost studies covered very specific water bodies and stressors, such as a boatable to swimmable

change in water quality for 13 recreation sites along the Monongahela River in Pennsylvania (Smith and

Desvousges, 1986), and a change in water quality measured by the Uttormark’s Lake Condition Index for

recreational use of Pike Lake in Wisconsin (Bouwes and Schneider, 1979). Beyond spatial and stressor

differences, the studies differ in their units for expressing value. The Monongahela River study used

benefits per household, with the largest value expressed for a change from boatable to swimmable ($51).

The Bouwes and Schneider (1979) study concluded that the total (aggregate) annual mean CS w as almost

$86,000.

There are several additional meta-analyses that describe methods of valuation for aquatic

ecosystems, but not specifically relating to effects caused by NO X or SO X

. Van Houtven et al. (2007)

analyzed WTP for fresh water quality improvements, covering 131 valuation estimates from 18 SP studies

that used or could be transformed to use the water quality ladder (boatable, fishable, swimmable)

modified into a 10–point scale. The analysis started with 90 studies but found that most were not

sufficiently comparable for statistical analyses. The studies ranged from 1977 to 2003 and covered rivers,

lakes and estuaries.

A meta-analysis by Johnston et al. (2005) examined the value of fish catch based on a total of 48

studies published in the U.S. and Canada between 1977 and 2001. The study statistically examined WTP

for fish relative to variations in resource, context, angler, and policy attributes, as well as methodological

attributes of the studies themselves. The attributes examined included species targeted, geographic region,

water body type, catch rate, angler demographics, and fishing method. Among 391 WTP observations,

122 were estimated using CV methods, 59 using TC methods and the remaining from discrete choice

models.

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32 Johnston et al. (2005) found that SP studies have generally resulted in lower WTP estimates than

RP studies, which is consistent with earlier multiple study reviews and meta-analyses. Dichotomous

choice CV also has produced lower WTP estimates than choice experiment or conjoint surveys, compared

to the default of open-ended surveys (including payment cards and iterative bidding). However, these

findings were not robust across all regression model specifications. The study supported previous findings

(Poe et al., 2000) that WTP is systematically associated with variations in resource, context, and angler

attributes. The study also concluded that WTP per fish varies systematically across study methods, but the

variation due to methodology accounted for a relatively small proportion of total WTP variation. Again,

these results demonstrate the value of recreational fishing, but do not address changes in water quality

that may be associated with reduced NO X /SO X emissions.



# F.5.1. Acidification

The Adirondack Park in New York is the best documented of all areas affected by acidic deposition

in the U.S. Due to air pollution that largely originated from power plants and other sources to the west

and southwest of the park, many watersheds in the park have experienced slow acidification of water and

soil since the late 18th century. This park has been the subject of numerous valuation studies in recent

decades.

Morey and Shaw (1990) applied the travel cost model and estimated recreational fishing values

associated with water quality change resulting from acidic deposition in the Adirondack Park area. The

results showed that the aggregate expected conditional compensating variation for a 50% increase in catch

rate of two trout species at four sites was $3,863 (1977 dollars; $13,175 in 2007 dollars), based on 607

survey respondents with an estimated standard deviation of $89 ($304 in 2007 dollars).

A travel cost model was also applied by Mullen and Menz (1985) to link acidic deposition and

recreational fishing in the Adirondack Park. A net average economic value per angler day was calculated

to be $20 (1976 dollars; $73 in 2007 dollars) for lakes, ponds, and streams in the Adirondacks, with an

estimated total loss in net economic value of $1.07 million (1976 dollars; $3.89 million in 2007 dollars)

per year for a 5% reduction in fishable area. These estimates excluded streams because limited knowledge

existed to assess the effect of pH fluctuations on aquatic life in streams. Shaw (1989) questioned the

reliability of these results, arguing that the demand equation was not compatible with economic theory

and that aggregating separate CS m easures from different equations was inappropriate.

Englin et al. (1991) assessed the economic impact on recreational fishing in four upper northeastern

states resulting from acidic deposition control. Biological effects of acidification were linked with

hedonic travel cost and random utility models through the acid stress index (ASI) and fish catch per unit

effort (CPUE). The analysis was based on multiple data sources and yielded positive social welfare for

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32 2030 under a scenario of reducing by 50% the ambient deposition in 1989. This level of decline in acidic

deposition was expected to occur on the basis of analyses conducted by the National Acid Precipitation

Assessment Program (NAPAP).

Cameron and Englin (1997) measured welfare under an assumption that people are not certain

about participation in fishing. Based on WTP results from a survey and a random utility model, surpluses

were estimated for preventing a 20% loss in the availability of high-elevation lakes for fishing in

northeastern states.

Randall and Kriesel (1990) employed a SP method to value a National Pollution Control Program,

which led to improved air and water quality by 25% within 5 years. A nationwide survey conducted in

multiple modes found an estimated annual willingness to pay of $694 ($1,098 in 2007 dollars) per

household. The researchers concluded that the estimated value was lower than their multimarket hedonic

estimates.

Banzhaf et al. (2006) conducted a CV survey, which was designed to estimate the total value (use

plus non-use values) of reducing acidification by liming the lakes and forests in the Adirondack Park to a

degree matching recent SO X and NO X reductions under emissions trading programs. The mean WTP for

improving 600 lakes “of concern,” and small increases in populations of two bird species and one tree

species was $48 (2003 dollars; $54 in 2007 dollars) per year per New York household. The mean WTP for

improving 900 lakes “of concern,” four bird species and three tree species was $54 (2003 dollars; $61 in

2007 dollars) per year per New York household.

Sequential implementation of several regulations, including in particular the CAA, has

substantially reduced acidic deposition, especially in the eastern part of the U.S. Burtraw et al. (1997)

conducted a benefit-cost analysis for the NAPAP program. Besides improving public health and visibility,

emissions controls contributed to decreased lake acidification which was projected to have economic

benefit associated with improved recreational fishing in the Adirondack Park. The ASI and CPUE

approaches were used in the Tracking and Analysis Framework (TAF) model to capture the response of

three recreationally fished species to water quality improvement in 33 statistically selected Adirondack

lakes. The per capita recreational fishing benefits in 2010 were estimated to be $0.62 (1990 dollars; $0.98

in 2007 dollars) annually per angler fishing in the Adirondack area.



# F.5.2. Eutrophication

As is the case for acidification, the endpoints most studied in the valuation literature for nutrient

enrichment concern fishing and non-use (or total) values. Change in aquatic recreational behavior is

another endpoint for looking at valuation of such ecosystems, although it is difficult to link those changes

due to ambient NO X or SO X concentrations.

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## F.5.2.1. Recreation

Bockstael et al. (1989a) measured the benefits of improvements in water quality of the Chesapeake

Bay using travel cost and SP methods. A telephone survey with 959 respondents revealed an aggregate

WTP for water quality improvement from current level to a level acceptable for swimming and/or other

water activity of about $91 million (1987 dollars; $166 million in 2007 dollars) for households in

Maryland. Based on three other surveys, a travel cost model yielded aggregate use values of about $34.7,

$4.7, and $1.4 million ($63.1, $8.6, and $2.6 million in 2007 dollars) for a 20% water quality

improvement for beach use, boating, and sport fishing, respectively.

Morgan and Owens (2001) simulated water quality change under a baseline (without additional

pollution control) and a scenario with pollution control in the Chesapeake Bay watershed and then

calculated aggregate benefits for beach use, boating, and striped bass sport fishing by transferring the

benefits from Bockstael et al. (1989a). Lower bound aggregate benefits for beach use, boating, and sport

fishing were $288.8, $6.7, and $288.8 million (1996 dollars; $380.4, $8.8, and $380.4 million in 2007

dollars), respectively for a 60% improvement in Chesapeake Bay water quality.

Lipton (2004) mailed out an open-ended contingent valuation survey to 2,510 randomly selected

Maryland boaters regarding willingness to pay for one unit improvement in the water quality of the

Chesapeake Bay. The boaters ranked their perception of water quality on a scale of one to five. The

median WTP for a one step improvement in water quality was $17.50 ($19.66 in 2007 dollars) per year

and the mean was $63 ($71 in 2007 dollars), with 38% of respondents expressing a zero WTP. It was

found that the boaters who keep their boats in the water would pay more than those who keep them on

trailers. Individuals who ranked ambient water quality lower and had more concern for the health effects

from water quality degradation would likely pay more for water quality improvement. The aggregated

WTP for the Chesapeake Bay boaters in Maryland was approximately $7.3 million ($8.2 million in 2007

dollars) per year with a $146 million ($164 million in 2007 dollars) present value (a 5% discount rate).

Smith et al. (1991) estimated the recreational fishing value of Albemarle-Pamlico Estuary in North

Carolina using a hedonic travel cost model. The analysis was based on an intercept survey with 1,012

interviews at 35 boat sites and 44 bank sites in 1981 and 1982. The estimated benefit derived from the

application of a conventional demand model to the boat site sample ranged from $1.49 to $2.58 (1982

dollars; $3.19 to $5.53 in 2007 dollar), and for the bank site sample ranged from $0.65 to $1.11 ($1.39 to

$2.38 in 2007 dollars) for an increase in catch rate of one fish per hour per person.

Four years later, Kaoru (1995) and Kaoru et al. (1995) used the information collected from the

same survey to estimate the recreation value from improvements in Albemarle and Pamlico Sounds using

a household production model and a random utility model. They linked the effluent loading and quality of

sport fishing with the fisherman’s decision on site selection. Instead of fish availability being estimated as

the average number of fish caught per person per hour for each entry point, Kaoru et al. (1995) used total

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35 catch per trip per person as their key measure of fishing success. They included not only the estimated N

loadings but also the effect on biochemical O 2 demand as an influence on fishing quality. The results

suggested that depending on aggregation methods, CS ranged between $7.05 and $36.19 ($9.56 and

$49.08 in 2007 dollars) for a 5% increase in total catch. The CS was $1.27 to $6.52 ($1.72 to $8.84 in

2007 dollars) for a 36% decrease in N loadings at all sites.

Whitehead et al. (2000) used a joint stated and RP model to estimate recreation value of

improvements to Albemarle and Pamlico Sounds in terms of CS per trip and per season. A telephone

survey was conducted in 1995 to learn how North Carolina residents would value improvements of water

quality resulting in a 60% increase in fish catch and a 25% increase in open shellfish beds. The analysis

was based on 765 completed responses. The CS per trip was estimated at $64 (1995 dollars; $87 in 2007

dollars) for current quality and $85 ($115 in 2007 dollars) for improved quality and the CS per season

was $121 ($164 in 2007 dollars) for current and $155 ($210 in 2007 dollars) for improved quality.

The Catawba River in North and South Carolina is used for electric power generation, recreation,

drinking water, and wastewater assimilation. With population growth and land use change, the health and

vitality of the river system are declining as the river flows downstream. Through a mail-phone survey of

1,085 residents randomly selected from 16 counties of Catawba River Basin, Kramer and Eisen-Hecht

(2002) presented a management plan designed to maintain the water quality at the current level over time.

The water quality in the entire watershed was classified as good, fair or poor on maps to illustrate the

distribution of the tributaries with different water quality. The mean WTP was estimated to be $139 ($160

in 2007 dollars) for protecting current water quality from deterioration.

A site choice model applied jointly with a trip frequency model was used by Needelman and Kealy

(1995) to assess the relative benefits of eliminating eutrophication, fecal bacteria, and oil and grease in

New Hampshire lakes. The site choices analysis was based on 53 individuals and 1,021 trips, while the

trip frequency model was based on a total of 519 individuals including the responses for the site choices

model. The mean seasonal benefits were valued at $1.40 ($1.90 in 2007 dollars) for eliminating

eutrophication from all sources ($1.33; $1.80 in 2007 dollars) for eliminating nonpoint source pollution

alone and $0.09 ($0.12 in 2007 dollars) for eliminating point source pollution alone) with an aggregate

seasonal benefit of $1.16 million ($1.57 million in 2007 dollars), with $1.11 million for nonpoint source

and $0.08 million for point source pollution. However, the economic benefit estimates were exclusively

for swimming and day trips. The measures of water quality were not from the year of the survey (1989)

but from a range of years from 1976 to 1991.

The Tar-Pamlico River has experienced declining fish catches, disease in fish and shellfish beds,

algae blooms, and aquatic grass losses. More than half of the pollution impairing one third of the river

was estimated to come from agricultural nonpoint sources. Whitehead and Groothuis (1992) proposed a

management program in which farmers used best management practices (BMPs) to significantly improve

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29 the water quality of the Tar-Pamlico River such that recreational anglers would be able to catch twice as

many fish per trip. A mail survey was sent to 179 households in four counties in Tar-Pamlico River basin

(with a 61% response rate). The mean WTP for doubling fish catch was estimated at $25 ($38 in 2007

dollars), which was bounded from above by a $35 use value ($53 in 2007 dollars) and from below by a

$21 ($32 in 2007 dollars) non-use value. The benefits of water quality improvement in the study area

were aggregated at $1.62 million (1991 dollars; $2.46 million in 2007 dollars) each year. The researchers

believed that non-use value could account for as high as 84% of the estimate. However, the study suffered

from small sample size and relatively high non-response rate (11%) to the WTP questionnaire.

Some portions of the coastal coral reefs in the Florida Keys are projected to disappear within 10 to

25 years. In 2000, the U.S. government announced a long-term plan to save coral reefs proposing that

20% of all coral reefs in American-controlled waters would become ecological preserves by 2010. Park

et al. (2002) investigated the WTP to preserve current water quality and health of the coral reefs in the

Florida Keys. Based on 460 responses to a CV survey and 4,035 respondents to a travel cost survey (460

responses used in the analysis), the annual use value was estimated at $481 per person per snorkeling trip

($553 in 2007 dollars). The mean predicted WTP per trip from a Tobit model was $735 ($844 in 2007

dollars). Over 85% of the predicted WTP value was within plus or minus $50 ($57 in 2007 dollars) of the

total trip expenses from the contingent valuation scenario.



## F.5.2.2. Commercial Fisheries

As one of six background studies for the National Science and Technology Council (NSTC), Diaz

and Solow (1992) used a time series estimation approach to examine the effects from hypoxia 1 in the Gulf

of Mexico. The study failed to confirm the relationship between the annual occurrence of hypoxia and

commercial fishery health, based on catch rate per unit effort of three major species from the 1960s to

1990s. Although the benefit assessment did not detect effects attributable to hypoxia, this does not

necessarily mean that the economic effects would not occur. NSTC (2000) identified alternatives for

reducing the adverse effects of hypoxia and examined the costs associated with reduction of N and P

inputs. A net cost estimated by the U.S. Mathematical Programming Model for Agriculture was about

$0.8 per kilogram ($0.36 per pound; $0.96 per kilogram/$0.36 per pound in 2007 dollars) for a 20% edgeof-field N loss reduction on agricultural lands, whereas restoring 5 million acres of wetland would have a

net cost of $1.00 per kilogram ($0.45 per pound) ($1.2 per kilogram/$0.54 per pound in 2007 dollars) of

N removal.

1 Hypoxia (DO d epletion) i s a phenomenon resulting from the overloading of n utrients (usually N o r P ) i n water. Most fresh waters are P-limited,

and therefore added N f rom atmospheric deposition does not have a substantial effect on productivity. In contrast, marine and estuarine waters

tend to be N-limited, and are therefore expected to respond to additional N inputs by increasing algal productivity. Excessive production of a lgae

can deplete the water o f o xygen when those algae die and are decomposed by oxygen-consuming microorganisms. If t he concentration of D O

decreases to very low levels, fish, and other l ife forms can die.

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29 The Neuse River in North Carolina is important to the commercial blue crab ( Callinectes sapidus )

fishery in the eastern U.S. It accounted for about a quarter of the blue crab harvest from 1994 to 2002.

Smith and Crowder (2005) simulated the progress of eutrophication in the Neuse River using a series of

ordinary differential equations, which linked changes in the quantity of nutrients, algal growth, spatial

population distribution of blue crab and its prey species with fishing efforts. Results suggested that a 30%

reduction in N loading in the Neuse River watershed over a 50–year period would result in about a $2.56

million ($2.71 million in 2007 dollars) discounted present value generated in fishery rent (the difference

between fishing revenues and costs including fixed and opportunity cost).



## F.5.2.3. Water Clarity

In addition to effects on fish populations, eutrophication reduces water clarity due to excessive

growth of algae. Boyle et al. (1999) studied the social welfare related to water clarity of four lakes in

Maine employing a two-stage hedonic demand model. Based on data from 1990 to 1995 on property sales

for 25 lakes, tax records, mail survey, and water clarity data from the Maine Department of

Environmental Protection (DEP), the CS f or water clarity improvement from the average ambient level

(3.78 m) to 5.15 m was estimated to be $3,677, $3,765, and $12,870 ($4,562, $4,671, and $15,967 in

2007 dollars) for differently specified demand systems (Cobb-Douglas, semilog, and linear demand

models, respectively). Social welfare loss for visibility deterioration from an average level to 2.41 m

ranged from $25,388 to $46,750 ($31,497 to $57,989 in 2007 dollars). One of the interesting findings in

the study was that the slope of the Cobb-Douglas demand model increased to 3.0 m, a threshold used by

the Maine Department of Environmental Protection to indicate improved water quality and public

preferences for it.

Poor et al. (2006) evaluated water quality in a small watershed of Maryland using a hedonic

property valuation model. The watershed is located in a peninsula surrounded by the Potomac and

Patuxent Rivers and the Chesapeake Bay. Due to nonpoint source pollution runoff, water quality has

deteriorated, which might have negative impacts on residential housing prices, especially for those close

to the river. To estimate the possible extent of the impact, total suspended solids (TSS) and dissolved

inorganic nitrogen (DIN) were averaged by year and included in the model as indicators of ambient water

quality. The estimates showed that a marginal increase in TSS reduced average housing prices by $1,086

($1,113 in 2007 dollars), and a marginal increase in DIN decreased housing prices by $17,642 ($18,087 in

2007 dollars).

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# F.5.3. Avoided Costs

Based on a model projection, IEc (1999c) estimated the benefits of decreased N deposition to the

estuaries in the eastern U.S. using the avoided cost method. According to their results, the annual avoided

cost in 2010 would range from $26 to $102 million if 12.8 million pounds of N loading was reduced

annually in Long Island Sound. An annual reduction of 58 million pounds of N loading into Chesapeake

Bay would avoid an annual cost of $349 to $1,278 million. Uncertainty associated with model

assumptions and the inappropriate use of avoided cost estimates to value benefits or damages are two

major sources of potential error in generating such estimates. In addition, the avoided costs method

generally does not measure values conceptually accurately, and these values also are not linked to benefits

from reduced pollution.



# F.5.4. Limitations and Uncertainties

Several general limitations apply to the valuation of water quality changes. First, the definition of

water quality is too ambiguous to quantify comparisons across studies. Second, the degree of water

quality improvement is not often clear. A common obstacle in any environmental economic valuation is

the availability of data. Problems stemming from lack of data extend from biological data on the

populations of target species to limitations of the available economic data on the value of commercial and

recreational fisheries to small samples of survey respondents (Smith and Crowder, 2005).

The geographical focus of valuation studies is another limitation. The studies reviewed here largely

focused on the eastern part of the U.S., represented by the Adirondacks, Chesapeake Bay, and Albemarle

and Pamlico Sounds. Several studies investigated the total value of aquatic ecosystems and the majority

of the studies calculated recreational values of water quality improvement. Only a few studies addressed

the economic value of commercial fisheries. There is considerable uncertainty in the estimates of benefits

as they vary significantly even for studies in the same area, with similar changes in the commodity, and

use of the same valuation methodology.

Almost every study reviewed here mentioned the problem of uncertainties about the natural science

(Diaz and Solow, 1992; NSTC, 2000). For example, the processes involved in the development of

hypoxia are not fully known. There is also no clear connection between a decrease in pollution level and

an increase in catch rate (Bockstael et al., 1989b). Additionally, uncertainty arises from the selection of

parameter values (Smith and Crowder, 2005) and models (Burtraw et al., 1997). Due to the uncertainty

about water quality improvement in tributaries of the Chesapeake Bay, the estimates provided by Morgan

and Owens (2001) excluded benefits from water recovery in the tributaries.

Study coverage also affects benefit estimates. For example, Morey and Shaw (1990) only included

those fishermen who could make day trips to one or more of the study sites, fishermen with complete

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32 records, and fishermen whose distance to the farthest site was less than 200 miles. Morey and Shaw

(1990) evaluated four sites and two kinds of trout in the Adirondacks while Englin et al. (1991) valued

only one trout species and lakes in four states.

Many studies relied on information from surveys, which may suffer from various biases. For

example, several studies reviewed here used intercept surveys, which are not necessarily representative of

the target population (Bockstael et al., 1989b; Smith et al., 1991). Double counting of values may also

introduce errors into value estimation. For example, people who are boating may also go fishing

(Bockstael et al., 1989b).



# F.6. Summary

Previous ecosystem valuations presented in AQCDs were very limited because they considered

only studies that could directly attribute monetary values to changes in emissions. The assessment of the

literature in this Annex has a different approach: the voluminous literature that values various ecosystem

services affected by emissions is included, whether or not the actual linkages all the way from emissions

to those effects have been quantified. This approach nevertheless necessitates that the natural science

underpinnings be examined in the context of preferences, that is, descriptions of damages (or benefits)

from NO X and SO X emissions (or reductions in those emissions) from the natural sciences that translate

into things that the public cares about. These include, for example, whether the water is boatable or

swimmable, the marketable yield associated with changes in a forest or a crop, and effects on aesthetics of

wetlands.

The physical endpoints and their corresponding valuation studies are divided into different

ecological and value endpoints applicable to terrestrial, transitional, and aquatic ecosystems. There is

significant valuation literature on the effects of O 3 on crops (and to a far lesser degree on forest yield), but

this topic is beyond of the scope of this assessment. Beyond the O 3 effects, there is little quantification of

the science describing the effects of pollution related to NO X and SO X on ecosystems. Valuation studies

are themselves classified into meta-analyses and original studies, the latter into market studies (e.g.,

commercial fishing), RP studies (e.g., those related to recreation behavior, property values, etc.), and SP

studies (those that ask people survey questions about their WTP for hypothetical ecosystem

improvements).

For valuation of terrestrial effects, survey methods are most common. Supplemented by travel cost

approaches, this literature leads to a variety of estimates of WTP for improved forest quality that could

prove useful for estimating benefits of N and S reductions (provided a number of linkages can be made),

even though some of the endpoints valued are related to insect damage. One meta-analysis is available

that summarizes this valuation literature.

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Table F­1. Commonly

preferences. adopted environmental valuation methods based on revealed or state

Methods Description Direct

Use Indirect

Use Total

Value

Revealed Preference

Methods

Avoided expenditure

method

Derived demand

functions

Hedonic valuation

method

Market analysis

Referenda

Replacement cost

Travel cost method

User fee Observing individual choices and behavior to predict their preferences for environmental

goods and services.

Predicting the cost of mitigating the effects of reduced environmental quality.

Estimate the value of environmental goods and services by deriving the demand functions of

households or firms for them (e.g. water use)

Estimating an implicit price for the environmental quality attributes of marketable goods, such

as housing.

Used for valuing market goods using data on prices and quantities of outputs and inputs. May

use prices of close substitutes, methods of deriving shadow prices, or simulation of changes

in market conditions.

Examining voting results related to environmental resources to predict values for them.

Measures expenditures incurred in replacing or restoring environmental good or service lost

(e.g. water filtration). Provides accurate valuation only under strict assumptions usually not

met.

Values the environmental attributes of recreational sites by examining visitation frequency

and cost differential incurred in reaching site with different attributes.

Examine user fees paid to gain access to an ecological resource such as a park to estimate

the lower bound of society’s value for that resource. X

X

X

X

X

X

X

X X

X

X

X

X

X

X

X For wetland valuation, three meta-analyses are available that summarized the valuation literature.

This literature is problematic because it focuses on values per wetland acre rather than WTP for changes

in services provided by a wetland affected by deposition of N and S p ollutants. However, some of the

available studies provide WTP for discreet ecosystem services, which is helpful in matching these values

to services affected by reductions in N and S deposition. Nevertheless, because there is a very poor

understanding of the scientific basis for linking NO X and SO X emissions to ecosystem health endpoints,

the ability to make the necessary linkages to estimate benefits of pollution reductions is very limited.

The aquatic service valuation literature is the most voluminous of the three categories reviewed. It

contains many recreation value studies, a number of property value studies, some total (use plus non-use)

value studies, some studies on commercial fishery damages and several good meta-analyses of primary

valuation studies, which provide estimates of the WTP of households for improvements in the aquatic

ecosystem related to N and S c hanges. Again, however, there is little focus on NO X or SO X as the cause of

alterations to the aquatic ecosystem.

Overall, there is a robust literature valuing a variety of ecosystem services that could be related to

reductions in N and S e missions. Therein addition, issues affecting the credibility of any individual study

and even the studies grouped by technique, such studies can only be used for general valuation purposes.

The most important limitation is establishment of the linkages between the physical, chemical, and

biological effects of air pollutants on natural ecosystems and changes in exposure to NO X and SO X

.

August 2008 F­35 DRAFT­DO NOT QUOTE OR CITEMethods Description Direct

Use Indirect

Use Total

Value

Stated Preferences Directly surveying individuals to predict their preferences for environmental goods and

Approach services.

Choice experiments, Elicits individuals’ choices from several alternatives associated with different environmental X X X

conjoint analysis and cost outcomes. Elicits data on WTP indirectly (by eliciting cost­environmental outcome

tradeoffs, similar to contingent ranking).

Contingent valuation Elicits data on individuals’ willingness to pay for environmental goods and services X X X

(hypothetical setting, limited by potential biases). Often implemented using a hypothetical

referenda.

Contingent ranking Elicits data on the ranking of several alternatives associated with different environmental and X X X

cost outcomes. Elicits data on WTP indirectly (by eliciting cost­environmental outcome

tradeoffs, similar to choice experiments, conjoint analysis).



### Table F­2. Economic effects

ozone criteria document. of ozone and other pollutants on agriculture, as reported in the 1996

Study

Adams

et al.

(1986a)b

Kopp

et al.

(1985)b

Adams

et al.

(1986b)

Adams

and

Crocker

(1989) Region

U.S.

U.S.

U.S.

U.S. Pollutant and

Concentration

Ozone, 25% reduction

from 1980 level for

each statec

Ozone, universal

reduction from 53 ppb

to 40 ppba

Acid deposition, 50%

reduction in wet acidic

deposition

Ozone, seasonal

standard of 50 ppb

with 95%

complianced; Includes

adjustments for 1985

Farm Bill Model Features Results (Annual 1980 U.S. Dollar)

Price

Changes

Yes

Yes

Yes

Yes Output

Substitutions

Yes

Yes

Yes

Yes Input

Substitutions

Yes

Yes

Yes

Yes Quality

Changes

No

No

No

No Crops

Corn,

soybeans,

cotton,

wheat,

sorghum,

barley

Corn,

soybeans,

wheat, cotton

Soybeans

Corn,

soybeans,

cotton,

wheat,

sorghum,

rice, hay,

barley Consumer Producer Total

Benefits Benefits Benefits

(Costs)

1160×106 550×106 1700×106

Not Not Not

reported reported reported

172×106 –30×106 142×106

905×106 769×106 1674×106

a Seven­h growing season geometric mean. Given a long­normal distribution of air pollution events, a 7–h seasonal ozone level of

approximately equal to an hourly standard o f 80 ppb, not to be exceeded more than once a year (Heck et al., 1983).

b Reported in 1986 criteria document.

c Reported All studies except Garcia et al. use NCLAN data to generate yield changes due to ozone.

d Seven­ and twelve­h growing season geometric mean. Analysis includes both fixed rollbacks (e.g., 25% and seasonal standards

compliance rates).

Source: Adams and Horst (2003) 40 ppb is

(with variable

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### Table F­3. Economic effects of ozone on marketable benefits from forests.

Reference Pollutant/Coverage Response and Air Quality Data Economic Model Annual Damages or

of Control Benefits

Callaway

et al.

(1986)a

Crocker

(1985)

Crocker and

Forster

(1986)

Haynes and

Adams

(1992)

Bentley and

Horst (1998) All pollutants. Forest products

(hardwood and softwood) in

the eastern U.S.

Acid deposition.

Forest products and forest

ecosystem service flows for

eastern U.S.

Acid deposition. Forest

products and forest

ecosystem services for

eastern Canada.

Air pollutants, including acid

precipitation. Losses

estimated for eastern U.S.

softwoods.

Ozone. All hardwoods and

softwoods except western

hardwoods. Assumes three arbitrary growth

reductions (10%, 15%, and 20%) for

hardwood and softwood tree species.

Assumes a 5% reduction in products due

to acid deposition: assumes a pristine

background pH of approximately 5.2.

Assumes 5% reduction in forest

productivity for all eastern Canadian

forests receiving $10 kg/ha/yr sulphate

deposition.

None; paper demonstrates a methodology for assessing economic

effects of yield (growth and inventory)

reductions due to any course. Assumes

losses from 6% to 21% for softwoods.

Dose­response based on survey of

experts scaled to partial attainment of

secondary standard relative to the

current primary standard. SUM06

exposure metric is based on a cumulative

daytime exposure during the growing

season. Spatial equilibrium

models of softwood and

hardwood stumpage

and forest products

industries in the U.S.

Naive; assumed

changes in output

multiplied by avg value

of those goods or

services.

Naive; assumed

changes in output

multiplied by avg value

of goods or services.

Econometric model of

U.S. timber sector

(TAMM).

Econometric model of

U.S. timber sector

(TAMM). $340 to $510 million; damage in

1984 dollars for assumed

reductions in growth levels.

$1.75 billion damage in 1978

dollars from current levels of acid

deposition.

$1.5 billion damage in 1981

Canadian dollars from current

levels of acid deposition.

Damages of $1.5 to $9.0 billion in

1988 dollars.b

Benefits of $14 million in 2010

(1990 dollars) for partial

attainment of 0.08 ppb 3rd max

secondary standard. This

standard was considered during

the standard development

process.

a We used the updated version of Crocker et al. from 1986, while

b We drew different numbers from the Crocker and Forster study

Source: Adams and Horst (2003), exceptions: Table 2 in Bentley Adams and Horst cited a prior

than those reported by Adams

and Horst (1998). version, from

and Horst. 1985.



### Table F­4. Forecasted average values for select activities, per day per person in 1996.

Activity Northeast Southeast Intermountain Pacific Coast Alaska USA

Swimming

General recreation* 14

30 9

25 19

34 9

25 14

30 14

30

Fishing

Waterfowl hunting

Big game hunting 37

40

45 32

35

40 41

44

50 32

34

40 37

40

45 37

39

45

*The activity category includes camping, picnicking, sightseeing, hiking, small game hunting, wildlife viewing, and other

meta­analysis, values for these activity categories do not differ statistically significantly. Source: from the meta­analysis

Rosenberger and Loomis (2001), Table 6. general recreation.

benefit function by In the



### Table F­5. Typical impacts of specific pollutants on the visual quality of forests.

Pollutant Geographic

Extent Injury

Type Major Types of Visual Injuries Notes

Ozone Area or regional

effects Direct

injuries Foliar injuries (e.g., pigmented stipple), increased needle/leaf

abscission, premature senescence of leaves. Pattern, size,

location, and shape of foliar injuries to indicator species can be

specific for ozone.Pollutant Geographic

Extent Injury

Type Major Types of Visual Injuries Notes

Acidic

Deposition

Sulfur

Dioxide

Hydrogen

Fluoride Area or regional

effects

Point source pollution

Point source pollution Indirect

Injuries

Indirect

Injuries

Direct

Injuries

Direct

Injuries Increased susceptibility to visual injuries may result from other

adverse environmental factors, such as insect attacks. For

example, increased needle/leaf abscission, elevated mortality

rates, and/or changes in species composition.

Increased susceptibility to visual injuries may result from other

adverse environmental factors, such as climatic factors. For

example, increased needle/leaf abscission, elevated mortality

rates, and/or changes in species composition.

Foliar injuries including leaf/needle discoloration and necrosis.

Pattern, size, location, and shape of foliar injuries to indicator

species can be specific for sulfur dioxide. At high

concentrations, elevated mortality rates of sensitive species and

changes in species composition may occur.

Foliar injuries including leaf/needle discoloration and necrosis.

Pattern, size, location, and shape of foliar injuries to indicator

species can be specific for sulfur dioxide. At high

concentrations, elevated mortality rates of sensitive species and

changes in species composition may occur. Acidic deposition can also cause direct

foliar injuries. Acids are, however, more likely to indirectly affect the visual

appearance of forest trees, unless

exposure levels are very high.

Sulfur dioxide may also cause indirect

injuries. Indirect injuries, however, are not

well documented.

Hydrogen fluoride may also cause indirect

injuries. Indirect injuries, however, are not

well documented.

Source: Exhibit 2 Industrial Economics (IEc) (1999a)



### Table F­6. Economic valuation studies related to forest aesthetics.

Study Method Study Area Description Economic

Endpoint Economic Value

Crocker

(1985)

Haefele

et al. (1992)

Holmes and

Kramer

(1996)a

Holmes

et al. (2006)

IEc

(1999b)b

Jakus and

Smith

(1991)

Jenkins

(2002) CV (open ended)

CV (payment

card,

dichotomous

choice)

CV (payment

card,

dichotomous

choice)

Hedonic pricing

Benefits transfer

CV (dichotomous

choice)

CV (open ended) Southern

California

Southern

Appalachian

Mountains

Southern

Appalachian

Mountains

New Jersey

National

Maryland and

Pennsylvania

Southern

Appalachian

Mountains Evaluating visits to recreational

sites with slight, moderate, and

severe O 3 induced damages to

ponderosa and Jeffery pine

stands.

Protect high­elevation spruce

firs from insect and air

pollution

Protect high­elevation spruce

firs from insect and air

pollution. Analysis compared

forest users and non­users.

Evaluate the effects of hemlock

forest health status on housing

prices. Hemlock health status is

potentially deprived by

hemlock woolly adelgid; an

exotic forest insect.

Protect trees from various

different types of damage (see

description of source studies)

Protect private homeowner

property and surrounding

public lands from gypsy moth

damages (reduce tree

defoliation by 25%)

Protect high­elevation spruce

firs from insects and air

pollution along roads and

throughout ecosystem Household

WTP

(per trip)

Household

WTP (per

yr)

Household

WTP (per

yr)

Housing

price

Welfare

Loss (1990–

2010)

Household

WTP

(for entire

program)

Household

WTP (per

yr) Slight damage: $2.09

Moderate damage: $0.66

Severe damage: $0.74

PC: $21

DC: $100

Forest users: $36

Non­users: $10

Hemlock forest health status is positively associated

with housing prices. For example, a 1–point

increment in (e.g., from 10% to 11%) in the % of

healthy hemlocks of all forests on the home parcel

was associated with a 0.66% sales price increase.

Similar changes in home’s near proximity are

associated with yet larger price increments.

$3 to $17 billion

Private Property Prgm (only): $254 to $420

Private and Public Prgm:

$314 to $527

$153

August 2008 F­38 DRAFT­DO NOT QUOTE OR CITEStudy Method Study Area Description Economic

Endpoint Economic Value

Kramer

et al. (2003)

Kramer and

Mercer

(1997)

Loomis

et al. (1996)

Leuschner

and Young

(1978b)

Miller and

Lindsey

(193)

Peterson

et al. (1987)

Treiman

(2006)

Walsh et al.

(1989)

Walsh et al.

(1990) CV (dichotomous

choice)

CV (payment

card and

dichotomous

choice)

CV (dichotomous

choice, open ended)

TCM

CV (dichotomous

choice)

CV, hedonic

property

CV (dichotomous

choice)

TCM

CV (iterative

bidding) Southern

Appalachian

Mountains

National (USA)

Oregon

Texas

New

Hampshire

Los Angeles

area

Missouri

Colorado

Rockies

Colorado Protect high­elevation spruce

firs from insects and air

pollution along roads and

throughout the entire

ecosystem

Protect 10% of tropical

rainforests as national parks or

forest reserves.

Protect old growth forests of

Pacific Northwest from fires.

Reduce crown density in

recreational areas.

Protect private homeowner

property from gypsy moth

damages (reduce tree

defoliation by various amounts)

Avoid O 3 induced damages to

trees in local national forests

for recreationists (in greater LA

area) and homeowners (with

property bordering forest).

Improve residential tree care

and maintenance in different

sized cities.

Reduce tree density in

recreational areas.

Protect ponderosa pine forests

from damages caused by the

mountain pine beetle. Household

WTP (per

yr)

Household

WTP (per

yr)

Household

WTP

(per yr)

Consumer

Surplus

(losses)

Household

WTP (per

yr)

Household

WTP

(per yr)

Consumer

Welfare

Household

WTP

(per yr)

Consumer

Surplus

(losses)

Household

WTP (per

yr) Road side only: $18

Entire ecosystem: $28

Non­use: 87% of total value

Use: 13% of total value

PC: $31

DC: $21

DC: $98

OE: $33

–0.69 to –6.5% (depending on

sites)

$55 to $86 (depending on level

defoliation)

Recreationist: $43

Homeowner: $131

$31 to $161 million

Urban areas: $14 to $16

–8.5 to –23.2% (for reductions

ranging between 10 to 30%)

$47

Non­use: 73% of total value

Use: 27% of total value level of substitute

of reduction in tree

in tree density

a Holmes and Kramer study applies same results found in Haefele

b Benefits transfer was based on results from Holmes and Kramer et al. (1991)

(1996), Peterson et al. (1987), Walsh et al. (1990).



### Table F­7. Summary of the monetized estimates of the annual value of forest quality changes

Reference Aesthetic Change

Valued Value of Change per

Household (Current

Dollars) Value of Change per

Household (1990

Dollars)i Total Value of

Change for Region

(Current Dollars) Total

Change

(1990 Value of

for Region

Dollars)i

Peterson et al. Ozone damage to San $6.31

­ $32.70ii $7.26

­ $37.62 $27

­ $140 million $31

­ $161million

(Peterson, 1987) Bernardino and Angeles

National Forests

Walsh et al. Visual damage to $47 $61.68 $55.7 million $73.09 million

(1990) Colorado’s Front Range

Holmes and Visual damage to spruce­ $10.81 nonusers $10.37 nonusers NA NA

Kramer (1996) fir forests in southern $36.22 users $34.76 users Appalachia

Source: Exhibit 4 Industrial Economics (IEc) (1999a).

August 2008 F­39 DRAFT­DO NOT QUOTE OR CITEAugust 2008 F­40 DRAFT­DO NOT QUOTE OR CITE 

### Table F­8. Estimated value of avoiding forest damage in the U.S.

Affected System States Included Value per

Household Householdsi Total Annual

Value Cumulative Value

(1990–2010)ii

Sierra Nevada and Los Angeles CA $7.26

­ $37.62 10.4 million $75.5 million

­ $1.02 billion

­ $5.27

Basin $391.2 million billion

Eastern Spruce Fir and Selected ME, VT, NH, MA, NY, PA, WV, $7.26

­ $37.62 23.2 million $168 million

­ $2.27 billion

­ $11.75

Eastern Hardwood TN, KY, NC, VA $872.8 million billion

Source: Exhibit 5 Industrial Economics (IEc) (1999a)



### Table F­9. Ecological wetland functions,

applicable valuation methods. economic goods and services, types of value, and

Ecological Function Economic Goods and Services Value

Type Commonly Used Valuation

(s)a Methods

Flood and flow control

Storm buffering

Sediment retention

Groundwater recharge/discharge

Maintenance/Nutrient retention

Habitat and nursery for plant and animal

species

Biological diversity

Micro­climate stabilization

Carbon sequestration

Ecological Function

Natural environment Flood protection

Storm protection

Storm protection

Water supply

Improved water quality

Waste disposal

Commercial fishing and hunting

Recreational fishing and hunting

Harvesting of natural materials

Energy resources

Appreciation of species existence

Climate stabilization

Reduced global warming

Economic Goods and Services

Amenity

Recreational activities

Appreciation of uniqueness to culture/

heritage Indirect use

Indirect use

Indirect use

Indirect use

Indirect use

Direct use

Direct use

Direct use

Direct use

Direct use

Non­use

Indirect use

Indirect use

Value Type

Direct use

Direct use

Non­use Replacement cost

Market prices

Opportunity cost

Replacement cost

Production function

Replacement cost

Production function

Production function

NFI Replacement cost

CVM

Replacement cost

Market prices, NFI

TCM, CVM

Market prices

Market prices

CVM

Production function

Replacement cost

Commonly Used Valuation

HP, CVM

CVM, TCM

CVM Methods (s)a

a Acronyms refer to the contingent valuation method (CVM), hedonic pricing (HP), net factor

Source: Table 1, Brander et al. (2006).

Source: with modifications adapted from Barbier (1991); Barbier et al. (1997); Brouwer et al. income (NFI), and the travel

(1999); and Woodward and cost

Wui method (TCM).

(2001).

### Table F­10. Economic

ecosystems. valuation studies related to acidification and eutrophication in aquatic

Reference Study Area Method Ecological

Endpoint Valuation

Endpoint Economic Value Note

Acidification

Banzhaf

et al. (2006)

Bockstael

et al.

(1989a)

Cameron

and Englin

(1997)

Englin et al.

(1991) Injured lakes

in

Adirondacks,

NY

Chesapeake

Bay, MA and

DC

Northeast U.S.

ME, NH, NY

(excluding

New York

City) and VT CV

CV and TC

CV and RUM

HTC and RUM Fish, bird

species, and

tree species

Fish

Trout fishing

(uncertain

about use)

Catch per

unit effort

(number of

fish caught in

an h) WTP (total

value)

Aggregate

benefit/

consumer surplus

(recreational

value)

Consumer

surplus

(recreational

value, existence

value)

Consumer

surplus

(recreational

value) 1. The mean WTP for base

version was $48 per yr per

household in New York State

while the mean WTP for scope

version was $54 per yr per

household in New York State

(discounting rate =3%)

1. 20%water quality

improvement resulting in

aggregate benefits for beach

use, boating, and sportfishing

were about $34.7, 4.7, and 1.4

millions.

2. aggregate WTP for water

quality improvement from

current to a level acceptable for

water activity was about $91

million (1987 dollars)

1. For passive user, surplus for

preventing a 20% loss in

currently fishable high altitude

lakes in the Northeast was $218

for S model and $215 for OP

model.

2. For active user, surplus for

preventing a 20% loss in

currently fishable high altitude

lakes in the Northeast was $283

for S model and $439 for OP­I

model and $436

for OP­D.

1. Under baseline scenario

(1989), welfare loss was

estimated at $0.27 m (HTC) and

$1.75 (RUM)

2. Under NAPAP scenario 1,

welfare loss was occurred in

2010 at $13.7 m (HTC) and $1.2

m (RUM) but social welfare

would gain in 2030 at $3.5 m (HTC) and $5.5 m (TCM)

3. Under NAPAP scenario 4,

welfare would gain in 2010 at $3

m (HTC) and $7.4 m (TCM) and

also gain in 2030 at $4.4 m (HTC) and $9.7 m (TCM) 1. Base version:

improvement in fish

population of 600 lakes,

small improvements in

the populations of two

bird and one tree

species.

2. Scope version:

improvement in fish

population of 900 lakes,

improvement in the

population of four bird

and three tree species.

1. TNP, product of N and

P, was included in the

model.

2. WTP was aggregated

for households in

Maryland.

1. S model: Surplus

interpretation of the WTP

responses

2. OP model: Option

price interpretation of the

WTP responses.

3. I: complete

independence between

yr­to­yr decisions

4. D: complete

dependence on previous

decisions

1. Acidic stress index

(ASI) measuring fish

biological tolerance to

acidity was included in

the model.

2. The benefits were

valued in 1989 dollars

3. Multiple datasets were

used in the analysis.

4. Only trout species

fishing was included.

5. The benefit estimates

were aggregated for four

states by accounting

income increase and

population change

resulting from baby

boom.

August 2008 F­41 DRAFT­DO NOT QUOTE OR CITEReference Study Area Method Ecological

Endpoint Valuation

Endpoint Economic Value Note

Kaoru et al. Albemarle and CV and RUM Fish Consumer 1. 35–site model: $36.19 for 5% 1. Estimated N loadings

(1995) Pamlico surplus increase of total catch at all sites and biochemical O 2 Sounds, NC (recreational (full­wage) demand, were used in

value) 2. 35–site model: $6.52 for 36% household production

decrease in N loadings at all

sites (full­wage) models to measure fishing quality.

2. The paper also

estimated benefit based

on opportunity cost at

1/3 wage for 23−site and

11–site models.

Morey and Four fishing CV and TC Brook and Consumer 1. $475.87 for 5% increase in 1. Economic values were

Shaw sites in Lake Trout surplus catch rates for trout (1977 aggregate CCV for 607

(1990) Adirondacks, (conditional dollars) survey respondents.

NY compensating

variations)

(recreational

value) 2. $2162.04 for 25% increase in

catch rates for trout (1977

dollars)

3. $3862.94 for 50% increase in 2. Catch rate, as an

indicator of acid

deposition, was included

as a variable in the

catch rates for trout (1977 model.

dollars)

Morgan and Chesapeake Water quality Water quality Aggregate 1. Lower bound total benefit for 1. The study also

Owens Bay model and benefit beach use was $288.8m from a provided aggregate

(2001) Benefit transfer (recreational 60% improvement in benefits at avg and high

value) Chesapeake Bay water quality. level for three

2. Lower bound total benefit for recreational uses.

trailered boating was $6.7m 2. Benefit transfer was

from a 60% improvement in based on the results in

Chesapeake Bay water quality. Bockstael et al. (1988).

3. Lower bound total benefit for

striped bass sport fishing was

$288.8m from a 60%

improvement in Chesapeake Bay

water quality.

Mullen and Adirondack, TC Fish Net economic 1. Net economic value per angler 1. The values were in

Menz (1985) NY value per angler day was avgd at $19.90 for 1976 dollars.

day/ Consumer entire waterbody including lakes, 2. Total value was surplus

(recreational

value) ponds, and streams.

2. Total angler value was estimated $31.3 million for entire aggregated by number of

trips.

water body.

3. The loss in net economic

value was estimated at $1.07

million for lakes and ponds due

to reduction in angler visitation.

Randall and Nationwide, CV Water/air WTP (total 1. The estimated annual 1. The study valued a

Kriesel U.S. quality value) willingness to pay was $694.42 National Pollution Control

(1990) per household. Program, which improved

air and water quality by

25 percent in 5 yrs.

Smith et al. Albemarle and HTC and demand Fish Benefit/ 1. Conventional demand model: 1. Benefit was calculated

(1991) Pamlico function consumer $2.58 for boat, $1.11 for bank for an one­fish increase

Sounds, NC surplus fishing in catch rate per h per (recreational

value) 2. Simple inverse demand

model: $0.79 for boat, $0.62 for person

2. Catch rate was

bank fishing included in the model.

3. Detailed inverse demand 3. The paper also

model: $0.88 for boat, $0.79 for estimated benefit based

bank fishing on opportunity cost at

1/3 wage.

August 2008 F­42 DRAFT­DO NOT QUOTE OR CITEReference Study Area Method Ecological

Endpoint Valuation

Endpoint Economic Value Note

Whitehead Albemarle and CV and TCM Fish catches Consumer 1. The consumer surplus per trip 1. The consumer surplus

et al. (2000) Pamlico and shellfish surplus per is $64 for current quality and per trip difference

Sounds, NC beds season/ per trip $85 for 60% increase in fish between two baseline

(recreational catch and 25% more open and scenario was not

value) shellfish beds. significant at the 0.10

2. The consumer surplus per

season is $121 for current level while the consumer surplus per season

quality and $155 for 60%

increase in fish catch and 25% difference between two

situations was.

more open shellfish beds. 2. The studies also

estimated annual

aggregate consumer surplus per season for 41

counties within the

watershed.

Eutrophication

Boyle, et al. Four lakes, HTC (two stages, Water clarity Consumer 1. The consumer surplus (+) for 1. The welfare was also

(1999) ME hedonic demand (visibility) surplus an avg visibility improvement measured in linear

model (recreational from 3.78m to 5.15m was demand model.

value) $3,765 and $3,677 for semi­log However, the own price

and Cobb­Douglas model. of water clarity is not

2. The welfare losses (–) for an significant.

avg visibility decrease from

3.78m to 2.41m was $25,388

and $46,750 for semi­log and

Cobb­Douglas model.

Diaz and Gulf of Mexico Time series Brown Mean catch rate 1. The study failed to quantify 1. Hypoxia was

Solow shrimp, white per unit effort economic effects of hypoxia measured in terms of

(1992) shrimp, and based on past data. area (zone) and index in

Menhaden the model to calculate

correlation.

Kramer and Catawba CV Water quality WTP (total 1. A mean willingness to pay 1. Phone­mail­phone and

Eisen­Hecht River, NC and value) was revealed at $139 for mail­phone survey (2002) SC maintaining current water quality formats applied.

over time.

Lipton Chesapeake CV Water quality WTP (use value) 1. The median WTP for a one 1. The aggregate WTP

(2004) Bay, MA scale improvement in water for the Chesapeake Bay

quality was $17.50 per boater boaters in Maryland was

per yr and the mean was $63, about $7.3 million per yr,

with 38% expressing a zero total improvement gets a

WTP. $146 million present

value.

Needelman Lakes, NH Site choice Water quality Benefit 1. The mean seasonal benefits 1. The study also

and Kealy model and a trip (recreational estimates were $1.4 for reported mean and

(1995) frequency model value) eliminating eutrophication from aggregate seasonal

all sources ($1.33 for nonpoint benefits for eliminating

source and $0.09 for point bacteria, oil and grease,

source) and the aggregate eutrophication problems,

seasonal benefits were and all pollution from all

estimated at $1,163,000 for sources. eliminating eutrophication from 2. The benefit estimates all sources ($1,105,000 for

nonpoint source and $75,000 for

point source). address exclusively

swimming and day trips.

vNSTC Gulf of Cost effective, Fish, shrimp, Social cost for 1 For 20% edge­of­field N­loss 1. The benefit to reduce

(2000) Mexico, simulation model and marine reduction/ reduction, the net cost was N loadings to the Gulf

Mississippi­ (US Mathematic ecosystem restoration estimated at $0.8/kg was difficult to calculate

Atchafalay

River Basin Programming

Model in ($0.36/pound), while restoring 5

million acres of wetland would because economic

analysis failed to show

Agriculture) have net cost of $1.00/kg direct effects on Gulf

($0.45/pound). fisheries, which was a background study

conducted by Diaz and

Solow (1992).

Park et al. Florida Keys, CV Water quality WTP per trip 1. The annual avg use value was (2002) FL and health of expenses $481.15 per person per

coral reefs (recreational snorkeling trip.

value)

August 2008 F­43 DRAFT­DO NOT QUOTE OR CITEReference Study Area Method Ecological

Endpoint Valuation

Endpoint Economic Value Note

Poor et al. St. Mary’s

(2006) River

watershed,

MA

Smith and Neuse River,

Crowder NC

(2005)

Whitehead  Tar­Pamlico

and River, NC

Groothuis

(1992)

Various Effects

Johnston U.S. and

et al. Canada

(Johnston,

2005) HPVM

Bio­economic

model

CV

Meta­analysis Ambient

water quality

Blue crab

Catch rate

Catch rate Avg housing

price within the

watershed

(commercial

value)

Fish rent

(commercial

value)

WTP (total value,

use, and non­use

value)

WTP

(recreational

use) 1. One unit (mg/L) increase in

TSS resulted in a $1,086 loss on avg housing prices within the

watershed.

2. One unit increase in the

dissolved inorganic N resulted in

a $17,642 loss on avg housing

prices.

1. A 30% reduction in N loadings

over a 50–yr time period

increases present value rents by

2.56 million, total catch by 12.4

million pounds, and total trips by

91,000.

1. The mean WTP resulting from

a 61% response rate was

estimated at $25, which was

bounded from above by a $35

use value, and from below a $21

nonuse value.

2. The aggregate benefits from

water quality improvement

estimated at $1.62 million each

yr.

1. WTP per fish over the sample

ranged from $.048 to $612.79,

with a mean of $16.82 1. Total suspended solids

and dissolved inorganic N

included as indicators of

ambient water quality.

1. Discounting rate was

2.5%.

1. The study proposed a

program in which

farmers are required to

use BMPs to improve the

water quality of the TarPamlico River so that

anglers would catch

twice as many fish per

trip.

2. A mail survey was sent

to 179 households in four

counties in Tar­Pamlico

River basin.

1. This study supports

previous findings that

WTP is systematically

associated with resource,

context, and angler

attributes.

August 2008 F­44 DRAFT­DO NOT QUOTE OR CITE

# ANNEX F

­ Glossary

A c i

d N e u t r a l i z

i n g C a p

a

c

i

t y ( A N C )

A key indicator of the ability of water to neutralize acidifying inputs. This ability depends largely on

associated biogeochemical characteristics.



## A c i d i f i

c a t i o

n

The process of decreasing the pH of a system. Systems that can be acidified by atmospheric deposition of

acidic or acidifying compounds include lakes, streams, and forest soils.



## A l g a e

Photosynthetic, often microscopic and planktonic, organisms occurring in aquatic ecosystems.



## A l g

a

l b l

o o m

A rapid and extreme increase of an algae population in a lake, river, or ocean.



## A l p i n

e

The biogeographic zone made up of slopes above the tree line

, and characterized by the presence of rosetteforming herbaceous plants and low, shrubby, slow-growing woody plants.



## A n t

h

r

o

p o g e

n

i c

Resulting from human activity or produced by human beings.



## A r i

d r e g i o n

An area receiving < 250 mm precipitation per year.



## A t m

o s p h e

r

e

The gaseous envelope surrounding the Earth. The dry atmosphere consists almost entirely of nitrogen and

O 2 , together with trace gases including carbon dioxide and ozone.



## B a s

e c a t i o n s a t u r a t

i

o n

The degree to which soil cation exchange sites are occupied with base cations (e.g. Ca 2+

, Mg 2+

, K+) as opposed to Al3+ and H+. Base cation saturation is a measure of soil acidification, with lower values being

more acidic. A marked increase in the sensitivity of soils to changes in base saturation occurs at a threshold

of approximately 20%.

August 2008 F­45 DRAFT­DO NOT QUOTE OR CITE

## B i o a c c u

m

u l a t i o n

The gradual increase in accumulation of some compounds in organisms with increasingly higher trophic

levels.



## B i o

d i v e r s

i

t

y

The total diversity of all organisms and ecosystems at various spatial scales (from genes to biomes).



## B u f f e r i n g c a p a c i t

y

The ability of a body of water and its watershed to resist changes in pH.



## C a r

b

o

n s e q u e

s

t

r

a

t

i

o n

The process of increasing the carbon content of a reservoir other than the atmosphere.



## C a t c

h m e n t

An area that collects and drains rainwater.



## C l i m a t e

Climate in a narrow sense is usually defined as the ‘average weather’, or more rigorously, as the statistical

description in terms of the mean and variability of relevant quantities over a period of time ranging from

months to thousands or millions of years. These quantities are most often surface variables such as

temperature, precipitation, and wind. Climate in a wider sense is the state, including a statistical

description, of the climate system

. The generally accepted period of time is 30 years, as defined by the

World Meteorological Organization (WMO).



## C r i t

i

c

a

l l o

a d

A quantitative estimate of an 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.



## D e n

i t r

i

f i

c a t

i

o

n

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

2 ON 2 O or

N

2 N 2 ), normally accomplished by denitrifying bacteria.



## D r y d e

p

o

s

i

t

i o n

The movement of gases and particles from the atmosphere to surfaces in the absence of precipitation (e.g.,

rain or snow) or occult deposition.



## E c o l o g i c a l c o m

m u n i t y

An assemblage of populations of different species, interacting with one another

A ugust 2008 F­46 DRAFT­DO NOT QUOTE OR CITE

## E c o s y s t e m s e r v i c e

s

Ecological processes or functions having monetary or non-monetary value to individuals or society at large.

They may be classified as (i) supporting services such as productivity or biodiversity maintenance; ii)

provisioning services such as food, fibre, or fish; iii) regulating services such as climate regulation or

carbon sequestration; and (iv) cultural services such as tourism or spiritual and aesthetic appreciation.



## E c o s y

s

t

e m

The interactive system formed from all living organisms and their abiotic (physical and chemical)

environment within a given area. Ecosystems cover a hierarchy of spatial scales and can comprise the entire

globe, biomes at the continental scale, or small, well-circumscribed systems such as a small pond.



## E u

t r o

p

h

i

c

a

t

i o n

The enrichment of a waterbody with nutrients, resulting in increased productivity (of algae or aquatic

plants), and sometimes also decreased dissolved O 2 levels.



## E u

t

r

o

p h y

Eutrophy generally refers to a state of nutrient enrichment, but it is commonly used to refer to condition of

increased algal biomass and productivity, presence of nuisance algal populations, and a decrease in

dissolved O 2 concentrations.



## E v a p o t

r a n

s p i r a

t

i

o

n

The combined process of water evaporation from the Earth’s surface and transpiration from vegetation.



## F e n

A phase in the development of the natural succession from open lake, through reedbed, fen and carr, to

woodland as the peat develops and its surface rises.



## F r

e s h

e t

A great rise or overflowing of a stream caused by heavy rains or melted snow.



## G r e e n h o

u

s

e g a s

Those atmospheric gasses that absorb and emit radiation emitted by the Earth’s surface, the atmosphere,

and clouds within the infrared portion of the spectrum. This property causes the greenhouse effect. Water

vapour (H 2 OH 2 O), carbon dioxide (CO 2 ), nitrous oxide (N 2 ON 2 O), methane (CH 4 ), and ozone (O 3 O 3 ) are

the primary greenhouse gases in the Earth’s atmosphere. Besides these, the Kyoto Protocol also deals with

the greenhouse gases sulphur hexafluoride (SF 6 ), hydrofluorocarbons (HFCs), and perfluorocarbons

(PFCs).



## G r o s s p r

i

m

a r y p r o

d u c t

i o n

The total carbon fixfixed by plant through photosynthesis.

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## H e a t h l a n

d

A wide-open landscape dominated by low-growing woody vegetations such as heathers and heathland

grasses. Heathlands generally occur on acidic, nutrient-poor, and often sandy and well-draining soils.



## H y p o x

i c

Events that lead to a deficiency of O 2

.



## I n v a s i v e s p

e

c i e

s a n d i n

v a s i v e a l i

e

n s p e c i e

s

A species aggressively expanding its range and population density into a region in which it is not native,

often through outcompeting or otherwise dominating native species.



## L e

a

c

h

i

n g

The removal of soil elements or chemicals by water movement through the soil.



## L o

w l a n d

In physical geography, lowland is any relatively flat area in the lower levels of regional elevation. The term

can be applied to the landward portion of the upward slope from oceanic depths to continental highlands, to

a region of depression in the interior of a mountainous region, to a plain of denudation, or to any region in

contrast to a highland.



## N e t e c

o

s

y s t e m e x

c

h a n

g e ( N

E E )

The net flux of carbon between the land and the atmosphere, typically measured using eddy covariance

techniques. Positive values of NEE usually refer to carbon released to the atmosphere (i.e., a source), and

negative values refer to carbon uptake (i.e., a sink)



## N e t e c o s y s t

e

m p r o d u c t

i

o n ( N E P )

The difference between net primary production (NPP) and heterotrophic respiration (mostly decomposition

of dead organic matter) of that ecosystem over the same area. NEP = -NEE, with positive values indicating

a sequestration of atmospheric carbon in to biosphere.



## N e t p r i m

a r y p r o

d u c t i o n ( N P P )

The gross primary production minus autotrophic respiration

, i.e., the sum of metabolic processes for plant

growth and maintenance, over the same area.



## N i t

r

i

f i c a

t

i o n

The biological oxidation of ammonia to nitrite and then to nitrate. This process is primarily accomplished

by autotrophic nitrifying bacteria that obtain energy by reducing ammonium and/or nitrite to nitrate.

## N i t

r

o

g

e n m i n e r a l i z a t

i

o

n

The conversion of organic nitrogen into plant-available inorganic forms (e.g. NH or NH +

3 4 NH +

4 ) by

microorganisms.



## N i t

r

o

g

e n

­ r

e

t e n

t i o

n c a p a c

i

t

y

The length of time that an ecosystem can retain nitrogen in (?) organisms (e.g., plant or microbe) and soilorganic matter. Nitrogen-retention capacity is highly affected by soil, vegetative, topographic, and land-use

factors.



## N i t r o

g e n s a

t

u

r a t

i

o n

The condition in which nitrogen inputs from atmospheric deposition and other sources exceed the

biological requirements of the ecosystem.



## O c c u l

t d e

p o s i t i o n

The transmission of gases and particles from the atmosphere to surfaces by fog or mist.



## O m b r

o

t

r o p

h

i

c b o g

An acidic peat-accumulating wetland that is fed by rainwater (instead of groundwater) and, thus, especially

poor in nutrients.



## p H

A measure of the relative concentration of hydrogen ions in a solution. The formula for calculating pH is:

pH = − -log [H +

10 ], where [H + ] represents the hydrogen ion concentration in moles per liter. The pH scale

ranges from 0 to 14. A pH of 7 is neutral. A pH less than 7 is acidic and a pH greater than 7 is basic.



## P h

y t o p l a n

k

t o n

The plant forms of plankton

. Phytoplankton are the dominant plants in the sea and are the basis of the entire

marine food web. These single-celled organisms are the principal agents of photosynthetic carbon

fixfixation in the ocean.



## P r

i m a r y P r o

d u c t i

o n

All forms of production accomplished by plants, also called primary producers. See GPP, NPP, and NEP

.



## S e m

i

­ a

r i d r e g i o n

s

Regions of moderately low rainfall (100– and 250–mm precipitation per year), which are not highly

productive and are usually classified as rangelands.

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## S e n

s i t

i

v i

t y

The degree to which a system responds to pollution (e.g. acidification, n-nutrient enrichment, etc.). The

response may be direct (e.g., a change in growth following a change in the mean, range, or variability of N

deposition) or indirect (e.g., changes in growth due to alterations in competitive dynamics between species

or decreased biodiversity

, themselves following N deposition).



## S t r

e

a m f l o

w

Water flow within a river channel. A synonym for river discharge

.



## S u r f a c e r u n o f f

The water that travels over the land surface to the nearest surface stream; runoff of a drainage basin that has

not passed beneath the surface since precipitation.



## T h r o u g h f

a

l l

The precipitation falling through the canopy of a forest and reaching the forest floor.



## T r o p h

i c l e

v

e

l

The position that an organism occupies in a food web.



## T u n d r a

A treeless, level, or gently undulating plain characteristic of the Arctic, sub-Arctic regions and some alpine

regions characterized by low temperatures and short growing seasons.



## U p l a n d t e

r r e s t

r

i

a l e c

o

s y s t

e

m

Generally considered to be the ecosystems located at higher elevations directly above riparian zones and

wetlands. Vegetation in an upland ecosystem is not in contact with groundwater or other permanent water

sources.



## V a l

u a t i o n

The economic or non-economic process of determining either the value of maintaining a given ecosystem

type, state, or condition or the value of a change in an ecosystem, its components, or the services it

provides.



## V u l

n

e r a b i

l i t y

Susceptibility to degradation or damage from adverse factors or influences. Vulnerability is a function the

exposureand its sensitivity.

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## W e l f a r

e e f f e c t s

Effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and

climate, damage to and deterioration of property, and hazards to transportation, as well as effects on

economic values and on personal comfort and well-being, whether caused by transformation, conversion,

or combination with other air pollutants (CAA 302(h)).



## W e t d e p o

s i t i o n

The transmission of gases and particles from the atmosphere to surfaces by rain or other precipitation.



## W e t l

a

n

d

Those areas that are inundated or saturated by surface or ground water at a frequency and duration

sufficient to support a prevalence of vegetation adapted to water-saturated soil conditions. Wetlands

include swamps, marshes, bogs, and similar areas.



## Z o o p

l a n k t

o

n

The animal forms of plankton. They consume phytoplankton or other zooplankton.

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36 

# ANNEX F

­ References

Adams, R. M.; Crocker, T. D. (1989) The agricultural economics of environmental change: some

lessons from air pollution. J. Environ. Manage. 28: 295-307.

Adams, R. M.; Horst, R. L., Jr. (2003) Future directions in air quality research: economic issues.

Environ. Int. 29: 289-302.

Adams, R. M.; Hamilton, S. A.; McCarl, B. A. (1986a) The benefits of pollution control: the

case of ozone and U.S. agriculture. Am. J. Agric. Econ. 68: 886-893.

Adams, R. M.; Callaway, J. M.; McCarl, B. A. (1986b) Pollution, agriculture and social welfare:

the case of acid deposition. Can. J. Agric. Econ. 34: 3-19.

Banzhaf, H. S.; Burtraw, D.; Evans, D.; Krupnick, A. J. (2006) Valuation of natural resource improvements in the Adirondacks. Land Econ. 82: 445-464.

Barbier, E. B. (1991) An approach to economic evaluation of tropical wetlands: with examples

from Guatemala and Nicaragua. In: Girvan, N. P.; Simons, D. eds. Caribbean Ecology

and Economics. St. Michael, Barbados: Caribbean Conservation Association; pp. 207­

231.

Barbier, E. B.; Acreman, M.; Knowler, D. (1997) Economic valuation of wetlands. A guide for

policy makers and planners. Gland, Switzerland: Ramsar Convention Bureau.

Bentley, J.; Horst, R. (1998) Commercial forestry benefits of alternative emission controls for

the NO

x 

### SIP Call. Research Triangle Park, NC: U.S. Environmental Protection Agency,

Office of Air Quality Planning and Standards, Science Applications International

Corporation (SAIC) Contract #68-D-98-113.

Bergstrom, J. C.; Taylor, L. O. (2006) Using meta-analysis for benefits transfer: theory and

practice. Ecol. Econ. 60: 351-360.

Bockstael, N. E.; McConnell, K. E.; Strand, I. E. (1988) Benefits from improvements in

Chesapeake Bay water quality, v. III of Benefit analysis using indirect or imputed market

methods. Washington, DC: U.S. Environmental Protection Agency Cooperative

Agreement CR-811043-01-0.

Bockstael, N. E.; McConnell, K. E.; Strand, I. E. (1989a) Measuring the benefits of

improvements in water quality: the Chesapeake Bay. Mar. Resour. Econ. 6: 1-18.

Bockstael, N.; McConnell, K.; Strand, I. (1989b) A r andom utility model for sportfishing: Some

preliminary results for Florida. Mar. Resour. Econ. 6: 245-260.

Bouwes, N. W.; Schneider, R. (1979) Procedures in estimating benefits of water quality change.

Am. J. Agric. Econ. 61: 535-539.

Boyd, J.; Banzhaf, S. (2007) What are ecosystem services? The need for standardized

environmental accounting units. Ecol. Econ. 63: 616-626.

Boyle, K. J.; Poor, P. J.; Taylor, L. O. (1999) Estimating the demand for protecting freshwater

lakes from eutrophication. Am. J. Agric. Econ. 81: 1118-1122.

August 2008 F­52 DRAFT­DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 B rander, L. M.; Florax, R. J. G. M.; Vermaat, J. E. (2006) The empirics of wetland valuation: a

comprehensive summary and a meta-analysis of the literature. Environ. Res. Econ. 33:

223-250.

Brouwer, R.; Langford, I. H.; Bateman, I. J.; Turner, R. K. (1999) A meta-analysis of wetland

contingent valuation studies. Reg. Environ. Change 1: 47-57.

Buhyoff, G. J.; Wellman, J. D. (1980) The specification of a non-linear psychophysical function

for visual landscape dimensions. J. Leisure Res. 12: 257-272.

Buhyoff, G. J., Leuschner, W. A.; Wellman, J. D. (1979) Aesthetic impacts of southern pine

beetle damage. J. Environ. Manage. 8: 261-267.

Buhyoff, G. J., Wellman, J. D.; Daniel, T. C. (1982) Predicting scenic quality for mountain pine

beetle and western spruce budworm damaged forests. For. Sci. 28: 827-838.

Burtraw, D.; Krupnick, A.; Mansur, E.; Austin, D.; Farrell, D. (1997) The costs and benefits of

reducing acid rain. Washington, DC: Resources for the Future. Discussion Paper 97-31­

REV.

Callaway, J. M.; Darwin, R. F.; Nesse, R. J. (1986) Economic valuation of acidic deposition

damages: preliminary results from the 1985 NAPAP assessment. In: Proceedings of

International Symposium on Acidic Precipitation; September; Muskoka, Ontario,

Canada. Richland, WA: U. S. Environmental Protection Agency; Pacific Northwest

Laboratory. Water Air Soil Pollut. 31: 1019-1034.

Cameron, T. A.; Englin, J. (1997) Welfare effects of changes in environmental quality under

individual uncertainty about use. Rand J. Econ. 28: S45-S70.

Carson, R. T.; Hanemann, M. W. (2005) Contingent valuation. In: Mäler, K.-G.; Vincent, J. R.,

eds. Handbook of Environmental Economics. Vol. 2. Valuation of Environmental

Changes. Amsterdam, The Netherlands: Elsevier; pp. 821-936.

Carson, R. T.; Mitchell, R. C. (1993) The value of clean water: The public's willingness to pay

for boatable, fishable, and swimmable quality water. Water Resour. Res. 29: 2445-2454.

Carson, R. T.; Flores, N. E.; Martin, K. M.; Wright., J. L. (1996) Contingent valuation and

revealed preference methodologies: comparing the estimates for quasi-public goods.

Land Econ. 72: 80-99.

Costanza, R.; d'Arge, R.; De Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.;

Naeem, S.; O'Neill, R. V.; Paruelo, J.; Raskin, R. G.; Sutton, P.; Van Den Belt, M. (1997)

The value of the world's ecosystem services and natural capital. Nature (London) 387:

253-259.

Crocker, T. D. (1985) On the value of the condition of a forest stock. Land Econ. 61: 244-254.

Crocker, T. D.; Forster, B. A. (1986) Atmospheric deposition and forest decline. Water Air Soil

Pollut. 31: 1007-1017.

De Steiguer, J. E.; Pye, J. M. (1988) Using scientific opinion to conduct forestry air pollution

economic analyses. In: Jöbstl, A., ed. Proceedings of symposium on the economic

assessment: of damage caused to forests by air pollutants; pp. 133-142. IUFRO Working

Party S4.04-02, September; Gmunden, Austria.

August 2008 F­53 DRAFT­DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 De Steiguer, J. E.; Pye, J. M.; Love, C. S. (1990) Air pollution damage to U.S. forests: a survey

of perceptions and estimates of scientists. J. For. 88: 17-22.

Diaz, R. J.; Solow, A. (1992) Ecological and economic consequences of hypoxia. Topic 2 report

for the integrated assessment on hypoxia in the Gulf of Mexico. In: Hypoxia in the Gulf

of Mexico. Progress towards the completion of an integrated assessment. Silver Spring,

MD: National Oceanic and Atmospheric Administration.

Englin, J. E.; Cameron, T. A.; Mendelsohn, R. E.; Parsons, G. A.; Shankle, S. A. (1991)

Valuation of damages to recreational trout fishing in the upper northeast due to acidic

deposition. Richland, WA: Pacific Northwest Laboratory, report no. PNL-7683.

Epp, D. J.; Al-Ani, K. S. (1979) The effect of water quality on rural nonfarm residential property

values. Am. J. Agric. Econ. 61: 529-534.

Federal Register. (1993) Report of the NOAA P anel on contingent valuation. F. R. (January 15)

58: 4601-4614.

Flowers, P. J.; Vaux, H. J.; Gardner, P. D.; Mills, T. J. (1985) Changes in recreation values after

fire in the northern Rocky Mountains. Berkeley, CA: U.S. Department of Agriculture

Forest Service, Pacific Southwest Forest and Range Experiment Station; Research Note

PSW-373.

Freeman, M. A. (2003) The measurement of environmental and resource values theory and

methods. Washington, D.C.: RFF Press.

Gramlich, F. W. (1977) The demand for clean water: the case of the Charles River. Natl. Tax J.

30(2): 183-194.

Haefele, M.; Kramer, R. A.; Holmes, T. P. (1992) Estimating the total value of forest quality in

high-elevation spruce-fir forests. In: The Economic Value of Wilderness: Proceedings of

the Conference; May, 1991; Jackson, WY. Asheville, NC: USDA F orest Service,

Southeastern Forest Experiment Station; General Technical Report SE-78, pp. 91-96.

Hammitt, W. E.; Patterson, M. E.; Noe, F. P. (1994) Identifying and predicting visual preference

of southern Appalachian forest recreation vistas. Landscape Urban Plan. 29: 171-183.

Hanemann, M. (1991) Willingness to pay and willingness to accept: How much can they differ?

T Am. Econ. Rev. 81(3): 635-647.

Hanley, N.; Shogren, J.; White, B. (1997) Environmental economics in theory and practice. New

York: Oxford University Press.

Haynes, R. W.; Adams, D. M. (1992) Assessing economic impacts of air pollution damage to

U.S. forests. In: de Steiguer, J. E., ed. The economic impact of air pollution on timber

markets: studies from North America and Europe. Asheville, NC: U.S. Department of

Agriculture, Southeastern Forest Experiment Station; general technical report no. SE-75.

Heal, G. M.; Barbier, E. B.; Boyle, K. J. (2005) Valuing ecosystem services: toward better

environmental decision making. Washington, DC: The National Academies Press.

Available: http://www.nap.edu/openbook.php?isbn=030909318X [ 6 November, 2007].

Heck, W. W.; Adams, R. M.; Cure, W. W.; Heagle, A. S.; Heggestad, H. E.; Kohut, R. J.; Kress,

L. W.; Rawlings, J. O.; Taylor, O. C. (1983) A reassessment of crop loss from ozone.

Environ. Sci. Technol. 17: 573A-581A.

August 2008 F­54 DRAFT­DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41 Hollenhorst, S. J.; Brock, S. M.; Freimund, W. A.; Twery, M. J. (1993) Predicting the effects of

gypsy moth on near-view aesthetic preferences and recreation appeal. For. Sci. 39(1): 28­

40.

Holmes, T. P. (1992) Economic welfare impacts of air pollution damage to forests in the

southern United States. In: de Steiguer, J. E., ed. The Economic Impact of Air Pollution

on Timber Markets: Studies from North America and Europe. Asheville, NC: U.S. Dept.

of Agriculture, Forest Service, Southeastern Forest Experiment Station. General

Technical Report SE-75; pp. 19-26.

Holmes, T. P.; Kramer, R. A. (1996) Contingent valuation of ecosystem health. Ecosyst. Health

2: 56-60.

Holmes, T. P.; Murphy, E. A.; Bell, K. P. (2006) Exotic forest insects and residential property

values. Agric. Res. Econ. Rev. 35: 155-166.

Industrial Economics, Inc. (1999a) Characterizing the commercial timber benefits from

tropospheric ozone reduction attributable to the 1990 Clean Air Act Amendments, 1990­

2010. Washington, DC: U.S. Environmental Protection Agency, Office of Policy.

Industrial Economics, Inc. (1999b) Characterizing the forest aesthetics benefits attributable to the

1990 Clean Air Act Amendments, 1990-2010. Washington, DC: U.S. Environmental

Protection Agency, Office of Policy.

Industrial Economics, Inc. (1999c) Benefits assessment of decreased nitrogen deposition to

estuaries in the United States attributable to the 1990 Clean Air Act Amendments, 1990­

2010. Washington, DC: U.S. Environmental Protection Agency, Office of Policy.

Jakus, P.; Smith, V. K. (1991) Measuring use and nonuse values for landscape amenities: a contingent behavior analysis of gypsy moth control. Washington, DC: Resources for the

Future; Discussion Paper no. QE92-07.

Jenkins, D. H.; Sullivan, J.; Amacher, G. S.; Nicholas, N. S.; Reaves, D. W. (2002) Valuing high

altitude spruce-fir forest improvements: importance of forest condition and recreation

activity. J. For. Econ. 8: 77-99.

Johnston, R. J.; Besedin, E. Y.; Ranson, M. H.; Helm, E. C. (2005) What determines willingness

to pay per fish? A m eta-analysis of recreational fishing values. Mar. Resour. Econ. 21: 1­

32.

Kaoru, Y. (1995) Measuring marine recreation benefits of water quality improvements by the

nested random utility model. Resour. Energy Econ. 17: 119-136.

Kaoru, Y.; Smith, V. K.; Liu, J. L. (1995) Using random utility models to estimate the

recreational value of estuarine resources. Am. J. Agric. Econ. 77: 141-151.

Kim, H. J.; Helfand, G. E.; Howitt, R. E. (1998) An economic analysis of ozone control in

California's San Joaquin Valley. J. Agric. Resource Econ. 23: 55-70.

Kopp, R. J.; Vaughn, W. J.; Hazilla, M.; Carson, R. (1985) Implications of environmental policy

for U.S. agriculture: the case of ambient ozone standards. J. Environ. Manage. 20: 321­

331.

Kramer, R. A.; Eisen-Hecht, J. I. (2002) Estimating the economic value of water quality

protection in the Catawba River basin. Water Resour. Res. 38: 10.1029/2001WR000755.

August 2008 F­55 DRAFT­DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Kramer, R. A.; Mercer, D. E. (1997) Valuing a global environmental good: U.S. residents'

willingness to pay to protect tropical rain forests. Land Econ. 73: 196-210.

Kramer, R. A.; Holmes, T. P.; Haefele, M. (2003) Contingent valuation of forest ecosystem

protection. In: Sills, E. O.; Abt, K. L., eds. Forests in a Market Economy. New Y ork, NY:

Kluwer Academic Publishers; pp. 303-320.

Kuik, O. J.; Helming, J. F. M.; Dorland, C.; Spaninks, F. A. (2000) The economic benefits to

agriculture of a reduction of low-level ozone pollution in The Netherlands. Eur. Rev.

Agric Econ. 27: 75-90.

Lefohn, A. S.; Oltmans, S. J.; Dann, T.; Singh, H. B. (2001) Present-day variability of

background ozone in the lower troposphere. J. Geophys. Res. [Atmos.] 106: 9945-9958.

Leuschner, W. A.; Young, R. L. (1978) Estimating southern pine beetle’s impact on reservoir

recreation. For. Sci. 24: 527-537.

Leuschner, W. A.; Young., R. L. (1978) Estimating the southern pine beetle's impact on reservoir

campsites. For. Sci. 43: 46-55.

Lipton, D. (2004) The value of improved water quality to Chesapeake Bay boaters. Mar. Resour.

Econ. 19: 265-270.

Loomis, J. B.; González-Cabán, A.; Gregory, R. (1996) A c ontingent valuation study of the

value of reducing fire hazards to old-growth forests in the Pacific Northwest. Albany,

CA: U.S. Department of Agriculture Forest Service, Pacific Southwest Research Station;

research paper PSW-RP-229-Web.

MacMillan, D.; Ferrier, B.; Hanley, N. (2001) Valuation of air pollution effects on ecosystems: a scoping study. Aberdeen, Scotland: University of Aberdeen, Department for

Environment, Food and Rural Affairs.

Mäler, K.-G.; Vincent, J. R., eds. (2005) Handbook of environmental economics. vol. 2.

Valuation of environmental changes. Amsterdam, The Netherlands: Elsevier.

Mathtech. (1994) The Regional Model Farm (RMF): an agricultural sector benefits assessment

model: Version 3.0 for personal computers. Washington, DC: Office of Air Quality

Planning and Standards, pp. 1-83.

McConnell, V.; Walls, M. (2005) The value of open space: evidence from studies of nonmarket

benefits. Washington, DC: Resources for the Future; RFF Report.

McLaughlin, S.; Percy, K. (1999) Forest health in North America: some perspectives on actual

and potential roles of climate and air pollution. Water Air Soil Pollut. 116: 151-197.

Millennium Ecosystem Assessment. (2003) Ecosystems and human well-being: a framework for

assessment. Washington, DC: Island Press.

Miller, J. D.; Lindsay, B. E (1993) Willingness to pay for a state gypsy moth control program in

New H ampshire: a contingent valuation case study. J. Econ. Entomol. 86: 828-837.

Morey, E. R.; Shaw, W. D. (1990) An economic model to assess the impact of acid rain: a

characteristics approach to estimating the demand for and benefits from recreational

fishing. In: Link, A. N.; Smith, V. K., eds. Recent Developments in the Modeling of

Technical Change and Modeling Demand for and Valuation of Recreation Resources.

August 2008 F­56 DRAFT­DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Greenwich, CT: JAI Press Inc., pp. 195-216. (Advances in Applied Microeconomics,

v. 5).

Morgan, C.; Owens, N. (2001) Benefits of water quality policies: the Chesapeake Bay. Ecol.

Econ. 39: 271-284.

Mullen, J. K.; Menz, F. C. (1985) The effect of acidification damages on the economic value of

the Adirondack Fishery to New Y ork anglers. Am. J. Agric. Econ. 67: 112-119.

Murphy, J. J.; Deluki, M. A.; McCubbin, D. R.; Kim, H. J. (1999) The cost of crop damage

caused by ozone air pollution from motor vehicles. J. Environ. Manage. 55: 273-289.

National Acid Precipitation Assessment Program. (1991) National Acid Precipitation

Assessment Program 1990 integrated assessment report. Washington, DC: National Acid

Precipitation Assessment Program.

National Science and Technology Council (NSTC). (2000) Integrated assessment of hypoxia in

the northern Gulf of Mexico [draft]. Washington, DC: U.S. National Science and

Technology Council, Committee on Environment and Natural Resources.

Needelman, M.; Kealy, M. J. (1995) Recreational swimming benefits of New H ampshire lake

water quality policies: An application of a repeated discrete choice model. Agric. Resour.

Econ. Rev. 24: 78-87.

Newman, D. H. (1987) An econometric analysis of the southern softwood stumpage

market:1950-l980. For. Sci. 33: 932-945.

Paquet, J.; Belanger, L. (1997) Public acceptability thresholds of clearcutting to maintain visual

quality of boreal balsam fir landscapes. For. Sci. 43: 46-55.

Park, T. B., Bowker, J. M.; Leeworthy, V. P. (2002) Valuing snorkeling visits to the Florida

Keys with stated and revealed preference models. J. Environ. Manage. 65: 301-312.

Peterson, D. G.; Rowe, R. D.; Schulze, W. D.; Russell, G. W.; Boyce, R. R.; Elliott, S. R.; Hurd,

B. (1987) Valuation of visual forest damages from ozone, a volume of improving

accuracy and reducing costs of environmental benefit assessments. In: Improving

Accuracy and Reducing Costs of Environmental Benefit Assessments. Report to U.S.

Environmental Protection Agency. Boulder, CO: University of Colorado, Center for

Economic Analysis.

Poe, G. L.; Boyle, K. J.; Bergstrom, J. C. (2000) A m eta-analysis of contingent values for

groundwater quality in the United States. Presented at: American Agricultural Economics

Association Conference. Tampa, FL.

Polasky, S.; Costello, C.; Solow, A. (2005) Economics of biodiversity. In: Mäler, K.-G.; Vincent,

J. R., eds. Handbook of Environmental Economics. Amsterdam, The Netherlands:

Elsevier; pp. 1517-1560.

Poor, J. P.; Pessagno, K. L.; Paul, R. W. (2006) Exploring the hedonic value of ambient water

quality: A l ocal watershed-based study. Ecol. Econ. 60: 797-806.

Randall, A.; Kriesel, W. (1990) Evaluating national policy proposals by contingent valuation. In:

Johnson, G. V., Johnson, R. L., eds. Economic Valuation of Natural Resources: issues,

theory and applications. Boulder, CO: Westview Press, pp. 153-178.

August 2008 F­57 DRAFT­DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 Rosenberger, R.; Loomis, J. (2001) Benefit transfer of outdoor recreation use values: a technical

document supporting the Forest Service Strategic Plan, (2000 revision). Fort Collins, CO:

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.

General Technical Report RMRS-GTR-72.

Ruddell, E. J.; Gramann, J. H.; Rudis, V. A.; Westphal, J. M. (1989) The psychological utility of

visual penetration in near-view f orest scenic-beauty models. Environ. Behav. 21: 393­

412.

Shaw, W. D. (1989) Valuing the effect of acidification damages on the Adirondack Fishery:

Comment. Am. J. Agric. Econ. 71: 217-220.

Shogren, J. F.; Shin, S. Y.; Hayes, D. J.; Kliebenstein, J. B. (1994) Resolving differences in

willingness to pay and willingness to accept. Am. Econ. Rev. 84: 255-270.

Smith, V. K. (2007) Reflections on the literature. Rev. Environ. Econ. Policy 1: 152-165.

Smith, V. K.; Desvousges, W. H. (1986) Measuring water quality benefits. Boston, MA: KluwerNijhoff.

Smith, V. K.; Huang, J.-C. (1995) Can markets value air quality? A m eta-analysis of hedonic

property value models. J. Polit. Econ. 103: 209-27.

Smith, V. K.; Pattanayak, S. K. (2002) Is meta-analysis a Noah's Ark for non-market valuation?

Environ. Resour. Econ. 22: 271-296.

Smith, V. K.; Palmquist, R. B.; Jakus, P. (1991) Combining Farrell frontier and hedonic travel

cost models for valuing estuarine quality. Rev. Econ. Stat. 73: 694-699.

Smith, M. D.; Crowder, L. B. (2005) Valuing ecosystem services with fishery rents: a lumpedparameter approach to hypoxia in the Neuse River Estuary. Milan, Italy: Fondazione Eni

Enrico Mattei (FEEM), NRM Nota di Lavoro (Natural Resources Management Working

Papers), 115.05. Available at SSRN: http://ssrn.com/abstract=825587 [5 November,

2007].

Smith, V. K.; Pattanayak, S.; Houtven, G. V. (2006) Structural benefits transfer: an example

using VSL estimates. Ecol. Econ. 60(2): 361-371.

Spash, C. L. (1997) Assessing the economic benefits to agriculture from air pollution control. J.

Econ. Surv. 11: 47-70.

Toman, M. A. (1998) Why not to calculate the value of the world's ecosystem services and

natural capital. Ecol. Econ. 25: 57-60.

Treiman, T.; Gartner, J. (2006) Are residents willing to pay for their community forests? Results

of a contingent valuation survey in Missouri, USA. Urban Stud. 43: 1537-1547.

U.S. Environmental Protection Agency. (1999) The benefits and costs of the Clean Air Act 1990

to 2010: EPA r eport to Congress. Washington, DC: Office of Air and Radiation; EPA

report no. 410-R-99-001. Available: http://www.epa.gov/air/sect812/1990­

2010/fullrept.pdf (19 December 2003).

U.S. Environmental Protection Agency. (2002) A framework for the economic assessment of

ecological benefits. Washington, DC: Science Policy Council, Social Sciences Discussion

Group, Ecological Benefit Assessment Workgroup.

August 2008 F­58 DRAFT­DO NOT QUOTE OR CITE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29 U.S. Environmental Protection Agency. (2006) Air quality criteria for ozone and related

photochemical oxidants, volume I. Research Triangle Park, NC: National Center for

Environmental Assessment; report no. EPA/600/R-05/004aF. Available:

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923 [19 March, 2007].

Van Houtven, G.; Powers, J.; Pattanayak, S. (2007) Valuing water quality improvements in the

United States using meta-analysis: Is the glass half-full or half-empty for national policy

analysis? Resour. Energy Econ. 29: 206-228.

Vaux, H. J., Jr.; Gardner, P. D.; Mills, T. J. (1984) Methods for assessing the impact of fire on

forest recreation. Berkeley, CA: U.S. Department of Agriculture Forest Service. Pacific

Southwest Forest and Range Experiment Station, General Technical Report PSW-79.

Walsh, R. G.; Ward, F. A.; Olienyk, J. P. (1989) Recreational demand for trees in national

forests. J. Environ. Manage. 28: 255-268.

Walsh, R. G.; Bjonback, R. D.; Aiken, R. A.; Rosenthal, D. H. (1990) Estimating the public

benefits of protecting forest quality. J. Environ. Manage. 30: 175-189.

Westenbarger, D. V.; Frisvold, G. B. (1995) Air pollution and farm-level crop yields: An

empirical analysis of corn and soybeans. Agric. Resour. Econ. Rev. 24: 156-165.

White, P. S.; Cogbill, C. V. (1992) Spruce-fir forests of eastern North America. In: Eagar, C.;

Adams, M.B., eds. Ecology and decline of red spruce in the eastern United States. New

York, NY: Springer-Verlag, pp. 3-39.

Whitehead, J. C.; Groothuis, P. A. (1992) Economic benefits of improved water quality: a case

of North Carolina's Tar-Pamlico River. Rivers 3: 170-178.

Whitehead, J. C.; Haab, T. C.; Huang, J.-C. (2000) Measuring recreation benefits of quality

improvements with revealed and stated behavior data. Resour. Energy Econ. 22: 339-354.

Willig, R. D. (1976) Consumer's surplus without apology. Am. Econ. Rev. 66: 589-597.

Wilson, M. A.; Carpenter, S. R. (1999) Economic valuation of freshwater ecosystem services in

the United States: 1971-1997. Ecol. Appl. 9: 772-783.

Woodward, R. T.; Wui, Y.-S. (2001) The economic value of wetland services: a meta-analysis.

Ecol. Econ. 37: 257-270.

August 2008 F­59 DRAFT­DO NOT QUOTE OR CITE