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. 

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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
 

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B.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
 

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ANNEX 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 N2O Flux __________________________________________________________________ C­1
 

C.1.1.4. Climate and N2O 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 N2O, CH4 and CO2 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
 

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D.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 NOY, NHX, and SOX 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 NOY, NHX, 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 NOX on Paper and Archival Materials_________________________________________________________________ E­12
 

E.9. Costs of Materials Damage from NOY, NHX, 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 NOX and SOX __________________________________________________________________ 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
 

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F.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

  

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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 NO3 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
 

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Table 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, CH4 emission and N2O 

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
 

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Table 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/NH3/SOX effects on metals. ___________________________ E-14
 

Table E-2. Studies on corrosive effects of NO /NH3/SOX 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
 

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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
 

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Figure 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 , NO3 , 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
 

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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 

Al3+ aluminum ion 

Ali inorganic aluminum 

Aln+ aluminum ion 

Alo 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 

Br2 molecular bromine 

BrCl bromine chloride 

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BrO bromine oxide 

BUV Backscatter Ultraviolet Spectrometer 

BUVD Beneficial Use Values Database 

C 

12C 

13C 

carbon; concentration 

carbon-12, stable isotope of carbon 

carbon-13, stable isotope of carbon 

Ca 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 

CaCl2 calcium chloride 

CaCO3 calcium carbonate 

CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation 

(satellite) 

Ca(NO3)2 calcium nitrate 

Ca(OH)2 calcium hydroxide 

CAPMoN Canadian Air and Precipitation Monitoring Network 

CaSO4·2H2O 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 

CH4 methane 

C2H4 ethene 

C2H6 ethane 

C5H8 isoprene 

CH3CHO acetaldehyde 

CH3C(O) acetyl radical 

CH3C(O)OO acetyl peroxy radical 

CH2I2 diiodomethane 

CH2O formaldehyde 

CH3OOH methyl hydroperoxide 

CH3-S-CH3 dimethylsulfide, DMS 

CH3-S-H methyl mercaptan 

(CH3)2SO dimethyl sulfoxide, DMSO 

CH3SO3H methanesulfonic acid 

CH3-S-S-CH3 dimethyl disulfide, DMDS 

Ci interstitial air concentration 

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CL critical load 

Cl chlorine 

Cl! chlorine ion 

Cl2 molecular chlorine 

CLaMS type of Lagrangian model 

CloudSat NASA Earth observation satellite 

ClNO2 nitryl chloride 

CMAQ Community Multiscale Air Quality (modeling system) 

CMSA consolidated metropolitan statistical area 

CO carbon monoxide 

CO2 carbon dioxide ! CO3 carbonate 

CONUS continental United States 

CPUE catch per unit effort 

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

CS Consumer surplus 

CS2 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-CH3 

DMS dimethyl sulfide, CH 3-S-CH3 

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 

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EMEFS 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 CO2 enrichment (studies) 

Fe iron 

FePO4 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 

Fx flux 

γN2O5 reaction potential coefficient for N2O5 

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 

gs 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 

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H+ 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 

HNO2, HONO nitrous acid 

HNO3, HOONO nitric acid 

HNO4 pernitric acid 

HO2 hydroperoxyl radical 

H2O2 hydrogen peroxide 

HO2NO2 peroxynitric acid 

HOBr hypobromous acid 

HOCl hypochlorous acid 

HOX hypohalous acid 

HP hedonic pricing 

HPVM ! HSO3 bisulfate ion 

! HSO4 sulfuric acid ion 

H2S hydrogen sulfide 

H2SO3 sulfurous acid 

H2SO4 sulfuric acid 

HTC 

hv photon with energy at wavelength v 

I iodine 

I2 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 

INO3 iodine nitrate 

INTEX-NA Intercontinental Chemical Transport Experiment - North America 

IO iodine oxide 

IPCC Intergovernmental Panel on Climate Change 

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IPCC-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 

Ka dissociation constant 

Kb dissociation constant 

KH Henry’s Law constant in M atm-1 

KNO3 potassium nitrate 

Kw ion product of water 

LAF Lake Acidification and Fisheries 

LAR leaf-area ratio 

LB laboratory bioassay 

LC0.01 lethal concentration at which 0.01% of exposed animals die 

LD33 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 

Mg2+ magnesium ion 

MIMS membrane inlet mass spectrometry 

MM5 National Center for Atmospheric Research/Penn State Mesoscale 

Model, version 5 

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Mn 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 14N nitrogen-14, stable isotope of nitrogen 15N nitrogen-15, stable isotope of nitrogen 

N2 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 

Na2MoO4 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 

Na2SO4 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 

NH3 ammonia 

NH2 amino (chemical group) + NH4 ammonium ion 

NH4Cl ammonium chloride 

NH4NO3 ammonium nitrate 

(NH4)2SO4 ammonium sulfate 

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NHX + category label for NH 3 plusNH4

NHY 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 

NO2 nitrogen dioxide ! NO2 nitrite 

! NO3 nitrate 

N2O nitrous oxide 

N2O5 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 

NOX sum of NO and NO 2 

NOY sum of NO X and NOZ; odd nitrogen species; total oxidized nitrogen 

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

HNO3, 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 

Nr 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 

O2 molecular oxygen 

O3 ozone 16O oxygen-16, stable isotope of oxygen 18O oxygen-18, stable isotope of oxygen 19O 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 

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OMI Ozone Monitoring Instrument 

O(

3

P) ground-state oxygen atom 

P phosphorus 

P, p probability value 

P1 1st percentile 

P5 5th percentile 

P95 95th percentile 

P99 99th percentile 

PAHs polycyclic aromatic hydrocarbons 

PAMS Photochemical Assessment Monitoring Stations 

PAN peroxyacetyl nitrate 

PANs peroxyacyl nitrates 

PARASOL Polarization and Anisotropy of Reflectances for Atmospheric Sciences 

coupled with Observations from a Lidar (satellite) 

Pb lead 

PBL planetary boundary layer 

PC payment card 

PCBs polychlorinated biphenyl compounds 

pH relative acidity 

P(HNO3) production of nitric acid 

PHREEQC model for soil and water geochemical equilibrium 

PIRLA Paleocological Investigation of Recent Lake Acidification (projects) 

pKa dissociation constant 

PM particulate matter 

PM2.5 particulate matter with aerodynamic diameter of #2.5 μm 

PM10 particulate matter with aerodynamic diameter #10 μm 

PM10-2.5 particulate matter with aerodynamic diameter between 10 and 2.5 μm 

PM-CAMx Comprehensive Air Quality Model with extensions and with particulate 

matter chemistry 

PnET Photosynthesis and EvapoTranspiration (model) 

PnET-BGC Photosynthesis and EvapoTranspiration-BioGeoChemical (model) 

PnET-CN Photosynthesis and EvapoTranspiration model of C, water, and N 

balances 

PnET-N-DNDC Photosynthesis and EvapoTranspiration-Denitrification-Decomposition 

(model) 

pNO3 

! particulate nitrate 

P(O3) production of O3 

PO4 

!

, PO4

3! phosphate 

POPs persistent organic pollutants 

ppb parts per billion 

PPN peroxypropionyl nitrate 

ppt parts per trillion 

PRB policy relevant background 

PRE-STORM Preliminary Regional Experiment for STORM 

PROFILE model using soil mineralogy as input 

PS producer surplus 

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2! pSO4 particulate sulfate 

2! 4 )P(SO production of sulfate 

Q flow rate; discharge 

Q10 temperature coefficient 

QAPP Quality Assurance Project Plan 

R generic organic group attached to a molecule 

R2 coefficient of determination 2 r correlation coefficient 

Ra aerodynamic resistance 

Rb boundary layer resistance 

Rc 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 

RO2 organic peroxyl; organic peroxy 

RONO2 organic nitrate 

RO2NO2 peroxynitrate 

RP revealed preferences 

RRX lognormal-transformed response ratio 

RuBisCO ribulose-1,5-bisphosphate carboxylase/oxygenase 

S sulfur 32S sulfur-32, stable isotope of sulfur 34S sulfur-34, stable isotope of sulfur 35S 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 

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SEARCH	 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 

SO2	 sulfur dioxide 

SO3	 sulfur trioxide 2!	 SO3 sulfite 

2!	 SO4 sulfate ion 

S2O	 disulfur monoxide 

SONEX	 Subsonics Assessment Ozone and Nitrogen Oxides Experiment 

SOS	 Southern Oxidant Study 

SOS/T	 State of Science/Technology (report) 

SOX	 sulfur oxides 

SP	 stated preferences 

SPARROW	 SPAtially Referenced Regressions on Watershed Attributes (model) 

Sr	 strontium 86Sr	 strontium-86, stable isotope of strontium 87Sr	 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) 

Tair	 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 

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TP total phosphorus 

TRACE-P Transport and Chemical Evolution over the Pacific 

TSI timber-stand improvement 

TSS total suspended solids 

Twater 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 

Vd 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 

XNO3 nitrate halogen-X salt 

XO halogen-X oxide 

Zn zinc 

ZnO zinc oxide 

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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
 

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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 and 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 deposition 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 and 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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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 and 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. 

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Source: 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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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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Source: Stoddard et al. (2003).

Figure A­2. Location LTM sites used 

in the 2003 Surface Water report.

New England 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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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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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. O3 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 (SO2) 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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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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�

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Bulk density, water content, and coarse fragment ( > 2–mm) content 

pH (water and 0.01 M CaCl2) 

Total carbon 

Total inorganic carbon (carbonates) (pH > 7.5 soils only) 

(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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� Total N 

� 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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Forest floor and litter samples are analyzed for:
 

�

�

�

Bulk density and water content 

Total carbon 

Total N
 

Soil chemical and physical 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 to 

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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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 O2. 

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 and 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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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. 

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Site 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

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Site 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

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Site 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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27 

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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 was 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 originated 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 is 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 is 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 is the long-term monitoring program. Section A.3.3.1 lists the 

major parameters included in the HBES long-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 

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changes in complex ecosystems. The long-term record at the HBEF provides: (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 where monitoring began in June 1963. Measured stream chemistry parameters 

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

conductance, dissolved O2, ANC, and PO4. 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. 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

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 of 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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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 range 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 O2. 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 are 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 are 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 were very influential in convincing scientists and policy makers that decreasing S emissions 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 there 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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6 

7 

8 

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11 

12 

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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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. 

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watershed hydrology and forest nutrient dynamics, 

forest micrometeorology, 

atmospheric deposition, 

International Biological Program Eastern Deciduous Forest Biome Project, 

trace element cycling and stream nutrient spiraling, and 

effects of acidic deposition on canopy processes and soil chemistry. 

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Daily climate data 

Monthly climate data 

Precipitation 

Atmospheric deposition 

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 have 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: 

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� Stream discharge and annual runoff 

� Stream chemistry 

� Vegetation 

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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 uses 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. 

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Recent research findings include the following: 

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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 (NO3 −

) 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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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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� 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 CH4) in wetland, forest, and talus soils, weekly to monthly; CO2 

concentrations in surface waters at 10–15 sites several times annually. 

� Snowmelt lysimeter discharge and chemistry, monitored for three years, currently inactive. 

Available water chemistry data include major ions, nutrients, DIC, DOC, and, for selected samples, 

a range of isotopes including 2

H/H, 18O/16O, 15N/14N and 18O/16O in nitrate ion, 35S, 34S/32S, 87Sr/86Sr, 13C, 

12C). Monitoring of precipitation and hydrology include the following elements: 

Atmospheric deposition 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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� 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 includes 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 m3

/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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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 CaCl2) 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 component 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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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 study 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 lithologic 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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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 and VTSSS programs 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 and 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 hydrochemical 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 program 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 program. Among these are: 

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� 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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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 data 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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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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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 NOX and SOX 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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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 deposition 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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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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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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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; NH4; SO 2−

4 ; NO3; Cl; and total inorganic Al), 

the stream discharge (Q), stream pH, sum of base cation (SBC) concentrations (SBC = Ca + Mg + Na + K 

+ NH4), sum of mineral acid anion (SAA) concentrations (SAA = Cl + SO 2− 

4 + NO3) 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 NO3) and the pH. The soil data used in the model include soil depth and bulk 

density, soil pH, soil cation-exchange capacity, and exchangeable bases in the soil (Ca, Mg, Na, and K). 

The atmospheric deposition inputs to the model must be estimates of total deposition, not just wet 

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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 model 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 based on expected 

optimal growth rates (input by the user and reduced in the event of nutrient limitation), user-defined 

litterfall, weathering, N and S mineralization rates, soil minerals composition, initial litter, soil organic 

matter pools, and C/N ratios. 

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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 , SO4 , H , and CO2; and (4) 

organic acids decay to NH +

4 , SO 2−4 , H+

, and CO2. 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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Phosphate adsorption is represented by a linear isotherm, and SO 2− 

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 (Ca2+, Mg2+, K+

, Na+, Si, S, P, Al3+, 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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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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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 Ca2+, Mg2+, Na+

, H+, Al3+, K+, and NH +

4 . A pH-dependent adsorption isotherm is used to describe 

the SO 2− 

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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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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 NH4 (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 Ca2+, Cl−

, K+, Mg2+, 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 stream 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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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 ( EF)

Precipitation (in) 

Avg slope (%) 

 Soilpermeability (in/h) 

Stream density (l/mi) 

Wetland (%) 

Instream loss (days) a 

 T1(Q#200 ft3

/s) 

 T2 (200 ft3

/s < Q #1,000 ft3/s) 

 T3(Q > 1,000 ft3

/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 

 aT, travel time 

Q, stream discharge 

lb/yr, pounds per yr 

EF, degrees Fahrenheit 

in/h, inches per hour 

mi, mile;
 

in, inches
 

%, percent
 

ft 3

/s, cubic feet per second
 

–, value not statistically significant
 

1 

2 

3 

4 

5 

6 

7 

8 

9 

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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Source: Castro et al. (2003).

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 NH3 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). NH3 

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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 Q10 value of 2 (Maag, 

1997; Stanford, 1975). A Q10 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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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 National 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 (r2 = 0.909) measured fluxes. 

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Source: Driscoll et al. (2003).

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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have applied WATERSN to address similar issues related to N loading to estuaries in other regions of the 

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 and 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 NH4 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 (CO2 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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1 

2 

3 

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

Waterfowl Acidification

Response Modeling System

A

A

A

A

A

S

S

N

N

N

1

2

3

4

McNicol

(1995),

(2002)

et al.

McNicol

QUAL2K 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 compartment­modeling program for

aquatic systems, including both the water column and the

underlying benthos. WASP allows the user to investigate 1,

2, and 3 dimensional systems, and a variety of pollutant

types. The time varying processes of advection, dispersion,

point and diffuse mass loading and boundary exchange are

represented in the model. WASP also can be linked with

hydrodynamic and sediment transport models that can

provide flows, depths velocities, temperature, salinity and 

sediment fluxes.

CE­QUAL­R1 is a one­dimensional, vertical reservoir model

and CE­QUAL­W2 is a two­dimensional (vertical and

longitudinal), laterally averaged, hydrodynamic and water

quality model. These two models are widely used by the

Corps of Engineers, other federal and state agencies, the

private sector, and agencies in other countries. CE­QUALRIV1 was developed for highly unsteady flow conditions,

such as storm water flows and streams below peaking

hydropower dams. CE­QUAL­ICM run in a 2D mode. This

approach has been used for large, shallow waterways,

harbors, and embayments.

RCA evaluates the fate and transport of conventional and

toxic pollutants in surface waterbodies in one, two, or three

dimensions. RCA has been linked to various hydrodynamic

models. Subroutines have been developed to model

coliforms, pathogens, BOD/DO, simple and advanced 

eutrophication, wetland systems, and toxic contaminants. A

sediment nutrient flux subroutine permits the coupling of the

water column and sediment bed.

WARMS includes an acidification model linked to fish and 

waterfowl models. WARMS uses pH, area, dissolved organic

carbon, total P, and presence of fish to estimate

preacidification, present and eventual steady­state values for

pH, fish presence and waterfowl breeding parameters under

proposed SO emission scenarios. 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. 

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Model 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 user­2 defined landscape units that may vary in shape and size (m

to km2). 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 CO2 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,

NH3 volatilization and denitrification, runoff and subsurface

leaching based on physical principles and parameter values

derived from extensive model testing and specific field

validation.

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Model 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 O2 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 CITE
             

Model 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 forconventional 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 NH3, 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 thatcalculates 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 CITE
             

Model 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.

ReNuMa Regional Nutrient

Management Model T* N 19 ReNuMa is based on the Generalized Watershed LoadingFunction (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 CITE
             

Model 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 

 (SO2, HNO3, 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    
 

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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 

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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 moves 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 budgets 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 deposition 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 budgets 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 retention at glaciated sites was attributed to relatively low SO 2− 

4 adsorption capacity in soils. Balanced 

S budgets implied that decreases in atmospheric deposition of S would 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 are 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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central Adirondack region of New York was about 1% of the organic S pool in the soil. Houle et al. 

(2001) estimated that annual S deposition at 11 sites in North America ranged from 1% to 13% of the 

organic S pool in soil. 

Courchesne et al. (2005) measured a downward trend in water-soluble SO4 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 cycling, 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 stored 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 and 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 have 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 deposition, the amount of S in runoff still exceeded 

inputs by 30%. Most of the S in runoff was attributed to mineralization of organic S in 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 Al3+. 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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depletion of soils had not been demonstrated at the time of the last SOX 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 Ca2+ 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 Ca2+ concentrations in both the O (combined Oa and Oe horizons) and B horizons from 

1930 to 1984. Richter et al. (1994) also observed Ca2+ 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 Ca2+, 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 Ca2+ and acid-extractable Ca2+ 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 Ca2+ 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 Ca2+ and Mg2+ concentrations in Oa/A horizons and 

throughout the B horizon. Courchesne et al. (2005) found higher concentrations of exchangeable Ca2+ 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 Mg2+ at the three locations in the O horizon. In the upper 10 cm of the 

B horizon, no significant differences were found in exchangeable Ca2+, but at two of three locations, 

exchangeable Mg2+ 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 Ca2+ 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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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 Ca2+ pools at HBEF and found a sustained decrease in exchangeable Ca2+ 

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 Ca2+ weathering fluxes in this 

analysis adds uncertainty to the magnitude of changes reported for the exchangeable Ca2+ pool (Bailey 

et al., 2003). Two additional mass balance studies used Sr isotopes to evaluate changes in soil Ca2+ 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 Ca2+ 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 

Ca2+ supply from weathering in the lower profile is not reaching the upper soil where most root activity 

occurs, and that Ca2+ depletion has occurred in the upper soil. 

The study of Yanai et al. (1999) investigated changes in Ca2+ and Mg2+ 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 Ca2+ 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 

Mg2+ concentrations, or Ca2+ or Mg2+ 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 Ca2+ and Mg2+ 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 Ca2+ depletion, and additional research on soil processes, a 

detailed understanding of the mechanisms of Ca2+ depletion has developed over the past two decades. 

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Ulrich (1983) explained Ca2+ 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 

Ca2+ 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 Ca2+ 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 (r2 = 0.44). However, these factors were strongly 

correlated in the Oa horizon (r2 = 0.92) (Lawrence et al., 1997). Because mineral weathering in the 

B horizon is the primary source of soil Ca2+, a strong relationship between weathering potential and 

exchangeable Ca2+ concentrations would be expected in this horizon. The weak correlation suggests that 

concentrations of Ca2+ 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 Ca2+ 

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 Ca2+ 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 Ca2+ from atmospheric deposition decreased sharply in the east through the 1980s 

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(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 Ca2+ 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 Ca2+ loss from forest harvesting 

continued for 30 years after disturbance, but the source of the additional Ca2+ 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 (Alo) 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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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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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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absorbed > 900 kg N/ha without significant NO3

− leaching during an 8–year N amendment study (Fenn 

et al., 1998). In contrast, NO3

− leaching losses were high at the Harvard Forest pine sites. In the 8–year 

experimental study, NO3

− 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 NO3

− 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 NH4

+ 

produced by mineralization. Further increase in the availability of N (for example by high levels of 

atmospheric N deposition) lessens competition for NH4

+ between plants and microbes and leads to 

increased production of NO3

− by autotrophic nitrifying bacteria. Some of this NO3− can be taken up by 

plants and microbes, but because much of the N demand is satisfied by NH4

+ under these conditions, 

NO3

− 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 NH4

+ has decreased to the point that net nitrification occurs. 

Substantial leaching of NO3

− 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 Ca2+ and Mg2+ (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 NO3

− 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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Analyses have been conducted in the northeastern U.S. and Europe to examine the relationships 

between N deposition and NO3

− leaching to surface waters. The relationship between measured wet 

deposition of N and streamwater output of NO3

− 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 (r2 = 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 NO3

− 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 NO3

− in surface waters were 

positively correlated with atmospheric deposition, whereas most watersheds with deposition less than 

7 kg/ha/yr had little or no NO3

− (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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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 NO3

− 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 NO3

− 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 NO3

− to surface waters 

prior to the additions, resulted in substantial increases in NO3

− 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 NO3

− plus NH4+ 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 (NO3

− + NH4+

) 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 (NO3

− + NH4+

) 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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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 NO3

− and Ca2+ 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 15NO3− and 15NO2 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 NO3

− to NO2− through oxidation of Fe2+

. Testing of this hypothesis in 

situ was not found in the literature, but the small amount of Fe2

+ 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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Fitzhugh et al. (2003) showed that NO2

− produced in the first step of nitrification may be directly 

converted to soluble organic N rather than becoming fully oxidized to NO3

−

. However, concentrations of 

introduced 15NO2 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 NO3

− 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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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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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 is 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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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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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 (pKa), 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 pKa 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., SO4

2−, NO3−

) 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 Aln+) as much 

as an exchange of SO4

2− and NO3− 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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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 ALo to Ali 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 

H2SO4 at HBEF in New Hampshire. Streamwater pH (4.4) was near the range of reported average pKa 

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 SO4

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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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 deposition 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 deposition. It is likely that a high 

percentage of this DOC originates from wetland soils within the monitored watersheds. This result 

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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. 

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Source: Stoddard et al. (2003).

Figure B­2. Cumlative frequency diagram (distribution) of slopes for SO 2−

4 , NO −3 , Gran ANC,

hydrogen ion, [Ca2+ + Mg2+], 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 and N. Chemical reduction 

reactions and biological uptake contribute to S and 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 reduction 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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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 NOX, SOX, 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 NOX. 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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Brook Watershed in Maine, one watershed with mixed coniferous and deciduous species received 

(NH4)2SO4 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 Ca2+ and 27 kg/ha/yr less 

exchangeable Mg2+ 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 (NH4)2SO4 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 NH4

+ versus SO42− additions. 

Kozlowski (1985) suggested that plants and soils act as sinks for SO2 deposition at low exposures 

of 1 to 4 μg/m3

, with no discernible effects on ecosystem structure at those levels. Shugart and 

McLaughlin (1985) cautioned that forest responses to SO2 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 NO3

− 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 SO4 -S and 8 to 10 kg/ha NO3 -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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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, (NH4)2SO4 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 NO3

− and Ca2+ (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 NO3

− 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 SO4 -S/ha/yr; 2 to 4 kg NO3 -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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� 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 

lakes (and presumably also streams) are acid-sensitive and have been acidified by atmospheric deposition 

of S and 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 each 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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� Southwestern Adirondacks 

�

�

�

�

New England Uplands 

Low Si Eastern Upper Midwest 

Forested Mid-Atlantic Highlands 

Mid-Atlantic Coastal Plain 

� Northern Florida Highlands 

In the 1990 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 for the NSS was 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 or 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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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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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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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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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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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 has one of the longest continuous records of precipitation and 

streamwater chemistry in the U.S. Compared to model hindcast approximations, current conditions at 

HBEF indicate 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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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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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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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 + NO3 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 Ali 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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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 (ΔANC = 

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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to predict. It is likely that monitoring programs of several decades or longer will be needed to separate 

trends in NO3

− 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 SO4

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 SO4

2− concentration were considered consistent with observed declines in S 

wet deposition. Surface water NO3

− concentrations also decreased, but only in the two regions that had 

the highest ambient surface water NO3

− 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 (Ca2+ + Mg2+; Figure B-4). The decline in base cations offset some of the 

decline in SO4

2−, and thus limited the increase in ANC or pH that occurred in response to lower SO42− 

concentrations. Surface water ANC and pH increased significantly in the 1990s; Ali 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 SO4

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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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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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 SO2 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 NOX 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 deposition. 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 adsorption 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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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. 

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Source: 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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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 deposition. 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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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 and 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 in 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 adsorption on soil is high, relatively high levels of S deposition 

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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 

SO4

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 km2

), 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 Ali (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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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 SO4

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 SO4

2− from atmospheric 

deposition. The amount of SO4

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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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 and 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 statistical 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 and 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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(~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 O3 standard, NOX 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 SO4

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 SO4

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 SO4

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 SO4

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 SO4

2− and NO3− 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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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 deposition. 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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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 survey 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 Aln+ is removed from 

soil solution by precipitation and ion exchange reactions within 75 cm depths (Grates et al., 1985), and 

relatively little Aln+ 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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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 

SO2 emissions and S deposition, which increased consistently between about 1945 and 1985 (Husar and 

Sullivan, 1991). 

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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 survey, 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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Sullivan (2000) summarized patterns in lakewater chemistry across the Upper Midwest from the 

ELS survey. 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 SO4 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 in 

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 (τw). 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 were 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 depositional 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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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− SO4 relative to base cation concentration, and had probably been acidified by acidic 

deposition. 

The concentration of lakewater (Ca2+ + Mg2+) 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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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 deposition (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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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 and 

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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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 SO4 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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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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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 SO4

2−, NO3−

, 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 NO3

− in the range of 10 

to 30 μeq/L, and likely had substantially higher NO3

− concentrations during spring. 

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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 SO2 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 SO2 emissions, and have since recovered to circumneutral values. Simulated ANC 

responded to both SO2 and NOX emissions, decreasing to annual values of 20 to 25 μeq/L during years of 

highest SO2 or NOX emissions compared with current mean annual ANC values near 50 μeq/L (Hartman 

et al., 2007). 

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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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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 Ali 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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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, SO4

2−, NO3−

, Aln+, 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 SO4

2− and especially NO3− 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 Ali 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(OH3)) 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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The mechanisms that produce acidic episodes can include dilution of base cations and flushing of 

NO3

−

, SO4

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 NO3

− during high flow periods, 

contributing hydrologically mobile SO4

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 NO3

− 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 

NO3

− 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 SO4

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 SO4

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: 

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� 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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Based on measurements of microbial biomass, CO2 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 Nr 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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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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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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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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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 NO3

− and SO42−, 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 

SO4

2− and organic acid anions, and to a lesser extent NO3−

, 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). 

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Source: 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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ANCmin = 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 ΔANC. Higher values of pre-episode ANC lead to larger ΔANC 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 ΔANC values for such streams are generally 

small. 

Webb et al. (1994) developed an approach to calibration of an episodic acidification model for 

VTSSS long-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 the model 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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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. In most cases where biological effects of acidification have been documented, the most 

important chemical parameters involved in those effects have been pH, Ali, and Ca2+. Less commonly, one 

or more base cations other than Ca2+ (e.g., Mg2+, 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 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., 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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length. Expressed as fish weight/length3, 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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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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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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Decreases in ANC and pH and increases in Ali 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 

Ca2+, and ANC, and had higher concentrations of Ali 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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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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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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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 km2 and ANC higher than 

75 μeq/L. In contrast, the majority (but not all) of the “runs” have watershed area smaller than 10 km2 and 

ANC less than 20 μeq/L. All of the streams that have watershed areas smaller than 10 km2 have three or 

fewer known species of fish present. All of the streams having larger watersheds (>10 km2

) 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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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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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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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 Ca2+ 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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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 processes 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 Ca2+ in female fish in acidified Canadian lakes lead to a higher probability for failure in 

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producing viable eggs. A depletion of Ca2+ 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 Ali 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 Ali 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 Ali 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-Ca2+ 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-Ca2+ (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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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, Ali, or Ca2+ (Baker et al., 1990a). Aquatic biota vary greatly 

in their sensitivity to episodic decreases in pH and increases in Al in waters having low Ca2+ 

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 Ali 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 Ali. 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 Ali 

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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-Ca2+ 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 Ali. 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 Ali was the best single predictor of 20–day mortality for both 

brook trout and sculpin, whereas the number of days with Ali > 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 Ali > 100 

to 200 μg/L. Acid -sensitive fish species were absent from streams with median high flow pH < 5.2 and 

Ali > 100 μg/L. More recently, Baldigo et al. (2007) found that mortality of brook trout young of the year 

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occurred at concentrations as low as 54 μg/L .Ali 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 Ali or ANC and Ali 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 Ali 

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 Ca2+

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 Ali 

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 Ali at higher Ca2+ concentrations, but in natural environments, elevated 

concentrations of Ali are only found in Ca2+-depleted systems. This effect is most important at low Ca2+ 

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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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 lower 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 deposition 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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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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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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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 SO4

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 SO4

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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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, SO2 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 SO2 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, Ca2+, 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 Ca2+, and thus underestimate predicted fish response to changes in acidic 

deposition. 

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An important consideration for measuring the success of S and 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 Ecosystem 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 NO2 

surpassed 40 μg/m3 and NOX surpassed 70 μg/m3

. 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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Peninsula. The effects were attributed to both acidification and fertilization processes (Lilleskov et al., 

2002). EMF communities are important in tree nutrition and C balance, and EMF trees tend to be 

dominant in N-limited forest ecosystems. A shift in EMF community structure could result in changes in 

tree species. 

Westman et al. (1985) summarized the literature of negative effects of SO2 on native plants, 

including decreased pollen germination and tube elongation in both angiosperms and gymnosperms. It is 

often difficult to separate the effects of SO2 exposure on plants from the effects of S deposition. This is 

because areas that experience high SO2 exposure generally also receive high S deposition. Kozlowski 

(1985) summarized relative susceptibility of different trees, lichens, and bryophytes to SO2. 

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 H2SO4:HNO3) 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 deposition (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 SO2 effects on grasses in the United Kingdom, Bell (1985) suggested that damage 

can occur at levels as low as 150 μg/m3

. 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 SO2

, 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/m3 of SO2 over 25 years. 

Studies of SO2 effects on timothy grass (Phleum pratense) showed diminished leaf production and 

increased leaf senescence in seedlings exposed to 0.120 ppm SO2 for 35 days (Mansfield and Jones, 

1985). In another experiment, Mansfield and Jones (1985) reported that exposure to 0.120 ppm SO2 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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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/m3

) and three elevated levels of SO2 (~60, 106, 184 μg/m3). 

Year-to-year S accumulation did not appear to occur over the 5–year course of the treatment, though 

progressive increases in root and rhizome S concentrations 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 deposition. 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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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 deposition 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 emissions 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 retention 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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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 showed 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 CO2 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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(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 NO3

− 

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 NO3

− 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 NO3

− 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 NO3

− concentrations are much lower than 

in the Adirondacks and Northern Appalachian Plateau (Source: Stoddard et al. (2003). 

Figure B-21), NO3

− concentrations in surface waters were unchanged during the 1990s. The 

Ridge/Blue Ridge province registered a small, but significant, decrease in NO3

− during the 1990s, but 

interpretation of trends for NO3

− 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 

NO3

− 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 NO3

− concentrations in surface waters was 

provided by studies of Mitchell et al. (1996) and Murdoch et al. (1998). A synchronous pattern in NO3

− 

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 NO3

− 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 NO3

− concentrations in most years in a Catskill 

watershed with elevated NO3

− 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 NO3

− 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 

NO3

− 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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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. 

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Source: Stoddard et al. (2003).

Figure B­21. Time series data for SO 2−

4 , NO −3 , base cations [Ca2+ + Mg2+], 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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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 

(Ca2+ + Mg2+) 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 (Ca2+ + Mg2+) concentrations were consistent with the expected pattern of high rates of cation leaching 

during the early stages of acidification from S deposition. 

Clow and Mast (1999) observed trends in (Ca2+ + Mg2+) concentrations that were generally 

consistent with SO 2− 

4 trends in five eastern rivers from 1968 to 1983. Decreasing trends in concentrations 

of (Ca2+ + Mg2+) and SO 2− 

4 concentrations were observed in a Maine river, and increasing trends in (Ca2+ 

+ Mg2+) and SO 2− 

4 concentrations were observed in an Ohio river. None of the three other rivers showed a 

decrease in concentrations of (Ca2+ + Mg2+), 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 (Ca2+ + Mg2+) showed no trend. Also, a negative trend in (Ca2+ + Mg2+) 

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 Ca2+ 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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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 Ca2+, 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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(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 Ali 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 Ali 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 Ali 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 Ali 

concentrations (Driscoll et al., 2007). The analysis of Stoddard et al. (2003) also observed an average 

decrease in Ali 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 showed decreases in Ali 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 Ali decreased, and no pH trend at the other two locations where Ali 

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 Ali. 

Most recently, Lawrence et al. (in review) found that 49 of 195 streams (25%) during August base flow in 

the western Adirondack region had Ali 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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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 (Ca2+ + Mg2+) 

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 (Ca2+ + Mg2+ 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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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 km2

) 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 km2

) 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 Ali 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 Ali 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 Ali concentrations increased from 1.6 to 7.3 μM in 6.5 h. 

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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 Ali 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 Ca2+ and other nutrient base cations (Mg2+, 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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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 SOX and NOY. 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 SOX and NOY 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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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 O3, 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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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 or 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 and 

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 and 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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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 Ca2+, and 7 to 16 kg/ha/yr of Mg2+ 

(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 NO3

− 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 SO4

2− adsorption on mineral soil, and therefore 

decreased the flux of SO4

2− in stream water. With subsequent vegetation regrowth, the adsorbed SO42− 

was released from the soil to drainage water, and streamwater SO4

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 NO3

− and Ali increased sharply during high flows after the first growing season (1997). 

Acid-Ali 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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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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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 NO3

− 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 NO3

− and base cation concentrations increased during high-runoff conditions, although the 

increase in base cations did not fully compensate for the episodic increase in NO3

−

. 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 NO3

− 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 SO4

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 SO4

2− response. 

Eshleman et al. (1998) reported NO3

− outputs from five small (<15 km2

) 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 NO3

− concentrations of about 30 to 55 μeq/L. Most of the 

increased NO3

− leaching occurred during storm flow conditions. 

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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 and 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 deposition. 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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(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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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 Ali and Ca2+. 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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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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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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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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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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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 Ali that is released from watershed soils. A number of studies reviewed by Baker et al. (1990a) 

reported threshold values of Ali for various species and effects. Those results are presented in Table B-25. 

The effects of low pH and high Ali can be ameliorated to an extent in the presence of increased Ca2+ 

concentration. A summary of the effect of increasing Ca2+ 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 Ali, DOC, and Ca2+, along with the 

timing and magnitude of episodic fluctuations in toxic acid and Ali 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 Ali 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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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 Ca2+ 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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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 Ali 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 Ali 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 Ali 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 Ca2+ metabolism (Peterson and Martin-Robichaud, 

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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 Ca2+ 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, Ca2+, 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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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 SiO2, 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, Ali, and Ca2+ in laboratory toxicity tests. These 

models had the advantage that they dealt directly with the interaction effects of pH, Al, and Ca2+, 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 Ca2+, 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 Ca2+, Ali, 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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27	 

28	 

29	 

31 

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 

goldeneye (McNicol, 2002). 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 emissions 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 emissions control scenarios 

targeted for 2010 (McNicol, 2002). The modeled emissions scenarios include: 

The number of 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 emissions 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

9.3a

10.2a

7.0–7.7
 

(as throughfall)
 

32c

15–20

3.1d

10.3d

26.6

7.5–8.0

23.3e

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	 

47c

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 NO3 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 NO3 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 − NO3 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       Ali > 100 L     �g/

ADIRONDACKS

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)

August 2008 B­138 DRAFT—DO NOT QUOTE OR CITE
             

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)

Acidic (ANC <

Low ANC (0 <

Moderate ANC

0 �eq/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− SO4

(�eq/L/yr)

− NO3

(�eq/L/yr)

Base Cations  

[Ca2+ + Mg2+]

(�eq/L/yr)

Gran ANC

(�eq/L/yr)

Hydrogen

(�eq/L/yr)

DOC

(mg/L/yr)

Aluminum

(�g/L/yr)

New 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.

August 2008 B­139 DRAFT—DO NOT QUOTE OR CITE
             

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)

Sulfate

−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

ANCa ANC

0

1

−2

−1

−2

7

−4

−4

DOCb

−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)
 

minus

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 STRATEGY1

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 STRATEGY1

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 CITE
             

Physiographic 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 ANC

(�eq/L)

SBC

(�eq/L)

2− SO4

(�eq/L)

− NO3

(�eq/L)

DOC

(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 SO4 ­ (�eq/L) and NO3 (�eq/L). The median (P50) and 90th percentiles are shown for DOC (mg/L).

99th (P95, P99) percentiles are

Table B­10. Population estimates of the − ANC and NO3 within defined ranges.

percentage of lakes in selected subregions of the West with 

ANC (�eq/L) NO3­ (�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)

Number of

Fish Species

Smaller Watersheds (<10 km2

)

North Fork Dry Run

Deep Run

White Oak Run

Two Mile Run

Meadow Run

Larger Watersheds (>10 km2

)

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

Diatom Inferred Measured ELS/NSWS Target Population 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 

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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

Modela

Brook Trout

Bayesian

LAF framework

pH

pCa/pH

pCa/pH, Al/DOC

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)
 

and 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

Responseb 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 CITE
             

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

Responseb 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

Cronan et al. 500–3000

(1989)

European beech

Asp and  300

Berggren

(1990)

Cronan et al. 500

(1989)

Peach

Edwards and  222

Horton (1977)

Scotch pine

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 CITE
             

Solution 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 NO3− 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 NO3− 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)

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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 et al. (1987)

Blacknose dace, 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

Lake Trout

Pearl dace

White 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

In 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

failure)

Source: Baker et al. (1990a)

1 

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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).

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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 Al

(�g/L) Observed 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)

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Table B­26. The effects of 2+ increasing Ca to ameliorate low pH and high Al.

Reference Type of

Study Taxa pH Al Range(�g/L)

2+ Ca     Range

(�eq/L) Observed 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) Expected 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

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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

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August    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 Increase in White sucker Recruitment failure Recruitment failure

fish	 minnow near extinction; abundance of pearl recruitment failure; for all species for all species

experience slimy sculpin dace, Suckers very no effect on adult

recruitment decline in abundant. growth and survival

failure abundance

Lake Increase in Increased  Lake trout Recruitment failure; Lake trout condition Lake trout condition

trout condition abundance of recruitment failure; no effect on adult poor; recruitment very poor;

i.e., “fatter” young­of­the­year condition similar to growth and survival failure; reduced recruitment failure;

preacidification adult survival reduced adult

survival

Source: (Baker, 1990)

Table B­30. Range of minimum pH of fish species occurrence in 11 lake surveys.
 

Family and Species	 High Minimum pH Low Minimum pH

Cyprinidae

Bluntnose minnow

Fathead minnow	 

Blacknose dace

Pearl dace	 

Northern redbelly dace

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

Lake 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

Black 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

Walleye

5.8

6.9

4.4

5.2
             

Family 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)

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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 percha

b Largemouth bass

Ecological health

    Brook trout

    Yellow perchc

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

LD33a

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
 

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Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N deposition 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 Experimental 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 papers. 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: 

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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) 

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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 N2O 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, N2O, and N2 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:N2O (relative “hole size” for NO 

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and N2O 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 N2O 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 N2O 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 N2O production in response to the highest N treatment (150 kg N/ha/yr) to a 

red pine (Pinus resinosa) stand, but N2O 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 

N2O 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 N2O 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 N2O 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 N2O 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 N2O 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 

N2 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, NO2, N2O, and N2) 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 15N 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 15N in soil, vegetation, and lysimeter leachate after 9 to 21 months amounted to 65% to 105% of 

added 15N (Tietema et al., 1998), providing a broad constraint on N gas emissions of no more than 35% of 

added 15N. The lowest rates of 15N 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 N2O Interactions

Rainfall events are an important feature controlling N2O 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 N2O emission was equivalent between 

the sites (Mohn et al., 2000). However, after rainfall events the maximum rate of N2O 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 N2O (118 g N2O 

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), N2O 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 

N2O 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 N2O 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 N2O 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, N2O production by denitrification decreases. Under these same environmental conditions, NO 

production by nitrification increases. Most laboratory studies show increasing temperature increases N2O 

production, however if water filled pore space (WFPS) increases to 70-80%, then N2 rather than N2O is 

the main product of denitrification and N2O emissions go down. This illustrates how N2O emissions 

increase with increasing soil moisture until soil moisture become more conducive to N2 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, N2 production, N2O accumulation in response to 

acetylene inhibition of N2O reduction, isotopic methods, and N2: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 N2O, or used the “acetylene block” technique of 

inhibiting transformation of N2O to N2 and monitoring the accumulation of N2O as a surrogate of the sum 

of N2O and N2. 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 method to measure 

small changes in dissolved N2 caused by denitrification in sediments. This technique allows measurement 

of N2 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 (CH4)-producing and CH4­

oxidizing microbial activity. The difference between the CH4 production and oxidation determines the 

magnitude of CH4 emission from soils. There is evidence to support that ammonium compounds reduce 

CH4 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 CH4 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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is that increases in vegetative cover caused by N addition increase C availability through root exudates, 

which in turn stimulates methanogenic bacteria and CH4 emissions (Granberg et al., 2001; Saarnio et al., 

2003; Zhang et al., 2007). 

Saarnio et al. (2003) observed moderate increases in CH4 emissions from boreal wetland soils with 

N fertilization rates of 30 kg N/ha/yr as ammonium nitrate (NH4NO3). Comparable N application rates 

and effects on CH4 emissions were also observed by Granberg et al. (2001) in a similar ecosystem type. 

Zhang et al. (2007) observed elevated CH4 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 CH4 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 CH4 

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 CH4 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 , O2, 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 N2 gas, it now appears that anaerobic ammonium oxidation 

(anammox) may account for up to 50% of the N2 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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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 NH3 and NOX 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. 

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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

—

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22

—

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—

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—

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Turner et al. (2001) found a strong correlation between population density (persons/ km2) and the 

total N loading from watershed to estuary (r2 = 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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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 CO2, 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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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 N2O, CH4

and CO2 Flux

Table C-2 summarizes key information from the experiments included in the meta- analysis 

presented in ISA section 3.3.4. 

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Table C­2. The study site, experimental condition, ecosystem type, N form,

and citations is presented for all studies used in NEE, EC, CH4 uptake, CH4

emission meta analyses.

amount of N addition 

emission and N2O

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

NH4NO3

NH4NO3

NH4NO3

NH4NO3

NH4NO3

NH4NO3

NH4NO3

.

slurry

NH4NO3

NH4NO3

.

.

.

NH4NO3

NH4NO3

.

NH4

NH4

DAP

DAP

NH4NO3

NH4NO3

NH4NO3

NH4NO3

NH4NO3

NH4NO3

NH4

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 CITE
                

Site experimental

condition ecosystem N form N addition(kg ha/yr) Reference

CH4 emission

Netherland incubation wetland NH4 . Aerts and Caluwe 1999

Netherland incubation wetland  NH4 . Aerts and Caluwe 1999

Polish incubation wetland NH4 . Aerts and Caluwe 1999

Netherland incubation wetland NH4 . Aerts and Toet 1997

Swiss FACE field grassland NH4NO3 84 Baggs and Blum 2004

Swiss FACE field grassland NH4NO3 84 Baggs and Blum 2004

Minnesota incubation wetland NH4 20 Keller et al 2005

Minnesota incubation wetland NH4 60 Keller et al 2005

Niwot Ridge, CO field grassland urea 250 Neef et al. 1994

Finland incubation wetland NH4NO3 30 Nykanen et al. 2002

Finland incubation wetland NH4NO3 100 Nykanen et al. 2002

Salmisuo, Finland field wetland NH4NO3 30 Sarrnio and Silvola 1999

Salmisuo, Finland field wetland NH4NO3 30 Sarrnio and Silvola 1999

Sanjiang mire, China field wetland NH4NO3 240 Zhang et al. 2007

CH4 uptake

Quebec, Canada incubation coniferous NH4 . Adamsen and King 1993

Quebec, Canada incubation coniferous NO3 . Adamsen and King 1993

Michigan field coniferous NH4NO3 10 Ambus and Robertson 2006

Michigan field coniferous NH4NO3 30 Ambus and Robertson 2006

Michigan field deciduous NH4NO3 10 Ambus and Robertson 2006

Michigan field deciduous NH4NO3 30 Ambus and Robertson 2006

Michigan field grassland NH4NO3 10 Ambus and Robertson 2006

Solling, Germany field coniferous NH4NO3 18.5 Borken et al. 2002

Solling, Germany field coniferous NH4NO3 24.2 Borken et al. 2002

Solling, Germany field coniferous NH4NO3 18.5 Borken et al. 2002

Solling, Germany field coniferous NH4NO3 24.2 Borken et al. 2002

Bousson, PA field deciduous NH4NO3 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 NH4 31.4 Castro et al. 1992

Bousson, PA field deciduous NH4NO3 100 Chan et al. 2005

Abisko, Sweden field tundra NH4NO3 100 Christensen et al. 1997

Finland field wetland NH4 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 NH4 150 Gulledge and Schimel 2000

LTER Alaska field deciduous NH4NO3 66.7 Gulledge and Schimel 2000

LTER Alaska field coniferous NH4NO3 12.3 Gulledge and Schimel 2000

LTER Alaska field deciduous NH4NO3 171.4 Gulledge and Schimel 2000

LTER Alaska field coniferous NH4NO3 142.9 Gulledge and Schimel 2000

Swiss FACE field grassland NH4NO3 560 Ineson et al. 1998

Swiss FACE field grassland NH4NO3 560 Ineson et al. 1998

August 2008 C­11 DRAFT—DO NOT QUOTE OR CITE
                

Site experimental

condition ecosystem N form N addition(kg ha/yr) Reference

Scotland incubation heathland NH4 40 Macdonald et al. 1997

Scotland incubation heathland NH4NO3 40 Macdonald et al. 1997

Scotland incubation heathland NO3 40 Macdonald et al. 1997

Finland field coniferous NH4NO3 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 NH4 150 Papen et. al 2001

Finland field wetland NH4NO3 30 Saarnio et al. 2003

Gjovelandsnesset, Norway incubation coniferous NH4NO3 30 Sitaula et al. 1995

Gjovelandsnesset, Norway incubation coniferous NH4NO3 90 Sitaula et al. 1995

Harvard forest, MA field coniferous NH4NO3 37 Steudler et al. 1989

Harvard forest, MA field deciduous NH4NO3 37 Steudler et al. 1989

Harvard forest, MA field coniferous NH4NO3 120 Steudler et al. 1989

Harvard forest, MA field deciduous NH4NO3 120 Steudler et al. 1989

Belgium incubation wetland NH4 . VanderNat et al. 1997

Belgium incubation wetland NH4 . 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)

N2O emission 

Michigan biological Station incubation deciduous NH4NO3 . Ambus and Robertson 1999

Michigan biological Station incubation deciduous NH4NO3 . Ambus and Robertson 1999

Michigan biological Station incubation deciduous NH4NO3 . Ambus and Robertson 1999

Michigan biological Station incubation deciduous NH4NO3 . Ambus and Robertson 1999

Michigan field coniferous NH4NO3 10 Ambus and Robertson 2006

Michigan field deciduous NH4NO3 10 Ambus and Robertson 2006

Michigan field grassland NH4NO3 10 Ambus and Robertson 2006

Michigan field coniferous NH4NO3 30 Ambus and Robertson 2006

Michigan field deciduous NH4NO3 30 Ambus and Robertson 2006

Hyytiälä incubation coniferous NH4 . Ambus et al. 2006

Speulderbos incubation coniferous NH4 . Ambus et al. 2006

San Rossore incubation coniferous NH4 . Ambus et al. 2006

Glencorse incubation coniferous NH4 . Ambus et al. 2006

Nyirjes incubation coniferous NH4 . Ambus et al. 2006

Achenkirch incubation coniferous NH4 . Ambus et al. 2006

Höglwald incubation coniferous NH4 . Ambus et al. 2006

Sorø incubation deciduous NH4 . Ambus et al. 2006

Bosco negri incubation deciduous NH4 . Ambus et al. 2006

Schottenwald incubation deciduous NH4 . 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 CITE
                

Site 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 NH4NO3 84 Baggs and Blum 2004

Swiss FACE field grassland NH4NO3 84 Baggs and Blum 2004

Swiss FACE field grassland NH4NO3 420 Baggs et al 2003

Swiss FACE field grassland NH4NO3 420 Baggs et al 2003

Swiss FACE field grassland NH4NO3 420 Baggs et al 2003

Swiss FACE field grassland NH4NO3 420 Baggs et al 2003

Swiss FACE field grassland NH4NO3 420 Baggs et al 2003

Swiss FACE field grassland NH4NO3 420 Baggs et al 2003

Solling, Germany field coniferous NH4NO3 18.5 Borken et al. 2002

Solling, Germany field coniferous NH4NO3 24.2 Borken et al. 2002

Solling, Germany field coniferous NH4NO3 18.5 Borken et al. 2002

Solling, Germany field coniferous NH4NO3 24.2 Borken et al. 2002

Harvard forest field deciduous NH4NO3 37 bowden et al. 1991

Harvard forest field coniferous NH4NO3 37 bowden et al. 1991

Harvard forest field deciduous NH4NO3 120 bowden et al. 1991

Harvard forest field coniferous NH4NO3 120 bowden et al. 1991

Solling, Germany field deciduous NH4 140 Brumme and Beese 1992

Gullane incubation deciduous NH4 . Castaldi and Smith 1998

Gullane incubation deciduous NO3 . Castaldi and Smith 1998

Ascutney , VI field coniferous NH4 31.4 Castro et al. 1993

Allt, UK field heathland NH4NO3 20 Curitis et al 2006

Allt, UK field heathland NH4NO3 20 Curitis et al 2006

Afon Gwy, UK field grassland NH4NO3 20 Curitis et al 2006

Afon Gwy, UK field grassland NH4NO3 20 Curitis et al 2006

Afon Gwy, UK field grassland NH4NO3 20 Curitis et al 2006

River Etherow, UK field heathland NH4NO3 20 Curitis et al 2006

River Etherow, UK field heathland NH4NO3 20 Curitis et al 2006

St. James Parish, LO field wetland NO3 100 Delaune et al. 1998

St. James Parish, LO field wetland NH4 100 Delaune et al. 1998

Saratoga field grassland NH4NO3 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 NH4NO3 560 Ineson et al. 1998

Swiss FACE field grassland NH4NO3 560 Ineson et al. 1998

Manaus, Brazil field tropical forest NO3 . Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NO3 . Keller et al. (Keller, 1988)

August 2008 C­13 DRAFT—DO NOT QUOTE OR CITE
                

Site experimental

condition ecosystem N form N addition(kg ha/yr) Reference

Manaus, Brazil field tropical forest NO3 . Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NO3 . Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NH4 . Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NH4 . Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NH4 . Keller et al. (Keller, 1988)

Manaus, Brazil field tropical forest NH4 . Keller et al. (Keller, 1988)

gardsjon watershed field coniferous NH4NO3 35 Klemedtsson et. al 1997

gardsjon watershed field coniferous NH4NO3 35 Klemedtsson et. al 1997
 

Avoyelles, LO field wetland NH4 100 Lindau et al. 1994

Avoyelles, LO field wetland NH4 300 Lindau et al. 1994
 

Avoyelles, LO field wetland NO3 100 Lindau et al. 1994

Avoyelles, LO field wetland NO3 300 Lindau et al. 1994
 

Harvard forest field coniferous NH4NO3 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 NH4 15.4 Munoz­Hincapie et al. 2002
 

Puerto Rico field wetland NH4 130.2 Munoz­Hincapie et al. 2002

Puerto Rico field wetland NH4 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 NH4 15.4 Munoz­Hincapie et al. 2002
 

Puerto Rico field wetland NH4 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 NH4 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 NH4 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 NH4NO3 30 Sitaula et al. 1995
 

Gjovelandsnesset, Sweden field coniferous NH4NO3 90 Sitaula et al. 1995

Gjovelandsnesset, Sweden field coniferous NH4NO3 30 Sitaula et al. 1995
 

Gjovelandsnesset, Sweden field coniferous NH4NO3 90 Sitaula et al. 1995

Gjovelandsnesset, Sweden field coniferous NH4NO3 30 Sitaula et al. 1995
 

Gjovelandsnesset, Sweden field coniferous NH4NO3 90 Sitaula et al. 1995

Scotland field coniferous NH4NO3 48 Skiba et al. 1998
 

Deepsyke forest field coniferous NH4NO3 48 Skiba et al. 1999

Deepsyke forest field coniferous NH4NO3 96 Skiba et al. 1999
 

August 2008 C­14 DRAFT—DO NOT QUOTE OR CITE
                

Site

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 NH4NO3 240 Zhang et al. 2007

1 

2 

3 

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5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

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 stoichiometry are closely relate, and affect the 

composition of autotrophic species that will occupy a particular habitat. The resulting stoichiometric 

balance of C:N:P in 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. 

August 2008 C­15 DRAFT—DO NOT QUOTE OR CITE
                

     

                 

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11 

12 

13 

14 

15 

16 

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 stoichiometry are closely relate, and affect the 

composition of autotrophic species that will occupy a particular habitat. The resulting stoichiometric 

balance of C:N:P in 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 

August 2008 C­16 DRAFT—DO NOT QUOTE OR CITE
                

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24 

25 

26 

27 

28 

29 

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 O3-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). 

August 2008 C­17 DRAFT—DO NOT QUOTE OR CITE
                

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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28

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 CITE
                

                         

                 

                     

                       

                   

                   

Source: 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 

2 

3 

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 

August 2008 C­19 DRAFT—DO NOT QUOTE OR CITE
                

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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 15N, NO − 

3

reductase activity, and mycorrhizal infection compared with N uptake quantified by stable isotope tracer 

additions in the greenhouse. 13C enrichment subsequent to 13C, 15N-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 NO3 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 

15N 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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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 O2 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 Forest 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 sarmentosa, Bryoria fuscescens,

Hypogymnia enteromorpha, Nephroma bellum, Nodobryoria

oregana

Polluted Air Regional nitrophytes Candelaria concolor, Physcia adscendens, Xanthoria polycarpa

* Includes only species with highest indicator value, used by Geiser and Neitlich (2007) to define air quality zones.

Source: Geiser and 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 concentrations > 0.6% and 0.07 %, and SO2 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 NOX and SOx deposition (McCune, 1988; Gauslaa, 

1995; van Haluwyn and van Herk, 2002). Other species respond positively to NHY 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 NH3 exposure to epiphytic 

macrolichens in the Sierra Nevada region. They found that lichens provide a relatively inexpensive tool 

for estimating fine-scale distributions of NH3 exposure to terrestrial ecosystems. Because NH3 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). 

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Number 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 emissions began to decrease substantially in response to the Clean Air Act 

amendments did the role of NOX, and still later, NH3, 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 in eutrophication of freshwaters for the past 40 years, largely due to the 

demonstrated role of P in 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 as 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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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 NO3

− 

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 NO3

−

, whereas lakes on the 

eastern side of the Continental Divide averaged 10.5 μeq/L of NO3

− concentration. In the Colorado Front 

Range, NO3

− concentrations in lakes above15 μeq/L have commonly been measured, suggesting some 

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Table C­5. Summary

ecosystems.

of additional evidence for N limitation on productivity of freshwater

Region Endpoint Observation Ecosystem

Type Reference

Sweden

Rocky Mountains

of Colorado and

Wyoming

Texas

Rocky Mountains

of Colorado and

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: the author concluded that the effects of atmospheric N deposition were

uncertain and that a widespread shift from N to P limitation had not been clearly

demonstrated.

some instances of seasonsal N­limitation, and other instances of year­round N­limitation

Review: Author stated that recent studies did suggest a change in diatom species

dominance in the 1950s, but widespread species changes across lakes in the region and 

the role of N deposition in these changes needed confirmation. Thus, the available data

were not clear at that time, but suggested that some changes had likely occurred in

some aquatic ecosystems.

lakes

rivers

Bergström et

al. (2005)

Burns (2004)

Stanley et al.,

1990

Burns (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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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 NO3

− and phosphate (PO4−

)-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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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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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 (NH4)2SO4 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 O2 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 NO3     ­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). 

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Species Common Name Life

Stage

N Concentration ­ (mg NO3     ­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

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Species Common Name Life

Stage

N Concentration ­ (mg NO3     ­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 

2 

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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� 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. 

sources, including both 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 number of 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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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 NOX 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 O2

        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 

2 
                

 

         

1 

2 

3 

4 

5 

6 

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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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 O2

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 O2

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).

1 

2 
                

             

                     

         

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

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 Ca2+ 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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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 Ca2+ 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 Nr (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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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 NO3

− 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 NO3

− 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 Nr deposition is thought to be impacting essential ecological attributes 

associated with terrestrial ecosystems and how they respond to disturbance. Effects of Nr 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 Nr 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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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 Nr 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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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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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 NOX 

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 NH3 emissions rather than as sinks (Grünhage et al., 

1992; Krupa, 2003). Atmospheric N deposition can contribute a quantitatively important component of 

the Nr 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 NHY to the atmosphere, some of which is 

deposited on coastal waters. 

Agriculture also experiences indirect effects of NOX emissions through the formation of groundlevel O3. 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 vegetation 

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 O3 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 O3. 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 NO3     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 O3 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 O3, which has 

damaged sensitive plant species, especially ponderosa and Jeffrey pine. Bytnerowicz (Bytnerowicz, 2002) 

summarized N/O3 interactions and consequent effects. 

Peak diurnal concentrations of atmospheric O3 and NO2 co-occur at Tanbork Flat in the San 

Bernardino Mountains (Bytnerowicz et al., 1987). They can have counteracting effects, with O3 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 O3 

pollution (Miller et al., 1983; Duriscoe and Stolte, 1989). In some areas of the western Sierra Nevada 

Mountains, O3 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 O3 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 O3 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). 

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Possibility of Broader Ecological or Location Level of Uncertainty Occurrence Reference Environmental Impact (at other sites)

Increased NO3− 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 O3 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)

NOX emissions as Southern California; Sierra Well­established effect	 Significant O3 injury to Miller and McBride (1999),

precursors for phytotoxic Nevada vegetation has not been Carroll et al. (2003)

levels of O3, leading to O3 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 deposition 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. 

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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 and 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 and 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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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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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 and 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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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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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 and 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 and 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. NH3, 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 NHX and/or NOY below which 

harmful effects in ecosystem structure and function do not occur according to present knowledge.” 

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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 D-1 

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

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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 eight general steps that must be taken to define the basic critical load question in any 

analysis. 

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4.	 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, NH4, HNO3, 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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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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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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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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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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� 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): 

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� 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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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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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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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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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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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 and 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 and N 

deposition to chemical indicator response are mostly process-based, whereas biological responses to 

acidification by S and 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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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 

SO42– 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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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., Ca2

+

, Mg2+, K+, Na+, Si, S, P, Al3

+

, 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 SO4

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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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., CO2 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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� 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: 

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� 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 

because 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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� 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 NH3 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

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� 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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� 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. Surface Waters

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� 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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� 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

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� 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 deposition 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 

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Annex E. Effects of NOY, NHX, and SOX 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 NOY, and NHX, SOX. 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, NOY, NHX, and 

SOX 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 and 

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 NOY/NHX/SOX 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 NOY/NHX/SOX 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 and 

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 NOY, NHX, and SOX 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 NOY/NHX/SOX 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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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 NOY/NHX/SOX 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 NOY/NHX/SOX 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 NO2, SO2, or nitric acid (HNO3), 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 (CaSO4·2H2O) forms from reaction of SO2 with the 

calcium carbonate (CaCO3) 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 HNO3 onto surfaces can increase the extent of wetting 

of surfaces, and promote the damaging effects of both HNO3 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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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 NH3 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 SO4 , 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 oxide 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 SO2 and NOX. 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 (HNO2), HNO3, sulfurous acid (H2SO3), and sulfuric acid (H2SO4)), which then 

dissociate to form nitrite, nitrate, sulfite, and sulfate ions. These acids are the primary species responsible 

for damage to materials by S and N pollutants. NH3, 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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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. NOX may enhance the oxidation of sulfite to 

sulfate and lead to faster rates of corrosion. The deposition velocity of SO2 and NOX may be influenced 

by the presence of HNO3 deposited to the surface due to the increased degree of surface wetting. 

E.2.4. Materials Damage Experimental Techniques

The NOY/NHX/SOX 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 

NOY/NHX/SOX 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 NOY/NHX/SOX 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 NOY/NHX/SOX species, and 

interconversions among species (e.g. SO 2−

X and SO4 , NOX and HNO3) 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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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 NO2 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, NO2 and ozone (O3) 

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 

NO2, 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 HNO3. A synergistic effect 

was observed between mechanical stress and NOX 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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Plastics and elastomers can be damaged by NO2, SO2, and O3, 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. NO2 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 

SO2 than by NO2. Elastomers are damaged more severely than plastics. In tests where light and NO2 have 

been present simultaneously, much of the damage observed in chamber tests has been attributed to O3, 

produced by the interactions of the pollutants and UV light, rather than to NO2 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 O3) 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. NO2 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). NO2 is formed when N-O bonds connecting cellulose 

rings are broken. The NO2 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 NOY/NHX/SOX 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 NOY/NHX/SOX air pollutants on metals. The studies listed in Table E-1 are 

discussed where applicable in the following sections. 

E.5.1. Role of NOY, NHX, and SOX in the Corrosion Process

In the atmosphere the NOY/NHX/SOX pollutants occur together, along with other pollutants such as 

O3 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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pollutants. It must be noted that in many studies the various NOY/NHX/SOX 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 NOX (or NOY) and SO2 together have 

been shown to be more damaging than SO2 alone. The combination of NO2 and SO2 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 NOY 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 HNO3 onto the surface 

(Dubowsky, 2004). The corrosion effect of HNO3 on zinc, copper, and steel is larger than that of SO2 

alone or a mixture of SO2 and NO2 (Samie et al., 2007). 

Although deposition of NOY/NHX/SOX 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 SO2 and NOX 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). SO2 is generally reported to be more corrosive 

than NOX, 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 pollutants. This is attributed to the increased wetting that can result from the 

presence of hygroscopic NO3 salts. Relative humidity has been shown to be very important in the 

corrosion of steels by SO2 with much slower corrosion rates observed when the relative humidity is below 

70% (Dehri et al., 1994). The presence of SO2 has been shown to reduce the corrosion pitting of iron 

induced by sodium chloride (NaCl) but there may be an overall synergistic effect among SO2, NO2, 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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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 SO2 and NO2 showed synergistic (i.e., greater than simply additive) 

corrosive effects on zinc (Svensson and Johansson, 1993a). SO2 slowed the NaCl−

induced corrosion of 

zinc while NO2 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 NOY/NHX/SOX pollutants have been observed. Minimal damage is caused to Al by NOX. 

The mixture of SO2 and NOX is variously said to be either more or less corrosive to Al than SO2 alone. 

The deposition rate of SO2 to Al was shown to increase in the presence of O3, but no effect on SO2 

deposition rate was found for NO2 (Blucher et al., 2005). Interaction between SO2 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 SO2 is more corrosive to Al than NO2 and that there is no difference in Al 

corrosion rate when exposed to NO or clean air. 

Mixtures of NOX and SO2 are more corrosive to copper than either pollutant alone. When hydrogen 

sulfide (H2S) and O3 were also evaluated for damage to copper, they also were found to be more 

damaging to copper than NOX. The corrosion rate of copper exposed to SO2 or NO2 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 NO2 is greater than in clean air. In the first 24 h of exposure to NO2, 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 SO2 and O3 (strong) 

and NO2 (weak) (Aastrup et al., 2000). High corrosion rates were observed for field exposures of copper 

at sites with a combination of SO2 and O3. 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 SO2, O3, 

and NO2 (Strandberg, 1998). 

Nickel is also damaged more severely by SO2 or chloride salts than by NOX. Nickel samples 

deployed at urban, industrial, and rural sites showed that corrosion rates increase with SO2 

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 SO2 and NO2 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 SO2 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 NO2 than by SO2, but a mixture of these two pollutants is 

more corrosive than either alone. The combination of SO2 and H2S is also less damaging than either NO2 

alone or a combination of NO2 and these pollutants. NO2 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 SO2 and moisture, 

and is more subject to damage by SO2 than by NO2. Sample weights increased with increasing NO2, but it 

is not clear if this indicates direct reaction of NO2 or an enhancement of the effects of SO2 and moisture 

by NO2. The effect of SO2 may be due to reaction with CaCO3 and zinc oxide (ZnO) present in the paint. 

Tests with various paints showed that NOX becomes incorporated into the paint surface upon long 

exposure, apparently by reaction with polymers that make up the cured paint. In other tests HNO3 was 

found to produce substantially more damage to paints containing both low and high levels of carbonate 

(CO3−) than did an equal mixing ratio of NO2. 

Grosjean et al. (1983) studied the fading of colorants on cellulose paper. Twelve colorants were 

exposed to various atmospheres including purified air, NO2, SO2, and a mixture of oxidants (O3, NO2, 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 SO2, NO2, 

and the mixture. For that colorant, the color change induced by the mixture of oxidants was 

approximately three times the color change with NO2 alone; the color change with SO2 was 

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approximately 70% of the color change with NO2. An increase in relative humidity results in increase

fading of colorants (Grosjean et al., 1993, 1994). Of 35 colorants exposed to a mixture of O3, NO2, a

PAN, nine exhibited substantial color changes and three exhibited moderate color changes (Grosjean


et al., 1993).
 

Paint samples were exposed to UV light, NOX, SO2, 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 NOY/NHX/SOX 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 NOY/NHX/SOX on stone, concrete, and mortars. Calcareous stone 

(i.e., that consisting of CaCO3, 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 SO2 in forming gypsum 

(CaSO @ 4 2H2O): 

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 SO2 to 

the stone surface between rain events is important, as it causes continued damage. The reaction of SO2 

with calcareous stone is more energetically favorable than the reaction of N oxides, and thus SO2 is the 

primary cause of damage to stone, however the combination of SO2 and NOX is more damaging than SO2 

alone. This effect may be due to enhanced wetting of the stone, to oxidation of SO2 by the N oxides, or to 

formation of calcium nitrate (Ca(NO3)2), which is much more soluble than CaCO3 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 

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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(NO3)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 SO2 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 SO2, 

play a role in the degradation of stone exposed to the atmosphere. The presence of particulate matter, in 

addition to SO2, has been shown to increase gypsum formation by 20% (Boke et al., 1999). Ambient SO2 

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 (SO2, NO2, NO) but by acid (H2SO4, HNO3) and salt 

(SO4

2−, NO3

−

) 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 (CO2) is an important mechanism by 

which stone samples are degraded, but additional acidity from the dissolution of SO2 and NO2 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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products. On mortars, a secondary damage mechanism exists in which gypsum reacts with calcium Al
 

hydrates present in the mortar to produce ettringite (Ca6Al @ 2(SO)4(OH)12 26H2O). 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 NOX on Paper and Archival Materials

The cellulose fibers that make up paper are reactive with NO2 and other NOY species, and storage 

condition standards have been set regarding acceptable levels of NOX for archives, libraries, and 

museums. Exposure of archival materials to NOY species in such facilities can arise from normal outdoor 

or indoor sources, but also from generation of such NOY 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 NOX and product species such as HNO3. 

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 HNO3 is a key reactant in the degradation of paper 

archives. The rapid deposition velocity of HNO3 and the numerous surfaces in archival facilities provide 

opportunity for attack by HNO3, and probably result in the effects of HNO3 being underestimated, 

relative to those of NOX, based on indoor air measurements. Artists’ pigments can also be damaged by 

extended exposure to ambient atmospheric NO2. 

The effects of SO2 and O3 on paper were studied by Johansson and Lennholm (2000). The 

deposition rate of SO2 to fresh paper was found to decrease rapidly with time and approached steady state 

after ten hours. The deposition rate of SO2 to fresh paper in the presence of O3 was found to be elevated 

compared to SO2 alone. The deposition rates to aged paper were much lower and there was no effect on 

the SO2 deposition rate observed in the presence of O3. 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. H2S has 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 NO2 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 SO2 or ammonium sulfate ((NH4)2SO4) particles, can 

damage, varnish, or cause discoloring of paint (Gysels et al., 2004). Paint models subjected to accelerated 

aging in SO2 (10 ppm) and NOX (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 NOY, NHX, 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 SO2 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 SO2 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 NOY/NHX/SOX 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 SO2 and 

NO2 have been implicated in damage processes for different materials. In general, damage to materials by 

SO2 is greater than by NO2. 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 SO2 and NO2 lead to increased damage 

rates for the gases in combination. Other species such as O3, NaCl, organics, or particulate matter have 

also been shown to have synergistic effects with SO2 and NO2. The corrosive effects of nitric acid have 

been found to be stronger than effects of other NOY/NHX/SOX 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 SO2 or HNO3 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 NOY/NH3/SOX 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 NO2 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.

SO2 slowed the corrosion of zinc with moderate to high surface

concentrations of NaCl due to the formation of sodium zinc

hydroxychloride sulfate. NO2 (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 SO2. With O3 present, an increased rate of mass gain

was measured, and copper sulfite was converted to copper

sulfate. NO2 increased the mass gain to a lesser extent than O3

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 SO2 and/or HCl (presentation rates of 27 ×

10−6 and 4.7 × 10−6 mg/cm2

s, respectively). A

synthetic acid rain solution was used to model wet

deposition.

Copper samples were exposed to 264 ppb NO2 in a

laboratory setting. Exposures were limited to 72 h to

study the initial corrosion behavior.

Zinc samples were exposed to SO2 (0.78 ppm)

and/or NO2 (1.06 ppm) for 420 h. Some samples

were treated with NaCl prior to exposure.

Samples were exposed to SO2 or NH3 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. SO2 deposition rates ranged from

10 mg/m2

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. SO2 was

supplied at 500 (± 5) ppb and 107 (± 2) ppb.

SO2 induced corrosion was found to be inversely dependent on

temperature. The maximum corrosion rate (at 107 ppb SO2) 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 SO2

(1.5 ppb, 0.5 ppm, 10 ppm), NO2 (10 ppm), NO (10

ppm), or O3 (10 ppm).

NO was found to have no effect on the corrosion of copper, zinc,

or aluminum. Copper in the presence of SO2 (10 ppm) and NO2

led to significant material loss initially with a slowing of the rate

with increasing time. O3 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 SO2. A small

effect on zinc was observed for NO2 with SO2 (10 ppm) resulting 

in the largest weight gain. SO2 at 0.5 ppm had a much larger

effect on zinc than on copper. For aluminum, O3 had the largest

effect followed by SO2 and NO2.

Corrosion rates in SO2 were found to be largely dependent on

relative humidity. No such humidity dependence was observed 

for corrosion induced by NH3. 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 SO2 (476 ppb) alone or

in combination with NO2 (450 ppb) or O3 (500 ppb).

Some samples were pretreated with carbon.

Tenorite reacted rapidly with SO2 to form brochantite and other

sulfate containing products. Cuprite reacted slowly with SO2

alone but addition of O3 formed antlerite and brochantite. NO2

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 SO2 in combination with O3 or NO2.

Samples were exposed to atmospheres containing

approximately 200 ppb of SO2, SO2 and O3, or SO2

and NO2. O3 and NO2 were introduced at different

times in the exposure scenarios.

reductions in NO2, NO, or NH3. In areas with low SO2 concentrations, reductions in NO2, O3, or 

particulate matter concentrations may reduce damage rates. 

August 2008 E­14 DRAFT—DO NOT QUOTE OR CITE
                

Materials Exposure Conditions	 Findings Reference

Mild Steel

Steel	 

Copper

Zinc

Nickel

Iron

Aluminum

Copper

Zinc

Steel

Steel samples were exposed to the atmosphere in

47 marine atmospheres with varying levels of

chloride and SO2. Atmospheres were separated for

data analysis based on chloride and SO2 deposition

rates.

Two types of steel were exposed to urban­industrial

and rural atmospheres for 20 yrs. Avg SO2 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. SO2,

NO2, and O3 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/m3

) (33, 8, and 2 ppb),

NO2 3 (50.1, 24.2, and 8.7 �g/m ) (26, 13, and 

5 ppb), SO2 3 (22.3, 29.0, and 12.2 �g/m ) (8, 11,

and 5 ppb), and O3 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, SO2

(200 ppb), SO2 and NO2 (each 200 ppb), or SO2 and 

O3 (each 200 ppb). The same exposure conditions

were used for iron samples with NaCl deposited on

the surface.

Aluminum samples were exposed to SO2 (96 ppb)

either alone or in the presence of other pollutants

(NaCl, NO2, or O3).

Samples were exposed to HNO     (50–180 ppb) in a 3 laboratory exposure chamber. Tests were conducted 

at 65% and 85% relative humidity.

Samples exposed at sites with moderate SO2 and chloride

deposition rates formed compact, rounded corrosion structures.

Samples at sites with high SO2 and moderate chloride exhibited 

cracking in the corrosion products. Samples at sites with high

chloride and moderate SO2 exhibited the highest corrosion rates

of the mixed atmospheres. The 1 site with high chloride and high

SO2 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 SO2 and O3. For zinc, the highest

corrosion losses were observed at the site with the highest SO2

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 SO2 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 SO2 alone was not enough

to initiate a change in corrosion behavior of the samples. When

an oxidant (NO2 or O3) was added to the humidified air/SO2

system, a significant increase in corrosion rate was observed.

SO2 was found to inhibit the NaCl induced corrosion of iron, but

the combination of SO2 and NO2 was found to accelerate NaCl

induced corrosion.

SO2 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 SO2. The combination of SO2 and NaCl

resulted in the largest mass gain (fastest corrosion rate). While

the rate of mass gain was highest with a combination of SO2 and 

NaCl, the pitting observed in the presence of NaCl alone was

significantly reduced. O3 was found to slightly increase the

deposition rate of SO2, no effect on SO2 deposition rate was

observed for NO2.

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 SO2 alone or a mixture of SO2 with O3

or NO2. No increase in corrosion was observed at 85% relative

humidity compared to 65% relative humidity.

Almeida 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 /NH3/SOX on stone. Y

Materials Exposure Conditions	 Comments Reference

August 2008	 E­15 DRAFT—DO NOT QUOTE OR CITE
August    2008 E­16	    DRAFT—DO          NOT QUOTE OR CITE

Materials Exposure Conditions	 Comments Reference

Marble	 

Limestone

Marble	 

Marble

Brick

Marble	 

Limestone

Mortars

Jaumont

limestone

Calcium

carbonate

Mortars

Mortars	 

Concrete

Marble	 

Laboratory exposure to HNO3 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) HNO3, 2.39 �g/m     (3 ppb) NH3, 4.84 �g/m − 3 3 NO3 , 14.61 �g/m SO 2–, and 5.01 �g/m     NH4+. 4 

Samples of differing thickness were exposed in the

field and runoff water collected for 5 mos. The avg  3 SO2 concentration was 60 �g/m     (23 ppb).

Laboratory exposure to 10 ppm of SO2 and NO2. Field 

exposure to either dry deposition or dry and wet

deposition in Louisville, Kentucky. Avg concentrations

of 10 ppb SO2 and 25 ppb NO2 for field exposures.

Samples exposed in the field for 8 days with multiple

fog episodes. Over 3 measurement campaigns, the

3 mean pollutant concentrations were 17.2 �g/m 3 (7 ppb) SO2, 265 �g/m     (139 ppb as NO2) NOX, and  3 131 �g/m suspended particles.

Samples were exposed to urban environments in both

sheltered and unsheltered configurations. Pollutant

concentrations were not reported.

3 Samples were exposed to 340 �g/m     (125 ppb) SO2 3 and 98 �g/m     (50 ppb) NO2 in the laboratory. Samples

were either exposed naked or sprinkled with fly­ash

particles. Field exposure was conducted for 1 yr with

samples sheltered from rainwater. Avg SO2

concentration during the field exposure was 107 3 �g/m     (40 ppb).

Powdered calcium carbonate was exposed to 10 ppm

SO2 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. The tunnel formerly held a railway and currently

houses a road with heavy automobile traffic. SO2 3 levels have declined from 350 �g/m     (132 ppb) in 3 1970 to ~10 �g/m     (4 ppb) in 2002. NOX

concentrations have remained relatively constant 3 around 100 �g/m     (52 ppb as NO2) over the same

time period.

Samples exposed to atmosphere and sheltered from

rain at 4 sites. SO2 concentrations ranged from ~2 to

20 ppb across the sites.

Marble was found to be a very good sink for HNO3. 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 (HNO3, H2SO4, etc.) on the surface rather

than the oxides (SO2, NOX).

Damage functions were developed to try to determine the ionic

sulfate content in runoff water from the ambient SO2

concentration. It was determined that the ambient SO2

concentration alone does not determine the sulfate

concentration in runoff water.

Gypsum crust thickness of 1.9 �m for rain sheltered samples

after 1 yr of exposure. SO2 was found 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 only stable mineral formed 

following exposure to fog water in a polluted environment.

Exposure to fog water may be a significant cause of corrosion

for materials sheltered from rainwater, but is of lesser

importance if a material is exposed to rain.

Sulfation was the primary 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

naked samples. The growth of gypsum crystals fixed the fly­ash

to the surface of the limestone. Fly­ash was found to be an

important factor in crust formation by facilitating gypsum

crystal formation. Fly­ash particles also darken gypsum crusts

from gray to black.

Reaction between calcium carbonate and SO2 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 SO2

concentration used is unrealistic for ambient conditions.

Sulfation was the primary damage mechanism observed. Sulfite

was found as an intermediate damage product in the sulfation

process. Ettringite was also found as a secondary damage

product due 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 mechanism was

found where gypsum reacts with calcium aluminum hydrates to

form ettringite, an insoluble sulfate. The presence of sulfur in

the damage products indicates SO2 as the most aggressive

atmospheric pollutant toward mortars.

The urban mixture of pollutants (SO2, NOX, CO2, and particles)

results in formation of dendritic crusts on concrete. Nitrates

were found to be present in the largely gypsum crusts. Soot

particles were found embedded in the crusts as well. Low

quality starting materials provide a more porous media that is

more susceptible to degradation by atmospheric pollutants. The

degradation of concrete is more similar to that of sandstone

than of limestone.

Sulfation was the primary damage mechanism observed. Marble

containing dolomite was less sensitive to SO2 than calcite

marble. For relative humidity greater than 72%, humidity was

an important factor in determining the sulfation rate.

Sikiotis and

Kirkitsos

(1995)

Torfs and van

Grieken (1996)

Yerrapragada

et al. (1996)

Del Monte and 

Rossi (1997)

Zappia et al.

(1998)

Ausset et al.

(1999)

Boke et al.

(1999)

Sabbioni et al.

(2001)

Sabbioni et al.

(2002)

Marinoni et al.

(2003)

Lan et al.

(2005)

Limestone	 

Sandstone

Samples were collected from 3 facades of a historical

building in Spain. Samples were collected from the

surface as well as 5 mm below the surface to

determine degradation and original compositions,

respectively. Pollutant concentrations over the life of

the building were not reported.

The main decay products on the surface were found to be

nitrate compounds. Samples with black crusts on the surface

were found to have predominantly gypsum and soot, but

nitrate compounds were identified in the crusts as well.

Sandstone samples were much more damaged than limestone

samples due to their higher porosity.

MartinezArkarazo

et al.(2007)
                

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ANNEX E ­ References
 

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Dubowski, Y.; Sumner, A. L.; Menke, E. J.; Gaspar, D. J.; Newberg, J. T.; Hoffman, R. C.; 

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Lan, T. T. N.; Thoaa, N. T. P.; Nishimurab, R.; Tsujinoc, Y.; Yokoid, M.; Maeda, Y. (2005) 

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SO2, NO2, NO and O3 on the corrosion of copper, zinc and aluminum. A short literature 

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Sabbioni, C.; Zappia, G.; Riontinoa, C.; Blanco-Varela, M. T.; Aguilerac, J.; Puertasc, F.; Van 

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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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Samie, F.; Tidblad, J.; Kucera, V.; Leygraf, C. (2007) Atmospheric corrosion effects of HNO3 ­

comparison of laboratory-exposed copper, zinc and carbon steel. Atmos. Environ. 41: 

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Yerrapragada, S. S.; Chirra, S. R.; Jaynes, J. H.; Bandyopadhyay, J. K.; Gauri, K. L. (1996) 

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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 NOX and SOX 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 

NOX and SOX emissions on terrestrial, transitional, and aquatic ecosystems. 

F.1.1. Valuation in the Context of NOX and SOX

Figure F-1 provides a schematic representation of how economic valuation is derived from changes 

to NOX and SOX secondary standards. Starting at the upper left-hand side, the NOX and SOX standards 

are set and emissions reductions occur to change the ambient concentrations of NOX and SOX. 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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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 O2, 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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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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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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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 NOX and SOX 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 journals1 and library 

databases2 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 Agricultural Economics, Canadian Journal of Economics, Canadian Journal of Forestry, 

Contemporary Economic Policy, Ecological Economics, Environment and Development Economics, Environmental and Resource Economics, 

Environmental Science and Technology, Forest Science, Forestry Chronicle, Journal of Agricultural and Applied Economics, Journal of Applied 

Econometrics, Journal of Agricultural and Resource Economics, Journal of Agricultural Economics, Journal of Environmental Economics and 

Management, Journal of Forest Economics, Journal of Forestry, Journal of Political Economy, Journal of Risk and Uncertainty, Land 

Economics, Marine Resource Economics, Resource and Energy Economics, Review of Agricultural Economics, Review of Economics and 

Statistics, Water Resources Research. 

2 AgEcon Search, Agricola, BioOne, CSA Illumina, EconLit, GeoRef, Google Scholar, SciSearch/Science Citation Index (Web of Science), 

SCOPUS, Sportfishing Values Database, SSRN. 

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� Does the study address an ecological endpoint sensitive to reductions in NO X andSOX
 

emissions?
 

� Does the study value quality changes in the ecological endpoint, which is actually or
 

potentially attributed to reductions in NO X and SOX 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 the 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 NOX/SOX). 

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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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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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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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 SOX or NOX 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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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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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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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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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 NOX and SOX, 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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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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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 NOX and SOX 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 NOX 

emissions comes from ambient ozone (O3), which is a byproduct of atmospheric reactions between 

Volatile Organic Compounds (VOCs) and NOX. In commercial forests, air pollution effects have not been 

addressed in economics beyond evaluating the potential effects of O3. Unlike in agriculture, the scientific 

understanding of O3 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 O3 

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 losses 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. 

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Source: Exhibit 1 in IEc (1999a).

Figure F­5. Linkages from emissions to forest aesthetics.

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Source: Figure 2 in IEc (1999a).

Figure F­6. Geographic distribution of forested areas historically affected by air pollution.

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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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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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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 NOX and SOX 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 O3 on 

terrestrial endpoints overshadow assessments of other effects of NOX and SOX 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 NOX and 

SOX 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 O3 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 O3. 

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 O3 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 O3. 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 NOX and 

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SOX. 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 O3 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 

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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 NOX or SOX. 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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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 NOX/SOX 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 NOX or SOX. 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 NOX and SOX on aquatic ecosystems. In economic valuation of the effects of NOX and 

SOX 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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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 was 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 NOX or SOX. 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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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 NOX/SOX 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 measures 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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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 SOX and NOX 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 NOX or SOX 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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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 O2 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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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 hypoxia1 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 depletion) is a phenomenon resulting from the overloading of nutrients (usually N or P) in water. Most fresh waters are P-limited, 

and therefore added N from 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 algae 

can deplete the water of oxygen when those algae die and are decomposed by oxygen-consuming microorganisms. If the concentration of DO 

decreases to very low levels, fish, and other life forms can die. 

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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 for 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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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 

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 NOX and SOX 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 O3 on crops (and to a far lesser degree on forest yield), but 

this topic is beyond of the scope of this assessment. Beyond the O3 effects, there is little quantification of 

the science describing the effects of pollution related to NOX and SOX 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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1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

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 pollutants. 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 NOX and SOX 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 changes. Again, however, there is little focus on NOX or SOX 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 emissions. 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 NOX and SOX. 

August 2008 F­35 DRAFT­DO NOT QUOTE OR CITE
                

Methods 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 O3 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

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Study 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 O3 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).

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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.

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Reference 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 O2

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.

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Reference 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)

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Reference 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.

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ANNEX F ­ Glossary

Acid Neutralizing Capacity (ANC)

A key indicator of the ability of water to neutralize acidifying inputs. This ability depends largely on 

associated biogeochemical characteristics. 

Acidification

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. 

Algae 

Photosynthetic, often microscopic and planktonic, organisms occurring in aquatic ecosystems. 

Algal bloom  A rapid and extreme increase of an algae population in a lake, river, or ocean. 

Alpine 

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. 

Anthropogenic

Resulting from human activity or produced by human beings. 

Arid region

An area receiving < 250 mm precipitation per year. 

Atmosphere

The gaseous envelope surrounding the Earth. The dry atmosphere consists almost entirely of nitrogen and 

O2, together with trace gases including carbon dioxide and ozone. 

Base cation saturation

The degree to which soil cation exchange sites are occupied with base cations (e.g. Ca2+, Mg2+, 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%. 

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Bioaccumulation  The gradual increase in accumulation of some compounds in organisms with increasingly higher trophic 

levels. 

Biodiversity

The total diversity of all organisms and ecosystems at various spatial scales (from genes to biomes). 

Buffering capacity  The ability of a body of water and its watershed to resist changes in pH. 

Carbon sequestration

The process of increasing the carbon content of a reservoir other than the atmosphere. 

Catchment

An area that collects and drains rainwater. 

Climate

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). 

Critical load

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. 

Denitrification

The anaerobic reduction of oxidized nitrogen (e.g., nitrate or nitrite) to gaseous nitrogen (e.g., N2ON2O or 

N2

N2), normally accomplished by denitrifying bacteria. 

Dry deposition

The movement of gases and particles from the atmosphere to surfaces in the absence of precipitation (e.g., 

rain or snow) or occult deposition. 

Ecological community  An assemblage of populations of different species, interacting with one another 

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Ecosystem services

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. 

Ecosystem  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. 

Eutrophication

The enrichment of a waterbody with nutrients, resulting in increased productivity (of algae or aquatic 

plants), and sometimes also decreased dissolved O2 levels. 

Eutrophy

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 O2 concentrations. 

Evapotranspiration  The combined process of water evaporation from the Earth’s surface and transpiration from vegetation. 

Fen 

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. 

Freshet

A great rise or overflowing of a stream caused by heavy rains or melted snow. 

Greenhouse gas  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 (H2OH2O), carbon dioxide (CO2), nitrous oxide (N2ON2O), methane (CH4), and ozone (O3O3) are 

the primary greenhouse gases in the Earth’s atmosphere. Besides these, the Kyoto Protocol also deals with 

the greenhouse gases sulphur hexafluoride (SF6), hydrofluorocarbons (HFCs), and perfluorocarbons 

(PFCs). 

Gross primary production

The total carbon fixfixed by plant through photosynthesis. 

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Heathland

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. 

Hypoxic

Events that lead to a deficiency of O2. 

Invasive species and invasive alien species

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. 

Leaching

The removal of soil elements or chemicals by water movement through the soil. 

Lowland

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. 

Net ecosystem exchange (NEE)

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) 

Net ecosystem production (NEP)  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. 

Net primary production (NPP)

The gross primary production minus autotrophic respiration, i.e., the sum of metabolic processes for plant 

growth and maintenance, over the same area. 

Nitrification

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. 
                

Nitrogen mineralization

The conversion of organic nitrogen into plant-available inorganic forms (e.g. NH or NH + 3 4 NH +

4 ) by 

microorganisms. 

Nitrogen­retention capacity

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. 

Nitrogen saturation

The condition in which nitrogen inputs from atmospheric deposition and other sources exceed the 

biological requirements of the ecosystem. 

Occult deposition  The transmission of gases and particles from the atmosphere to surfaces by fog or mist. 

Ombrotrophic bog  An acidic peat-accumulating wetland that is fed by rainwater (instead of groundwater) and, thus, especially 

poor in nutrients. 

pH

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. 

Phytoplankton

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. 

Primary Production

All forms of production accomplished by plants, also called primary producers. See GPP, NPP, and NEP. 

Semi­arid regions   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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Sensitivity   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). 

Streamflow   Water flow within a river channel. A synonym for river discharge. 

Surface runoff

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. 

Throughfall

The precipitation falling through the canopy of a forest and reaching the forest floor. 

Trophic level

The position that an organism occupies in a food web. 

Tundra

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. 

Upland terrestrial ecosystem  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. 

Valuation

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. 

Vulnerability

Susceptibility to degradation or damage from adverse factors or influences. Vulnerability is a function the 

exposureand its sensitivity. 

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Welfare effects

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)). 

Wet deposition

The transmission of gases and particles from the atmosphere to surfaces by rain or other precipitation. 

Wetland

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. 

Zooplankton

The animal forms of plankton. They consume phytoplankton or other zooplankton. 

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1

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21

22

23

24

25

26

27

28

29

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31

32

33

34

35

36

ANNEX F ­ References
 

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Adams, R. M.; Callaway, J. M.; McCarl, B. A. (1986b) Pollution, agriculture and social welfare: 

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Bentley, J.; Horst, R. (1998) Commercial forestry benefits of alternative emission controls for 

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