UnitedStates                               August 2008
Agency menta' Pr°teCti°n                        EPA/600/R-08/083
      Annexes for the
      Integrated Science Assessment
      for Oxides of Nitrogen and
      Sulfur- Environmental Criteria
      (Second External Review Draft)

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                                           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-RTF Division
         Office of Research and Development
         U.S. Environmental Protection Agency
            Research Triangle Park, NC

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                                  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
List of Figures
Acronyms and Abbreviations
Annex A. Ecosystem Monitoring and Models
A.1. Introduction
A.2. Ecosystem Monitorina
A.2.1. Environmental Monitorina and Assessment Proaram
A. 2. 2. Surface Water Chemistry Monitorina
A.2.2.1. TIME Project
A.2.2.2. Lona-Term Monitorina Project
A. 2. 3. Forest Inventory and Analysis
A.2.3.1. Lichens
A.2.3.2. Soil Quality
A.2.4. USGS Monitorina Proarams
A. 2. 4.1. National Water Quality Assessment Proaram
A.2.4.2. Hydroloaic Benchmark Network
A.2.4.3. New York City Water Quality Network
A.2.4.4. Catskill Lona-Term Monitorina Sites
A.2.4.5. Buck Creek, New York
A.2.5. NSF Lona-Term Ecoloaical Research Network
A. 2. 5.1. Hubbard Brook Experimental Forest
A.2.5.2. Coweeta
A.2.5.3. Walker Branch
A.2.6. Water, Eneray, and Bioaeochemical Budaets Proaram
A.2.6.1. Sleepers River
A.2.6.2. Loch Vale
A. 2. 7. Other Monitorina Proarams
A.2.7.1. Bear Brook
A.2.7.2. Shenandoah Watershed Study
A.2.7.3. Fernow
A. 2. 7. 4. National Ecoloaical Observatory Network
A.3. Modelina
A. 3.1. Principal Ecosystem Models Used in the U.S.
A.3.1.1. MAGIC
A.3.1.2. NuCM Model
A.3.1.3. PnET-BGC
A.3.1.4. DayCent-Chem
A.3. 1.5. SPARROW
A.3.1.6.WATERSN
A.3.2. Additional Effects Models Used Widely in Europe
A. 3.2.1 . The Very Simple Dynamic Model
A.3.2.2. SMART
A.3.2.3. SAFE
A.3.3. Other Models
A.3.3.1. Current Lona-term Monitorina Data Sets Developed throuah the Hubbard Brook Ecosystem Study
ANNEX A -References
Annex B. Acidification Effects
B.1. Effects on Bioaeochemical Processes alona Acidification Pathways
viii
xi
xiii
A-1
A-1
A-2
A-3
A-4
A-4
A-6
A-8
A-9
A-11
A-1 2
A-1 2
A-1 3
A-1 4
A-1 4
A-1 4
A-1 5
A-1 8
A-21
A-23
A-24
A-24
A-26
A-28
A-28
A-29
A-32
A-34
A-34
A-34
A-35
A-39
A-41
A-44
A-46
A-49
A-54
A-54
A-55
A-55
A-55
A-62
A-63
B-1
B-1
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        B.1.1. Atmospheric Deposition and Canopy Interaction	B-1
        B.1.2. Interactions with Soil	B-1
              B. 1.2.1. Sulfur Retention and Release	B-2
              B.1.2.2. Base Cation Depletion	B-3
              B.1.2.3. Aluminum Mobilization	B-7
              B.1.2.4. Soil Acidification	B-8
              B.1.2.5. N Saturation	B-9
              B.1.2.6. Nitrate Leaching	B-16
        B.1.3. Interactions with Transitional Ecosystems	B-16
              B.1.3.1. S Storage and Release in Transitional Ecosystems	B-16
              B.1.3.2. Organic Acidity in Transitional Ecosystems and Downstream Surface Waters	B-17
    B.2. Factors That Determine Ecosystem Sensitivity	B-23
        B.2.1. Transitional Ecosystems	B-23
              B.2.1.1. Wetlands and Peatlands	B-23
              B.2.1.2. Ponds	B-24
        B.2.2. Streams and Lakes	B-24
        B.2.3. Other Types of Ecosystems	B-25
    B.3. Distribution and Extent of Ecosystem Effects	B-25
        B.3.1. Terrestrial Ecosystems	B-25
        B.3.2. Transitional Ecosystems	B-28
        B.3.3. Aquatic Ecosystems	B-28
              B.3.3.1. Status of Surface Waters - Regional Overview	B-29
              B.3.3.2. Recent Changes in Surface Water Chemistry	B-31
        B.3.4. Regional Assessments	B-33
              B.3.4.1. Northeastern Surface Waters	B-33
              B.3.4.2. Southeastern Surface Waters	B-47
              B.3.4.3. Upper Midwest	B-55
              B.3.4.4. West	B-58
              B.3.4.5. Temporal Variability in Water Chemistry	B-64
    B.4. Effects on Biota	B-76
        B.4.1. Types of Effects of Acidification on Biota	B-77
              B.4.1.1. Individual Condition Factor	B-77
              B.4.1.2. Species Composition	B-80
              B.4.1.3. Taxonomic Richness	B-80
              B.4.1.4. Community Structure	B-86
              B.4.1.5. Indices of Ecological Effects	B-87
        B.4.2. Timing of Effects	B-87
              B.4.2.1. Life Stage Differences in Sensitivity	B-87
              B.4.2.2. Biological Effects of Episodes	B-90
              B.4.2.3. Timing of Recovery from Acidification	B-95
        B.4.3. Effects by Ecosystem Type	B-99
              B.4.3.1. Terrestrial Ecosystems	B-99
              B.4.3.2. Aquatic Ecosystem	B-102
    B.5. Effects on Watersheds and Landscapes	B-112
        B.5.1. Interactions among Terrestrial, Transitional, and Aquatic Ecosystems	B-112
        B.5.2. Interactions with Land Use and Disturbance	B-113
              B.5.2.1. Timber Harvest	B-115
              B.5.2.2. Insect Infestation	B-117
        B.5.3. Wind or Ice Storm Damage	B-119
              B.5.3.1. Fire	B-120
              B.5.3.2. Multiple Stress Response	B-120
    B.6. Ecological  indicators of acidification	B-121
        B.6.1. Biological Indicators	B-121
              B.6.1.1. Phytoplankton	B-122
              B.6.1.2. Zooplankton	B-124
              B.6.1.3. Benthic Invertebrates	B-125
              B.6.1.4. Fish	B-127
              B.6.1.5. Amphibians	B-132
              B.6.1.6. Fish-Eating Birds	B-133
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ANNEX B - References	B-160

Annex C. Nutrient Enrichment Effects from N	C-1
    C.1. Effects on Biogeochemical Pathways and Cycles	C-1
        C.1.1. N Cycling in Terrestrial Ecosystems	C-1
             C. 1.1.1. N Deposition Effects on DON Leaching	C-1
             C.1.1.2. Interactions between snow melt and nitrate leaching	C-1
             C.1.1.3. Denitrification: NO and  N20 Flux	C-1
             C.1.1.4. Climate and N20 Interactions	C-3
        C.1.2. N Cycling in Transitional Ecosystems	C-4
             C.1.2.1. Denitrification: Measurement Techniques	C-4
             C.1.2.2. N Deposition Effects on Methane	C-4
        C.1.3. N Cycling in Estuarine Ecosystems	C-5
             C.1.3.1. Denitrification and Anammox in Estuarine Ecosystems	C-5
             C.1.3.2. N Budgets	C-6
        C.1.4. Timing of Chemical Change	C-8
             C.1.4.1. Interannual Change: Nitrate Leaching	C-8
             C.1.4.2. Episodic Change	C-8
             C.1.4.3. Reversibility of Impacts	C-9
        C.1.5. Tables Supporting Cross Ecosystem Evaluation of N20, CHUand C02 Flux	C-9
    C.2. Terrestrial Ecosystems	C-15
        C.2.1. General C cyling	C-15
        C.2.2. Forest Growth Interactions with Herbivores	C-15
        C.2.3. Forest Growth Interactions with Herbivores	C-16
        C.2.4. Southern California Coniferous Forest	C-16
        C.2.5. Boreal forests	C-17
        C.2.6. Alpine	C-18
        C.2.7. Arctic Tundra	C-20
        C.2.8. Arid Land	C-21
        C.2.9. Lichens	C-21
    C.3. Transitional Ecosystems	C-23
    C.4. Aquatic Ecosystems	C-24
        C.4.1. History of Evaluating N Enrichment in Freshwater Aquatic Ecosystems	C-25
        C.4.2. Interactions between N and P loading	C-25
        CAS. Aquatic Species Affected	C-27
             C.4.3.1. Phytoplankton and Plants	C-27
        C.4.4. Seasonal N Input and Cyanobacteria	C-29
        C.4.5. Nitrate Toxicity: Invertebrates	C-29
        C.4.6. Nitrate Toxicity: Amphibians and Fish	C-30
    C.5. Estuary and Coastal Ecosystems	C-33
        C.5.1. Interacting Factors with Productivity	C-35
        C.5.2. Hydrology Interactions  with Phytoplankton Biomass	C-35
    C.6. Effects on Watersheds and Landscapes	C-36
        C.6.1.1 nteractions among Terrestrial,  Transitional, and Aquatic Ecosystems	C-36
        C.6.2. Interactions with Land Use and Disturbance	C-41
        C.6.3. Timber Harvest and Fire	C-42
        C.6.4. Insect Infestation and Disease	C-44
        C.6.5. Urbanization	C-46
        C.6.6. Agriculture	C-46
        C.6.7. Other Disturbances	C-47
        C.6.8. Multiple Stress Response	C-47

ANNEX C - References	C-50

Annex D. Critical  Loads	D-1
    D.1. Background	D-1
        D. 1.1. The Critical Load Process	D-3
        D.1.2. Organization of this Annex	D-4
    D.2. Definitions and Conceptual Approach	D-5
        D.2.1. Critical Load Definitions	D-5
        D.2.2. Critical Load Analysis Procedures	D-6
        D.2.3. Target Load Definition	D-10
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    D.3. Time Frame of Response	D-11
        D.3.1. Steady State Critical Loads	D-12
        D.3.2. Dynamic Critical Loads	D-12
        D.3.3. Receptor Responses	D-13
        D.3.4. Deposition Schedules	D-14
        D.3.5. Long-Term Implications	D-18
        D.3.6. Steady State and Dynamic Critical Loads - Complementary Information	D-19
    D.4. Calculation of Critical Loads	D-20
        D.4.1. Empirical Models	D-20
        D.4.2. Acidification Effects of Sulfur and N	D-21
        0.4.3. Nutrient Effects of N	D-21
        D.4.4. Process-Based Models	D-21
        0.4.5. Steady State Models	D-22
        0.4.6. Dynamic Models	0-22
    D.5. Use of Critical Loads in the U.S. - Current Status	0-24
        D.5.1. Current Recommendations on Critical Loads Uses in the U.S.	0-25
        D.5.2. Questions and Limitations Regarding Critical Loads Uses in the U.S.	0-26
        D.5.3. Critical Loads Research and Monitoring Needs	0-26
        D.5.4. Emissions and Deposition	D-26
        0.5.5. Soils	D-27
        0.5.6. Surface Waters	D-27
        0.5.7. Biological Effects	D-28
        0.5.8. Critical Loads Models	D-28

ANNEX D - References	D-30

Annex E. Effects of NOY, NHX, and S0xon Structures and Materials	E-1
    E.1. Introduction	E-1
    E.2. Environmental  Exposures of Materials	E-2
        E.2.1. Mechanisms of Materials Damage	E-2
        E.2.2. Deposition Processes	E-2
        E.2.3. Chemical Interactions of N and S Oxide Species	E-3
        E.2.4. Materials Damage Experimental Techniques	E-4
    E.3. Effects on Dyes and Textiles	E-5
        E.3.1. Fading of Dyes	E-5
        E.3.2. Degradation of Textile Fibers	E-5
    E.4. Effects on Plastics and Elastomers	E-5
    E.5. Effects on Metals	E-6
        E.5.1. Role of NOv, NHx, and SOX in the Corrosion Process	E-6
        E.5.2. Effect on Economically Important Metals	E-7
        E.5.3. Effects on Electronics	E-9
    E.6. Effects on Paints	E-9
    E.7. Effects on Stone and Concrete	E-10
    E.8. Effects of NOx on Paper and Archival Materials	E-12
    E.9. Costs of Materials Damage from N0y, NHx, and SOX	E-13
    E.10. Summary	E-13

ANNEX E - References	E-17

Annex F. Valuation of the Environmental Effects of N and S (non-materials)	F-1
    F.1. Introduction	F-1
        F. 1.1. Valuation in the Context of NOx and SOx	F-1
             F. 1.1.1. Ecosystem Services	F-2
             F. 1.1.2. Use of the Valuation Literature to Define Adversity	F-4
             F.1.1.3. Methods for Selecting Literature for this Assessment	F-5
    F.2. Conceptual Framework	F-7
        F.2.1. Taxonomy of Values for Environmental Goods and Services	F-7
        F.2.2. Welfare Economics	F-9
        F.2.3. Benefit Estimation Approaches	F-11
    F.3. Valuation of forests and terrestrial ecosystems	F-15
        F.3.1. Use Values	F-15
        F.3.2. Total Values	F-18
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        F.3.3. National-Scale Valuation	F-19
        F.3.4. Valuation of Degrees of Injury	F-19
        F.3.5. Limitations and Uncertainties	F-21
    F.4. Valuation of Transitional Ecosystems	F-23
        F.4.1. Use and Non-Use Values	F-23
        F.4.2. Limitations and Uncertainties	F-25
    F.5. Valuation of Aquatic Ecosystems	F-25
        F.5.1. Acidification	F-27
        F.5.2. Eutrophication	F-28
             F.5.2.1. Recreation	F-29
             F.5.2.2. Commercial Fisheries	F-31
             F.5.2.3. Water Clarity	F-32
        F.5.3. Avoided Costs	F-33
        F.5.4. Limitations and Uncertainties	F-33
    F.6. Summary	F-34

ANNEX F - Glossary	F-45

ANNEX F - References	F-52
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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 (ueq/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
             western U.S.3	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 offish
             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 (pre-
             industrial) 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
   Table B-26. The effects of increasing Ca2+ 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 offish species occurrence in 11 lake surveys.	B-156
   Table B-31. Studies3 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 (ug/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, ChU uptake, ChU 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 NOY/NH3/SOX effects on metals.	E-14
   Table E-2. Studies on corrosive effects of NOY/NHs/SOx on stone.	E-15
   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
   Figure A-3. Long-term record of SO42~ 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 SO42~ 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 offish 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 ueq/L ANC categories, based on data  collected by the Adirondack
              Lakes Survey Corporation.	B-83
   Figure B-18. Number offish 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
   Figure B-21. Time series data for SO42", NO3", base cations [Ca2+ + Mg2+], 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 NOs~ 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
                      A13+           aluminum ion
                      Al;            inorganic aluminum
                      Aln+           aluminum ion
                      A10            organic aluminum
                      A1(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
                       BUY          Backscatter Ultraviolet Spectrometer
                       BUVD         Beneficial Use Values Database
                       C              carbon; concentration
                       12C            carbon-12, stable isotope of carbon
                       13C            carbon-13, stable isotope of carbon
                       Ca             ambient air concentration
                       Ca             calcium
                       Ca2+           calcium ion
                       C AA          Clean Air Act
                       C AAA         Amendments to the Clean Air Act
                       CAAAC        Clean Air Act Advisory Committee
                       CaCl2          calcium chloride
                       CaC03         calcium carbonate
                       CALIPSO      Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
                                      (satellite)
                       Ca(N03)2       calcium nitrate
                       Ca(OH)2        calcium hydroxide
                       CAPMoN      Canadian Air and Precipitation Monitoring Network
                       CaS04-2H20    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(0)       acetyl radical
                       CH3C(0)00    acetyl peroxy radical
                       CH2I2          diiodomethane
                       CH20          formaldehyde
                       CH3OOH       methyl hydroperoxide
                       CH3-S-CH3     dimethylsulfide, DMS
                       CH3-S-H       methyl mercaptan
                       (CH3)2SO       dimethyl sulfoxide, DMSO
                       CH3S03H      methanesulfonic acid
                       CH3-S-S-CH3   dimethyl disulfide, DMDS
                       Ci             interstitial air concentration
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                       CL             critical load
                       Cl             chlorine
                       Cr             chlorine ion
                       C12             molecular chlorine
                       CLaMS         type of Lagrangian model
                       CloudSat       NASA Earth observation satellite
                       C1N02          nitryl chloride
                       CMAQ         Community Multiscale Air Quality (modeling system)
                       CMSA          consolidated metropolitan statistical area
                       CO             carbon monoxide
                       C02            carbon dioxide
                       C03~           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
                       A              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
                       DIG            dissolved inorganic carbon
                       DIN            dissolved inorganic nitrogen
                       DMDS          dimethyl disulfide, CH3-S-S-CH3
                       DMS           dimethyl sulfide, CH3-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 Long-
                                      range 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 C02 enrichment (studies)
                       Fe             iron
                       FeP04         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
                       yN205          reaction potential coefficient for N205
                       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-ID AS    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
                       2H             hydrogen-2, deuterium, stable isotope of hydrogen
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                       H+
                       ha
                       HAPs
                       HBEF
                       HBES
                       HBN
                       HC
                       HCHO
                       HC1
                       Hg
                       HN02, HONO
                       HN03, HOONO
                       HN04
                       H02
                       H202
                       H02N02
                       HOBr
                       HOC1
                       HOX
                       HP
                       HPVM
                       HS03~
                       HS04~
                       H2S
                       H2S03
                       H2S04
                       HTC
                       hv
                       I
                       I2
                       IA
                       IADN
                       1C
                       ICARTT

                       ILWAS
                       IPC
                       lEc
                       IIASA
                       IMPROVE
                       ICARTT

                       IN03
                       INTEX-NA
                       10
                       IPCC
proton, hydrogen ion; relative acidity
hectare
hazardous air pollutants
Hubbard Brook Experimental Forest
Hubbard Brook Ecosystem Study
Hydrologic Benchmark Network
hydrocarbon
formaldehyde
hydrochloric acid
mercury
nitrous acid
nitric acid
pernitric acid
hydroperoxyl radical
hydrogen peroxide
peroxynitric acid
hypobromous acid
hypochlorous acid
hypohalous acid
hedonic pricing

bisulfate ion
sulfuric acid ion
hydrogen sulfide
sulfurous acid
sulfuric acid

photon with energy at wavelength v
iodine
molecular iodine
Integrated Assessment
Integrated Atmospheric Monitoring Deposition Network
intracloud (lightning flash)
International Consortium for Atmospheric Research on Transport and
Transformation
Integrated Lake-Watershed Acidification Study
International Cooperative Programme
Industrial Economics
International Institute for Applied Systems Analysis
Interagency Monitoring of Protected Visual Environments
International Consortium for Atmospheric Research on Transport and
Transformation
iodine nitrate
Intercontinental  Chemical Transport Experiment - North America
iodine oxide
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
                       KN03           potassium nitrate
                       Kw              ion product of water
                       LAP            Lake Acidification and Fisheries
                       LAR            leaf-area ratio
                       LB              laboratory bioassay
                       LCo.oi           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 Ground water 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
                       (ieq            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
                       MOBILE6
                       MODIS
                       MOPITT
                       MOZAIC
                       MOZART
                       MPAN
                       MSA
                       Mt
                       N
                       N,n
                       14N
                       15N
                       N2
                       NA
                       Na
                       Na+
                       NAAQS
                       NaCl
                       NADP
                       Na2Mo04
                       NAMS
                       NANI
                       NAPAP
                       NASQAN
                       NARSTO

                       NAS
                       NASA
                       Na2S04
                       NASQAN
                       NATTS
                       NAWQA
                       NCore
                       NEE
                       NEG/ECP
                       NEI
                       NEON
                       NEP
                       NFI
                       NH3
                       NH2
                       NH4+
                       NH4C1
                       NH4N03
                       (NH4)2S04
manganese
Highway Vehicle Emission Factor Model
Moderate Resolution Imaging Spectroradiometer
Measurement of Pollution in the Troposphere
Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft
Model for Ozone and Related Chemical Tracers
peroxymethacrylic nitrate
metropolitan statistical area
million tons
nitrogen
number of observations
nitrogen-14, stable isotope of nitrogen
nitrogen-15, stable isotope of nitrogen
molecular nitrogen; nonreactive nitrogen
not available; insufficient data
sodium
sodium ion
National Ambient Air Quality Standards
sodium chloride
National Atmospheric Deposition Program
sodium molybdate
National Air Monitoring Stations
Net anthropogenic nitrogen inputs
National Acid Precipitation Assessment Program
National Stream Quality Accounting Network
program formerly known as North American Regional Strategy for
Atmospheric Ozone
National Academy of Sciences
National Aeronautics and Space Administration
sodium sulfate
National Stream Quality Accounting Network
National Air Toxics Trends (network)
National Water Quality Assessment (program)
National Core Monitoring Network
net ecosystem exchange
New England Governors and Eastern Canadian Premiers
National Emissions Inventory
National Ecological Observatory  Network
net ecosystem productivity
net factor income
ammonia
amino (chemical group)
ammonium ion
ammonium chloride
ammonium nitrate
ammonium sulfate
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                       NHX            category label for NH3 plus NH4+
                       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
                       N02            nitrogen dioxide
                       N02~           nitrite
                       N03~           nitrate
                       N20            nitrous oxide
                       N205           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 N02
                       NOY            sum of NOX and NOZ; odd nitrogen species; total oxidized nitrogen
                       NOZ            sum of all inorganic and organic reaction products of NOX (HONO,
                                       HN03, HN04, organic nitrates, paniculate nitrate, nitro-PAHs, etc.)
                       NPOESS        National Polar-orbiting Operational Environmental Satellite System
                       NPP            net primary production
                       NFS            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
                       02              molecular oxygen
                       03              ozone
                       160             oxygen-16, stable isotope of oxygen
                       180             oxygen-18, stable isotope of oxygen
                       190             oxygen-19, radioactive isotope of oxygen
                       OC             organic carbon
                       OCO           Orbiting Carbon Observatory
                       OCS            carbonyl sulfide
                       0(:D)           electronically excited oxygen atom
                       OH             hydroxyl radical
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                       OMI            Ozone Monitoring Instrument
                       0(3P)           ground-state oxygen atom
                       P               phosphorus
                       P, p             probability value
                       P!              1 st 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
                       PEL            planetary boundary layer
                       PC             payment card
                       PCBs           polychlorinated biphenyl compounds
                       pH             relative acidity
                       P(HN03)        production of nitric acid
                       PHREEQC      model for soil and water geochemical equilibrium
                       PIRL A         Paleocological Investigation of Recent Lake Acidification (projects)
                       pKa             dissociation constant
                       PM             paniculate matter
                       PM2.5           paniculate matter with aerodynamic diameter of <2.5 (im
                       PM10           paniculate matter with aerodynamic diameter < 10 (im
                       PM10.2.5         paniculate matter with aerodynamic diameter between 10 and 2.5 (im
                       PM-CAMx      Comprehensive Air Quality  Model with extensions and with paniculate
                                       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)
                       pN03~          paniculate nitrate
                       P(03)           production of 03
                       P04~, P043~     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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                        pS042          paniculate sulfate
                        P(S042~)        production of sulfate
                        Q              flow rate; discharge
                        Qio            temperature coefficient
                        QAPP          Quality Assurance Project Plan
                        R              generic organic group attached to a molecule
                        R2             coefficient of determination
                        r2              correlation coefficient
                        Ra             aerodynamic resistance
                        Rb             boundary layer resistance
                        Rc             internal resistance
                        RADM         Regional Acid Deposition Model
                        RAMS         Regional Atmospheric Modeling System
                        RAP S          Regional Air Pollution Study
                        RCOO-s        strongly acidic organic anions
                        RC(0)00      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
                        R02            organic peroxyl; organic peroxy
                        RON02         organic nitrate
                        R02N02        peroxynitrate
                        RP             revealed preferences
                        RRX            lognormal-transformed response ratio
                        RuBisCO       ribulose-l,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

                       Si
                       SIP
                       SJAQS
                       SLA
                       SLAMS
                       SMART
                       8MB
                       SO
                       S02
                       S03
                       S032~
                       S042~
                       S20
                       SONEX
                       SOS
                       SOS/T
                       SOX
                       SP
                       SPARROW
                       Sr
                       86Sr
                       87Sr
                       SRB
                       SRP
                       SSWC
                       STE
                       STN
                       SUM06
                       SVOC
                       SWAS
                       T
                       T
                       TAP
                       Taff
                       TAMM
                       TAR
                       TC
                       TCM
                       TOLAS
                       Tg
                       TIME
                       TN
                       TOMS
                       TOR
Southeastern Aerosol Research and Characterization Study (monitoring
program)
silicon
State Implementation Plan
San Joaquin Valley Air Quality Study
specific leaf area
State and Local Air Monitoring Stations
Simulation Model for Acidification's Regional Trends (model)
Simple Mass Balance (model)
sulfur monoxide
sulfur dioxide
sulfur trioxide
sulfite
sulfate ion
disulfur monoxide
Subsonics Assessment Ozone and Nitrogen Oxides Experiment
Southern Oxidant Study
State of Science/Technology (report)
sulfur oxides
stated preferences
SPAtially Referenced Regressions on Watershed Attributes (model)
strontium
strontium-86, stable isotope of strontium
strontium-87, stable isotope of strontium
sulfate-reducing bacteria
soluble reactive phosphorus
Steady State Water Chemistry  (model)
stratospheric-tropospheric exchange
Speciation Trends Network
seasonal sum of all hourly average concentrations > 0.06 ppm
semivolatile organic compound
Shenandoah Watershed Study
atmospheric lifetime
time; duration of exposure
Tracking and Analysis Framework (model)
air temperature
Timber Assessment Market Model
Third Assessment Report
total carbon; travel cost
travel cost method
Tunable Diode Laser Absorption Spectrometer
teragram
Temporally Integrated Monitoring of Ecosystems (program)
total nitrogen
Total Ozone Mapping Spectrometer
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
                       XN03           nitrate halogen-X salt
                       XO             halogen-X oxide
                       Zn             zinc
                       ZnO            zinc oxide
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                 Annex A.  Ecosystem  Monitoring
                                      and  Models
      A.1.  Introduction
 1         A tremendous amount of research has been conducted in the U.S., and elsewhere, over the past
 2    three decades on the ways in which atmospheric deposition of sulfur (S) and nitrogen (N) affect the
 3    health, condition, and vitality of aquatic, transitional, and terrestrial ecosystems. Much of this work has
 4    focused on developing a better understanding of acidification and nutrient enrichment processes. Some of
 5    this work has been highly quantitative, allowing researchers to determine key process rates in multiple
 6    ecosystem compartments. Nevertheless, quantification of overall ecosystem response requires a higher
 7    level of process rate aggregation. It is important to develop quantitative understanding of the extent of
 8    past ecosystem effects in response to atmospheric S and N deposition, the extent to which conditions will
 9    worsen or recover under continued or reduced deposition levels, and the sustained loads of deposition that
10    would be required to prevent further ecosystem damage and to allow damaged ecosystems to recover.
11    This kind of quantitative understanding cannot evolve directly out of process-based research. It requires
12    development of mathematical models that encode process knowledge and link it in such a way as to
13    produce quantitative estimates of change in resource conditions over time in response to changes in the
14    major forcing functions, including atmospheric deposition, climate, and landscape disturbance. As
15    described in this Annex, many such models have been developed and used to estimate past and future
16    changes in ecosystem condition. Such models cannot be validated, per se, because environmental systems
17    are never closed and because important processes yield conflicting, often opposing, results. Therefore, a
18    model can produce the right answer for the wrong reason (cf. Oreskes, 1994). Similarly, a particular
19    process may not be  important at a particular site where a model is tested, but assume much greater
20    importance elsewhere. For these reasons, it is critical that environmental models be tested and confirmed
21    at multiple locations that exhibit differing conditions and pollutant loads before they are used as the
22    foundation for public policy (Sullivan, 2000).
23         Some of the best data with which to test and confirm environmental models are derived from
24    long-term monitoring sites. These are locations where one or more attributes of a natural ecosystem
25    compartment (i.e., surface water, soil, plants) is periodically sampled and analyzed over a long period of
26    time. Such data are  often especially valuable for sites which experience rather large changes in one of the
27    forcing functions (often atmospheric deposition). This enables evaluation of the extent to which the model
28    accurately captures  the dynamics of ecosystem response(s) that occur. Because many environmental
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 1    attributes undergo rather substantial intra- and inter-annual variability in response to climatic variation
 2    and other changes, a long period of record is required before a monitoring data set can be used for
 3    evaluation of ecosystem response or for model confirmation.
 4          Long-term monitoring data provide not only data with which to test model projections, but also a
 5    reality check on scientific understanding of damage and recovery processes. If observed (monitored)
 6    changes are not in agreement with process understanding, it is possible or perhaps likely that one or more
 7    key processes is not well-understood or well-formulated in the model.
 8          This Annex summarizes the primary long-term monitoring sites and programs in the U.S., and the
 9    principal mathematical models used to simulate environmental responses to atmospheric S and N
10    deposition. Quantitative data derived from the model projections and from trends analyses of the
11    monitoring data provide an important part of the foundation for evaluating the past, current, and future
12    effects of S and N deposition, and expected recovery as emissions levels decrease in the future.
      A.2. Ecosystem  Monitoring
13          The effects of acidic and N nutrient deposition on ecosystems require long-term study. Changes in
14    ecosystems often occur gradually, and sustained monitoring of key variables provides the principal record
15    of change over time. Monitoring data are also useful for establishing a baseline of resource conditions and
16    determining if short-term events were unusual or extreme (Lovett, 2007). There are limited monitoring
17    programs and data to document ecosystem responses to changes in atmospheric deposition in the past. It
18    is often difficult to sustain funding for ecosystem monitoring, perhaps because results are produced
19    slowly and because results are seldom viewed as novel. Nevertheless, monitoring data provide some of
20    the best means for evaluating the completeness of the scientific knowledge base and for testing how
21    robust our projections of future conditions might be. This section describes some of the more important
22    and useful monitoring programs for evaluating the effects of N and S deposition on ecosystems in the
23    U.S.
24          There are long-term monitoring sites scattered throughout the U.S. where samples are periodically
25    collected and analyzed to determine the condition of aquatic, transitional, or terrestrial ecosystem
26    elements. Some have been in operation for only a short period of time; others have continued for decades.
27    None extend back far enough to have documented resource conditions prior to the advent of high levels of
28    atmospheric S and N deposition. Some of the monitoring sites exist as an individual entity, or small
29    collection of sites, often established primarily for research purposes. Despite the research focus, many of
30    these long-term research sites include collection of monitoring data. Other long-term monitoring sites
31    exist as part of large regional programs with a specific focus on long-term monitoring. The most
32    significant individual monitoring sites and networks are discussed below.

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 1          Lovett et al. (2007) reviewed the characteristics of successful environmental monitoring programs,
 2    and argued that monitoring is a fundamental part of environmental science and policy. Their analysis
 3    underscored the fact that environmental monitoring costs little relative to the value of the resources that it
 4    protects and the policy that it informs. Monitoring data also have substantial added value because they
 5    can be used for multiple purposes, including various research objectives.
 6          Ecosystems also require long-term study because most changes occur slowly. When more rapid
 7    change does occur, for example in response to an extreme event, a long-term record is needed to put the
 8    effects of the extreme event into proper context.


            A.2.1. Environmental Monitoring and Assessment Program
 9          The EPA Environmental Monitoring and Assessment Program (EMAP) began regional surveys of
10    the nation's surface waters in 1991 with a survey of Northeastern U.S. lakes. Since then, EMAP and
11    Regional-EMAP (REMAP) surveys have been conducted on lakes and streams throughout the country.
12    The objective of these EMAP surveys is to characterize ecological condition across populations of surface
13    waters. EMAP  surveys are probability surveys where sites are picked using a spatially balanced
14    systematic randomized sample so that the results can be used to make estimates of regional extent of
15    condition (e.g., number of lakes, length of stream). EMAP sampling typically consists of measures of
16    aquatic biota (fish, macroinvertebrates, zooplankton, and periphyton), water chemistry, and physical
17    habitat.
18          Of particular interest with respect to acidic deposition effects were two EMAP surveys conducted
19    in the 1990s, the Northeastern Lake Survey and the Mid-Atlantic Highlands Assessment of streams
20    (MAHA). The Northeastern Lake Survey was conducted in  summer from 1991 to 1994 and consisted of
21    345 randomly selected lakes in the states  of New York, New Jersey, Vermont, New Hampshire, Maine,
22    Rhode Island, Connecticut, and Massachusetts (Whittier, 2002). To make more precise estimates of the
23    effects of acidic deposition, the sampling grid was intensified to increase the sample  site density in the
24    Adirondacks and New England Uplands areas known to be  susceptible to acidic deposition. The MAHA
25    study was conducted on  503  stream sites from 1993 to  1995 in the states of West Virginia, Virginia,
26    Pennsylvania, Maryland, Delaware, and the Catskill Mountain region of New York (Herlihy, 2000).
27    Sampling was done during spring baseflow. Sample sites were restricted to first through third order
28    streams as depicted on the U.S. Geological Survey (USGS)  1:100,000 digital maps used in site selection.
29    To make more precise estimates of the effects of acidic deposition, the sampling grid was intensified to
30    increase the sample site density in the Blue  Ridge, Appalachian Plateau, and Ridge section of the Valley
31    and Ridge  ecoregions. Results from both  of these surveys were used to develop and select the sampling
32    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
 1          There are two surface water chemistry monitoring programs, administered by EPA, that are
 2    especially important to inform the assessment of aquatic ecosystem responses to changes in atmospheric
 3    deposition. These are the TIME program (Stoddard, 2003) and the Long-term Monitoring (LTM) program
 4    (Ford, 1993; Stoddard, 1998). These efforts focus on portions of the U.S. most affected by the acidifying
 5    influence of S and N deposition, including lakes in the Adirondack Mountains of New York and in New
 6    England, and streams in the Northern Appalachian Plateau and Blue Ridge in Virginia and West Virginia.
 7    Both projects are operated cooperatively with numerous collaborators in state agencies, academic
 8    institutions and other federal agencies. The TIME and LTM projects have slightly different objectives and
 9    structures, which are outlined below. Stoddard et al. (2003) conducted a thorough trends analysis of the
10    TIME and LTM data.

      A.2.2.1. TIME Project
11          At the core of the TIME project is the concept of probability sampling, whereby each sampling site
12    is chosen statistically from a pre-defined target population. Collectively, the monitoring data collected at
13    the sites are representative of the target population of lakes or streams in each study (Figure A-l). The
14    target populations in these regions include lakes and streams likely to be responsive to changes in acidic
15    deposition, defined in terms of acid neutralizing capacity (ANC),  which represents an estimate of the
16    ability of water to buffer acid. It can be either calculated (calculated ANC = sum of base cations - sum of
17    mineral acid anions, where all concentrations are in (ieq/L) or titrated in the laboratory (Gran ANC).
18    Measurement of Gran ANC uses the Gran technique to find the inflection point in an acid-base titration of
19    a water sample (Gran, 1952). In the Northeast, the TIME target population consists of lakes with a Gran
20    ANC less than 100 (ieq/L. In the Mid-Atlantic, the target population is upland streams with Gran ANC
21    less than 100 (ieq/L. In both regions, the sample sites selected for future monitoring were selected from
22    the EMAP survey sites in the region (Section A.2.1) that met the TIME target population definition.
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   Acid-Sensitive Regions of the Eastern United States
    Temporally Integrated Monitoring of Ecosystem (TIME) Sites
                                    V.
                                                                  .
                                                                  •
                                       L
                                                                    New England     *
                                                                         •       ff
                                                                               »T
                               Northern
                          Appalachian Plateau
       •
     ^ _B
                                                                          Source: Stoddard et al. (2003).
Figure A-1. Location of acid-sensitive 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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 1          Each lake or stream is sampled annually (in summer for lakes; in spring for streams), and results
 2    are extrapolated with known confidence to the target population(s) as a whole using the EMAP site
 3    population expansion factors or weights (Larsen,  1993; Larsen, 1994; Stoddard, 1996; Urquhart, 1998).
 4    TIME sites were selected using the methods developed by the EMAP (Paulsen, 1991; Herlihy, 2000). The
 5    TIME project began sampling northeastern lakes in 1991. Data from 43 Adirondack lakes can be
 6    extrapolated to the target population of low-ANC lakes in that region. There are about 1,000 low-ANC
 7    Adirondack lakes, out of a total population of 1830 lakes with surface area greater than  1 ha. Data from
 8    30 lakes (representing about 1,500 low-ANC lakes, out of atotal population of 6,800) form the basis for
 9    TIME monitoring in New England. Probability monitoring of Mid-Atlantic streams began in  1993.
10    Stoddard et al. (2003) analyzed data from 30 low-ANC streams in the Northern Appalachian Plateau
11    (representing about 24,000 km of low-ANC stream length out of a total stream length of 42,000 km).
12          The initial 1993-1995 EMAP-MAHA sample in the Mid-Atlantic was not dense  enough to obtain
13    enough sites in the TIME target population in the Blue Ridge and Valley and Ridge ecoregions. In 1998,
14    another denser random sample was conducted in these ecoregions to identify more TIME sites. After
15    pooling TIME target sites taken from both MAHA and the 1998 survey, there are now 21 TIME sites in
16    the Blue Ridge and Ridge and Valley that can be used for trend detection in this aggregate ecoregion in
17    the Mid-Atlantic in addition to the Northern Appalachian Plateau ecoregion.

      A.2.2.2. Long-Term Monitoring Project
18          As a complement to the statistical lake and stream sampling in TIME, the LTM project samples a
19    subset of generally acid-sensitive lakes and streams that have long-term data, many dating back to the
20    early 1980s (Figure A-2). These sites are sampled 3 to 15 times per year. This information is used to
21    characterize how some of the most sensitive of aquatic systems in each region are responding to changing
22    deposition, as well as giving information on seasonal variation in water chemistry. In most regions, a
23    small number of higher-ANC (e.g., Gran ANC greater than 100 (ieq/L) sites are also sampled, and help
24    separate temporal changes due to acidic deposition from those attributable to other disturbances (e.g.,
25    climate, land use change). Because of the availability of long-term records (more than two decades) at
26    many LTM sites, their trends can also be placed in a better historical context than those  of the TIME sites,
27    where data are only available starting in the 1990s.
28          Monitored water chemistry variables include pH, ANC, major anions and cations, monomeric
29    aluminum (Al), silicon (Si), specific conductance, dissolved organic carbon (DOC), and dissolved
30    inorganic carbon (DIC). The field protocols, laboratory methods, and quality assurance  procedures are
31    specific to each team of investigators. This information is contained in the cited publications of each
32    research group. The EMAP and TIME protocols and quality assurance methods are generally consistent
33    with those of the LTM cooperators. Details of LTM data from each  region are given below.

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         Acid-Sensitive Regions of the Northern and Eastern United States
                        Long-Term Monitoring (LTM) Sites
  ;V"
  pper
, Midwest,  •-•      V.rt
                            '
                                                     New England
                                   ^~k""^           " V" ' * ^       -jj/*'
                                            Adironcldpk^ *     ', ^r
                                       -*    ^r!^:      V
                                  •x--
                                       -W-       ;
                                        /Cf4  «4L
                                          • '  ft    -3 n
                                                               .
                                                                             Source: Stoddard et al. (2003).
                                                                  Figure A-2. Location LTM sites used
                                                                  in the 2003 Surface Water report.
 1          New England Lakes: The LTM project collects quarterly data from lakes in Maine (sampled by the
 2    University of Maine; Kahl, 1991; Kahl, 1993) and Vermont (data collected by the Vermont Department of
 3    Environmental Conservation'; \Stoddard, 1993; Stoddard, 1998). Data from 24 New England lakes were
 4    available for the trend analysis reported by Stoddard et al. (2003) for the time period 1990 to 2000. In
 5    addition to quarterly samples, a subset of these lakes have outlet samples collected on a weekly basis
 6    during the snowmelt season; these data are used to characterize variation in spring chemistry. The
 7    majority of New England LTM lakes have mean Gran ANC values ranging from -20 to 100 (ieq/L; two
 8    higher ANC lakes (Gran ANC between 100 and 200 (ieq/L) are also monitored.
 9          Adirondack Lakes: The trend analysis of Stoddard et al. (2003) included data from 48 Adirondack
10    lakes, sampled monthly by the Adirondack Lake Survey Corporation (Driscoll, 1993; Driscoll, 1995); a
11    subset of these lakes are sampled weekly during spring snowmelt to help characterize spring season
12    variability. Sixteen of the lakes have been monitored since the early 1980s; the others were added to the
13    program in the 1990s. The Adirondack LTM dataset includes seepage and drainage lakes, most with Gran
14    ANC values in the range of-50 to 100 (ieq/L; three lakes with Gran ANC between 100 (ieq/L and 200
15    (ieq/L are also monitored.
16          Appalachian Plateau Streams: Stream sampling in the Northern Appalachian Plateau is conducted
17    about 15 times per year, with the samples spread evenly between baseflow (e.g., summer and fall) and
18    high flow (e.g., spring) seasons. Data from four streams in the Catskill Mountains (collected by the U.S.
19    Geological Survey; Murdoch, 1993, and five streams in Pennsylvania (collected by Pennsylvania State
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 1    University; DeWalle and Swistock, 1994) were analyzed by Stoddard et al. (2003). All of the Northern
 2    Appalachian LTM streams have mean Gran ANC values in the range -25 to 50 (ieq/L.
 3          Upper Midwest lakes: Forty lakes in the Upper Midwest were originally included in the LTM
 4    project, but funding in this region was terminated in 1995. The Wisconsin Department of Natural
 5    Resources (funded by the Wisconsin Acid Deposition Research Council, the Wisconsin Utilities
 6    Association, the Electric Power Research Institute and the Wisconsin Department of Natural Resources)
 7    has continued limited sampling of a subset of these lakes, as well as carrying out additional sampling of
 8    an independent subset of seepage lakes in the state. The data reported by Stoddard et al. (2003) included
 9    16 lakes (both drainage and seepage) sampled quarterly (Webster, 1993) and 22 seepage lakes sampled
10    annually in the 1990s. All of the Upper Midwest LTM lakes exhibit mean Gran ANC values from -30 to
11    80 (ieq/L.
12          Ridge/Blue Ridge Streams: Data from the Ridge and Blue Ridge provinces consist of a large
13    number of streams sampled quarterly throughout the 1990s as part of the Virginia Trout Stream
14    Sensitivity Study (Webb, 1989, and a small number of streams sampled more intensively (as in the
15    Northern Appalachian Plateau). A total of 69 streams, all located in the Ridge section of the Ridge and
16    Valley province, or within the Blue Ridge province, and all within the state of Virginia, had sufficient data
17    for the trend analyses by Stoddard et al. (2003). The data are collected cooperatively with the University
18    of Virginia and the National Park Service. Mean  Gran ANC values for the Ridge and Blue  Ridge data
19    range from -15 to 200 (ieq/L, with 7 of the 69  sites exhibiting mean Gran ANC greater than 100 (ieq/L.
            A.2.3.  Forest Inventory and Analysis
20          The USDA Forest Service's Forest Inventory and Analysis (FIA) program authority is mandated
21    under the Forest and Rangeland Renewable Resources Research Act of 1978 (PL 95-307). Earlier, the
22    program was known as the Forest Survey and has been run continuously since the 1930s. The FIA
23    Program collects, analyzes, and reports information on the status and trends of America's forests: how
24    much forest exists, where it exists, who owns it, and how it is changing, as well as how the trees and other
25    forest vegetation are growing and how much has died or has been removed in recent years. (See
26    http://www.fia.fs.fed.us for more information).
27          Currently,  FIA plots are divided into three phases. Phase 1 establishes approximately three million
28    samples via remote sensing (aerial photographs, digital orthoquads, and satellite imagery). These samples
29    are used to classify land as forest or nonforest. As defined by the U.S. Forest Service, forest land is any
30    land > 1 acre in size that is at least 10% stocked by forest trees of any size. Phase 2 establishes a subset of
31    the Phase 1 plots for ground sampling, approximately one field sample site for every 6,000 acres for a
32    total of about 125,000 plots. The forest characteristics measured include forest type, site attributes, tree

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 1    species, tree size, and overall tree condition. Phase 3 plots are the Forest Health Monitoring (FHM) plots,
 2    and are a subset of Phase 2 plots. Phase 2 and Phase 3 plots are remeasured every 5 years. Each year,
 3    different plots are sampled within defined regions, so that statistical trends for the overall region can be
 4    developed on an annual basis.
 5          Phase 2 measurements consist primarily of basic vegetation measurements and general site
 6    description. From these measurements information is obtained on tree diameter, tree length, tree quality
 7    for use as lumber, tree damage, stocking and seedling and sapling counts.
 8          Approximately one of every 16 Phase 2 plots is measured for forest health attributes in addition to
 9    Phase 2 attribute measurements. These plots are known as Phase 3 plots. There is approximately one
10    Phase 3 plot for every 96,000 acres, for a total of roughly 7,800 plots. Measurements for Phase 3 plots
11    include tree crown conditions, lichen community composition, understory vegetation, down woody
12    debris, and soil attributes. 03 injury to vegetation is also monitored at some Phase 3 plots. Comprehensive
13    reviews of the FIA sampling strategy may be found in Brand et al. (2000) and McRoberts et al. (2004).
14    Goodale et al. (Goodale, 2002) used FIA data to generate estimates of carbon sequestration in forest
15    ecosystems in the U.S. in 1990 and 1991. The home page for the National FIA program can be found at:
16    http://fia.fs.fed.us./

      A.2.3.1. Lichens
17          Lichens are organisms consisting of both fungi and algae. Lichens are very responsive to
18    environmental stressors in forests, including changes in forest structure, air quality, and climate. Many
19    studies have documented the close relationship between lichen communities and air pollution, especially
20    sulfur dioxide (862) and acidifying or fertilizing N- and S-based pollutants. The composition of an
21    epiphytic lichen community is one of the best biological  indicators of air pollution in forests, because
22    epiphytic lichens rely totally on atmospheric sources of nutrition.
23          In several studies (e.g.\ \Muir,  1988), lichens have given much clearer responses to N and S
24    pollutants (in terms of diversity, total abundance, and community composition) than either leaf symptoms
25    or tree growth, and have been one of the few components of terrestrial ecosystems to show a clear
26    relationship to gradients of acidic deposition in the eastern U.S.
27          Although trees may respond to moderate, chronic levels of air pollution deposition, all of the other
28    influences on tree growth, such as variation in soils, make the responses of trees to pollutants difficult to
29    measure in the field. Epiphytic lichens may be used to assess potential air quality impacts on forest
30    ecosystem health and productivity that are difficult to measure directly. Long-term observation of lichen
31    community change provides early indication of improving  or deteriorating air quality. Epiphytic lichens
32    also have important biological roles in many forests, including N fixation, food  for animals (deer, caribou,
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 1    voles, and flying squirrels), and nesting material for small mammals and birds. Typically, there are 10 to
 2    50 lichen species per FIA plot.
 3          Lichen community information contributes to the investigation of several key forest ecosystem
 4    concerns: the contamination of natural resources, biodiversity, and productivity/sustainability. Lichen
 5    community data are collected by personnel that receive FIA training on field procedures at the beginning
 6    of each field season. They are trained to observe the presence or absence of lichen species, to estimate the
 7    abundance of each species,  and to collect lichen specimens for identification by a specialist, a
 8    lichenologist. Identifications by the lichenologist are part of quality assurance and quality control
 9    procedures. Errors are further minimized by audits of the field. The method has two parts that are
10    performed simultaneously. (1) In each standard 0.38 ha FIA plot (see http://www.fia.fs.fed.us/library/
11    field-guides-methods-proc/docs/2006/p3_3_0_sec 10_10_2005 .pdf for plot design and field methods) the
12    field crew searches for macrolichens on woody plants and collects samples of each lichen believed to be a
13    distinct species. Tree and shrub bases below 0.5 m are excluded from sampling. Lichens on fallen
14    branches and other lichen litter may be included. Given the large plot area, fallen branches typically
15    provide an excellent sample of the canopy lichens. The collection represents the species diversity of
16    macrolichens in the plot as fully as possible, with a maximum time limit of 2 hours. (2) The field crew
17    estimates the abundance of each species using a four-step scale: 1 = rare (<3 individuals in plot); 2 =
18    uncommon (4-10 individuals in plot); 3 = common (>10 individuals in plot but less than half of the boles
19    and branches have that species present); and 4 = abundant (more than half of boles and branches in the
20    plot have the subject species present). As plots are finished, specimens are sent to specialists for
21    identification. Note that the field crew need not accurately assign species names to the lichens, but must
22    be able to distinguish among species, and be able to estimate abundances accurately.
23          Two procedures are used for constructing plot-level indices: (1) Species richness: A component of
24    diversity, species richness is the total number of epiphytic macrolichen species found in the lichen plot.
25    (2) Community gradients: The dominant gradients across the region are determined using accepted
26    statistical methods. Relationships of these gradients to forest structure, climate and air quality are then
27    analyzed. Scores for air quality and climate are calculated for each plot and are used to answer questions
28    about air quality and forest productivity/sustainability and biodiversity. Data and more information on
29    FIA lichen surveys can be found at: http://fia.fs.fed.us/lichen/.
30          The lichen community indicator was developed in the Southeast in 1990-93 by Bruce McCune and
31    Jonathan Dey, funded jointly by EPA and the USDA Forest Service (EMAP Program). FHM pilot projects
32    were run in 1994-96 in a variety of eastern states and Colorado, Oregon, Washington and California in
33    the west. The lichen community indicator was included in regular permanent plot surveys starting in 1997
34    (FHM 1997-1999, FIA after 1999). Regional gradient models have been developed for the southeast
35    (1994), the northeast (1997), and Colorado (1998), and are being developed for the Midatlantic  States
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 1    (1999-2000) the Pacific Northwest (2001), and California (2003). Lichen communities are now a Phase 3
 2    indicator in the FIA.

      A.2.3.2. Soil Quality
 3          Another type of forest health data that the FIA program collects on their Phase 3 plots is the Soil
 4    Quality Indicator. The FIA program began consistent sampling related to the Soil Quality Indicator in
 5    2001. The Soil Quality Indicator collects information through field measurements on FIA sample plots
 6    and laboratory analyses. Soil condition indicators such as erosion, compaction, and soil chemistry are
 7    monitored over time and used to demonstrate trends. Details of the Soil Quality Indicator measurements
 8    of FIA can be found here: http://nrs.fs.fed.us/ fia/topics/soils/.
 9          FIA field personnel collect soil data during the Phase 3 field season, which begins in early June and
10    ends in September. Soil samples are sent to the laboratory immediately after collection where they are
11    stabilized by air drying. Laboratory analyses are conducted throughout the fall and winter following the
12    field season. On-plot measurements include soil compaction and bare soil observations. Soil compaction,
13    the percentage of the soil surface exhibiting evidence of soil compaction as well as the type of
14    compaction, is measured by ocular estimation. The relative amount of bare soil is also estimated. Field
15    measurements related to erosion and compaction estimates are made on all four subplots on the Phase 3
16    field plot. Soil samples are collected on FIA sample plots along soil sampling lines adjacent to subplots 2,
17    3, and 4.  Soils are collected if the soil sampling location is in a forested condition. A total of five samples
18    are collected on each plot (three forest floor, two mineral soil). The entire forest floor layer is sampled
19    from a known area after measuring the thickness of the  litter and duff layers at the north, south, east, and
20    west edges of a 12-inch diameter sampling frame. Only organic material that is < one-fourth-inch
21    diameter is collected; rocks and larger woody materials are discarded prior to collection.
22          Once the forest floor has been removed, mineral  and organic soils are sampled volumetrically by
23    collecting cores from two depths: 0 to 4 inches and 4 to 8 inches. The texture of each layer is estimated in
24    the field and characterized as organic, loamy, clayey, sandy, or coarse sandy. Following soil sampling, the
25    depth to any restrictive horizon within the top 20 inches is estimated using a soil probe. Soil samples are
26    mailed to the regional laboratory for physical and chemical analysis.
27          In  the lab, mineral soil samples collected from FIA plots are analyzed for a suite of physical and
28    chemical properties including:
          •   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)
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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)
 1    Forest floor and litter samples are analyzed for:
          •   Bulk density and water content
          •   Total carbon
          •   Total N
 2          Soil chemical and physical properties can be highly variable in the field and are expensive to
 3    analyze. As a result, interpretation of soil chemical data is confounded by spatial variability within the
 4    plot. In addition, depending upon the soil type, both the number of samples and the methods used in
 5    collecting these samples may vary between plots, complicating compilation and estimation procedures.
 6    Finally, soil samples reflect conditions only in the forest floor and upper 20 cm of the soil. In many
 7    systems, the upper portion of the soil profile is likely to be more responsive to disturbance, providing a
 8    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
 9          The National Water Quality Assessment (NAWQA) Program was created in 1991 by the USGS to
10    assess the nation's water quality in 51 study units defined primarily by major drainage divides. These
11    units comprise  approximately 50% of the conterminous U.S. Its major of the program are to  determine (1)
12    the condition of the nation's streams, rivers, and ground water, (2) whether these conditions are changing
13    over time, and (3) how these conditions are affected by natural features and human activities.
14          The major priority of the NAWQA Program since its inception has been on watersheds that have
15    experienced impacts from agriculture and various forms of development. The location of sites, sampling
16    frequency, and  types of measurements taken, all reflect this priority. Each study unit runs on a 9-year
17    cycle, with approximately one-third of the study units beginning the cycle every 3 years. Each 9-year
18    cycle is comprised of 3 years of intensive data  collection and 6 years of low-level assessment. Three types
19    of sampling sites are established within each study unit: integrator sites, indicator sites, and synoptic sites.
20    Integrator sites are located on major rivers at points that drain much or the entire study unit. Indicator
21    sites drain large fractions of the  study unit that are representative of a particular landscape or land use
22    type. Some indicator sites are also located to evaluate point sources of water pollution, and some are
23    located downstream of undisturbed drainages to provide reference, or background conditions. Reference
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 1    indicator sites are generally located too low in the drainage basin for assessment of surface water
 2    acidification. Sites associated with synoptic studies are chosen for the purpose of improving spatial
 3    resolution of data collection within the study unit. The strategies for site selection, sampling, and analysis
 4    for synoptic sites are issue-specific and keyed to hydrologic conditions, times, and places of specific
 5    interest for the targeted water quality issue.
 6          During the 3-year intensive sampling period, integrator and indicator sites are sampled multiple
 7    times, both periodically and in association with high flows. The sampling approach for synoptic studies
 8    varies depending on the issue of interest, but is usually done within the second or third year of the
 9    intensive sampling period. During the six years of the low-level assessment, sampling usually involves
10    base-flow sampling of high priority integrator sites, and possibly some sampling of indicator sites.
11          Water quality measurements vary among study units, but usually include pathogens, nutrients
12    (including N and S), trace elements, pesticides, industrial organics, suspended sediment, salinity,
13    temperature, acidity, and dissolved O2.
14          NAWQA studies have resulted in over 1000 reports on an extensive list of water quality issues,
15    including freshwater and marine eutrophication associated with N pollution. None deal with acidification,
16    however. Program details and access to publications can be obtained at http://water.usgs.gov/nawqa/.

      A.2.4.2. Hydrologic Benchmark Network
17          The Hydrologic Benchmark Network (HBN) was started in 1963 by the USGS and gradually grew
18    to include 57 river gauging stations and 1 lake-stage station in 39 states by 1990. Most of the stations
19    have been established at the outlet of watersheds that were virtually free of human activities, located in
20    places such as in national parks and forests, wilderness areas, or nature preserves. Streamflow was
21    initially monitored continuously at each station, and samples were collected every month for water-
22    quality analyses that included concentrations of nutrients and all major ions. The frequency of water
23    sampling at HBN stations was decreased to quarterly in 1986 because of budgetary restrictions. Sampling
24    was discontinued in October 1997, except for a small  study in the eastern U.S. that focused on the initial
25    response  of rivers to decreases in industrial emissions mandated by the Clean Air Act Amendments of
26    1990 (http://www.epa.gov/oar/oaq_caa.html/).
27          All HBN watersheds were evaluated in 2002 to determine whether upstream development had
28    made them unsuitable as reference watersheds. The 36 sites that best met the network criteria were
29    selected for continued streamflow monitoring, and water sampling was reinitiated at 15 of those 36 sites.
30    In 2003,  15 of the original HBN stations were equipped with refrigerated, automated samplers and
31    telemetry systems that allow program coordinators to  monitor stream conditions and adjust sampling
32    frequency and capture unique stream conditions or special sampling needs. The automated sampling
33    system is designed to collect samples through a wide range of flow conditions and to transmit data by

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 1    satellite. About 25 water samples are collected annually at each HBN water quality station and
 2    refrigerated on site until retrieved by field personnel who visit the sites regularly. The most recent trends
 3    analysis was done by Clow and Mast (1999) to evaluate long-term trends in stream chemistry with respect
 4    to the Clean Air Act. The program is further described in a fact sheet that can be found at:
 5    http://ny.water.usgs.gov/pubs/fs/fs20053135/.

      A.2.4.3. New York City Water Quality Network
 6          The New York District of the U.S. Geological Survey operates a water quality network throughout
 7    the water supply watershed for New York City, in the Catskill Mountains region. The purpose of the
 8    network is to provide stream flow and water quality data at key locations within the watershed. There are
 9    currently 34 sites throughout the network at which stream flow data are collected, and at thirteen of those
10    sites stream water quality data are also collected. The water quality network is composed of paired
11    "nodes" consisting of one or more "upper nodes" that provide water quality of undeveloped, forested
12    watersheds, and "lower nodes" that provide downstream water quality data that may reflect some level of
13    development within the watershed.
14          Water quality sampling for this program began at the 13 sites in  1998-99. Water samples are
15    collected biweekly and during high flow for approximately 6 storms per year. All water  samples are
16    analyzed for concentrations of nutrients and major ions. Because streams in this area are also affected by
17    acidic deposition, acid-neutralizing capacity and 3 forms of Al are also measured. Further details on the
18    program are available at: http://ny.cf.er.usgs.gov/ nyc/unoono.cfm.

      A.2.4.4. Catskill Long-Term Monitoring Sites
19          Within the Catskill Mountains region of New York State, stream samples are collected and stream
20    flow is measured at three locations within the Neversink River basin, and at one site on  Rondout Creek.
21    Water samples are collected biweekly and during most storms. These sites are currently  part  of the EPA
22    LTM program, but also are affiliated with other programs. Sampling at two of the four sites began in the
23    mid  1980s, whereas sampling at the remaining two sites began in 1991. The primary purpose of these
24    sites is to monitor effects of acidic deposition on stream chemistry. The full suite of analytes  needed to
25    assess acidic deposition effects are measured on these water samples.

      A.2.4.5. Buck Creek, New York
26          Stream flow and water chemistry are monitored at three locations within Buck Creek watershed, in
27    the western Adirondack Region of New York. Samples are collected biweekly and during most storms at
28    each location. Sampling began in 1998 at two sites and 2001  at the third site. The full suite of analytes

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

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

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

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

 5     flow monitoring.



              A.2.5. NSF Long-Term Ecological Research Network

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

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

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

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

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

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

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

13     for evaluation of long-term effects of N and sulfur deposition are discussed in greater detail below.
       Table A-1. LTER site locations and basic site description information.
Site
H.J. Andrews
Experimen-
tal Forest
(AND) 44.2,
-122.2
Arctic Tundra
(ARC) 68.6,
-149.6
LandsatWRS
Path 46 Row 29;
Lat/Long: 44°14'N/
122°11'W
Path 73, Row 12;
Lat/Long: 68°38'N/
149°34'W
Institutional Affiliations
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
Principal Biome/
Main Communities
Temperate coniferous forest.
Douglas-fir/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
Research Topics
Successional changes in ecosystems;
forest-stream 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
       Baltimore
       Ecosystem
       Study (BES)
       39.1, -76.3
Path 15, Row 33;
Lat/Long 38°54" 04' (N),
76°52" 04' (W)
       Bonanza Creek  Path 69, Row 15;
       Experimen-    Lat/Long: 64°45'N/
       tal Forest      148°00'W
       (BNZ) 64.8,
       -148.0
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
Eastern deciduous forest/
Suburban Agriculture fringe,
urban parks, residential and
commercial patches, riparian and
stream habitats
                                         Taiga. Areas of boreal forest
                                         including permafrost-free uplands
                                         and permafrost-dominated north
                                         slopes and lowlands; floodplain
                                         seres
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; plant-
mediated changes in resource and
energy availability for decomposers;
herbivorous control of plant species
composition; hydrologic regime and
stream ecology
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Site
                    LandsatWRS
                                         Institutional Affiliations
                                                           Principal Biome/
                                                          Main Communities
                                                                                                              Research Topics
Cedar Creek    Path 27, Row 28;
Natural History  Lat/Long: 45°24'N/
Area (CDR)     93°12'W
45.4, -93.2
Central
Arizona -
Phoenix (CAP)
33.5, -11.2
Path 36, Row 37 and
Path 36, Row 36° These
have been used by CAP
although Path 37, Row 37
is centered more closely
in the City of Phoenix
                         University of Minnesota
Arizona State University
(Main and West)
Coweeta
Hydrologic
Laboratory
(CWT) 35.0,
-83.5
Path 18, Row 36;
Lat/Long: 35°00'N/
83°30'W
Harvard Forest
(HFR)
42.5, -72.2
Path 13, Row 30;
Lat/Long: 42°32'N/
72°10'W
Hubbard Brook Path 13, Row 29;
Experimental   Lat/Long: 43°56'N/
Forest (HER)   71°45'W
43.9,  -71.8
Jornada
Experimen-
tal Range
(JRN) 32.5,
-106.8
Path 33, Row 37;
Lat/Long: 32°30'N/
106°45'W
W.K. Kellogg    Path 21, Row 31;
Biological       Lat/Long:  85°24'W/
Station (KBS)   42°24'N
42.4, -5.4
Konza Prairie   Path 28, Row 33;
Research       Lat/Long: 39°05'N/
Natural         96°35'W
Area(KNZ)
39.1, -94.6
Eastern deciduous forest and
tallgrass prairie. Old fields; oak
savanna and forest, conifer bog;
lakes; pine forest; wetland marsh
and carr

Sonoran Desert scrub. Urban
parks, residential, interior
remnant desert patches,
commercial and industrial
patches, urban fringe, regulated
river and floodplain (dry),
effluent-dominated river
University of Georgia; USDA
Forest Service,
Southeastern Forest
Experiment Station
                         Kansas State University
Eastern deciduous forest.
Hardwood forests and white pine
plantations
Harvard University;
Universities of New
Hampshire and
Massachusetts; The
Ecosystem Center, Marine
Biological  Laboratory
                         Yale, Cornell, and Syracuse
                         Universities; Institute of
                         Ecosystem Studies; USDA
                         Forest Service,
                         Northeastern Forest
                         Experiment Station
New Mexico State
University; USDA ARS
Jornada Experimental
Range; Duke University;
NOAA, RTP, NC; University
of New Mexico; Dartmouth
College, NH; Oregon  Gradu-
ate Center; Texas
Technological University;
SUNY Buffalo; University of
Keele, UK; Kings College,
London, UK; EPA-EMAP, Las
Vegas, NV

Michigan State University,
Michigan Agricultural
Experiment Station
Eastern deciduous forest.
Hardwood-white-pine-hemlock
forest; spruce swamp forest;
conifer plantations
                           Eastern deciduous forest.
                           Northern hardwood forests in
                           various developmental stages,
                           spruce-fir forests; streams and
                           lakes
Hot desert. Playa, piedmont, and
swale; bajada, basin, mountain
and swale shrubland; mesquite
dunes
                                                     Row-crop agriculture.
                                                     Conventional and organic-based
                                                     corn-soybean-wheat cultivation;
                                                     perennial biomass cultivation;
                                                     native successional communities
                           Tallgrass prairie. Tallgrass
                           prairie; gallery forest; prairie
                           stream
Successional dynamics; primary
productivity and disturbance patterns;
nutrient budgets and cycles; climatic
variation and the wetland/upland
boundary; plant-herbivore dynamics

Interactions of ecological and socio-
economic systems in an urban
environment; influence of land  use
change on ecological patterns and
processes; movement of nutrients
through highly manipulated, urban
flowpaths; interactions of introduced
and native species in urban
environment; millennium- and century-
scale geomorphic change in landforms
and interaction with engineered
landscapes

Long-term dynamics of forest
ecosystems including forest disturbance
and stress along an  environmental
gradient; stream ecosystems along  an
environmental gradient; and the riparian
zone as a regulator of terrestrial-aquatic
linkages

Long-term climate change, disturbance
history and vegetation dynamics;
comparison of community, population,
and plant architectural responses to
human and natural disturbance; forest-
atmosphere trace gas fluxes; organic
matter accumulation, decomposition and
mineralization; element cycling, fine root
dynamics and forest microbiology

Vegetation structure and production;
dynamics of  detritus in terrestrial and
aquatic ecosystems; atmosphere-
terrestrial-aquatic ecosystem linkages;
heterotroph population dynamics;
effects of human activities on
ecosystems

Desertification; factors affecting primary
production; animal-induced  soil
disturbances; direct and indirect
consumer effects; vertebrate and
invertebrate  population dynamics;
grazing effects on ecosystem structure
and function; biodiversity and
ecosystem function; small mammal
effects on soil and vegetation
heterogeneity; soil microbial processes;
surface hydrology; trace gas emissions
from soils; eolian processes
                                Ecological interactions underlying the
                                productivity and environmental impact
                                of production-level cropping systems;
                                patterns, causes, and consequences of
                                microbial, plant, and insect diversity in
                                agricultural landscapes; gene transfer,
                                community dynamics,  biogeochemical
                                fluxes

                                Effects of fire, grazing and climatic
                                variability on  ecological patterns and
                                processes in tallgrass prairie
                                ecosystems, use of remotely sensed
                                data and geographic information
                                systems to evaluate grassland structure
                                and dynamics
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Site
                    LandsatWRS
                                         Institutional Affiliations
                                                           Principal Biome/
                                                          Main Communities
                                                                                                              Research Topics
Luquillo        Path 4, Row 47 and 48;
Experimental   Lat/Long: 18°18'N/
Forest (LUQ)   65°47'W
18.3, -65.8
McMurdo Dry   Path 56, Row 116
Valleys -
Antarctica
(MCM) -78.0,
+ 165.0
Niwot Ridge/   Path 34, Row 32;
Green Lakes    Lat/Long: 40°03'N/
Valley (NWT)   105°37'W
40.1, -105.6
North
Temperate
Lakes (NTL)
46.0, -89.7
and 43.1,  89.4
Palmer Station
(PAL)
Antarctica
-64.7, -64.0
Plum Island
Sound (PIE)
42.67, -70.99
Path 25, Row 28 and
Path 24, Row 30
Lat/Long: 46°00'N/
89°40'W and
89°24/ 43°06
Path 219 , Row 105;
Lat/Long: 64°40'S/ 64°W
                         Center for Energy and
                         Environment Research,
                         University of Puerto Rico;
                         Institute of Tropical
                         Forestry, USDA Forest
                         Service, Southern
                         Experiment Station

                         Desert Research Institute,
                         Reno, Nevada; U.S.
                         Geological Survey, Boulder,
                         Colorado
                         Institute of Arctic and
                         Alpine Research, University
                         of Colorado
Center for Limnology,
University of Wisconsin-
Madison, Wisconsin
Sevilleta
National
Wildlife Refuge
(SEV) 34.3,
-106.8
Path 12, Row 30; Lat/
Long: 42°40'/ 70°59' Site
has the following X and Y
bounds in decimal
coordinates: X min =
-71.22 X max = -70.75.
Y min = 42.50 Y max =
42.83. The total area is
approximately 37 km x 37
km or 1369 km2

Path 33, Row 36;
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
Shortgrass      Path 33, Row 32;
Steppe (SGS)   Lat/Long: 40°49'N/
40.8, -104.8   104°46'W
University of California,
Santa Barbara; Old
Dominion  University
The Ecosystems Center,
Marine Biological
Laboratory; Universities of
South Carolina and New
Hampshire; Massachusetts
Audubon; Wells, Maine,
NERRS
University of New Mexico;
U.S. Fish and Wildlife
Service
                           Tropical rainforest. Tabonuco
                           forest; palo Colorado forest; palm
                           brake; dwarf forest and montane
                           streams
                         Colorado State University;
                         USDA Forest Service; USDA
                         Agricultural Research
                         Service
                                                                    Polar desert oases
Alpine tundra; Fellfield; meadow;
herbaceous and shrub tundras;
cliffs and talus; glacial lakes;
streams and wetlands

Northern temperate lakes in
glacial landscapes in urban,
agricultural and forested
watersheds. Oligotrophic,
dystrophic and eutrophic lakes;
temporary forest ponds; warm
and cold streams; sphagnum-
leatherleaf bog; conifer swamp;
mixed deciduous and coniferous
forests

Polar marine. Coastal and open
ocean pelagic communities;
seabird  nesting areas
Coastal estuary
                                Patterns of and ecosystem response to
                                different patterns of disturbance; land-
                                stream interactions; effect of
                                management on  ecosystem properties;
                                integration of ecosystem models and
                                geographic information systems
Microbial ecosystem dynamics in arid
soils, ephemeral streams, and closed
basin lakes; resource and environmental
controls on terrestrial, stream and lake
ecosystems;  material transport between
aquatic and terrestrial ecosystems;
ecosystem response to greater
hydrologic flux driven by warming
climate

Patterns and controls of nutrient cycling;
trace gas dynamics, plant primary
productivity and species composition;
geomorphology, and paleoecology

Physical, chemical and biological
limnology; hydrology and geochemistry;
climate forcing; producer and consumer
ecology; ecology of invasions;
ecosystem variability; lakescape and
landscape ecology
Oceanic-ice circulation and models; sea-
ice dynamics; biological/physical
interactions; effect of sea ice on primary
production, consumer populations and
apex predators; bio-optical models of
primary production; spatial distribution
and recruitment in consumer
populations; seabird population
dynamics and reproductive ecology

Linkages between land and coastal
waters involving organic  carbon and
organic N inputs to estuarine
ecosystems from watersheds with
various land covers and uses
Multiple: intersection of subalpine
mixed-conifer 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
shortgrass steppes; tallgrass
swales; riparian communities

Floodplain; shrubland;
saltmeadow
Landscape and organism population
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
                                                           Soil water; above- and belowground net
                                                           primary production; plant population
                                                           and community dynamics; effects of
                                                           livestock grazing; soil organic matter
                                                           accumulation and losses, soil nutrient
                                                           dynamics; and ecosystem recovery from
                                                           cultivation
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      Site            LandsatWRS     Institutional Affiliations      Principal Biome/             Research Topics
                                                          Mam Communities
      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; life-form modeling of
                                                                             succession
      A.2.5.1. Hubbard Brook Experimental Forest
 1          The Hubbard Brook Ecosystem Study (HBES) at Hubbard Brook Experimental Forest (HBEF) is
 2    the longest-running precipitation and stream chemistry (1963 to present) monitoring program in the U.S.
 3    (see http://www.hubbardbrook.org). HBEF was established in 1955 as a major center for hydrologic
 4    research in New England. The site is located within the boundaries of the White Mountain National Forest
 5    in central New Hampshire. The 3138-ha, bowl-shaped valley has hilly terrain, ranging from 222 to 1015
 6    m elevation. The HBES originated in 1960 with the intention of applying the small watershed approach to
 7    the study of element fluxes and cycles. The goal of the study is to develop a better understanding of
 8    ecological patterns and processes that characterize the northern forest in eastern North America, and its
 9    response to both natural and human disturbances. In 1987, HBEF joined the NSF's LTER network
10    (http://www.lternet.edu). Hubbard Brook is renowned for its long-term record of measurements,
11    landscape-scale experiments of whole watersheds, and the involvement of scientists from diverse
12    disciplines and institutions.
13          The HBEF is entirely forested, mainly with deciduous northern hardwoods: sugar maple (Acer
14    saccharum), beech (Fagus grandifolid), and yellow birch (Betula allegheniensis), and some white ash
15    (Fraxinus americand) on the lower and middle slopes. Other less abundant species include mountain
16    maple (Acer spicatum), striped maple (Acer pensylvanicum), and trembling aspen (Populus tremuloides).
17    Red spruce (Picea rubens), balsam fir (Abies balsamed), and white birch (Betulapapyrifera var.
18    cordifolia) are abundant at higher elevations and on rock outcrops. Hemlock (Tsuga canadensis) is found
19    along the main Hubbard Brook. Pin cherry (Prunus pensylvanicd), a shade intolerant species, dominates
20    all sites for the first decade following a major forest disturbance. Logging operations ending around
21    1915-1917 removed large portions of the conifers and better quality,  accessible hardwoods. The present
22    second-growth forest is even-aged and composed of about 80 to 90% hardwoods  and 10 to 20% conifers.
23          The HBEF is an oblong basin about  8 km long by 5 km wide. Hubbard Brook is the single major
24    stream draining the basin. Numerous smaller tributary streams of varying size drain into Hubbard Brook
25    including Watershed 6 (WS-6), which is the biogeochemical reference watershed.
26          One of the strengths of the HBES is  the long-term monitoring program. Section A.3.3.1 lists the
27    major parameters included in the HBES long-term monitoring study.  The monitoring data illustrate that
28    short-term observations can be misleading  and that decades of monitoring may be required to detect real

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 1    changes in complex ecosystems. The long-term record at the HBEF provides: (1) insight into ecosystem
 2    function; 2) empirical data for testing models and generating hypotheses; 3) a record of extreme or
 3    unusual events; and (4) information that is relevant to regional national and global environmental issues.
 4          Some of the monitoring is done on experimentally manipulated watersheds. There are nine gaged
 5    watersheds at the HBEF, four of which have been treated experimentally. A tenth ungauged watershed
 6    was also treated. Table A-2 includes summary data on the various watersheds. Datasets for long-term
 7    monitoring can be found at http://www.hubbardbrook.org/ data/dataset_search.php. The datasets most
 8    often used to examine ecosystem response to ambient deposition of N and S are from WS-6 and Mirror
 9    Lake, since they have not been experimentally manipulated. Watershed 6 is the biogeochemical reference
10    catchment at HBEF where monitoring began in June 1963. Measured stream chemistry parameters
11    include major anions and cations, pH, silica, dissolved organic and inorganic carbon, specific
12    conductance, dissolved O2, ANC, and PO/i. Stream chemistry data can be accessed at
13    http://www.hubbardbrook.org/ data/dataset.php?id=8. The normal sampling interval for WS-6 is weekly,
14    with more frequent samples taken at times of increased discharge.
Table A-2. Study watersheds
ws
1
2
3
4
5
6
7
8
9
10
Area (ha)
11.8
15.6
42.4
36.1
21.9
13.2
77.4
59.4
68.4
12.1
Slope (°)
18.6
18.5
12.1
15.6
15.4
15.8
12.4
14.0


at HBEF
Aspect
S22°E
S31°E
S23°W
S40°E
S24°E
S32°E
N16°W
N12°W
NE
SE

Elevation (m)
488-747
503-716
527-732
442-747
488-762
549-792
619-899
610-905
685-910
470-595

Gauge Type
V-notch weir
V-notch weir
V-notch wir
V-notch weir
V-notch weir, San Dimas flume
V-notch weir, San Dimas flume
V-notch weir, San Dimas flume
V-notch weir, San Dimas flume
V-notch weir
None

Initial Yr.
1956
1957
1957
1960
1962
1963
1965
1968
1995
1970
15
16          Monitoring of streamflow and water chemistry has shown that the study watersheds have similar
17    characteristics. Within each watershed there are a variety of soils, vegetation, microtopographical
18    features, and micro-climate. Nevertheless, the composition of these variables seems to be similar from
19    watershed-to-watershed. Thus, the effects of experimental manipulations of watersheds can be adequately
20    evaluated by comparison with neighboring unmanipulated watersheds.
21          The most conspicuous streamflow characteristic is the seasonal shift from large volume of flow in
22    spring to very low flow in late summer and early autumn. These yearly highs and lows reflect seasonal
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 1    spring snowmelt that often occurs over a few days or weeks and the slow progressive decrease in flow
 2    from the transpirational draft in summer, respectively. The numerous streams in the HBEF range from
 3    small ephemeral channels that often dry up during summer to a large perennial 5th-order stream (Hubbard
 4    Brook).
 5          Mirror Lake is a 15-ha oligotrophic clearwater lake adjacent to HBEF. The lake normally mixes in
 6    spring and fall, and is ice-covered from about December 1st to April 15th each year. Part of the drainage
 7    to the lake originates in the Experimental Forest. The lake water is dilute, slightly acidic, and quite clear,
 8    with low productivity and low concentrations of nutrients in the water. Numerous studies have been
 9    conducted on Mirror Lake since the mid-1960s, including extensive physical, chemical, biological, and
10    paleoecological research (cf Likens, 1985). Data are available since 1967 for lakewater concentrations at
11    discrete depths for base cations, pH, and dissolved O2. Ammonium, major anions, phosphate, and
12    dissolved silica have been measured routinely since 1970, although some data are available prior to these
13    dates for each solute. Other standard monitoring data include temperature and specific conductance at
14    each depth. Prior to  1990, not all records had complete solute arrays. Since  1990, DIC and ANC have also
15    been measured on a routine basis, although some  prior data do exist for those parameters. The usual
16    sampling interval for Mirror Lake is four to six times each year, especially at times  of maximum and
17    minimum thermal stratification. Data for Mirror Lake and inlet and outlet streams can be found at:
18    http://www.hubbardbrook.org/data/ dataset_search.php.
19          The soils, vegetation, and climate at the HBEF are characteristic of the northern hardwood forest
20    complex, which spans much of the north-central and northeastern U.S. and southeastern Canada.
21    Streamflow and stream chemistry reflect the landscape characteristics of the drainage area. Consequently,
22    results from the relatively small watersheds at the HBEF are to a first approximation representative of a
23    much larger regional area. During the scientific debate that occurred prior to passage of the Clean Air Act
24    Amendments of 1990, the trends in sulfate (SO42  ) concentrations in streamwater and precipitation at
25    HBEF were very influential in convincing scientists and policy makers that decreasing S emissions would
26    yield large decreases in the concentration of SC>42 in precipitation and streamwater in the northeastern
27    U.S. (Lovett, 2007). Monitoring data collected since 1963 (Figure A-3) played a major role in
28    development of the watershed-ecosystem concept and methods for analyzing and understanding
29    watershed biogeochemical cycles (Bormann, 1967; Likens, 1978; Likens, 1995; Lovett, 2007).
30          An extensive  effort has been made to bring together some of the results of research that had been
31    done at Hubbard Brook over the last several decades. Over the duration of the HBES there have been six
32    books and more than 1,000 papers published. In addition, more than 500 abstracts were published and
33    more than 100 graduate theses completed. A complete list of titles is available at
34    http://www.hubbardbrook.org/pubs/pub_search.php. To date, four synthesis volumes have been completed
35    (Likens, 1977; Bormann, 1979; Likens, 1985; Likens, 1992).
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        O
         S
         I
        I
160

140 -

120

100

 80

 60

 40

 20 -
               00
                                    I
                        Stream water
Precipitation
                            I        !         I         I                 111
                 1960    1965     1970    1975     1980     1985    1990    1995    2000    2005
                                                    Water year
                                                                         Source: Lovett et al. (2007) ; updated from Likens et al. (2002).
      Figure A-3. Long-term record of S042~ concentration in streamwater and precipitation at Watershed
      6 of HBEF.
      A.2.5.2. Coweeta
 1          The Coweeta LTER research program (http://coweeta.ecology.uga.edu/) in North Carolina is based
 2    in the eastern deciduous forest of the Blue Ridge Physiographic Province of the southern Appalachian
 3    Mountains. The program entails long-term cooperation between the University of Georgia and the U.S.
 4    Department of Agriculture (USDA) Forest Service Coweeta Hydrologic Laboratory. The research
 5    program centers on the effects of disturbance and environmental gradients on biogeochemical cycling,
 6    and the underlying watershed ecosystem processes that regulate and respond to those cycles. Coweeta
 7    represents one of the longest continuous environmental studies of any North American landscape.
 8          The research at Coweeta focuses largely on how water, soil,  and forest resources respond to
 9    management practices, natural disturbances, and the atmospheric environment. It also aims to identify
10    practices that mitigate impacts on these watershed resources. Current topics of emphasis include (1)
1 1    analyses of long-term changes in hydrology, nutrient cycling, and productivity in response to management
12    practices and natural disturbances; 2) assessment of prescribed burning effects on the forest environment;
13    3) interdisciplinary implementation of ecosystem management on the national forests; 4) effects of
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 1    climatic change on productivity; 5) impacts of atmospheric deposition on forest processes and
 2    ecosystems; 6) cumulative effects of land use practices on water quality; 7) physiological studies of forest
 3    carbon balance and competition; and (8) biodiversity.
 4          Investigators at the Coweeta Hydrologic Laboratory have recorded N dynamics of streams and
 5    precipitation in mature mixed hardwood-covered watersheds since 1972. Research has been conducted on
 6    responses to management practices such as clearcutting, selective cutting, conversion of native hardwood
 7    to coniferous forest, and old-field succession. Reference watersheds were characterized as in a transition
 8    phase between stage 0 and stage 1 of watershed N saturation. Evidence for stage 3 of N saturation, where
 9    the watershed is a net source of N rather than a N sink, was found for the most disturbed watershed at
10    Coweeta.
11          The Coweeta Basin comprises 2185 hectares within the Blue Ridge geologic province in North
12    Carolina. The laboratory has been dedicated to forest hydrology research since its establishment in 1933.
13    Elevations range from 679 to 1592 m. More than 50 km of streams drain the area.
14          Coweeta is the first major mountain range contacted by air masses moving over the industrialized
15    Piedmont region to the south. Analyses of precipitation chemistry have shown the influence of both local
16    and regional activities on nutrient inputs to forest ecosystems.
17          Since Coweeta was established, 32 weirs have been installed on streams. Seventeen of these weirs
18    are currently operational. Stream gauging was initiated on most watersheds between 1934 and 1938, and
19    stream chemistry measurements date back to 1968.
20          Research has been conducted on eight mixed hardwood control areas  and  13 catchments where
21    forest management prescriptions have been applied. Past treatments  have included varying intensities of
22    cutting, ranging from light selection through clear-cutting; conversion of hardwoods to grass and
23    subsequent succession to hardwoods; multiple-use management; mountain farming; and the application of
24    herbicides and fertilizers.
25          Research and monitoring data from Coweeta has been extensively analyzed and reported in the
26    scientific literature. Example recent publications include Swank and Vose (1997, Grossman et al. (1998,
27    Schofield et al. (2001, and Scott and Helfman (2001).
28          Long-term changes in soils have been identified in reference and managed watersheds over two
29    decades (Knoepp, 1994). For example, changes in exchangeable soil cation content varied with aspect:
30    concentrations decreased on a north-facing slope but were stable on  a south-facing slope. The
31    demonstrated impacts of forest  management practices have varied considerably. Soils  in a white pine
32    plantation showed stable C levels, but cations declined.
33          Commercial sawlog harvest resulted in large increases in soil  C and cation concentrations, which
34    remained elevated for 17 years. Whole-tree harvest resulted in decreased soil C for the next 14 years.
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 1    Clearly soil response to harvest varies with type of harvest and site. Long-term studies like these have
 2    proven useful in guiding ecosystem management projects in the southern Appalachians (Meyer, 1996).

      A.2.5.3. Walker Branch
 3          Walker Branch Watershed is located on the U.S. Department of Energy's Oak Ridge Reservation in
 4    Tennessee. The 97.5 ha Walker Branch watershed has been the site of long-term, intensive environmental
 5    studies since the late-1960s (see http://walkerbranch.ornl.gov/).
 6          The forest soils are acidic, very cherty, infertile, and permeable. They are formed over dolomitic
 7    bedrock, but retain little evidence of their carbonate parent material. The forest vegetation is primarily
 8    oak-hickory with scattered pine on the ridges and mesophytic hardwoods in the valleys.
 9          Initially, the research and monitoring of Walker Branch centered primarily on the geologic and
10    hydrologic processes that control the amounts and chemistry of water moving through the watershed. Past
11    projects have included:
12          •   watershed hydrology and forest nutrient dynamics,

13          •   forest micrometeorology,

14          •   atmospheric  deposition,

15          •   International Biological Program Eastern Deciduous Forest Biome Project,

16          •   trace element cycling and stream nutrient spiraling, and

17          •   effects of acidic deposition on canopy processes and soil chemistry.

18          These projects have all contributed to a more complete understanding of how forest watersheds
19    function and have provided insights into the solution of energy-related problems associated with air
20    pollution, contaminant transport, and forest nutrient dynamics. Available long-term data at this site
21    include:
22          •   Daily climate data

23          •   Monthly climate data

24          •   Precipitation

25          •   Atmospheric deposition
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 1          •  Stream discharge and annual runoff

 2          •  Stream chemistry

 3          •  Vegetation


            A.2.6. Water, Energy, and Biogeochemical Budgets Program
 4          The Water, Energy, and Biogeochemical Budgets (WEBB) Program was started in 1991 at five
 5    small watersheds in the U.S. to examine water, energy, and biogeochemical fluxes and to determine the
 6    effects of atmospheric deposition, climatic variables, and human influences on watershed processes. The
 7    five sites are at Loch Vale, Colorado; Luquillo Experimental Forest, Puerto Rico; Panola Mountain,
 8    Georgia; Sleepers River, Vermont; and Trout Lake, Wisconsin. These sites are supported, in part, by other
 9    programs in the USGS, other Federal and State Agencies, and Universities. Two of these sites, Loch Vale
10    and Sleepers River, have been used extensively to evaluate the effects of atmospheric sulfur and N
11    deposition, and are described here. Each of those sites is also part of the LTER network.

      A.2.6.1. Sleepers River
12          The Sleepers River Research Watershed in northeastern Vermont was established by the
13    Agricultural Research Service (ARS) of the USDA in 1959 and is now operated jointly by the USGSand
14    the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL), with collaboration from
15    several other Federal agencies and universities (see http://nh.water.usgs.gov/ projects/sleepers/index.htm).
16    The USGS uses hydrologic measurements and chemical and isotopic tracing techniques to determine how
17    water moves from the hillslope to the stream, and what processes cause chemical changes, including the
18    neutralization of acid rain. Research results provide insights on how pollutants move through ecosystems,
19    and how ecosystems  may respond to climatic change.
20          The watershed is covered by 1 to 4 m of glacial till, a compacted fine silty material that formed
21    underneath glacial  ice as it moved overland. The till was formed primarily from local bedrock, which is  a
22    calcareous granulite/quartz-mica phyllite. About 60 to 80 cm of soil has developed in the till. Weathering
23    of calcite in the till and bedrock causes highly buffered streamflow, compared to most streams in New
24    England, and a nutrient-rich biological environment. Sleepers River is, therefore, an end member in
25    regional biogeochemical cycling studies (Hornbeck, 1997).
26          The Sleepers River area has reverted from a predominately cleared, agricultural landscape to a
27    mostly forested one. A Northern Hardwood forest, dominated by sugar maple, white ash, yellow birch,
28    and beech, with lesser amounts of red spruce and balsam fir, now covers two-thirds of the area; the
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1    remaining open land is primarily pasture and hayfields. Dairy farming and logging are the primary human
2    enterprises in the watershed. The average annual temperature is 6 °C and the average annual precipitation
3    is 1.1 m, 20% to 30% of which falls as snow.
4          Sleepers River has one of the longest historical hydrologic and climatologic data bases for a cold-
5    region area in the U.S., featuring measurements of precipitation and streamflow since 1959, snow depth
6    and corresponding water content since 1960, soil frost depth since 1984 (Shanley, 1999, and ground-water
7    levels since 1991. These and other measurements constitute a valuable resource for hydrologic modeling
8    and for the evaluation of climatic changes.  Sampling site locations are shown in Figure A-4.
                                                                          VERMONT
                   Snowmelt
                   research
                   station
                A Stream-gaging station
                • Meteorological station                    0               2 mi
     Figure A-4. Location of sampling stations in Sleepers River watershed, Vermont.
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 1          Recent research findings include the following:
 2          •   Precipitation is acidic, but streamflow is well-buffered from calcite weathering in till and
 3              bedrock.

 4          •   Infiltrating snowmelt causes ground water to rise into the permeable soil zone, where it moves
 5              rapidly downslope.

 6          •   Naturally occurring isotopic and chemical tracers indicate that "old" water dominates
 7              streamflow, and that water acquires solutes from weathering and biogeochemical processes
 8              along both deep and shallow flowpaths.

 9          •   Nitrate (NO3 ) in streamflow is supplied primarily by mineralization and nitrification in the
10              soil, rather than directly by the N content of precipitation.

11          The fate of NO3  in the forest ecosystem is being investigated by analysis of both the N and O
12    isotopes of the NC>3  ion. The isotopic composition of NC>3  in streamflow matches that of NC>3 produced
13    by mineralization and nitrification in the soil, indicating that streamflow NC>3 is derived from the soil and
14    not from the rain or snowmelt that causes the high flow (Kendall, 1995). This finding suggests that most
15    incoming atmospheric N is incorporated at least temporarily in the soil where it is utilized by the biota.

      A.2.6.2. Loch  Vale
16          The Loch  Vale Watershed is a 661-ha alpine/subalpine basin located in the south-central Rocky
17    Mountains, about 100 km northwest of Denver, Colorado. The basin is in a roadless area in Rocky
18    Mountain National Park and is accessed by a 5-km hike or ski from the trailhead near Bear Lake. The
19    western boundary of the basin is the Continental  Divide; streams drain to the northeast. Basin elevations
20    range from 4192 m (13,15 3 ft) at Taylor Peak to 3110 m (10,200 ft) at the outlet. There are two main
21    subbasins in Loch Vale: Andrews Creek drains the northern subbasin, and Icy Brook drains the southern
22    subbasin. These two creeks join above The Loch, which is the lowest of three lakes in the basin. Stream
23    gauges are operated on Andrews Creek, Icy Brook, and at The Loch outlet. Water chemistry monitoring
24    occurs at The Loch and on both inlet streams  (see http://nh.water.usgs.gov/projects/sleepers/index.htm).
25          Large glaciers that covered much of Rocky Mountain National Park during the late Pleistocene
26    sculpted the basin into characteristic glacial landforms, including steep U-shaped valleys, cirques, and
27    aretes. When the glaciers retreated about 12,500 years ago, they deposited till of varying thickness, which
28    is confined mostly  to the forested, lower part of the basin. Smaller, more recent glacial advances left
29    younger till, talus, and rock deposits in the upper parts of the basin. The younger glacial and periglacial
30    deposits are  largely unvegetated.

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 1          Available water chemistry data include major ions, nutrients, DIC, DOC, and, for selected samples,
 2    a range of isotopes including 2H/H, 18O/160,15N/14N and 18O/16O in nitrate ion, 35S, 34S/32S, 87Sr/86Sr, 13C,
 3    12C). Monitoring of precipitation and hydrology include the following elements:
 4           •   Precipitation: quantity — 3 sites continuous; chemistry, 1 site, biweekly.

 5           •   Stream discharge, 2 sites (Andrews Creek-Loch Vale, Icy Brook-Loch Vale).

 6           •   Stream chemistry, (Andrews Creek, Icy Brook-Loch Vale).

 7           •   Spring discharge, conductance, and temperature, 3 sites continuous.

 8           •   Spring water chemistry, 3 sites biweekly, 20-30 sites once during low-flow season.

 9           •   Soil lysimeters, 5 sites, biweekly to monthly during summer and fall.

10           •   Snowpack amount and chemistry (depth, snow-water equivalent) basin-wide survey at
11              maximum accumulation, index sites biweekly to monthly.

12           •   Selected microenvironment runoff, e.g.,  rock outcrop, talus fields, weekly to monthly.

13           •   Meteorology: 3 sites (wind speed, wind direction, air temperature, incoming and outgoing
14              radiation, relative humidity), continuous.

15           •   Gas flux (CC>2 and CH4) in wetland, forest, and talus soils, weekly to monthly; CC>2
16              concentrations in surface waters at 10-15 sites several times annually.

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

18          Atmospheric deposition of N to Loch Vale is high compared to most other sites in the Rockies,
19    although considerably lower than most impacted sites in eastern North America and Europe. The
20    alpine/subalpine  ecosystem at Loch Vale exhibits symptoms of advanced watershed N saturation,
21    indicating sensitivity to N deposition. Talus landscapes contribute substantially to N export in streamflow,
22    and soil microbial processes are important in cycling N, even in areas such as talus that have little soil
23    development. Research at this site indicates that N export is a function of both deposition and internal N-
24    cycling processes that are affected by variability in  climate.
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            A.2.7. Other Monitoring  Programs

      A.2.7.1. Bear Brook
 1          The Bear Brook Watershed (BBW) is located in eastern Maine (44°52'15" latitude, 68°06'25"
 2    longitude), approximately 60 km from the Atlantic coastline. The BBW is a paired watershed study
 3    funded by EPA since 1987 as part of The Watershed Manipulation Project (WMP) within the National
 4    Acid Precipitation Assessment Program (NAPAP) (see http://hydromodel.com/bbwm.htm;
 5    http://www.umaine.edu/DrSoils/bbwm/bbwm.html). As a long-term research watershed, the BBW
 6    includes bench-scale, micro-site, plot, and whole watershed investigations. The major purposes of the
 7    BBW project are to:
 8          •   Identify and quantify the major processes that control surface water acidity, with a major
 9              emphasis on (1) the role of excess SO42 and nitrate provided via atmospheric deposition and
10              experimental application, and (2) the rate of cation supply from chemical weathering and
11              cation desorption.

12          •   Assess the quantitative and qualitative responses at the watershed  level to different (both
13              increased and decreased) levels  of acidic deposition.

14          •   Evaluate the ability of existing models of water acidification to predict short- and long-term
15              chemical variations in surface water chemistry and to predict watershed soil responses to
16              increased and decreased loading of strong acids.

17          The watershed includes two first order streams: East Bear Brook (EBB) and West Bear Brook
18    (WBB). On each stream, a catchment outlet was selected and gauged so that both streams have  about the
19    same catchment area (EBB = 10.7 ha and WBB =10.2 ha). Since streams are close and face the same
20    slope direction, the watersheds are geographically similar and are appropriate for a paired watershed
21    zintervals. Both watersheds have a maximum discharge of about 0.01 mm/ha/sec or 0.15 m3/s. Annual
22    water yield relative to incoming precipitation for WBB ranges from 68 to 77% and EBB ranges from 62-
23    68%.
24          Stream channels in each watershed are well defined. Each stream bed is approximately 1  m wide at
25    the weir and water flows over exposed bedrock in places. Elsewhere, the streambeds are comprised of
26    boulders and gravel. Both streams have undergone intermittent dry periods during summer over the
27    course of the study. One V-notch weir was  constructed on each of the streams during winter 1987-1988.
28    Mean discharge in each stream is about 0.13 cfs.
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 1          Sampling frequency at the weirs was every three weeks during the winter of 1986-1987 and at
 2    least weekly thereafter. On the basis of sampling conducted prior to beginning the manipulation
 3    experiment (1987-1989), the streams had the following characteristics: ANC, -5 to 90 (ieq/L; air-
 4    equilibrated pH, 4.7 to 7.2; specific conductance of approximately 26 (iS/cm; and DOC of 1 to 4 mg C/l.
 5          Soils in the Bear Brook watersheds are primarily Spodosols. The average depth of the overburden
 6    in the watersheds is 0.5 m, with a range of 0 to 5.2 m. Soil pH (0.01M CaC^) values ranged from 2.9 in
 7    the O horizon, to 3.9 in the B horizon, to 4.4 in the  C horizon. The bedrock is primarily metamorphosed
 8    and folded politic graded beds  and quartzites, with granitic dikes. The surficial material is till.
 9          The forest is comprised primarily of deciduous species with areas of conifers. Tree species include
10    American beech (Fagus grandifolid), birch (Betula sp.), maple (Acer sp.), red spruce (Picea rubens),
11    balsam fir (Abies balsamea), white pine (Pinus strobus), and hemlock (Tsuga canadensis). Coniferous
12    stands, which occupy approximately 17% of the total watershed area, occur more commonly in the upper,
13    steeper portions of the watersheds.
14          Although the Bear Brook project was intended as an experimental manipulation of West Bear
15    Brook, there is also great value in the long-term monitoring data collected at East Bear Brook, the non-
16    manipulated reference watershed. This two-decade long monitoring record provides information on the
17    response of an acid-sensitive low-order stream in Maine to changes that have occurred in atmospheric
18    deposition since 1986.
19          Results of the Bear Brook project have been widely published (Norton, 1994; cf Kahl,  1993;
20    Norton, 1999; Norton, 1999).

      A.2.7.2. Shenandoah  Watershed Study
21          The Shenandoah Watershed Study (SWAS) program is a monitoring and research network focused
22    on low-order, high-gradient streams  associated with public lands in western Virginia (see
23    http://swas.evsc.virginia.edu/). The objectives of the program are to increase  understanding of factors that
24    govern biogeochemical conditions and stressor-response relationships in forested mountain watersheds of
25    the central Appalachian region. Success in addressing these scientific and problem-oriented objectives has
26    been achieved through development of a data collection network that accounts for spatial gradients, as
27    well as temporal variation, in the chemical composition of the region's relatively undisturbed headwater
28    streams.
29          The program is notable for the length of the continuous data record that has been obtained,
30    including the longest-running record (28 years) of stream  water composition and discharge in the National
31    Park System. The SWAS component of the program, which now includes 14 streams in Shenandoah
32    National Park, was initiated in 1979. The Virginia Trout Stream Sensitivity Study (VTSSS) component,
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 1    which now includes 51 streams in National Forests and other conservation lands, was initiated in 1987.
 2    The distribution of SWAS-VTSSS study sites in relation to public lands is shown in Figure A-5.
 3          The SWAS-VTSSS program has been maintained as a cooperative effort involving the Department
 4    of Environmental Sciences at the University of Virginia, the National Park Service, the EPA, the USDA
 5    Forest Service, the U.S. Geological Survey, the Virginia Department of Game and Inland Fisheries, and
 6    Trout Unlimited. The monitoring sites account for ecological variation among the region's forested
 7    mountain watersheds with a data-collection strategy that represents: (1) spatial variation through the
 8    distribution of hydrochemical monitoring within a lithologic classification system; and (2) temporal
 9    variation through long-term data collection at fixed locations sampled at different frequencies.
                                                               Shenandoah
                                                               National Park
                                                   George Washington and
                                                  Jefferson National Forests
      Figure A-5. SWAS-VTSS Program Study Sites. The length of the stream water chemical composition
      record for SWAS-VTSSS 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).

10          The lithologic classification system includes 6 classes based on the physical and chemical
11    properties of bedrock formations in the region. ANC and concentrations of related acid-base constituents
12    in stream waters, as well as other biotic and abiotic properties of watersheds, differ among the lithologic
13    classes.
14          The SWAS-VTSSS data collection framework is most-well developed in the Blue Ridge Mountains
15    Province within Shenandoah National Park, where stream water composition data are collected seasonally
16    at 14 sites, weekly at 6 sites, and every four hours during episodic high-flow conditions at 3 sites with
17    continuous discharge gauging. Stream water composition data are collected on a seasonal basis at an
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 1    additional 51 sites located outside of the Park, in both the Blue Ridge Mountains and Ridge and Valley
 2    Provinces.
 3          Stream water samples collected through the SWAS and VTSSS programs are analyzed for ANC,
 4    pH, and the major anions (SO42 , nitrate, and chloride) and cations (calcium, magnesium, potassium, and
 5    sodium) by methods appropriate for low-ionic strength natural waters. Both the SWAS and VTSSS
 6    sample streams were selected based on geographic distribution, representation of the major bedrock types
 7    underlying the mountain ridges in the region, and minimization of recent watershed disturbance. All but a
 8    few of the sample streams currently support reproducing populations of native brook trout. All of the
 9    sample streams supported brook trout populations historically.
10          Sustained data collection in a network constructed of intensively studied sites nested within a
11    geographically extensive set of less intensively studied sites has allowed detection and interpretation of
12    change that has occurred in a context of multiple time scales and stressors. Responses to multi-year
13    changes in acidic deposition have been reflected in long-term  trends in quarterly concentrations of SO42 ,
14    ANC, and other acid-base constituents of streams in the network. Expectations for southeastern
15    watersheds with soils that retain sulfur, for example, have been confirmed by the lack of regional
16    improvement in stream water quality following reductions in acidic deposition mandated by the Clean Air
17    Act. The acid-base chemistry of streams in the network also varies seasonally and on shorter time scales.
18    Weekly and higher-frequency automated stream water sampling during periods of high runoff have
19    supported the study of episodically more-acidic conditions, including the study of fish sensitivity with in-
20    stream bioassays and development of models to predict severity and recurrence intervals.
21          By accounting for significant spatial gradients and temporal patterns in the region, the SWAS-
22    VTSSS hydrochemical data collection program provides a basis for both observing and interpreting
23    watershed-scale change, as well as an informed foundation for process-oriented research. Monitoring data
24    and research findings obtained through the SWAS-VTSSS program have contributed to increased
25    scientific understanding, as well as to policy formulation and implementation.
26          The mathematical model, Model of Acidification of Groundwater in Catchments  (MAGIC), was
27    first calibrated using data obtained for White Oak Run, a SWAS-VTSSS study stream in Shenandoah
28    National Park. MAGIC is the most widely used acid-base chemistry model in the U.S. and Europe and the
29    principal model used by the National Acid Precipitation Assessment Program in the 1980s to estimate
30    future damage to lakes and streams in the eastern U.S. The MAGIC model has since been applied in a
31    number of regional assessments that relied extensively on stream water and soils data obtained through
32    the SWAS-VTSSS program. Among these are:
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 1          •   The Southern Appalachian Mountain Initiative, a multi-state effort to evaluate alternative
 2              approaches to solving regional air-pollution problems. MAGIC projections indicated that even
 3              ambitious emission control strategies would not result in near-term recovery of the region's
 4              most acidified surface waters - a consequence of base-cation depletion in soils exposed to
 5              decades of acidic deposition.

 6          •   The Shenandoah Assessment, an assessment of acidification effects on aquatic systems in
 7              Shenandoah National Park. MAGIC reconstructions indicated that Park streams associated
 8              with base-poor bedrock lost  about 70 (ieq/L between 1900 and 1990. MAGIC projections
 9              indicated that some streams may recover given prospective reductions in acidic deposition, but
10              others will not.

11          Data and findings provided through the SWAS-VTSSS program have also proven relevant to the
12    evaluation and implementation of national air pollution control policies. The SWAS-VTSSS program
13    provides data for the EPA's long-term monitoring of surface water response to legislated reductions in
14    sulfur emissions. Whereas SO42 concentrations in surface water declined during the 1990-2000 period
15    for four northeastern regions with sensitive surface waters, the SWAS-VTSSS study region, in contrast,
16    experienced increasing stream-water SO42 concentrations and continuing acidification.
17          Recent publications that were based on analyses of SWAS-VTSSS data include Cosby et al. (1991,
18    Stoddard et al. (1993, Sullivan et al. (Sullivan, 2003), 2004), and Webb et al. (2004).

      A.2.7.3. Fernow
19          The Fernow Experimental Forest, established in 1934, is located just south of the city of Parsons in
20    the most mountainous region of West Virginia. It is surrounded by the Monongahela National Forest,
21    which comprises about 900,000 acres of rugged, hilly terrain. Most research at Fernow is focused on
22    improvement of forest management (see http://www.fs.fed.us/ne/parsons/fefhome .htm).
23          Scientists at Fernow are developing information and techniques for sustainably managing
24    hardwood forests in the central Appalachians. The mixed hardwood forest covers about 78% of West
25    Virginia and supplies important timber products, provides recreational opportunities, and supports a
26    diverse assemblage of wildlife and plant species.
27          The Fernow Experimental Forest was heavily logged between  1905 and 1911. The forest now
28    contains about 1900 ha of second- and third-growth Appalachian hardwood stands, which are
29    representative of average to better than  average sites found on approximately 4 million ha of the forest
30    type in West Virginia and  surrounding states. At the lowest elevations, the original forests consisted
31    mainly of hardwoods, with eastern hemlock (Tsuga canadensis [L.] Carr.) along stream bottoms and on
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 1    north slopes. Forests at the higher elevations were dominated by red spruce (Picea rubens Sarg.) and
 2    hemlock. Small patches of pure spruce occurred on the tops of the mountains.
 3          Elevations in the Fernow range from 533 to  1112 m, with slopes of 10% to 60%. A rock layer
 4    composed of fractured hard sandstone and shale underlies most of the Fernow. A majority of the soils are
 5    of the Calvin and Dekalb series, which originated from these rocky materials (loamy-skeletal mixed
 6    mesic Typic Dystrochrepts). On the southeastern part of the forest, Greenbrier limestone outcrops to
 7    produce a midslope zone of limestone soil of the Belmont series (fine-loamy mixed mesic Typic
 8    Hapludalfs). Almost all Fernow soils, including the sandstone, shale, and limestone soils, are well-
 9    drained, medium textured loams and  silt loams. Average soil depth is about 1 m, and average soil pH is
10    about 4.5.
11          A rainy, cool climate is typical on the Experimental Forest. Precipitation, which averages about 145
12    cm per year, is evenly distributed throughout the year. Mean annual temperature is about 9 °C, and the
13    length of the growing season is approximately 145 days.
14          The forest types and conditions today reflect the site qualities and past history of the area. Oaks
15    (Quercus spp.) are most common and are  found on all sites along with American beech (Fagus
16    grandifolia Ehrh.) and sweet birth (Betula lenta L.). Excellent sites in coves and on north slopes support
17    primarily northern red oak (Quercus rubra L.), sugar maple (Acer saccharum Marsh.), yellow-poplar
18    (Liriodendron tulipifera L.), black cherry  (Prunus serotina Ehrh.), white ash (Fraxinus americana L.),
19    basswood (Tilia americana L.), cucumbertree (Magnolia acuminata L.), and beech. Fair sites on south
20    and east slopes usually support oak stands composed of red oak, white oak (Quercus alba L.), chestnut
21    oak (Quercus prinus L.), and scarlet oak (Quercus coccinea Muenchh.). Other fair site species include red
22    maple (Acer rubrum L.), sweet birch, black gum (Nyssa sylvatica Marsh.), sassafras (Sassafras albidum
23    Nutt.), and sourwood (Oxydendrum arboreum [L.]  DC.). Good sites commonly support a mixture of
24    excellent and fair site species. Black locust (Robiniapseudoacacia L.), sweet birch, and Fraser magnolia
25    (Magnolia fraseri Walt.) are consistent but generally minor components of the forest on all sites.
26    American chestnut was a major forest component until it was eliminated by the chestnut blight.
27          The Fernow Experimental Forest encompasses practically the entire Elk Lick Run drainage, which
28    is about 5.8 km long and 3.5 km across at the widest point. Elk Lick Run has seven major tributaries
29    including Big Spring, which drains a  headwater limestone formation. Headwater areas on two of these
30    tributaries have been gauged to show how forest management influences  streamflow.
31          Research on the Fernow Experimental Forest by the Timber and Watershed Project scientists is
32    done in cooperation with the Monongahela National Forest, West Virginia University, Marshall
33    University, Pennsylvania State University, Virginia Tech, and the West Virginia Division of Natural
34    Resources.
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 1          Scientific studies on the Fernow have followed two lines of research, with considerable overlap.
 2    Silvicultural research, focused mostly on mixed hardwood stands, addresses questions relating to
 3    regenerating, growing, tending, and harvesting trees and stands. Watershed research has addressed some
 4    of the more basic questions about water use by forests and forest hydrology, as well as critical issues
 5    affecting roads, best management practices, and forest management effects on water and soil resources.
 6    The Fernow also has been in the forefront of research on acidic deposition and N saturation. A whole-
 7    watershed acidification study has been conducted since 1989. Recently, research on threatened and
 8    endangered species has assumed a more prominent role, due to the presence of Indiana bat and running
 9    buffalo clover on the Fernow.

      A.2.7.4. National Ecological Observatory Network
10          The National Ecological Observatory Network (NEON) is a continental-scale research platform
11    that is primarily focused on discovering and understanding the impacts of climate change, land-use
12    change, and invasive species on ecology. It will also generate data that will be useful for assessing effects
13    of NOx and SOx deposition on ecosystems. NEON has not yet been implemented; it is described here
14    because it represents an ambitious monitoring program that is expected to be very useful in the near
15    future. NEON will gather long-term data on ecological responses of the biosphere to changes in land use
16    and climate, and on feedbacks with the geosphere, hydrosphere, and atmosphere. NEON is proposed as a
17    national observatory, consisting of distributed sensor networks and experiments, linked by advanced
18    cyber infrastructure to record and archive ecological data for at least 30 years. Using standardized
19    protocols and an open data policy, NEON is intended  to gather essential data for developing scientific
20    understanding and theory required to manage the nation's ecological challenges. The program description
21    is found at www.neoninc.org/.
      A.3. Modeling

            A.3.1. Principal Ecosystem  Models Used  in the U.S.
22          It is particularly difficult to study endpoints at the larger levels of biological organization (e.g., at
23    the population, community, biogeochemical, and ecosystem-level) with monitoring studies. Geographic
24    areas are larger, and timeframes are longer, rendering it difficult to obtain data in sufficient quantity to
25    detect impacts unless they are exceptionally severe. Therefore, the most common approach to study
26    endpoints at these scales is to develop and apply a model. Models may be calibrated using data from
27    monitoring, survey, or laboratory or field experiments and are useful tools in predicting larger-scale,
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 1    longer-term impacts. However, verifying the predictions and assessing the overall validity of the model
 2    can be challenging.
 3          Some of the most frequently used ecosystem models designed to quantify effects of atmospheric N
 4    and S deposition are discussed below. It is important to note that the ecosystem models are parameterized
 5    for specific areas and may not be readily applicable to other locations  without significant re-
 6    parameterization.
 7          There are four principal models that are currently being used in the U.S. to assess the effects of S
 8    and N deposition on terrestrial and freshwater aquatic ecosystems: MAGIC, NuCM, PnET/BGC, and
 9    DayCent-Chem. Two models, SPARROW and WATERSN, are commonly used to evaluate N loading to
10    large river systems and to estuaries. These six models are briefly reviewed in the following sections. Each
11    review begins with a summary of the provenance and conceptual basis of the model and contains
12    references to some of the published applications. This is followed by a more detailed description of the
13    processes included in the model, the inputs required, and the output variables simulated by the model.
14          The ranges of process complexity, temporal resolution and spatial discretization represented in
15    these models are considerable. These ranges make comparative summaries of inputs, outputs, and
16    processes across the models problematic. The models are all currently in use because they are, in a sense,
17    complementary to each other, with each providing an approach or satisfying requirements unique to their
18    own structure and intended applications. As a result, there is no good way to develop satisfying
19    comparative equivalences among the components of the various structures. It is also beyond the scope  of
20    this document to present the level of detail necessary to run any of the models. The  descriptions below
21    must of necessity be brief. References to appropriate texts designed to provide more detail are given for
22    each model.
23          Following the discussion of the four models most frequently used in the U.S., there are brief
24    descriptions of the most important models of S and N deposition effects that are being used in Europe and
25    elsewhere.

      A.3.1.1. MAGIC
26          The MAGIC model (Cosby, 1985; Cosby, 1985; Cosby, 1985) is a mathematical model of soil and
27    surface water acidification in response to atmospheric deposition based on process-level information
28    about acidification. MAGIC has been applied extensively in North America and Europe to both individual
29    sites and regional networks of sites, and has also been used in Asia, Africa and South America. The utility
30    of MAGIC for simulating a variety of water and soil acidification responses at the laboratory, plot,
31    hillslope, and catchment scales has been tested using long-term monitoring and experimental
32    manipulation data.
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 1          MAGIC has been widely used in policy and assessment activities in the U.S. and in several
 2    countries in Europe (Cosby, 1985; Cosby, 1990; Cosby, 1995; Cosby, 1996; Ferrier, 2001; Jenkins, 1990;
 3    Clair, 2004; Sullivan, 1998; Moldan, 1998; Hornberger, 1989; Whitehead, 1997; Whitehead, 1988;
 4    Wright, 1994; Wright, 1998; e.g.', \Beier, 1995; Sullivan, 2006).

      MAGIC Model Structure
 5          MAGIC is a lumped-parameter model of intermediate complexity, developed to predict the long-
 6    term effects of acidic deposition on surface water chemistry (see Figure A-6). The model simulates soil
 7    solution chemistry and surface water chemistry to predict the monthly and annual average concentrations
 8    of the major ions in these waters. MAGIC consists of: (1) a section in which the concentrations of major
 9    ions are assumed to be governed by  simultaneous reactions involving SO42  adsorption, cation exchange,
10    dissolution-precipitation- speciation of aluminum, and dissolution-speciation of inorganic carbon; and (2)
11    a mass balance section in which the  flux of major ions to and from the soil is assumed to be controlled by
12    atmospheric inputs, chemical weathering, net uptake and loss in biomass and losses to runoff. At the heart
13    of MAGIC is the size of the pool of exchangeable base cations in the soil. As the fluxes to and from this
14    pool change over time owing to changes in atmospheric deposition, the chemical equilibria between soil
15    and soil solution shift to give changes in surface water chemistry. The degree and rate of change of
16    surface water acidity thus depend both on flux factors and the inherent characteristics of the affected soils.
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          Major Pools and Fluxes
                              Atmospheric
                               Deposition
               Weathering
      Figure A-6. Conceptual structure of the MAGIC model showing major pools and fluxes included in
      simulation of effects of S and N deposition.

 1         Cation exchange is modeled using equilibrium (Gaines-Thomas) equations with selectivity
 2    coefficients for each base cation and aluminum. SO42  adsorption is represented by a Langmuir isotherm.
 3    Aluminum dissolution and precipitation are assumed to be controlled by equilibrium with a solid phase of
 4    aluminum trihydroxide. Aluminum speciation is calculated by considering hydrolysis reactions as well as
 5    complexation with SO42 , fluoride and dissolved organic compounds. Effects of carbon dioxide on pH and
 6    on the speciation of inorganic carbon are computed from equilibrium equations. Organic acids are
 7    represented in the model as tri-protic analogues. Weathering rates are assumed to be constant. Two
 8    alternate mechanisms are offered for simulation of nitrate and ammonium in soils and water: (1) first
 9    order equations representing net uptake and retention; or (2) a set of equations and compartments
10    describing process-based N dynamics controlled by C and N pools and fluxes in the compartments.
11         Atmospheric deposition fluxes for the base cations and strong acid anions are required as inputs to
12    the model. These inputs are generally assumed to be uniform over the catchment. Atmospheric fluxes are
13    calculated from concentrations of the ions in precipitation and the rainfall volume into the catchment. The
14    atmospheric fluxes of the ions must be corrected for dry deposition of gas, particulates and aerosols and
15    for inputs in cloud/fog water. The volume discharge for the catchment must also be provided to the model.
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 1    In general, the model is implemented using average hydrologic conditions and meteorological conditions
 2    in annual or seasonal simulations, i.e., mean annual or mean monthly deposition, precipitation and lake
 3    discharge are used to drive the model. Values for soil and surface water temperature, partial pressure of
 4    carbon dioxide and organic acid concentrations must also be provided at the appropriate temporal
 5    resolution.
 6          The MAGIC model can be implemented as a one- or two-soil representation of a catchment with or
 7    without wetlands. Atmospheric deposition enters the soil compartment(s) and the equilibrium equations
 8    are used to calculate soil water chemistry. The water is then routed to the stream compartment, and the
 9    appropriate equilibrium equations are reapplied to calculate runoff chemistry. Input-output mass balance
10    equations are provided for base cations and strong acid anions, and charge balance is required for all ions
11    in each compartment (for complete details of the model see \Cosby, 1985; Cosby,  1985; Cosby,  1985;
12    Cosby, 2001).
13          For most applications, model outputs for 15 stream water variables are used. These variables
14    consist of the concentrations of 10 ions (H; Ca; Mg; Na; K; NF^; SO42 ; NC^; Cl; and total inorganic Al),
15    the stream discharge (Q), stream pH, sum of base cation (SBC) concentrations (SBC = Ca + Mg + Na + K
16    + Nty, sum of mineral acid anion (SAA) concentrations (SAA = Cl + SO42 + NC^) and the charge
17    balance acid neutralizing capacity (ANC = SBC - SAA). These variables are expressed in units of m/yr
18    (or m/mo) for Q, (imol/L for inorganic Al, and (ieq/L for all other variables. In addition, model output for
19    7 soil and soilwater variables are frequently used, the total base saturation and individual cation
20    saturations for Ca, Mg, Na, and K, the soilwater pH and the Ca/Al ratio in soil water.
21          The aggregated nature of the model requires that it be calibrated to observed data from a system
22    before it can be used to examine potential system response. Calibrations are based on volume weighted
23    mean annual or seasonal fluxes for a given period of observation. The length of the period of observation
24    used for calibration is not arbitrary. Model output will be more reliable if the annual flux estimates used in
25    calibration are based on a number of years rather than just one year. There is a lot of year-to-year
26    variability in atmospheric deposition and catchment runoff. Averaging over a number of years reduces the
27    likelihood that an "outlier" year (very  dry, etc.) is used to specify the primary data on which model
28    forecasts are based. On the other hand, averaging over too long a period may remove important trends in
29    the data that need to be simulated by the model.
30          The calibration procedure requires that stream water quality,  soil chemical and physical
31    characteristics, and atmospheric deposition data be available for each catchment. The water quality data
32    needed for calibration are the concentrations of the individual base cations (Ca, Mg, Na,  and K) and acid
33    anions (Cl, SO42  , and NOs) and the pH. The soil data used in the model include soil depth and bulk
34    density, soil pH, soil cation-exchange capacity, and  exchangeable bases in the soil (Ca, Mg, Na, and K).
35    The atmospheric deposition  inputs to the model must be estimates of total deposition, not just wet
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 1    deposition. In some instances, direct measurements of either atmospheric deposition or soil properties
 2    may not be available for a given site with stream water data. In these cases, the required data can often be
 3    estimated by: (a) assigning soil properties based on some landscape classification of the catchment; and
 4    (b) assigning deposition using model extrapolations from some national or regional atmospheric
 5    deposition monitoring network.
 6          Soil data for model calibration are usually derived as aerially averaged values of soil parameters
 7    within a catchment. If soils data for a given location are vertically stratified, the soils data for the
 8    individual soil horizons at that sampling site can be aggregated based on horizon, depth, and bulk density
 9    to obtain single vertically aggregated values for the site, or the stratified data can be used directly in the
10    model.
11          Calibration of the model (and estimation of historical changes at the modeled sites) requires a
12    temporal sequence of historical anthropogenic deposition. Current understanding of ecosystem responses
13    to acidic  deposition suggests that future ecosystem responses can be strongly conditioned by historical
14    acid loadings. Thus, as part of the model calibration process, the model should be constrained by some
15    measure of historical deposition to the site. However, such long-term, continuous historical deposition
16    data may not exist. The usual approach is to  use historical emissions data as a surrogate for deposition.
17    The emissions for each year in the historical period can be normalized to emissions in a reference year (a
18    year for which observed deposition data are  available). Using this scaled sequence of emissions, historical
19    deposition can be estimated by multiplying the total deposition estimated for each site in reference year
20    by the emissions scale factor for any year in the past to obtain deposition for that year.

      A.3.1.2. NuCM Model
21          The current NuCM model is based on the original Integrated  Lake Watershed Acidification Study
22    (ILWAS) model of the 1980s (cf Chen, 1984;  Goldstein, 1984; Gherini, 1985). NuCM was developed as
23    an extension to the ILWAS model by investigators in the Integrated  Forest Study see (Johnson, 1992), and
24    the model code was written by Tetra-Tech, Inc. (Liu, 1991). NuCM  was developed to explore potential
25    effects of atmospheric deposition, fertilization and harvesting in  forest ecosystems. Because NuCM was
26    designed primarily for simulating the effects of atmospheric deposition on nutrient cycling processes, its
27    construction emphasizes soil and soil solution  chemistry (Liu, 1991). As a stand4evel model, NuCM
28    incorporates all major nutrient cycling processes (uptake, translocation, leaching, weathering, organic
29    matter decay, and accumulation). Vegetation is divided into leaf, bole and root compartments for under-
30    and overstory vegetation. NuCM simulates the cycling of N, P, K, Ca, Mg, Na, and S based on expected
31    optimal growth rates (input by the user and reduced in the event  of nutrient limitation), user-defined
32    litterfall,  weathering, N and S  mineralization rates, soil minerals composition, initial litter, soil organic
33    matter pools, and C/N ratios.

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 1          The model has been calibrated for different vegetation types, including a loblolly pine (Pinus taeda
 2    L.) stand at Duke University (Johnson, 1995), a mixed deciduous stand at Walker Branch (Johnson, 1993)
 3    and a red spruce (Picea rubens Sarg.) stand in the Great Smoky Mountains (Johnson, 1996). The NuCM
 4    model was used as part of the  Southern Appalachian Mountain Assessment (Sullivan, 2002).

      NuCM Model Structure
 5          In NuCM, the ecosystem is represented as a series of vegetation and soil components. The
 6    overstory consists of one generic conifer and one generic deciduous species of specified biomass and
 7    nutrient concentration (foliage, branch, bole, roots). For mixed species stands, average values for biomass
 8    and nutrient concentration by component must be used. NuCM also includes an understory, which can be
 9    divided  into canopy, bole, and roots. Maximum potential vegetative growth in the model is defined by the
10    user and is constrained in the model by the availability of nutrients and moisture. The forest floor is
11    simulated from litterfall inputs and litter decay. Litterfall mass inputs are defined by the user, and litter
12    decay is represented as a four stage  process where: (1) coarse litter decays to fine litter; 2) fine litter
13    decays to humus and cations; 3)  humus decays to organic acids, NH4+, SC>42  , ft", and CC^; and (4)
14    organic  acids decay to NH4+, SC>42 , ft", and CC>2. Each stage is represented as a first-order equation.
15          The soil includes multiple layers (up to 10), and each layer can have different physical and
16    chemical characteristics. The user defines bulk density, cation exchange capacity, exchangeable cations,
17    adsorbed phosphate and SO42  , and  four soil minerals and their composition. These inputs define the
18    initial soil exchangeable/adsorbed pools and total pools. Initial total soil N pools are simulated from
19    litterfall and decay, as described above, and user-defined C/N ratios. Vegetation, litter, and soil pools
20    change over a simulation in response to growth, litterfall and decomposition, and nutrient fluxes via
21    deposition, leaching and weathering.
22          The processes that govern interactions among these pools include translocation, uptake, foliar
23    exudation and leaching, organic  matter decay, nitrification, anion adsorption, cation exchange and mineral
24    weathering. Translocation, defined as the removal of nutrients from foliage prior to litterfall, is user-
25    specified. Maximum uptake is calculated from biomass and nutrient concentrations; actual uptake is equal
26    to this maximum value when sufficient nutrients are available and reduced when nutrients become
27    limiting. Reduced uptake first allows reduced nutrient concentrations in plant tissues, then causes a
28    reduction in growth. Foliar exudation and leaching rates are simulated as proportional to foliar
29    concentrations using user-defined coefficients.
30          Mineral weathering reactions are described in the model using rate expressions with dependencies
31    on the mass of mineral present and solution-phase hydrogen-ion concentration taken to a fractional power.
32    Cation exchange is represented by the Gapon equation. The model simulates a tri-protic organic acid with
33    a fixed charge density. Nitrification is represented in the form of a Michaelis-Menton rate expression.
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 1    Phosphate adsorption is represented by a linear isotherm, and SC>42 adsorption is represented by a
 2    Langmuir adsorption isotherm.
 3          Climate inputs to the NuCM model are through input meteorological files (typically 1 to 5 years
 4    long), which are repeated in order to generate long-term simulations. The meteorological files contain
 5    daily values for precipitation quantity, maximum and minimum air temperature, cloud cover, dewpoint,
 6    atmospheric pressure, and wind speed. Monthly soil temperature data are also required.
 7          Precipitation is routed through the canopy and soil layers and evapotranspiration, deep seepage,
 8    and lateral flow are simulated. The movement of water through the system is simulated using the
 9    continuity equation, Darcy's equation for permeable media flow, and Manning's equation for free surface
10    flow. Percolation occurs between layers as a function of layer permeability's and differences in moisture
11    content. Nutrient pools associated with soil solution, the ion exchange complex, minerals, and soil organic
12    matter are all tracked explicitly by NuCM.
13          Wet deposition is calculated from precipitation amounts and user-input air quality files which
14    define precipitation concentrations on a monthly basis. Dry deposition is calculated from air
15    concentrations in the air quality files combined with user-defined deposition velocities and simulated leaf
16    areas. Leaching is calculated from soilwater percolation and simulated soil solution concentrations using
17    the soil chemical and biological algorithms defined above for each soil horizon.
18          The only processes in the NuCM model that are explicitly temperature-dependent are evaporation,
19    occurrence of precipitation as rainfall versus  snowfall, snowpack melting, litter decay, and nitrification.
20    Temperature affects processes such as cation exchange, mineral weathering, and uptake only indirectly.
21    Precipitation effects are manifested strictly through the hydrologic simulations; none of the nutrient
22    processes are dependent explicitly upon moisture.

      A.3.1.3. PnET-BGC
23          PnET-BGC is an  integrated dynamic biogeochemical model that simulates chemical transforma-
24    tions of vegetation, soil and drainage water. The PnET-BGC model was  formulated by linking two
25    submodels  (vegetation and biogeochemical) to allow for the simultaneous simulation of major element
26    cycles in forest and interconnected aquatic ecosystems. The vegetation submodel is based on PnET-CN
27    (Aber, 1992; Aber, 1997; Aber, 1997), a simple generalized model of monthly carbon, water, and N
28    balances that provides estimates of net primary productivity, N uptake, and water balances. The
29    biogeochemical submodel BGC (Gbondo-Tugbawa, 2001, expands PnETto include vegetation and
30    organic matter interactions of other elements (Ca2+, Mg2+, K+, Na+, Si, S, P, A13+, Cl, and F ), abiotic soil
31    processes, solution speciation, and surface water process.
32          PnET-BGC was initially developed for and applied to the northern hardwood forest ecosystem. The
33    model has been tested using vegetation, soil and water chemistry data from the Hubbard Brook

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 1    Experiment Forest (HBEF) (Gbondo-Tugbawa, 2001). The model has subsequently been applied to
 2    intensively studied watersheds in the Adirondack and Catskill regions of New York and applied regionally
 3    to the Adirondacks (Chen, 2005) and northern New England (Chen, 2005; Chen, 2005). PnET-BGC has
 4    also been used to evaluate the effects of current and future atmospheric deposition scenarios (Gbondo-
 5    Tugbawa, 2002; Sullivan, 2006).

      PnET-BGC Model Structure
 6         PnET/BGC simulates major biogeochemical processes, such as forest canopy element
 7    transformations, hydrology, soil organic matter dynamics, N cycling, geochemical weathering, and
 8    chemical equilibrium reactions in solid and solution phases, and allows for simulations of land
 9    disturbance (Gbondo-Tugbawa, 2001) (see Figure A-7). The model uses mass transfer relationships to
10    describe weathering, canopy interactions and surface water processes. Chemical equilibrium relationships
11    describe anion adsorption, cation exchange and soil solution and surface water speciation. Soil solution
12    equilibrium reactions are described using the tableau approach (Morel,  1993). A more detailed description
13    of the model can be found in Gbondo-Tugbawa et al., 200 la.
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                Carbon/Nutrient (C/Nut)
            10
                                       •t    'I    t
                                                 116
                                       Foliar Canopy
                  Wood
                  C/Nut

                  ^
                  Wood
                  23 ^
                  Dead
                  Wood
               \    \  26
               2sx^  \      ir
                        Soil Organic Matter

                      Cation Exchange Sites

                      Anion Adsorption Sites
                                                           19

                                                           20
     Water

          12
                                                                     13
                                                                           14
                                                                               Snow
                                                                            15
                                                                             16
  T
Soil Solution
(Speciation)
      A
       11
                                                                             31
                                                                                  Surface Water
                                                                                    Processes
         Processes Depicted:
         1. Gross Photosynthesis
         2. Foliar Respiration
         3. Transfer to Mobil C
         4. Growth and Maintenance Respiration
         5. Allocation to Buds
         6. Allocation to Fine Roots
         7. Allocation to Wood
         8. Foliar Production
         9. Wood Production
         10. Soil Respiration
         11. Weathering Supply
                                        12. Precipitation
                                        13. Interception
                                        14. Snow-Rain Partition
                                        15. Snowmelt
                                        16. Shallow Flow
                                        17. Water Uptake
                                        18. Transpiration
                                        19. Deposition (Wet + Dry)
                                        20. Foliar Nutrient Uptake
                                        21. Foliar Exudation
                                        22. Throughfall & Stemflow
         23. Wood Litter
         24. Root Litter
         25. Foliar Litter
         26. Wood Decay
         27. Mineralization/Immobilization
         28. Nutrient Uptake
         29. Cation Exchange Reactions
         30. Anion Adsorption Reactions
         31. Drainage
                                                                                    Source: Gbondo-Tugbawa et al. (2001).
     Figure A-7. Structure of the PnET-BGC model illustrating the compartments and flow paths of
     carbon and nutrients (C/Nut) within the model.
1
2
3
4
5
6
1
      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+, FT, A13+, K+, and NH4+. A pH-dependent adsorption isotherm is used to describe

the SO42  adsorption process. The speciation of monomeric aluminum is calculated in the model,
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 1    including both organic and inorganic forms. Organic acids are described using a triprotic analogue
 2    (Driscoll, 1994, and the total amount of organic acids is estimated as a certain fraction (based on the
 3    charge density) of DOC. The model simulates ANC in surface waters as an analogue to ANC measured by
 4    Gran plot analysis, by considering the contributions of DIG, organic anions and Al complexes (Driscoll,
 5    1994).
 6          The PnET/BGC model requires inputs of climate, wet and dry deposition chemistry, and
 7    weathering data. Climate inputs consist of minimum and maximum air temperature, solar radiation, and
 8    precipitation. The model uses a constant dry-to-wet deposition ratio by default, but a variable ratio can
 9    also be applied (Chen, 2004). The model inputs utilize canopy enhancement factors to depict the
10    increased dry deposition observed in coniferous and mixed forest stands compared to hardwood forests.
11    Deposition and weathering fluxes for all major elements are required as model inputs. Weathering rates
12    are assumed to remain constant over time.
13          Calibration of PnET-BGC is based on empirical relationships and observations. The model uses
14    historical reconstructions of climate, atmospheric deposition, and land disturbance in  order to construct
15    hindcasts of the response of forests to past acidic deposition. The model can also be used to predict the
16    response of acid-sensitive forest ecosystems to future changes in acidic deposition, for example in
17    response to controls on atmospheric emissions. A detailed description of the model, including a detailed
18    uncertainty analysis of parameter values, is available in Gbondo-Tugbawa et al. (2001).

      A.3.1.4. DayCent-Chem
19          DayCent-Chem links two widely accepted and tested models, one of daily biogeochemistry for
20    forest, grassland, cropland, and savanna systems, DayCent (Parton, 1998), and the other of soil and water
21    geochemical equilibrium, PHREEQC (Parkhurst, 1999). The linked DayCent/PHREEQC model was
22    created to capture the biogeochemical responses to atmospheric deposition and to explicitly consider
23    those biogeochemical influences on soil and surface water chemistry.  The linked model expands on
24    DayCent's ability to simulate N, P, S, and C ecosystem dynamics by incorporating the reactions of many
25    other chemical species in surface water.
26          Hartman et al. (2007) used DayCent-Chem to investigate how wet and dry deposition affect biolo-
27    gical assimilation, soil organic  matter composition, ANC and pH of surface waters, and also Al mobiliza-
28    tion, soil base cation depletion, and base cation flux. Model results were tested against a long-term data
29    set available from Andrews Creek in Loch Vale Watershed, Rocky Mountain National Park,  Colorado.

      DayCent-Chem Model Structure
30          DayCent is the daily time-step version of CENTURY, a non-spatial, lumped parameter model that
31    simulates C, N, P, S, and water dynamics in the soil-plant system at a monthly timestep over time scales


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 1    of centuries and millennia (Parton, 1994). CENTURY can represent a grassland, crop, forest, or savanna
 2    system with parameters that describe the site-specific plant community and soil properties. DayCent, the
 3    daily timestep version of CENTURY, adds layered soil temperature, a trace gas submodel, a more detailed
 4    soil hydrology submodel, and explicitly represents inorganic N as either NC>3 or NH4+ (Kelly, 2000) Del
 5    Grosso, 2001; Parton, 1998). DayCent 5  is an object-oriented model written in the C++ programming
 6    language that implements a layered soil structure and algorithms to manage soil layers. The model is
 7    initialized with an organic soil depth and up to 10 soil layers, where each layer has a specified thickness,
 8    texture, bulk density,  field capacity, wilting point, and saturated hydraulic conductivity.
 9          PHREEQC is a model based on equilibrium chemistry of aqueous solutions interacting with
10    minerals, gases, exchangers, and sorption surfaces. The model is written in the C programming language
11    and has an extensible chemical database. Version 2.7 of PHREEQC is used in the linked DayCent-Chem
12    model to compute aqueous speciation, ion-exchange equilibria, fixed-pressure gas-phase equilibria,
13    dissolution and precipitation of mineral phases to achieve equilibrium, and irreversible aqueous mineral
14    phase reactions. The aqueous model uses ion-association and Debye Huckel expressions. Ion-exchange
15    reactions are modeled with the Gaines-Thomas convention and equilibrium constants are derived from
16    Appelo and Postma (1993).
17          The DayCent-Chem model inputs  are climate drivers consisting of daily precipitation, and mini-
18    mum and maximum air temperatures. The model also requires daily atmospheric wet deposition concen-
19    trations for precipitation species Ca2+, Cl~, K+, Mg2+, Na+, NH4+, NOs , SC>42 , and H+ and daily dry depo-
20    sition amounts or dry/wet ratios for all precipitation species. Initial conditions for model simulations in-
21    elude: (1) initial snowpack water content and chemical composition; 2) initial soil solution concentra-
22    tions; and (3) initial exchangeable cations in each soil layer. Potential annual denudation rates for each
23    mineral phase that could be dissolved in  the soil, groundwater, or stream solutions must also be  provided.
24          DayCent-Chem implements a geochemical submodel of layered pools and properties that provides
25    information exchange, such as of water fluxes and solute concentrations, between the coupled models,
26    and calculates daily geochemical outputs. The geochemical  submodel defines soil  layers and a
27    groundwater pool that correspond to those in Day-Cent 5's original soil class. Surface water
28    concentrations are computed in a two-step process where solutes are first transported, and then
29    PHREEQC undertakes solution reactions. At each timestep, the model updates exchangeable base cation
30    pools and soil solutions in each soil layer, along with groundwater and stream solutions.
31          DayCent 5 output includes daily evapotranspiration; soilwater content; outflow; inorganic and
32    organic C, N, P, and S stream fluxes; C, N, P, and S contents in soil and plant pools; net primary
33    production (NPP); nutrient uptake; trace  gas flux; and heterotrophic respiration. In addition to standard
34    DayCent 5 outputs, at each daily timestep the model writes the  solution chemistry for soil layers,
35    groundwater, and stream.
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      A.3.1.5. SPARROW
 1          SPAtially Referenced Regressions on Watersheds (SPARROW) is a hybrid statistical/deterministic
 2    model used to estimate pollutant sources and contaminant transport in surface waters. SPARROW can be
 3    used to estimate pollutant loading to downstream receiving waters for a number of water quality
 4    constituents. The model as constructed for evaluating N export to estuaries will be presented here.
 5          SPARROW was first described by Smith et al. (1997) as a water quality model designed to reduce
 6    problems with interpreting watershed data as a result of sparse sampling, network bias, and basin hetero-
 7    geneity. SPARROW combines regression techniques and process information regarding contaminant
 8    transport and retention in watershed and riverine systems. Literature values for watershed retention rates
 9    are used; in-stream retention of N is estimated by a first-order decay function (Smith, 1997).
                                                   S
                                                   0
                         where
                         Lj =   load in reach /;
                         n,N =  source index where N is the total number of considered sources;
                         J(i) =  the set of all reaches upstream and including reach /, except
                                those containing or upstream of monitoring stations
                                upstream of reach /;
                        fin -   estimated source parameter;
                         Sn,/ =  contaminant mass from source n in drainage to reachy;
                         a =   estimated vector of land-to-water delivery parameters;
                         Zy =   land-surface characteristics associated with drainage to reach./:
                         6  =    estimated vector of instream-loss parameters; and

                         Tj I =  channel transport characteristics.
                                                                                      Source: Preston and Brakebill (1999).
      Figure A-8. Mathematical form of the SPARROW model.

10          Others have developed similar regression models relating in-stream water quality measurements to
11    watershed nutrient sources and basin attributes (Howarth, 1996; Mueller, 1997; Jaworski, 1997). These
12    simple correlative models assume that contaminate sources and sinks are homogenously distributed and
13    do not make a distinction between watershed and in-stream loss processes. SPARROW is distinct from
14    these methods by incorporating spatial representation of basin attributes in the model. Model correlations
15    between basin attributes and water quality measurements are strengthened by incorporating these spatial

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 1    references (Alexander, 2001; Smith, 1997). Spatially referenced basin attributes include land use, point
 2    and non-point N sources, temperature, soil permeability, and stream density, among others (Figure A-8).
 3    (Preston, 1999) shows the mathematical form of the SPARROW model (Figure A-8). Smith et al. (1997)
 4    provided an example of SPARROW model development for application to the conterminous U.S. Their
 5    exploratory model included five N sources and eight land surface characteristics as potential factors that
 6    deliver N from land to water. In-stream decay coefficients for three stream size classes were also tested
 7    for significance (Table A-3).
 8          The final model resulted in the inclusion of each of the five N sources and three (temperature, soil
 9    permeability,  and stream density) of the eight land to water delivery factors. Parameter selection was
10    primarily based on statistical significance. Further discussion regarding the exclusion of precipitation and
11    irrigated land, both of which were determined to be significant, can be found in Smith et al. (1997).
12    Parameter estimates are evaluated for robustness through the use of bootstrap analysis.
13          The bootstrap procedure involves randomly selecting, with replacement, M monitored loads and
14    associated predictor variables from among the observations in the  data set (Mis the number of monitored
15    reaches in the reach network). Where a sampled observation has an upstream monitored load as one of its
16    predictors, the monitored value is used, regardless of whether the upstream station appears in the
17    bootstrap sample. Coefficient values are estimated from the bootstrap sample. The bootstrap process is
18    repeated 200 times, resulting in 200 estimates of each coefficient. From these estimates, the mean
19    coefficient value (called the bootstrap estimate), minimum confidence interval, and probability that the
20    estimated coefficient has the wrong sign are determined (Smith, 1997).
21          Spatial referencing in the model occurs in two ways: (1) land surface polygons are mapped in
22    conjunction with nonpoint contaminant sources and the land-water delivery variables (temperature, soil
23    permeability,  stream density, etc.) and (2) the stream reach network is mapped along with point sources,
24    channel transport characteristics, and measured transport rates. The positive impacts of this spatial
25    referencing can be quantified by eliminating the channel decay coefficients from the model and creating a
26    new model with only the contaminant sources and land-water delivery variables in the original model
27    (Smith, 1997). Removing this spatial reference provided by the reach network results in a model with
28    significantly higher mean squared error and lower predictive capacity (Table A-4).
      Table A-3. Parameter Estimates, Probability Levels, and Regression Results for the Chesapeake Bay
      Total Nitrogen Sparrow Model
Explanatory Variables
Nitrogen sources
Point Sources
Urban area (acres)
Parameter Estimates
P
1.496
7.008
Probability Level
O.005
0.010
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                 Explanatory Variables
 Parameter Estimates
        Probability Level
              Fertilizer application (Ib/yr)             0.2790
              Livestock waste production (Ib/yr)       0.3361
              Atmospheric deposition (Ib/yr)          1.024

      Land-to-water deli very                         a
              Temperature (°F)
              Precipitation (in)
              Avg slope (%)
              Soil permeability (in/h)                 0.0754
              Stream density (I/mi)
              Wetland (%)
                           0.005
                           O.005
                           O.005
                           0.095
Instream loss (days)3
Tl (Q <200ft3/s)
T2(200ft3/sl,OOOft3/s)
Tu (reservoir retention)
R-squared
Mean square error
Number of observations
5
0.7595
0.3021
0.0669
0.4145
0.961
0.1669
79

O.005
0.005
0.005
O.005



      aT, travel time
      Q, stream discharge
      Ib/yr, pounds per yr
      °F, degrees Fahrenheit
      in/h, inches per hour
  mi, mile;
  in, inches
  %, percent
  ftVs, cubic feet per second
  -, value not statistically significant
2           SPARROW has also been applied to spatially identify N sources at the scale of the Chesapeake Bay
3    watershed (Preston, 1999). A similar set of N sources, land-to-water delivery parameters, and in-stream
4    loss rates to those used in Smith et al. (1997) were considered for this model (Preston, 1999). Only
5    estimates for parameters that were used in the final model are given in Table A-5.
6           The final model included five N sources, one land-to-water deliver parameter (soil permeability),
7    and four in-stream loss rates (including reservoir retention). Comparisons between predicted and observed
8    N loading provided an r2 value of 0.961 (Preston, 1999). Because the data that are used in SPARROW are
9    spatially referenced, model results can be mapped.
     Table A-4. Effect of spatial referencing on measures of regression model performance for predicting
     total N flux using the sparrow model.
     Model Components
                     Mean Square Error
                                                                                                     R2
     Includes full spatial referencing (SPARROW)a
                                                                    0.4544
                                                                                                 0.8743
     Excludes in-stream decay and reservoir retention
                                                                    0.9659b
                                                                                                 0.7307
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      A.3.1.6. WATERSN
 1          The Watershed Assessment Tool for Evaluating Reduction Strategies for Nitrogen (WATERSN)
 2    model is a steady-state numerical N budgeting model that estimates the amount of N exported to rivers
 3    and estuaries from forest, agricultural and urban land uses. The model is intended to provide an
 4    understanding of the relative contribution of N export from these land uses to estuaries, and to evaluate N
 5    export reduction strategies that are specific to each land use type (Driscoll, 2007).
 6          Figure A-9 shows a conceptual diagram of the N budgeting system used in WATERSN. A detailed
 7    description of the original model calculations is provided in Castro et al. (2001). Subsequent model
 8    applications (2003; Castro, 2002) Driscoll, 2003; Whitall, 2004) have developed modifications to the
 9    approach originally described in Castro et al. (2001).
10          WATERSN uses calculations described in Jordan and Weller (1996) to estimate N inputs to the
11    watershed/estuary system. Estimated anthropogenic sources of N inputs to the modeled watershed/estuary
12    system include: (1) crop and lawn fertilizer application, (2) biotic N fixation by leguminous crops and
13    pastures, (3) atmospheric deposition of wet and dry inorganic N (NH4+, NC>3  ), (4) net N import of food
14    for human consumption, and (5) net N import of feed for livestock (Castro, 2002).

      Agricultural Areas
15          N available for water-borne export to  estuaries from agricultural lands is determined as the
16    difference between N inputs and outputs (Castro, 2001).  Modeled N inputs to agricultural lands consist of
17    wet and dry atmospheric NH4+ and NOs deposition, N fertilization, biotic N fixation, and livestock waste
18    (Castro, 2002). Wet and dry deposition are derived from  NADP and CASTNET data. Average annual wet
19    deposition rates of NH4+ and NOs are taken from NADP sites in or near the study watersheds. Dry
20    deposition of NH4+ and NO3  is calculated as an average of all CASTNET sites nearest to the study
21    watersheds. WATERSN assumes that dry deposition of both NH4+ and NO3 to the estuary surface is 25%
22    less than dry deposition to the watershed (Castro, 2002). Meyers et al. (2000) described the uncertainty of
23    estimates of wet and dry deposition and considered it to be no less than a factor of 2. Estimates of N
24    fertilization are taken from agricultural census data. WATERSN assumes that all fertilizer sold in a county
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                                                      N Inputs
                            Net Food Import
Atmospheric
NH/andN03
Deposition
f
Atmospheric
NH/andNO3
Deposition
N Fixation
N Fertilization
Livestock Waste
Atmospheric
NH4* and NO3
Deposition
N Fixation
                     Human Population
                     Point    Septic
                     Sources  Systems
                        Nitrogen inputs to the watershedfestuary and nitrogen loss/retention in the watershed, streams,
                        and rivers
                        Nitrogen inputs to the watershed above the fall line
                        Nitrogen inputs to the watershed below the fall line
                                                                                            Source: Castro et al. (2003).
      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.
 1    is applied in that county. This is considered to be the most certain N input to the model (±25%) (Castro,
 2    2002). WATERSN estimates both non-symbiotic and symbiotic N fixation for crops, pastures, hay fields
 3    and upland forests. Non-symbiotic rates were taken from literature values for crops, orchards, upland
 4    forests, and non-wooded pastures (Hendrickson, 1990; Stevenson, 1982; Woodmansee, 1978). Symbiotic
 5    rates of N fixation are based on type of legume, crop N harvest, N in unharvested portions of crops, soil N
 6    availability, and fertilization rate. These estimates are less certain than for N fertilization, but are noted as
 7    being a relatively minor N source in most of the study watersheds. Livestock waste was calculated as the
 8    difference between livestock consumption of N in feed and production of N in meat, milk, and eggs for
 9    human consumption (Jordan, 1996).
10          N outputs from agricultural land include crop harvest, pasture grazing, volatilization of Nft} and
1 1    denitrification. Data regarding crop harvest are obtained from agricultural census. N removed through
12    crop harvest is estimated by multiplying the crop harvest by the percent N in each crop. Estimates for
1 3    grazing are based on sheep, cattle, and horse populations (USDA online database in \Castro, 2002, their
14    dietary N requirements, and proportion of dietary N obtained from grazing (Jordan, 1996). NH3
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 1    volatilization is assumed to be 10% of the N input from fertilizer and atmospheric deposition, and 20% of
 2    livestock manure inputs (Schlesinger, 1992). Denitrification rates were originally estimated as 10 to 30%
 3    of the N inputs from fertilizer and atmospheric deposition and 20% of livestock N waste. Subsequent
 4    applications of WATERSN (Castro, 2003) modified denitrification rates from agricultural lands to vary
 5    with the mean watershed temperature and are based on a denitrification activity Qio value of 2 (Maag,
 6    1997; Stanford, 1975). A Q10 value of 2 suggests that the denitrification rate used by the model will
 7    change by a factor of 2 for every 10 degree change in temperature based on a direct relationship between
 8    temperature and denitrification.

      Urban Areas
 9          N inputs to urban areas include atmospheric and non-atmospheric sources. The total atmospheric N
10    deposition input to urban areas is taken as the total (wet + dry) inorganic (NC>3  and NH4+) N deposition
11    rate to the watershed multiplied by the total urban area in the watershed. Non-atmospheric  sources include
12    point sources (primarily waste water  treatment plants) and non-point sources (septic systems and
13    pervious/impervious surface runoff) of N in urban areas (Castro, 2002).
14          N outputs from urban areas include waste water treatment plant effluent, septic system leachate,
15    and total N runoff from pervious and impervious lands. Measured total N data are used to calculate N
16    export for wastewater treatment plants that have available data. A strong regression relationship between
17    measured total N discharged from wastewater treatment plants and human populations that use
18    wastewater treatment facilities is used to estimate total N discharges from wastewater treatment plants
19    that do not have total N monitoring data available. Septic system output is determined by multiplying
20    watershed specific human per capita N excretion rates by the human population of the watershed.
21    WASTERSN assumes  that 75% of this N is exported to the estuary (Castro, 2002). The soil water
22    assessment tool (SWAT) is used to estimate non-point source non-atmospheric total N runoff from
23    pervious and impervious urban lands. SWAT is a distributed parameter, continuous time model applicable
24    at the watershed scale. Required inputs to SWAT include climatic variables, soil properties, elevation,
25    vegetation information, and land use. SWAT is designed to predict land use and land management impacts
26    on water, sediment, and agricultural yields in large watersheds (Castro, 2002). The model assumes that
27    75% of atmospheric N inputs to urban areas is exported to the estuary (Fisher, 1991). Alternatively, this N
28    export term can be modified.

      Upland Forests
29          N inputs to forests are assumed to be in the  form of atmospheric deposition and non-symbiotic N
30    fixation. Outputs from forests are estimated with a non-linear regression relationship between wet
31    deposition of inorganic N and stream water export of dissolved inorganic nitrogen (DIN) developed using
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 1    results from a multitude of forest watershed studies. Exported dissolved organic nitrogen (DON) was
 2    assumed to be equal to 50% of the inorganic N export (Castro, 2002).

      Watershed and In-Stream N Retention
 3          Model validation efforts using measured N fluxes from the USGS National Stream Quality
 4    Accounting Network (NASQAN) have shown that WATERSN tends to overestimate N export from
 5    watersheds to estuaries (Castro, 2002). These differences are not unexpected since WATERSN does not
 6    account for watershed and in-stream N sinks. Attempts have been made to improve flux estimates by
 7    accounting for watershed and in-stream N retention (Castro, 2002; Castro, 2001; Castro, 2003; Castro,
 8    2003). A summary of the N retention rates applied to WATERSN in these studies is given in Castro and
 9    Driscoll (2002) assumed that 30% of the total N that entered rivers above the fall line was lost during
10    transport to the fall line and that inputs that enter the river below the fall line were not attenuated because
11    of the relatively short travel times to the estuary See Table A-5. This 30% in-stream N retention value
12    represents the median retention value obtained in previous studies of northeastern U.S. rivers (Castro,
13    2001) and falls within the range of retention values estimated by Howarth et al. (1996) and Alexander
14    et al. (2000). Castro and Driscoll (2002) also  incorporated watershed N retention fractions specific to
15    individual land uses. They assumed that 60% of the excess N from agricultural land and septic systems
16    was lost (retained within the watershed) due to watershed processes. Support for this value of N retention
17    was given by several reports of riparian N removal rates from agricultural land, ranging from about 50 to
18    90% (Jordan,  1993; Jacobs, 1985;  Peterjohn,  1984; Lowrance, 1983). After incorporating these
19    assumptions, predicted fluxes closely matched (r2 = 0.909) measured fluxes.
20
Table A-5. Summary of N retention rates used in recent WATERSN studies.
Study
Castro et al. (Castro, 2001)

Castro and Driscoll (Castro, 2002)

Castro et al. (2003)


Retention Type
In Stream*
Agriculture
In Stream*
Agriculture
Agriculture
Septic System
In Stream

30%
50%
30%
60%
40%
40%
Adjusted
°/o N Retention






until predicted N flux matched observed fluxes
      *In-stream 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).
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               40 -i
W
, _ k.
.2 «
8-0
** Ł

!!
Z1S
(J
'E i
o) c>
               30-
               20-
               10-
                                                                     EH Sewage
                                                                     EH Agriculture
                                                                     (• Urban
                                                                     EH Forest
                                                                     EH Atmospheric Deposition
                                                                                         1 - 1


                                                                                     Source: Driscoll et al. (2003).
     Figure A-10. WATERSN model estimates of anthropogenic N inputs to the estuaries of the
     northeastern U.S., in kilograms per hectare per year.

1          Driscoll et al. (2003) applied WATERSN to investigate anthropogenic N loading to estuaries in the
2    northeastern U.S. The objectives of the study were to apply WATERSN to (1) quantify the inputs of
3    reactive N to the region (Error! Reference source not found.), (2) discuss the ecological effects of
4    regional elevated anthropogenic reactive N inputs, and (3) evaluate management options aimed at
5    mitigating the effects of these elevated anthropogenic N inputs. Modeled N reduction scenarios included
6    reductions atmospheric N emissions, increased N removal efficiencies of wastewater treatment plants,
7    offshore pumping of wastewater, reductions  in agricultural N runoff to surface waters, and an integrated
8    management scenario consisting of a combination of N reductions from multiple sources. Other studies
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 1    have applied WATERSN to address similar issues related to N loading to estuaries in other regions of the
 2    U.S. (Whitall, 2004; Castro, 2003) Whitall, 2006).


            A.3.2. Additional Effects Models Used Widely in Europe
 3          The models of the effects of S and N deposition described below have been used primarily in
 4    Europe. These descriptions are derived in part from the UNECE Convention of Long-Range
 5    Transboundary Air Pollution Modelling and Mapping manual (Posch, 2003).

      A.3.2.1. The Very Simple Dynamic Model
 6          The Very Simple Dynamic (VSD) soil acidification model is frequently used in Europe to simulate
 7    acidification effects in soils when observed data are sparse. It only includes weathering, cation exchange,
 8    N immobilization processes,  and a mass balance for cations, sulfur and N. It resembles the model
 9    presented by Reuss (1980) which, however, did not consider N processes. In the VSD model, the various
10    ecosystem processes have been limited to a few key processes. Processes that are not taken into account
11    include (1) canopy interactions, (2) nutrient cycling processes, (3) N fixation and NH4 adsorption, (4)
12    SO42 transformations (adsorption, uptake, immobilization, and reduction), (5) formation and protonation
13    of organic anions,  and (6) complexation of Al.
14          The VSD model consists of a set of mass balance equations, describing the soil input-output
15    relationships, and a set of equations describing the rate-limited and equilibrium soil processes. The soil
16    solution chemistry in VSD depends solely on the net element input from the atmosphere (deposition
17    minus net uptake minus  net immobilization) and the geochemical interaction in the soil (CC>2 equilibria,
18    weathering of carbonates and silicates, and cation exchange). Soil interactions are described by simple
19    rate-limited (zero-order) reactions (e.g., uptake and silicate weathering) or by equilibrium reactions (e.g.,
20    cation exchange). It models the exchange of Al, H, and Ca + Mg + K with Gaines-Thomas or Gapon
21    equations.
22          Solute transport in VSD is described by assuming complete mixing of the  element input within one
23    homogeneous soil compartment with a constant density and a fixed depth. Since  VSD is a single layer soil
24    model neglecting vertical heterogeneity, it predicts the concentration of the soil water leaving this layer
25    (mostly the rootzone). The annual water flux percolating from this layer is taken as being  equal to the
26    annual precipitation excess. The time step of the model is one year, and therefore seasonal variations are
27    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
 1          The Simulation Model for Acidification's Regional Trends (SMART) model is similar to the VSD
 2    model, but somewhat extended. It is described in De Vries et al. (1989) and Posch et al. (1993). As with
 3    the VSD model, the SMART model consists of a set of mass balance equations, describing soil input-
 4    output relationships, and a set of equations describing the rate-limited and equilibrium soil processes. It
 5    includes most of the assumptions and simplifications given for the VSD model; and justifications for
 6    them can be found in De Vries et al. (1989).
 7          SMART models the exchange of Al, H, and divalent base cations using Gaines Thomas equations.
 8    Additionally, SC>42  adsorption is modeled using a Langmuir equation (as in MAGIC) and organic acids
 9    can be described as mono-, di-, or tri-protic. Furthermore, it does include a balance for carbonate and Al,
10    thus allowing application to a range of site conditions, from calcareous soils to completely acidified soils
11    that do not have an Al buffer left. Recently, a description of the complexation of aluminum with organic
12    acids has been included. The SMART model has been developed with regional applications in mind, and
13    an early example of an application to Europe can be found in De Vries et al. (1994).

      A.3.2.3. SAFE
14          The Soil Acidification in Forest Ecosystems (SAFE) model has been developed at the University of
15    Lund (Warfvinge,  1993) and a recent description of the model can be found in Alveteg and Sverdrup
16    (2002). The main differences between the SMART and MAGIC models are: (a) weathering of base
17    cations is not a model input, but it is modeled with the PROFILE (sub-)model, using soil mineralogy as
18    input (Warfvinge, 1992) ; b) SAFE is oriented to soil profiles in which water is assumed to move
19    vertically through several soil layers (usually 4); and c) Cation exchange between Al, H, and (divalent)
20    base cations is modeled with Gapon exchange reactions, and the exchange between soil matrix and the
21    soil solution is diffusion-limited.
22          The standard version of SAFE does not include SC>42 adsorption although a version, in which
23    SC>42  adsorption is dependent on SC>42 concentration and pH has recently been developed (Martinson,
24    2003). The SAFE model has been applied to many sites and more recently also regional applications have
25    been carried out for Sweden (Alveteg, 2002) and Switzerland (Kurz, 1998).


            A.3.3. Other  Models
26          There are scores of models that can be useful in the context of developing a better understanding of
27    the ecological effects of atmospheric S and N deposition. In the preceding sections, we have attempted to
28    summarize a relatively small number of models that are most commonly used for this purpose in the U.S.
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1     and Europe, in particular those that contribute to substantive conclusions presented in the ISA. There are

2     many other models that are not covered in the discussion presented in this Annex. Several are highlighted

3     in Table A-6.
      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.
      Model
                               Name
                        Type1  Support2    Reference3
                                                                                                      Notes
      QUAL2K
      WASP7
                       Water Quality Analysis
                       Simulation Program
      CE-QUAL-RIV1;
      CE-QUAL-R1;
      CE-QUAL-W2; CE-
      QUAL-ICM
Water quality models
(river, reservoir, and
estuary/ coastal)
supported by USAGE
      RCA
                       Row Column AESOP
      WARMS
                       Waterfowl Acidification
                       Response Modeling System
                                        McNicoletal.
                                        (1995), McNicol
                                        (2002)
QUAL2K is one dimensional river and stream water quality
model. QUAL2K assumes: that he channel is well-mixed
vertically and laterally; steady state hydraulics; diurnal
water-quality kinetics. QUAL2K addresses point and non-
point loads, BOD/DO, non-living particulate organic matter
(detritus); denitrification; sediment-water interactions;
bottom  algae; pH (both alkalinity and total inorganic
carbon).

WASP is a dynamic compartment-modeling 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.

CE-QUAL-R1 is a one-dimensional, vertical reservoir model
and CE-QUAL-W2 is a two-dimensional (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. CE-QUAL-
RIV1 was developed for highly unsteady flow conditions,
such as storm water flows and streams below peaking
hydropower dams. CE-QUAL-ICM 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
conforms, 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  offish to estimate
preacidification, present and eventual steady-state values for
pH, fish presence and waterfowl breeding parameters under
proposed SO emission scenarios.
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Model
                            Name
Type1 Support2     Reference3
                                                                                                          Notes
GT-MEL
                   Georgia Tech hydrologic
                   model and the Multiple
                   Element Limitation model
ILWAS
                   Integrated Lake-Watershed
                   Acidification Study
                  Gherini et al.
                  (1985)
THMB/IBIS
                                                               6,7
BIOME-BGC
DNDC
EPIC
                   Biome-BGC is a multi-
                   biome generalization of
                   FOREST-BGC
                   Denitrification-
                   decomposition model
                   Agricultural dynamic
                   simulation model
                  11
GT-MEL is a spatially distributed, process-based
ecohydrology model that links a land surface hydrology
model with a terrestrial biogeochemistry model. GT-MEL
differs from other available ecohydrology models in its
simplicity, flexibility, and theoretical foundation. The coupled
GT-MEL simulates the cycling and transport of water and
nutrients (C, N and P) within hillslopes and watersheds. The
model runs on a daily time step and can be applied to user-
defined  landscape units that may vary in shape  and size (m2
to km2). Thus, GT-MEL 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.
GT-MEL simulates the effects of multiple interacting
stressors, including changes in land use, land cover, climate,
atmospheric CO2 and N  deposition.

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 physical-chemical processes which
change the acid-base characteristics of the water are
simulated by rate (kinetic) and equilibrium expressions and
include mass transfers between gas, liquid and solid phases.

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 time-varying 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 inter-annual variability in the water and N  budget
from 1960 to 1994.

Biome-BGC is a computer program that estimates fluxes and
storage  of energy, water, carbon, and N for the vegetation
and soil  components of terrestrial ecosystems. The primary
model purpose is to study global and regional interactions
between climate, disturbance, and biogeochemical cycles.

DNDC was  initially developed to quantifying nitrous oxide
(N2O) emissions from agricultural soils in the U.S. The
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 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 water-quality. EPIC simulates N cycling processes
in soils-including mineralization, nitrification, immobilization,
Nhh 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
Type1 Support2     Reference3
                                                                                                          Notes
GLEAMS           Groundwater Loading
                  Effects of Agricultural
                  Management Systems
                  12
Hole-in-the-pipe     Hole-in-the-pipe
                                                               Davidson et al.
                                                               (2000)
MERLIN
                   Model of Ecosystem
                   Retention and Loss of
                   Inorganic Nitrogen
                  Cosby etal.
                  (1997), Kjonaas
                  and Wright (1998)
NLM
                  Waquoit Bay Nitrogen
                  Loading Model
                  RT1 International
                  (2001)
GLEAMS was developed from both EPIC and CREAMS and
employs a more explicit description of soil water content. In
GLEAMS, the concentration of nitrate-N 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 micro-zones when the soil is not at field capacity
or saturation.

The Hole-in-Pipe model relates the emissions of nitrous
oxides to common soil processes. It regulates soil emissions
of NO and N2O at two levels: 1st, the rate of N cycling
through ecosystems, which is symbolized by the amount of
N flowing through the pipes, affects total emissions of NO
and NO; 2nd, soil water content and perhaps other factors
affect the ratio of NO: NO emissions, symbolized by the
relative sizes of thelioles 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,  N2O, and N2

MERLIN is a catchment-scale mass-balance 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).

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 long-term avg output. It
is not currently endorsed  by a federal agency but has been
published in peer-reviewed 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).
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Model
                            Name
                          Type1  Support2    Reference3
                                                                                                         Notes
Simple Mass
Balance
Method/Steady
State Mass
Balance
"Mass balance approach"
                                            Bhattacharya
                                            et al. (2004),
                                            Likens et al.
                                            (1996), Rodriguez
                                            and  Macias (2006)
HSPF/LSPC
Hydrological Simulation
Program - FORTRAN
                                            T*
                                                               12, 13
PLOAD
                  Pollutant Loading Model
                                            T*
                                                               14
SWAT
                  Soil and Water Assessment
                  Tool
                          T*
                                            van Griensven and
                                            Bauwens (2001),
                                            15
WARMF
                  Watershed Analysis Risk
                  Management Framework
                          T*
                                            16
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
long-term avg sources of acidity and alkalinity in order to
determine the maximum acid input that will balance the
system at a biogeochemical  safe-limit. 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 simulates watershed hydrology and water quality for
conventional and toxic pollutants.  HSPF incorporates
watershed-scale ARM and NPS models into a basin-scale
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 In-stream hydraulic and sediment-chemical
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 text-
file output by  subwatershed  for all land-layers, 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 CE-QUAL-W2.

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 (GlS)-based data coverages to rapidly estimate N
loading to the bay using pass-through  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 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 includes a CIS-based watershed model that
calculates daily runoff, shallow groundwater flow, hydrology
and water quality of a river basin. A river basin is divided
into a network of land catchments (including canopy and soil
layers), stream segments, and lake layers for hydrologic and
water quality simulations. Inputs include meteorology, air
quality, point source, reservoir release, and flow diversion
data. WARMF also includes two watershed approach
modules for Consensus building and TMDL calculation.
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Model
                           Name
Type1 Support2    Reference3
                                                                                                       Notes
DRAINMOD
                                           T*
                                                             17
INCA
                                                             Wade et al.
                                                             (2005)
LWWM
                  Linked
                  Watershed/Waterbody
                  Model
                  18
ReNuM"
                  Regional Nutrient
                  Management Model
T*
                  19
RHESS
                                           T*
                                                             Boyer etal.
                                                             (2006)
DRAINMOD quantifies N losses and transport from
agricultural lands with shallow water tables where artificial
drainage systems are used. Watershed-scale 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 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 land-use type and
subcatchment. Biogeochemical reactions are limited to the
soil zone from which water and N are leached to deeper
groundwater.

The original release of the LWWM coupled the RUNOFF
Block of the EPA's SWMM model (Version 4.21) with the
EPA's Water Quality Analysis Program (WASP5). All
components were accessed via a user-friendly operating
shell. The LWWM included a CIS 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 pre-
processors 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 post-processor for the review of output
from all  model components. The post-processor was also the
means by which nonpoint source loading  files from RUNOFF
were mapped to WASP segments.

ReNuMa is based on the Generalized Watershed Loading
Function (GWLF) model that has been used widely for
purposes such asTMDL 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 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  source-area 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
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Model                     Name           Type1  Support2     Reference3                            Notes

'Type: A = aquatic; I = integrated aquatic/terrestrial; T = terrestrial; T* = watershed
2 Support: S = currently supported by EPA; N = currently not supported by EPA
3Websites:
     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. cfm7Topic=model&Type=watqual
     4: Hydroqual: Row Column AESOP (RCA) Modeling Code Description and Technical Capabilities;
http://www. hyd roq ua I. 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: Biome-BGC: Terrestrial Ecosystem Process Model, Version 4.1.1; http://www.daac.ornl.gov/MODELS/guides/biome-bgc_guide.html
     9: Denitrification Modeling Workshop: Model Summary; http://marine.rutgers.edu/BGC/RCNsite/WSl/WSlmodels/DNDC-2.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/soiLwater/drainmod/
     18:  Linked Watershed Waterbody  Model at the Southwest Florida Water Management District;
http: //www. swfwmd. state .f I. us/softwa re/I wwm .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
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A.3.3.1. Current Long-term 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               Solution Chemistry
        Instantaneous streamflow (9 stations)             Weekly bulk precipitation (6-10
        Daily precipitation (24 stations)                        stations)
        Class A weather station data                     Monthly soil solution WS5, WS6
        Weekly snow depth on snow courses             Weekly stream at weirs of WS19
        Daily soil temperature and moisture              Monthly stream within WS5, WS6

 Air Chemistry                                Organisms
        (SO2, HNO3, particulates, ozone)                 Bird populations
                                                     Phytophagous insect populations
 Mirror Lake                                              WS2, WS4, WS5, WS6
        Instantaneous streamflow (3 inlets,               Vegetation biomass, chemistry
              outlet)
        Daily precipitation (2 stations)            Soils
        Weekly chemistry (3 inlets, outlet)               Forest floor mass, chemistry (WS6,
        Bi-monthly limnology (temp, chemistry,                WS5; 5-yr intervals)
        plankton)                                     Chemical and physical properties
                                                           from soil pits (WS5)
                                                     Chemical and physical properties
                                                           from soil bags
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                           ANN EX A-References
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10           nitrogen to estuaries in the United States. Estuaries 26: 803-814.
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18          soils. Madison, WI: American  Society of Agronomy.
19    Stoddard, J. L.; Kellogg, J. H.  (1993) Trends and patterns in lake acidification in the state of
20          Vermont: evidence from the Long-Term Monitoring project. Water Air Soil Pollut. 67:
21          301-317.
22    Stoddard, J. L.; Urquhart, N. S.; Newell, A. D.; Kugler, D. (1996) The Temporally Interated
23          Monitoring of Ecosystems (TIME) project design 2. Detection of regional acidification
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26          acidification trends for the northeastern U.S., 1982-1994. Environ. Monitor. Assess. 51:
27          399-413.
28    Stoddard, J. L.; Driscoll, C. T.; Kahl, S.; Kellogg, J. (1998b) Can site-specific trends be
29          extrapolated to the regional level? A lake acidification example for the northeastern U.S.
30          Ecol. Appl. 8: 288-299.
31    Stoddard, J.; Kahl, J. S.; Deviney, F. A.; DeWalle, D. R.; Driscoll, C. T.; Herlihy, A. T.;
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34          U.S. Environmental Protection Agency, Office of Research and Development, National
35          Health and Environmental Effects Research Laboratory. EPA 620/R-03/001.
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 1    Sullivan, T. I; Cosby, B. I; Laurence, J. A.; Dennis, R. L.; Savig, K.; Webb, J. R.; Bulger, A. I;
 2          Scruggs, M.; Gordon, C.; Ray, J.; Lee, H.; Hogsett, W. E.; Wayne, H.; Miller, D.; Kern,
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24          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
 1         Inputs of N and S in wet, dry, and occult deposition first interact with the vegetative canopy. This
 2    interaction can occur a few centimeters above the ground in some alpine or grassland ecosystems to over
 3    100 m above the ground in some forest canopies. In the canopy, deposited pollutants (especially N) can be
 4    taken up by the plants or by organisms that live within the canopy or on the leaf surface. Most of the
 5    deposited S moves as throughfall to the soil where it can be temporarily, or permanently, adsorbed on the
 6    soil. Sulfur that is not adsorbed on the soil moves readily into drainage water.
 7         Earlier reviews (i.e., Hosker and  Lindberg, 1982; Taylor et al., 1988) summarized information on
 8    the deposition of N to vegetation surfaces and interactions between pollutant deposition and canopy and
 9    leaf surfaces. Deposited N that is not taken up within the canopy then falls to the ground as throughfall,
10    where plants, bacteria, and fungi compete for it. This competition for deposited N has long been known to
11    play an important role in determining the extent to which N deposition will stimulate plant growth and the
12    degree to which added N is retained within the ecosystem (U.S. Environmental Protection Agency, 1993).
13    The available surface area of vegetation, onto which N gasses readily diffuse, has a significant effect on
14    the dry deposition of N (Heil and Bruggink, 1987). Coniferous forests tend to increase deposition rates
15    (both dry and wet) relative to deciduous forests, and landscape features such as elevation, aspect, and
16    forest edge can play an important role in creating high levels of variability in deposition rates in complex
17    terrain (Weathers et al., 2000).


           B.1.2. Interactions  with Soil
18         Air pollution is not the sole cause of soil acidity. High rates of soil acidification occur in low-
19    deposition regions of the western U.S. because of internal soil processes, including tree N uptake and
20    nitrification associated with extensive N fixation, for example on sites occupied by red alder trees (Alnus
21    rubrd) (Johnson et al., 1991b). Acidic deposition is not a necessary condition for having acidic soils, as
22    evidenced by the common occurrence of acidic soils in unpolluted forests of the northwestern U.S. and
23    Alaska (Johnson et al., 1991b).
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      B.1.2.1. Sulfur Retention and Release
 1          Soils in the U.S. that most effectively adsorb SO42 occur south of the maximum extent of
 2    glaciation that occurred during the most recent ice age (Rochelle and Church, 1987; Rochelle et al.,
 3    1987). Sulfate adsorption is strongly pH dependent, and a decrease in soil pH resulting from acidic
 4    deposition can enhance the ability of soil to adsorb SC>42  (Fuller et al., 1987).
 5          Considerable effort in the 1980s went into the computation of S budgets for watersheds and forest
 6    plots, to evaluate S retention and release. These budgets were subject to complications from fluxes that
 7    could not be measured directly, such as dry deposition and weathering, but generally indicated net S
 8    retention at sites south of the line of glaciation—a result attributed to net adsorption of SC>42 (Rochelle
 9    et al., 1987; Cappellato et al., 1998). Through the 1990s little or no decrease in SC>42 concentrations
10    occurred in streams below the glaciation line, despite regional decreases in atmospheric deposition of S
11    (Webb et al., 2004). This lack of response has been generally attributed to the net release of adsorbed
12    SC>42 , resulting from a shift in equilibrium between the adsorbed and solution phases  under conditions of
13    decreased atmospheric inputs of SC>42 . This interpretation is supported by a decrease in concentrations of
14    adsorbed SC>42  from 1982 to 1990 in a Piedmont soil in South Carolina that received decreasing levels of
15    S deposition during this period (Markewitz et al.,  1998). This same soil  also experienced an increase in
16    adsorbed SO42  from 1962 to 1972 (Markewitz et al., 1998). The only published S budget more recent
17    than 1992 for an unglaciated site in the U.S. (Castro and Morgan, 2000) also suggests  a net release of
18    SO42 . This upland Maryland watershed released 1.6 times more SO42 than measured in throughfall in
19    1996-97. Additional information was obtained in the German study of Martinson et al. (2005) in which a
20    "clean-roof was used to exclude acidic deposition since 1989. Data collection enabled calibration of a
21    model that  predicted elevated concentrations of desorbed SC>42 in soil water for at least several decades.
22    Although decreased levels of deposition are most likely resulting in net  SC>42 desorption, limited research
23    is available on sulfate desorption over time periods relevant to the time scale of decreased levels of S
24    deposition (Johnson and Mitchell, 1998).
25          Numerous S budgets were also compiled in the 1980's for glaciated sites,  and results generally
26    indicated that inputs approximately equaled outputs on an annual basis (Rochelle et al., 1987). Little or no
27    S retention at glaciated sites was attributed to relatively low SC>42  adsorption capacity in soils. Balanced
28    S budgets implied that decreases in atmospheric deposition of S would lead directly to decreases in SC>42
29    leaching, and the strong correlation between decreases in atmospheric deposition and decreases in SC>42
30    concentrations in surface waters is widely recognized as an indication of this direct linkage (Stoddard
31    et al., 2003). However, considerable  evidence also indicates that S inputs in glaciated ecosystems do not
32    behave conservatively, but instead are cycled through microbial and plant biomass (David et al., 1987;
33    Alewell and Gehre, 1999; Likens et al., 2002). As a result, large quantities of S are stored in organic
34    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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 1    central Adirondack region of New York was about 1% of the organic S pool in the soil. Houle et al.
 2    (2001) estimated that annual S deposition at 11 sites in North America ranged from l%to 13%ofthe
 3    organic S pool in soil.
 4          Courchesne et al. (2005) measured a downward trend in water-soluble SO4 from 1993 to 2002 in
 5    glaciated soils in Quebec, and attributed this response to net desorption of SC>42 rather than release of
 6    organically associated S. However, during this period, deposition of SC>42 was essentially unchanged.
 7    They attributed this discrepancy to a delay in the release of adsorbed SO42 in response to a decrease in S
 8    deposition over the previous decade. These authors did not provide a mechanism to explain how
 9    desorption can continue under conditions of constant SO42 inputs, however. On the basis of the abundant
10    evidence of biological S cycling, it seems more likely that the delay observed by Courchesne et al. (2005)
11    is the result of biological controls over the release of S.
12          Much of the organic S stored in soil is in carbon-bonded forms that are relatively unreactive, but
13    can be mineralized to SC>42  in oxic conditions, typically found in moderately well-drained to well-
14    drained soils (Johnson and Mitchell, 1998). Furthermore, strong correlations have been shown between
15    levels of atmospheric deposition of S and concentrations of S in soil (Driscoll et al., 2001; Novak et al.,
16    2001). Long-term increases in concentrations of total S in soils that are at least partially attributable to
17    increases in organic S have also been documented (Knights et al., 2000; Lapenis et al., 2004). The study
18    of Houle etal. (2001) did not find a relation between these factors, however. A Swedish "clean-roof
19    study also provides some  insights into the role of organic S in possibly delaying recovery (Morth  et al.,
20    2005). After 9 years of pre-industrial levels of S deposition, the amount of S in runoff still exceeded
21    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
22          Base cations are common in rocks and soils, but largely in forms that are unavailable to plants.
23    There is a pool of bioavailable base cations (termed exchangeable base cations) that are adsorbed to
24    negatively charged surfaces of soil particles. They can enter solution by exchanging with other dissolved
25    cations including acidic cations such as H+ or A13+. Base cations in this pool are gradually leached from
26    the  soil in drainage water, but are constantly resupplied through weathering. Weathering slowly breaks
27    down rocks and minerals, releasing base  cations to the pool of adsorbed base cations in the soil. The
28    balance between base cation supply and base cation loss determines whether the pool of available base
29    cations is increasing or decreasing in size. Net forest growth can also potentially lower exchangeable base
30    cation concentrations through uptake of nutrient cations (Ca, Mg, and K), but these cations remain in the
31    terrestrial ecosystem and can become available in the future through mineralization or canopy  leaching.  It
32    has long been known that leaching of base cations by acidic deposition might deplete the soil of
33    exchangeable bases faster than they are resupplied (Cowling and Dochinger, 1980). However, base cation

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 1    depletion of soils had not been demonstrated at the time of the last SOx Air Quality Criteria Document
 2    (AQCD) (U.S. Environmental Protection Agency, 1982).
 3          Data that clearly showed soil base cation depletion in the U.S. did not become available until the
 4    1990s, although decreases in exchangeable Ca2+ concentrations between the periods 1947 to 1950 and
 5    1987 to 1988 had been identified in European soils through repeated sampling (Billett et al., 1990;
 6    Falkengren-Grerup and Eriksson,  1990). In the only repeated sampling in the U.S. in which the original
 7    soil sample pre-dated acidic deposition, Johnson et al. (Johnson et al., 1994b) documented a decrease in
 8    exchangeable Ca2+ concentrations in both the O (combined Oa and Oe horizons) and B horizons from
 9    1930 to 1984. Richter et al. (1994) also observed Ca2+ depletion in the B horizon from 1960 to 1990, in
10    repeated sampling of Piedmont soil in South Carolina. The studies of Johnson et al. (Johnson et al.,
11    1994b) and Richter et al. (1994) acknowledged the potential role of acidic deposition in causing the loss
12    of Ca2+, but focused on net forest growth as the primary cause.
13          Through reanalysis of archived soils, Lawrence et al. (1995) measured decreases in concentrations
14    of exchangeable Ca2+ and acid-extractable Ca2+ in Oa horizons of spruce stands from 1969-70 to 1987-92
15    and presented relationships in soil chemistry that were not consistent with changes expected from
16    vegetation uptake effects, but that could be explained by acidic deposition. Drohan and Sharpe (1997)
17    also observed a decrease in Ca2+ concentrations in Oa and A horizons at 11 sites across Pennsylvania that
18    were sampled in 1957 or 1959 and again in  1993, although effects of vegetation and acidic  deposition
19    were not distinguished.
20          The most thorough soil re-sampling study in the U.S. was conducted by Bailey et al.  (Bailey et al.,
21    2005) in northwestern Pennsylvania. Between 1967 and 1997, pronounced decreases, attributed largely to
22    acidic deposition, were measured in exchangeable Ca2+ and Mg2+ concentrations in Oa/A horizons and
23    throughout the B horizon. Courchesne et al. (2005) found higher concentrations of exchangeable Ca2+ in
24    the  O horizon (combined Oe and Oa horizons) in 2002 than in 1994 at one of three sampling areas within
25    a 5.1 ha watershed, but no significant differences at the other two locations. No significant differences
26    were found for exchangeable Mg2+ at the three locations in the O horizon. In the upper 10 cm of the
27    B horizon, no significant differences were found in exchangeable Ca2+, but at two  of three locations,
28    exchangeable Mg2+ concentrations were lower in 2002 than in 1994. The 8-year interval between
29    sampling in this study is the shortest time in which changes in exchangeable base cations have been
30    reported for North American soils.
31          In a regionally designed assessment of changes in soil-exchange chemistry,  Sullivan  et al. (2006a)
32    found that base saturation and exchangeable Ca2+ concentrations in the Adirondack region of New York
33    had decreased in the upper 10 cm of the B horizon between the mid 1980s and 2003, in watersheds of
34    lakes with ANC less than 200 (ieq/L.  Soil chemistry in 36 lake watersheds in the mid 1980s was
35    compared to soil chemistry in 32 lake watersheds in 2003. Although this study did not involve repeated
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 1    sampling of the same sites, the comparison could be made on a regional basis because the sampling
 2    locations were selected randomly in both the mid 1980s and in 2003, and a large and similar number of
 3    sites were included in both samplings.
 4          In a widely cited article, Likens et al. (1996) used a watershed mass balance approach to estimate
 5    changes in ecosystem Ca2+ pools at HBEF and found a sustained decrease in exchangeable Ca2+
 6    concentrations from 1963 to 1993. The maximum depletion rate occurred in 1972, at the estimated peak
 7    in acidic deposition levels. The dependence on Ca:Na ratios to estimate Ca2+ weathering fluxes in this
 8    analysis adds uncertainty to the magnitude of changes reported for the exchangeable Ca2+ pool (Bailey
 9    et al., 2003). Two additional mass balance studies used Sr isotopes to evaluate changes in soil Ca2+ pools
10    and fluxes. The study of Bailey et al. (1996) estimated substantial depletion rates in a watershed in the
11    White Mountains of New Hampshire. Miller et al. (1993) estimated that inputs from weathering and
12    atmospheric deposition approximately equaled leaching losses at a site in the Adirondack Mountains in
13    New York. The different findings in these two studies are related to differences in the mineralogical
14    composition of the  respective soils. However, the Miller et al. (1993) study also estimated that 50 to 60%
15    of the Ca2+ in vegetation and the forest floor was derived from the atmosphere, despite the fact that the
16    weathering flux was estimated to be three times the rate of atmospheric inputs. This result suggests that
17    Ca2+ supply from weathering in the lower profile is not reaching the upper soil where most root activity
18    occurs, and that Ca2+ depletion has occurred in the upper soil.
19          The study of Yanai et al. (1999) investigated changes in Ca2+ and Mg2+ concentrations and content
20    in northeastern hardwood stands over time intervals ranging from 10 to 21 years. The general conclusion
21    of this study was that little or no  change in O horizon (Oi, Oe, and Oa horizons) exchange chemistry
22    occurred. However, a decrease in exchangeable Ca2+ concentrations in the Oa horizon was observed in
23    this study at the HBEF from 1978 to 1997, although no change was observed in this  soil in exchangeable
24    Mg2+ concentrations, or Ca2+ or Mg2+ content in the Oa horizon. Results of this study were complicated
25    by high spatial variability and differences in field sampling techniques between the original collection and
26    the resampling (Yanai et al., 1999). Yanai et al. (2005) also found little difference over 15 years in
27    exchangeable and extractable Ca2+ and Mg2+ concentrations in Oa horizons at 6 sites, and in O horizons
28    (combined Oe  and Oa horizons) at 13 sites, in hardwood stands in New Hampshire. In this study, it was
29    also estimated that a difference greater than 50% would be needed to be statistically  detected due to a
30    large degree of spatial variability. Although most repeated sampling studies did identify decreases in
31    exchangeable base cations in the Oa or O horizon, the results of Yanai et al. (2005) indicated that this
32    change may not occur at all sites and may be difficult to detect in some soils due to inconsistencies in
33    identifying horizon separations during sampling.
34          Through direct and inferred evidence of Ca2+ depletion, and additional research on  soil processes, a
3 5    detailed understanding of the mechanisms of Ca2+ depletion has developed over the past two decades.
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 1    Ulrich (1983) explained Ca2+ depletion as a three-stage process in which buffering of acidity in the
 2    mineral soil is first accomplished by weathering of carbonates and other mineral forms that weather
 3    relatively rapidly. Once these mineral forms are depleted, buffering is accomplished largely by cation
 4    exchange, in which fT is substituted for base cations and concentrations of exchangeable base cations
 5    decrease. Once the buffering capacity provided by cation exchange is depleted, acid neutralization is
 6    accomplished by weathering of crystalline minerals that contain large amounts of silicon (Si) and Al and
 7    relatively small amounts of base cations. At this stage, Al is mobilized within the soil and exchangeable
 8    Al concentrations increase. The shift in acid buffering from base cation exchange to alumino-silicate
 9    weathering and exchangeable Al was documented in Russian soils sampled three times over 75 years
10    (Lawrence etal., 1995).
11          The effect of decreasing concentrations of exchangeable base cations on cation leaching in mineral
12    soil was shown in simulation modeling by Reuss (1983). Below a base saturation of 20%, leaching of
13    Ca2+ decreases substantially and becomes less sensitive to variations in acid inputs as base saturation
14    decreases further. This relationship was later shown experimentally by Lawrence et al.  (1999a). Samples
15    from the upper B-horizon in nearly all of the Adirondack lake watersheds sampled by Sullivan et al.
16    (2006a) had base saturation values less than 20%, as did soils at 11 sites in New York, Vermont, New
17    Hampshire, and Maine in a regional study of mature spruce-fir forests (David  and Lawrence, 1996).
18    Exchangeable Ca2+ concentrations (expressed as a percentage of cation exchange capacity [CEC]) in the
19    regional spruce-fir study were weakly correlated with an estimate of the relative weathering potential of
20    parent material in the upper 10 cm of the B horizon (r2 = 0.44). However, these factors were strongly
21    correlated in the Oa horizon (r2 = 0.92) (Lawrence et al.,  1997). Because mineral weathering in the
22    B horizon is the primary source of soil Ca2+, a strong relationship between weathering potential and
23    exchangeable Ca2+ concentrations would be expected in this horizon. The weak correlation suggests that
24    concentrations of Ca2+ had decreased into the Al-buffering range sometime in  the past.  The parent
25    material signature in the Oa horizon was likely maintained through vegetative recycling—uptake of Ca2+
26    from the O and B horizons, followed by transport back into the O horizon in litterfall.
27          In summary, evidence from repeated sampling and studies of soil processes indicate that decreases
28    in exchangeable base cation concentrations in both Oa and B horizons are common and widespread in the
29    eastern U.S. Factors such as logging and net forest growth are likely to have contributed to this decrease
30    in varying degrees, but acidic deposition has played a major role (Lawrence et al.,  1987; Huntingdon,
31    2000). The magnitudes and rates at which Ca2+ depletion has occurred are less clear.
32          These base cation depletion issues relate directly to the chemical recovery potential of acidified
33    soils and surface waters. Replenishment of exchangeable base cation concentrations on soils will require
34    that inputs from weathering and atmospheric deposition exceed losses from leaching and vegetative
35    uptake.  Inputs of Ca2+ from atmospheric deposition decreased sharply in the east through the 1980s
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 1    (Hedin et al., 1994), and have remained relatively stable since that time (http://nadp.sws.uiuc.edu/).
 2    Atmospheric deposition of SC>42  currently remains several factors higher than that of Ca2+ even at sites
 3    where SC>42 levels are relatively low (http://nadp.sws.uiuc.edu/), so chemical recovery at current acidic
 4    deposition levels will require inputs of base cations from weathering that are considerably greater than
 5    inputs from the atmosphere.
 6          Because of the importance of weathering to the base-cation status of soils, a great deal of effort has
 7    been made to estimate in situ weathering flux with a variety of methods (Miller et al., 1993; Likens et al.,
 8    1996; Bailey et al., 2003).  The complexity and variability of factors that affect weathering flux rates, such
 9    as soil mineralogy, particle surfaces, soil organic matter, moisture flux, and a host of other factors that are
10    difficult to  quantify, add large uncertainties to weathering flux estimates. Weathering rates estimated in
11    geochemical models are generally assumed to be constant overtime, but lower weathering rates were
12    observed in a soil sampled in 1987 than in the same soil sampled and archived in 1949-50 (Zulla and
13    Billett, 1994). Further complexity in weathering flux rates results from the possible role of mycorrhizae in
14    penetrating silicate minerals to extract base cations while remaining isolated from the soil solution
15    (Van Breemen et al., 2000; Blum et al., 2002). Lastly, as yet unidentified sources of base cations may
16    exist in forest soils. Bailey et al. (2003) found that elevated rates of Ca2+ loss from forest harvesting
17    continued for 30 years after disturbance, but the source of the additional Ca2+ being lost could not be
18    identified. Until estimates of in situ weathering  fluxes are better constrained and more data become
19    available from repeated soil sampling, predictions of recovery of exchangeable base cation concentrations
20    will be highly uncertain.

      B.1.2.3. Aluminum Mobilization
21          Through the natural process of podzolization, dissolved organic acids derived from partially
22    decomposed organic matter in the O horizon move into the mineral soil where they weather soil particles
23    and release Al into solution. As soil solution moves deeper into the  profile, acidity is  neutralized and Al is
24    deposited as a secondary mineral or more likely as an organic Al (A10) complex (DeConinck, 1980). The
25    limited mobility of organic anions results in retention of most Al within the mineral soil (often in the
26    Bh horizon). Complexation with dissolved organic matter can increase the mobility of Al within the soil
27    and lead to transport of organic Al into surface waters from shallow soils that are high in organic matter
28    (Lawrence  etal., 1986).
29          Increased concentrations of exchangeable Al in the mineral soil have been identified through
30    repeated sampling in the U.S.  and Europe over periods ranging from 17 years to 41 years in studies by
31    Billet et al. (1990), Falkengren-Grerup and Eriksson (1990), Bailey et al. (Bailey et al., 2005), and
32    Lawrence et al. (Lawrence et al., 1995). In areas of Europe with excessively high acidic deposition  levels,
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 1    evidence of Al depletion in the mineral soil has also been found (Mulder et al., 1989; Lapenis et al.,
 2    2004), but Al depletion has not been documented in the U.S.
 3          Increases in exchangeable Al concentrations in the O horizon have been documented over periods
 4    from 17 to 30 years (Lawrence et al., 1995; Drohan and Sharpe, 1997; (Bailey et al., 2005), although the
 5    study of Yanai et al. (2005) did not find consistent changes in Oa horizons over 15 years.
 6          Numerous papers have evaluated solubility controls on Al in both the mineral soil and the
 7    O horizon. These papers have commonly related Al solubility to gibbsite (A1(OH)3) or a gibbsite-like
 8    mineral to determine if inorganic Al concentrations could be predicted from gibbsite solubility constants
 9    and pH (e.g., Johnson et al., 1981; David and Driscoll, 1984; Cronan and Goldstein, 1989; Lawrence and
10    David, 1977). These efforts have shown that inorganic Al concentrations are often undersaturated with
11    respect to gibbsite and do not support Al-trihydroxide as the primary control in natural systems. Gibbsite
12    solubility should therefore be considered a useful point of reference in evaluating Al-solubility rather a
13    mineral form that is an important control of Al solubility in natural systems.
14          Through the 1990s, evidence accumulated to indicate that secondary Al in the mineral soil is in a
15    form associated with organic matter, and in some soils, imogolite (Dahlgren and Walker, 1993; Mulder
16    and Stein, 1994; Berggren and Mulder, 1995; Simonsson and Berggren, 1998; Skyllberg, 1999). Organic
17    matter also plays a major role in controlling Al solubility in O horizons. This interaction has been
18    described by Cronan et al. (1986) in O horizons through the bound Al ratio, which reflects the equivalents
19    of adsorbed Al per mol of carboxyl groups (Cronan et al., 1986). Tipping et al. (1995) described Al
20    solubility on organic and mineral soil horizons through equilibrium humic ion binding. Each of these
21    approaches has had success in describing dissolved Al concentrations in organic soils as a function of pH
22    through formulations that rely on concentrations of solid-phase organic bound Al. Further work has
23    shown these relationships to be  specific to the particular horizon, and the pool sizes of Al and humic
24    substances (Lofts et al., 2001). However, inputs of acidity may alter concentrations of solid-phase organic
25    bound Al (Lawrence and David, 1977). Changes in atmospheric deposition levels may therefore shift
26    these relationships over time as soils further acidify or recover.

      B.1.2.4. Soil Acidification
27          In the B horizon of soils north of the maximum extent of glaciation, CEC is largely derived from
28    organic  matter, whereas in older southern soils the surface charge of highly weathered clay minerals is the
29    primary source of CEC. The CEC derived from organic matter is pH-dependent. Decreases in pH result in
30    a decreases in CEC. In both cases, the CEC of the B horizon is much lower than in organic-rich
31    surface horizons (Oa or A horizons). Less acidity from organic matter and a limited capacity for buffering
32    due to low CEC makes the B horizon more susceptible to a lowering of pH from acidic deposition, and
33    decreases in pH lower the CEC, further reducing the acid-buffering capacity from cation exchange. Two

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 1    studies in the U.S. have provided measurements to assess changes in soil pH in the B horizon from acidic
 2    deposition. Bailey et al. (Bailey et al., 2005) found lower pH values in the upper B horizon in
 3    northwestern Pennsylvania soils in 1967 than in 1997, at 50 cm depth (p < 0.001) and at 100 cm depth (p
 4    < 0.001), which were largely attributable to acidic deposition. Markewitz et al. (1998) also found
 5    pronounced decreases in soil pH down to 60 cm in highly weathered Piedmont soils from 1962 to 1990.
 6    The latter study was conducted in a former cotton field in which loblolly pines were planted in 1956-57.
 7    Forest regrowth undoubtedly played a large role in the soil pH changes that were measured, but
 8    atmospheric deposition was estimated to account  for 38% of the H+ inputs during the 28 years that
 9    elapsed between measurements.
10          Other studies in Europe have found similar decreases in soil pH of the B horizon that could be
11    attributed, at least in part, to acidic deposition. These include the study of Lawrence  et al. (Lawrence et
12    al., 1995) in northwestern Russia, which documented decreases in soil pH in the B horizon down to 90
13    cm, from 1926 to 1964, and further decreases from 1964 to 2001. Acidic deposition was identified as the
14    probable primary cause of decreasing pH in this study. The study of Lawrence et al.  (Lawrence et al.,
15    1995) also observed a decrease in CEC in this soil, as did a previous study of Russian soils (Lapenis et al.,
16    2004). The decrease in pH was likely to have contributed to the decreased CEC of these soils, but a more
17    important factor may  have been a decrease in organic carbon concentrations that was also measured. To
18    our knowledge, data to assess possible changes in CEC in soils in the U.S. has not become available, but
19    change in CEC has implications for recovery potential of soils from acidic deposition effects (Sullivan
20    et al., 2006b). Increased CEC driven by increases in pH could foster soil recovery by increasing the
21    opportunity for adsorption of base cations,  as soil solution becomes less acidic. Decreases in soil organic
22    matter driven by climate and/or vegetation  changes, such as those seen in Russian soils, would result in a
23    decrease in acid-buffering capacity through cation-exchange. There are currently no  data in the U.S. that
24    indicate increases in soil pH associated with recent declines in acidic deposition levels. These data
25    limitations make future projections of recovery of soil pH highly uncertain.

      B.1.2.5. N Saturation
26          Severe symptoms of N saturation, have  been observed in high-elevation, nonaggrading spruce-fir
27    ecosystems in the Appalachian Mountains, as well as in the eastern hardwood watersheds at Fernow
28    Experimental Forest near Parsons, WV and throughout the northeastern U.S. Mixed  conifer forests and
29    chaparral watersheds  with high smog exposure in the Los Angeles Air Basin also are N-saturated and
30    exhibit the highest stream water NC>3 concentrations documented within wildlands in North America
31    (Bytnerowicz and Fenn, 1996; Fenn et al.,  1998).
32          Some examples of N-saturated forests in North America, including estimated inputs  and outputs,
33    are shown in Table B-l (Fenn et al., 1998). The Harvard Forest hardwood stand in western Massachusetts

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 1    absorbed > 900 kg N/ha without significant NC>3 leaching during an 8-year N amendment study (Fenn
 2    et al., 1998). In contrast, NC>3 leaching losses were high at the Harvard Forest pine sites. In the 8-year
 3    experimental study, NC>3  leaching was observed in the pine stand after the first year (1989) in the high-N
 4    application plots, and further increases were observed in 1995 and 1996. The hardwood stand did not
 5    show significant increases in NC>3 leaching until 1996. The differences in response of the pine and
 6    hardwood stands indicate that the mosaic of community types across the landscape must be considered
 7    when evaluating landscape-scale responses to N deposition (Magill et al., 2000).
 8          Utilization of N in the terrestrial ecosystem is accomplished through complex interactions between
 9    plants and microbes that are not fully understood (Schimel and Bennett, 2004). Long-term N retention is
10    largely accomplished by incorporation of N into soil organic matter through biological assimilation (Aber
11    et al., 1998), and to a lesser extent by abiotic processes that are not well understood (Bail et al., 2001).
12    The forms in which N is assimilated by plants and microbes are determined by availability, as described
13    in Schimel and Bennett (2004). In the most N-limited ecosystems, competition between plants and
14    microbes is high and N is assimilated primarily in depolymerized organic forms, resulting in low
15    mineralization rates and minimal buildup of inorganic N in the soil. Increased availability of N increases
16    the  mineralization rate, which enhances competition between plants and microbes for available NH4+
17    produced by mineralization. Further increase in the availability of N (for example by high levels of
18    atmospheric N deposition) lessens competition for NH4+ between plants and microbes and leads to
19    increased production of NC>3 by autotrophic nitrifying bacteria. Some of this NC>3 can be taken up by
20    plants and microbes, but because much of the N demand is satisfied by NH4+ under these conditions,
21    NOs  tends to be mobile within the  soil, enabling it to leach to drainage water. Based on the definitions of
22    Aber et al.  (1989, 1998) and Stoddard (1994), the first stage of N saturation is reached when competition
23    between plants and microbes for NH4+ has decreased to the point that net nitrification occurs.
24          Substantial leaching of NO3  from forest soils to streamwater can acidify downstream waters
25    (Webb et al., 1995), eutrophy estuaries and marine waters (Fisher and Oppenheimer, 1991), and deplete
26    soils of nutrient base cations, especially Ca2+ and Mg2+ (Likens et al., 1998). Considerable evidence is
27    available to link N deposition to acidification of soils. Much of this evidence comes from the northeastern
28    U.S., where increased accumulation of N in soil is suggested by a strong positive correlation between
29    atmospheric deposition levels and total N concentration in the Oa horizon, at sites in New York, Vermont,
30    New Hampshire, and Maine (Driscoll et al.,  2001b). Further evidence that atmospheric deposition has
31    increased availability of N in soil is shown by a strong negative correlation between atmospheric
32    deposition levels and the C:N ratio of the Oa horizon in this region (Aber et al., 2003). If the C:N ratio
33    falls below about 25, nitrification is stimulated, resulting in elevated NOs in surface waters (Aber et al.,
34    2003). Similar results were found in Europe, where a C:N ratio of 24 was identified as the critical level
35    below which nitrification occurred (Emmett et al., 1998).
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 1          Analyses have been conducted in the northeastern U.S. and Europe to examine the relationships
 2    between N deposition and NC>3  leaching to surface waters. The relationship between measured wet
 3    deposition of N and streamwater output of NC>3  was evaluated by Driscoll et al. (1989) for sites in North
 4    America (mostly eastern areas), and augmented by Stoddard (1994). The resulting data showed a pattern
 5    of N leaching at wet inputs greater than approximately 5.6 kg N/ha/yr. Stoddard (1994) presented a
 6    geographical analysis of patterns of watershed loss of N throughout the northeastern U.S. He identified
 7    approximately 100 surface water sites in the region with sufficiently intensive data to determine their N
 8    status. Sites were coded according to their presumed stage of N retention, and sites ranged from Stage 0
 9    (background condition) through Stage 2 (chronic effects). The geographic pattern in watershed N
10    retention depicted by Stoddard (1994) followed the geographic pattern of N deposition. Sites in the
11    Adirondack and Catskill Mountains in New York, where N deposition was about 11 to 13 kg N/ha/yr,
12    were typically identified as Stage 1 (episodic effects) or Stage 2. Sites in Maine, where N deposition was
13    about half as high, were nearly all Stage 0. Sites in New Hampshire and Vermont, which received
14    intermediate levels of N deposition, were identified as primarily Stage 0, with some Stage  1 sites. Based
15    on this analysis, a reasonable threshold of N deposition for transforming a northeastern site from the
16    "natural" Stage 0 condition to Stage 1 would correspond to the deposition levels found throughout New
17    Hampshire and Vermont, approximately 8 kg N/ha/yr. This agreed with Driscoll et al.'s (1989)
18    interpretation, which would probably correspond to total N inputs near 8 to 10 kg N/ha/yr. This is  probably
19    the approximate level at which episodic aquatic effects of N deposition would become apparent in many
20    watersheds of the eastern U.S.
21          Analysis of data from surveys of N outputs from 65 forested plots and catchments throughout
22    Europe were conducted by Dise and Wright (1995) and Tietema and Beier (1995). Below the throughfall
23    inputs of about 10 kg N/ha/yr, there was very little N leaching at any of the study sites. At throughfall
24    inputs greater than 25 kg N/ha/yr, the study catchments consistently  leached high concentrations of
25    inorganic N. At intermediate deposition values (10 to 25 kg N/ha/yr), Dise and Wright (1995) observed a
26    broad range of watershed responses. Nitrogen output was most highly correlated with N input (r2  = 0.69),
27    but also significantly correlated with S input, soil pH, percent slope, bedrock type, and latitude. A
28    combination of N input (positive correlation) and soil pH (negative correlation) explained 87% of the
29    variation in N output at the study sites (Dise and Wright, 1995).
30          The threshold level of atmospheric deposition that causes release of NC>3 to surface waters was
31    identified by Aber et al. (Aber et al., 2003) as approximately 7 kg N/ha/yr for the northeastern U.S. In
32    watersheds receiving N deposition above this level,  concentrations of NC>3  in surface waters were
33    positively correlated with atmospheric deposition, whereas most watersheds with deposition less than
34    7 kg/ha/yr had little or no NC>3  (undetectable at most sites) in their surface waters (Aber et al., 2003). The
3 5    threshold value of 7 kg/ha/yr was based on atmospheric deposition levels for the base of forested
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 1    watersheds. When scaled to include higher deposition levels expected at upper elevations this value was
 2    estimated to equal about 10 kg/ha/yr, similar to the European estimate of Dise et al. (1998).
 3          The common deposition threshold for release of NC>3 to surface waters  in forested watersheds
 4    found in the northeastern U.S. and Europe represents an important advance in relating N inputs to
 5    ecosystem effects, but a considerable amount of variability in ecosystem response has also been
 6    demonstrated. Lovett et al. (2000b) found that 39 watersheds in the Catskill region of New York State
 7    retained from 49% to 90% of atmospheric N inputs. Castro and Morgan (2000) showed that NO3 export
 8    from watersheds in eastern North America can range from nearly 0 to over 400 eq/ha/yr in watersheds
 9    that receive similar levels of inorganic N in wet deposition in the range of 400  to 500 eq/ha/yr.
10          Experimental additions of N to plots and watersheds have also demonstrated variations in terrestrial
11    retention of N. Additions of N (approximately twice ambient deposition) to hardwood watersheds in
12    Maine (25 kg N/ha/yr) and West Virginia (35.5 kg N/ha/yr), which were releasing NC>3  to surface waters
13    prior to the additions, resulted in substantial increases in NC>3  concentrations in soil water and stream
14    water within the first treatment year (Kahl et al., 1993a; Peterjohn et al., 1996). Additions of 25 kg
15    N/ha/yr to spruce plots in Vermont (ambient bulk deposition 5.4 kg N/ha/yr), in which net nitrification did
16    not occur prior to treatment, triggered net nitrification in the second year of treatment, whereas
17    nitrification was not triggered until the third year in plots receiving 19.8 kg N/ha/yr (McNulty et al.,
18    1996). Similar results to these were seen in two studies from Colorado. Additions of 25  kg N/ha/yr to old-
19    growth spruce plots in Loch Vale watershed (ambient bulk deposition -4-5 kg  N/ha/yr) doubled N
20    mineralization rates and stimulated nitrification, while the addition of the same amount  to plots receiving
21    ambient bulk deposition of ~2.0 kg N/ha/yr in Fraser Experimental Forest elicited no microbial response
22    but significantly increased foliar and organic soil horizon N (Rueth et al., 2003). A comparison study of
23    old-growth spruce plots across a depositional gradient in Colorado found mineralization rates to be higher
24    where N deposition ranged from 3 to 5 kg N/ha/yr than where N deposition ranged from 1 to 2 kg
25    N/ha/yr, with measurable nitrification rates at sites with the highest deposition  amounts (Rueth and Baron,
26    2002). In marked contrast to these results, concentrations of NC>3 plus NH4+ were not detected until the
27    seventh year in hardwood plots in Harvard Forest, which received additions of 150 kg N/ha/yr (Magill
28    et al., 2004). Concentrations of (NC>3  + NFit+) in hardwood plots receiving 50 kg N/ha/yr were not yet
29    detectable in the 15th year of treatments. The same treatments were applied to  red pine  (Pinus resinosa)
30    plots, which exhibited elevated concentrations of (NC>3 + NH4+) in soil water after 1 year of 150 kg
31    N/ha/yr doses, and after 5 years of 50 kg N/ha/yr doses.
32          In general, deciduous forest stands in the eastern U.S. have not progressed toward N-saturation as
33    rapidly or as far as spruce-fir stands. Deciduous forests may have a greater capacity for  N retention than
34    coniferous forests. In addition, deciduous forests tend to be located at lower elevation and receive lower
35    atmospheric inputs of N. Many deciduous forests have higher rates of N uptake and greater N requirement
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 1    than spruce-fir forests. Decreased growth and increased mortality have more commonly been observed in
 2    high-elevation coniferous stands than in lower elevation hardwood forests, and these differences have
 3    been partially attributed to excess inputs of N (Aber et al., 1998). Indeed, many of the lower elevation
 4    deciduous stands are N-deficient and are therefore likely to benefit (i.e., grow faster), at least up to a
 5    point, with increased inputs of N.
 6          There are examples of N saturation in lower-elevation eastern forests, especially in West Virginia.
 7    For example, progressive increases in streamwater NO3  and Ca2+ concentrations were measured at the
 8    Fernow Experimental Forest in the 1970s and 1980s (Edwards and Helvey, 1991; Peterjohn et al., 1996;
 9    Adams et al., 1997, 2000). This watershed has received higher N deposition (average throughfall input of
10    22 kg/ha/yr of N in the  1980s) than is typical for low-elevation areas of the eastern U.S., however (Eagar
11    et al., 1996), and this may help to explain the observed N saturation.
12          Varying responses to N additions reflect differences in N status of the treatment sites. These
13    variations have most often been attributed to disturbance history, dating back a century or more (Goodale
14    and Aber, 2001). Sites which have undergone disturbances that cause loss of soil N,  such as logging, fire,
15    and agriculture, tend to be most effective at retaining atmospheric and experimental  inputs  of N. Nitrogen
16    retention capability often decreases with stand age, which suggests that older forests are more susceptible
17    than younger forests to  becoming N-saturated (Hedin et al.,  1995). Aber et al. (1998) surmised that land
18    use history may be more important than cumulative atmospheric deposition of N in determining the N
19    status of a forest ecosystem.
20          Although considerable progress has been made in understanding the factors that control N
21    retention, efforts to quantify net N retention through known processes have not been fully successful.
22    Assimilation of N by mycorrhizae followed by exudation as dissolved organic matter was proposed by
23    Aber et al.  (1998) as a possible explanation for unaccounted conversion of inorganic N into soil organic
24    matter. However, Frey et al. (2004) found that elevated N inputs reduced active mycorrhizal biomass,
25    fungal diversity and fungal:bacterial biomass ratios. These results suggested a decreased role for
26    mycorrhizae in fixation of N under elevated N inputs.
27          Abiotic transformation of inorganic N into soil organic matter has also been proposed as a possible
28    mechanism to explain high rates of N retention in soil, and some evidence has been presented to support
29    this possibility. Bail et al. (2001) observed retention of 15NC>3- and  15NC>2 in sterile soil, but the method of
30    sterilization may have increased dissolved organic carbon (DOC) concentrations and artificially increased
31    the opportunity for formation of soluble organic N compounds. Davidson et al. (2003) developed the
32    ferrous wheel hypothesis to explain incorporation of inorganic N into organic matter. The hypothesized
33    mechanism involves conversion of NC>3 to NC>2 through oxidation of Fe2+. Testing of this hypothesis in
34    situ was not found in the literature, but the small amount of Fe2+ that typically occurs in the forest floor,
35    where presumably much of the conversion to organic N occurs, may limit the importance of this pathway.
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 1    Fitzhugh et al. (2003) showed that NC>2 produced in the first step of nitrification may be directly
 2    converted to soluble organic N rather than becoming fully oxidized to NC>3 . However, concentrations of
 3    introduced 15NC>2 in this experiment were several orders of magnitude higher than that normally seen in
 4    forest soils. Therefore, the evidence at this time for abiotic retention of N is not fully convincing, and the
 5    importance of this process requires further research.
 6          In addition to our limited understanding of N retention mechanisms, there is no direct information
 7    on ecosystem recovery from N saturation in the U.S. This may be at least partly because atmospheric
 8    deposition of N has been relatively stable in the eastern U.S. over the past two to three decades. An
 9    important source of information on N recovery responses has been provided by the European NITREX
10    study, which reduced ambient N deposition for 5 years with roofs constructed over experimental plots in
11    Germany and The Netherlands. At the German site,  deposition was reduced from approximately 38 kg
12    N/ha/yr to levels that varied from 10 to 20 kg N/ha/yr. At the Dutch site, deposition was reduced from
13    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
14    the experiment were approximately three to four times greater than the highest deposition levels
15    commonly found in the eastern U.S., whereas after the reduction, levels at the Dutch site fell within the
16    range of deposition in the eastern U.S. over the past two decades, and values at the German site were
17    somewhat higher than this range (Ollinger et al., 1993); Emmett et al., 1998). The decrease in ambient N
18    inputs resulted in a marked decrease in N outputs at each site within 2 to 3 years. The responses at the two
19    sites were somewhat different, however. At the Dutch site, outputs of N exceeded inputs both before and
20    after experimental reduction of inputs. At the German site, inputs exceeded outputs before and after
21    reduction of inputs, but outputs were more similar to inputs  after the reduction. At both sites,  outputs after
22    the reduction in deposition remained two to three times higher than outputs commonly measured in the
23    eastern U.S.
24          Thus, atmospheric deposition of N has increased N availability in soils, which has led to increased
25    nitrification and associated acidification of soil and soil water. The N retention capacity of soils is
26    strongly dependant on land-use history, however, so the relationships between N deposition and
27    ecosystem N status and percent of terrestrial retention are variable. In general, however, atmospheric
28    deposition of 10 kg N/ha/yr or higher is required for appreciable amounts of NC>3  to leach to surface
29    waters in the eastern U.S. and northern Europe. Future projections of chemical recovery from N-driven
30    acidification are uncertain because (1) retention mechanisms are not fully understood, and (2) there are
31    only limited data on recovery responses. European experiments that reduced inputs of N found decreased
32    outputs  of N within 2 to 3 years, which indicates a relatively rapid response to decreased deposition
33    levels. However, these studies are difficult to directly apply  to the U.S. because deposition levels were
34    much higher at the European sites prior to the experiment, and the 5-year duration of the experiments
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 1    only demonstrated recovery to levels of N saturation that are higher than the more heavily affected sites in
 2    the eastern U.S.
 3          High concentrations of lake or streamwater NO3 , indicative of ecosystem saturation, have been
 4    found at a variety of locations throughout the U.S., including the San Bernardino and San Gabriel
 5    Mountains within the Los Angeles Air Basin (Fenn et al., 1996), the Front Range of Colorado (Baron
 6    et al., 1994; Williams et al., 1996a,b), the Allegheny Mountains of West Virginia (Gilliam et al., 1996),
 7    the Catskill Mountains of New York (Murdoch and Stoddard, 1992; Stoddard, 1994), the Adirondack
 8    Mountains of New York (Wigington et al., 1996), and the Great Smoky Mountains in Tennessee (Cook
 9    et al., 1994). All of these regions, except Colorado, received relatively high (more than about 10 kg
10    N/ha/yr) atmospheric deposition of oxidized N throughout the  1980s and 1990s. In contrast, the Front
11    Range of Colorado receives less than about 4 or 5 kg N/ha/yr of total (wet plus dry) deposition (Sullivan
12    et al., 2005), less than half of the total N deposition received at these other locations. The cause of N-
13    saturation at high-elevation western watersheds that receive low to moderate levels of atmospheric
14    deposition has been  a subject of debate. High concentrations of NO3  in surface waters in the western
15    U.S. are not widespread. Nitrate concentrations during the fall  sampling season were low in most western
16    lakes sampled  in the Western Lakes Survey (WLS). Only 24 sampled lakes were found to have NO3
17    concentrations greater than 10 (ieq/L. Of those, 19 lakes were situated at high elevation, most above 3,000
18    m. Cold temperatures in such lakes undoubtedly play an important role in maintaining high NO3
19    concentrations by limiting biological uptake processes. The high NO3 concentrations are likely to affect
20    acid-base chemistry only where ANC is low. Eight lakes showed high NO3  (>10 (ieq/L) and low ANC
21    (<50 (ieq/L), all of which occurred at elevations higher than 3,100 m. Four were located in Colorado, two
22    in Wyoming, and one each in California and Utah. In all cases, pH was above 6.5 and ANC was greater
23    than or equal to 15 (ieq/L. Such lakes are sensitive to episodic pulses of NO3 acidity; such pulses have
24    been reported from Colorado Front Range lakes (Williams and Tonnessen, 2000). Episodic acidification
25    of western lakes could be important biologically.
26          In the Uinta Mountains of Utah and the Bighorn Mountains of central Wyoming, 19% of the lakes
27    included within the WLS had NO3  > 10 (ieq/L. This suggests that N deposition in these areas may have
28    exceeded the capability of these  systems to assimilate N. It is unknown if these concentrations of NO3
29    represent effects from anthropogenic sources or if this constituted a natural condition associated with
30    inhibited NO3  assimilation in cold alpine  environments.
31          Williams et al. (1996a,b) contended that N-saturation is occurring throughout high-elevation
32    catchments of the Colorado Front Range. Many lakes in the Colorado Front Range have chronic NO3
33    concentrations greater than 10 (ieq/L and concentrations during snowmelt are frequently much higher, due
34    at least in part  to leaching from tundra, exposed bedrock, and talus areas. Although biological N demand
35    may be  high in subalpine forests, uptake is limited in alpine areas by large N inputs from snowmelt,  steep
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 1    watershed gradients, rapid water flushing, extensive areas having little or no soil development, and
 2    limitations on the growth of phytoplankton in some alpine lakes by factors other than N (e.g., phosphorus
 3    [P], temperature) (Baron et al., 1994).

      B.1.2.6. Nitrate Leaching
 4          Nitrate leaching losses from soils to drainage waters are governed by a complex suite of ecosystem
 5    processes in addition to N inputs from atmospheric deposition. In particular, mineralization and
 6    nitrification processes play important roles in regulating the quantity of, and temporal variability in, the
 7    concentration of NC>3  in soil solution, and consequently leaching losses from the rooting zone (Reuss and
 8    Johnson, 1985; Joslin et al., 1987; Johnson et al., 1991b,c). Thus, NC>3 leaching is mostly under
 9    biological control and typically shows pronounced seasonal variability (Van Miegroet et al., 1993). Peak
10    concentrations of NO3  in soil solution appear to be largely responsible for the potentially toxic peaks in
11    Al concentration that sometimes occur in soil solution, although SC>42  may also play a role by serving to
12    elevate chronic Al concentrations (Eagar et al., 1996).
13          High leaching of NC>3  in soil water and streamwater draining high-elevation spruce-fir forests has
14    been documented in numerous studies in the Southern Appalachian Mountain region (cf. Joslin et al.,
15    1992; (Joslin et al., 1992)Van Miegroet et al., 1992a,b; Joslin and Wolfe, 1994; Nodvin et al., 1995). This
16    high NC>3 leaching has been attributed to a combination of high N deposition, low N uptake by forest
17    vegetation, and inherently high N release from soils. Forest age is another major factor-affecting uptake,
18    with mature forests requiring minimal N for new growth and, hence, often exhibiting higher NC>3
19    leaching that younger,  faster growing stands (Goodale and Aber, 2001). Old-growth red spruce stands in
20    the Southern Appalachians have been demonstrated to have significantly slower growth rates than stands
21    younger than 120 years (Smith and Nicholas, 1999). The latter feature is associated with low C:N ratios in
22    mineral soil, high N mineralization potential and high nitrification (Joslin  et al., 1992)Eagar et al., 1996).
23          In most terrestrial ecosystems in the U.S., N is strongly retained and there is limited  mobility of
24    NO3 . Exceptions to this pattern tend to occur in spatially limited regions that receive high  levels of total
25    N deposition (higher than about 10 to 20 kg N/ha/yr) and in alpine and subalpine environments that have
26    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
27          Although S is generally mobile in upland soils in most parts of the U.S., wetlands act as both
28    sources and sinks of atmospherically deposited S. Wetlands retain and release S in response to variations

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 1    in hydrology, which in turn affect oxidation and the reduction process in wetland soils. Ito et al. (2005)
 2    evaluated the influence of land cover types on SO42 fluxes in Adirondack lake watersheds. They found
 3    that SO42  concentration in drainage water decreased in association with increased wetland area within the
 4    lake watershed (adj. r2 = 0.58, p > 0.001). They attributed this observed pattern to dissimilatory SO42
 5    reduction in anaerobic wetland soils.
 6          Sulfur storage in wetland soils to some degree prevents or delays the acidification of downstream
 7    surface waters with mineral acidity. However, the water table in wetland areas typically drops during
 8    drought conditions, and this allows development of aerobic conditions in surface wetland soils. Under
 9    aerobic conditions, stored S is re-oxidized to SO42 , which can then be rapidly mobilized under high-flow
10    conditions that occur in response to rainfall or snowmelt. This can cause substantial episodic pulses of
11    acidity in surface waters that receive drainage water from wetlands. Thus, wetlands buffer downstream
12    receiving waters against chronic acidity to some degree, but can be an important source of periodic
13    episodes of more extreme acidity.

      B.1.3.2. Organic Acidity in Transitional Ecosystems and Downstream Surface Waters
14          Organic acids in fresh water originate from the degradation of biomass in upland areas, wetlands,
15    near-stream riparian zones, the water column, and stream and lake sediments (Hemond,  1994). The
16    watersheds of surface waters that have high concentrations of organic matter (DOC > about 400 (iM)
17    often contain extensive wetlands and/or extensive organic-rich riparian areas (Hemond, 1990; Sullivan,
18    2000).
19          Organic acids contributed by wetlands to downstream drainage waters can influence surface water
20    acid-base chemistry, particularly in dilute waters having moderate to high (greater than about 400 (iM)
21    DOC concentrations. Organic acids in surface waters include a mixture of functional groups having both
22    strong and weak acid character. Some lakes and streams are  naturally acidic as a consequence of organic
23    acids contributed to solution by wetlands. The presence of organic acids also provides buffering to
24    minimize pH change in response to changes in the amount of SO42 and NO3  derived from acidic
25    deposition.
26          There are many lakes and streams that are chronically acidic or low in ANC mainly due to the
27    presence of organic acids. In many cases, the principal source of these organic acids is the wetlands
28    within the watershed. The NAPAP (1991) concluded that about one-fourth of all acidic lakes and streams
29    surveyed in the National Surface Water Survey (NSWS) (Linthurst et al., 1986a; Kaufmann et al., 1988)
30    were acidic largely as a consequence of organic acids. A survey of 1400 lakes in the Adirondack
31    Mountains by the Adirondack Lake Survey Corporation (ALSC) (Kretser et al., 1989), which included
32    many small lakes and ponds (1 to 4 ha) having relatively high DOC, revealed that about 38% of the lakes
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 1    had pH < 5 due to the presence of organic acids, and that organic acids generally depressed the pH of
 2    Adirondack lakes by 0.5 to 2.5 pH units in the ANC range of 0 to 50 (ieq/L (Baker et al., 1990b).
 3          Specification of the acid-base character of water high in DOC is somewhat uncertain. Attempts
 4    have been made to describe the acid-base behavior of organic acids using a single FT dissociation
 5    constant (pKa), despite the fact that organic acids in natural waters are made up of a complex mixture of
 6    acidic functional groups. A portion (perhaps one-third) of the acidity in organic acids is quite strong, with
 7    some ionization occurring at pH values well below 4.0 (Driscoll et al., 1994; Hemond, 1994). A number
 8    of modeling approaches have been used to estimate the acidity of organic acids in fresh waters, often as
 9    simple organic acid analogs having different pKa values (Oliver et al., 1983; Perdue et al., 1984; Driscoll
10    etal., 1994).
11          The importance of naturally occurring organic acids as agents of surface water acidification was
12    reinforced by a modeling study (Sullivan et al., 1996a) that showed that inclusion of organic acids in the
13    Model of Acidification of Groundwater in Catchments  (MAGIC) had a substantial effect on model
14    predictions of surface water pH, even in waters where DOC concentrations were only moderate. MAGIC
15    hindcasts of pre-industrial lakewater pH of Adirondack lakes showed poor agreement with diatom
16    inferences of pre-industrial pH when organic acids were not considered in the MAGIC model (Sullivan
17    et al., 1996a). Revised MAGIC hindcasts of pre-industrial lakewater pH that included an organic acid
18    representation (Driscoll et al., 1994) showed considerably closer agreement with diatom inferences
19    (Figure B-l). The mean difference between MAGIC and diatom estimates of pre-industrial pH was
20    reduced from 0.6 pH units to  0.2 pH units when organic acids were included in the model, and the
21    agreement for individual lakes improved by up to a full pH unit (Sullivan et al., 1996a).
22          Rosenqvist (1978) and Krug et al. (Krug et al., 1985) hypothesized that a significant component of
23    the mobile acid anions contributed from atmospheric deposition (e.g., SO42 , NO3 ) replace  organic
24    anions that were previously present in solution. Under this anion substitution hypothesis, the net result of
25    acidic deposition is not  so much an increase in cations (including potentially toxic FT and Aln+) as much
26    as an exchange of SO42 and NO3 anions for organic anions, with little or no change in ANC and pH.
27    This hypothesis has received  some support from paleolimnological studies, which suggested historic
28    decreases in DOC concentrations during the period of lakewater acidification in the  1900s (Davis et al.,
29    1985a,b; Kingston  and Birks, 1990; Dixit et al., 2001). Other studies have found a decrease  in organic
30    acidity which was at least partly attributable to the extent of organic acid protonation. David et al. (1999)
31    measured a decrease in  organic anion concentrations in stream water in response to the experimental
32    whole-watershed acidification experiment at the Bear Brook Watershed in Maine. Wright et al. (1993)
3 3    concluded that ANC increases in a small watershed in Norway, where rates of acidic deposition were
34    experimentally reduced, were limited by the increasing role of organic acids that accompanied decreasing
35    acid deposition load.
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 1          Complexation of organic acids by metals (Aimer et al., 1974; Lind and Hem, 1975; Dickson, 1978;
 2    Cronan and Aiken, 1985) and pH-dependent changes in dissociation of organic acids (Oliver et al., 1983;
 3    Wright et al., 1988a) are  probably important components of the organic acidity response. Loss of DOC in
 4    response to acidic deposition can also cause a shift in Al species composition towards lesser complexation
 5    with organic ligands. Such a shift from AL0 to Al; increases toxicity of the Al to aquatic biota (Baker and
 6    Schofield, 1982). Changes in pH can alter the charge density of organic solutes and thus influence organic
 7    contributions to acidity (e.g., Wright et al., 1988a,b). David et al. (1999) found that the charge density of
 8    organic acids decreased by about 1 (ieq/L/mg C at West Bear Brook in Maine, in response to 6 years of
 9    experimental acidification, probably due to greater protonation of organic acid anions at the lower pH.
10    Similar results were reported by Lydersen et al. (1996) at Lake Skjervatjern in Norway. Values of the
11    organic acid charge density in the ALSC lakes in the Adirondack Mountains increased with increasing pH
12    between pH values of 5.0 to 7.0 due to the presence of weakly acidic functional groups  (Driscoll et al.,
13    1994).
14          Hedin et al. (1990) artificially acidified a small, moderately high-DOC (725 (iM C) stream with
15    H2SO4 at HBEF in New  Hampshire. Streamwater pH (4.4) was near the range  of reported average pKa
16    values for organic acids,  suggesting that the capacity of organic acids to buffer mineral acidity should be
17    high. The acid loading rate was adjusted to achieve an increased streamwater SO42 concentrations of
18    150 (ieq/Latthe downstream sampling point 108 m below the point of acid addition. Adjustments were
19    made for dilution by soil water or inflow from small tributaries. Although streamwater DOC did not
20    change significantly, the  concentration of organic anions (as calculated from the charge balance)
21    decreased by 17 (ieq/L. Thus, the overall capacity of organic anions to neutralize mineral acid inputs
22    offset about 11% of the added acid (Hedin et al., 1990). This experiment only considered interactions
23    between mineral acid and organic matter within the stream. Any additional buffering that may have  been
24    provided within the terrestrial catchment was not represented in the experimental design. Also, any
25    possible catchment-mediated influences of the experimental acidification on organic acid properties or
26    terrestrial DOC mobilization were excluded from the experiment because the acid was not applied to the
27    catchment soils.
28
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MAGIC Hindcast Pre-industrial pH
7 5
7
6 5
Q
5 5
5
4 5
4

Organic Acids Not Included
. . V-
• • • V .x"
• • •* *• ^.
%*'••* ^^ "
^
^


ii i i i i
(A
S
73
i
o
 8-

7.5-

 7-

6.5.

 6 •

5.5-
<  4.5-
                   4.5
                          5     5,5     6     6-5     7
                           Diatom-inferred Pre-industrial pH
                                                          7.5
                 Organic Acids Not Included
                    I
                   4,5
                                5.5     6     6.5     7
                           Diatom-inferred Pre-industrial pH
                                                          7,5
                                                                                         Source: Sullivan etal. (1996).
                                                                    Figure B-1. MAGIC model hindcast
                                                                    estimates of pre-industrial pH
                                                                    versus diatom-inferred 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.
 1
 2          Results of a resurvey of 485 Norwegian lakes sampled in both 1986 and 1995 provided evidence in
 3    support of an increase in organic acid anion concentrations in association with decreased lakewater SO42~
 4    concentration (Skjelkvale et al., 1998). The organic acid anion concentration increased by an amount
 5    equal to between 9% and 15% of the decrease in SO42 concentration in the four regions of the country
 6    most heavily affected by the decrease in S deposition during the intervening 10 year period. Lakewater
 7    SC>42  concentrations decreased by 9 (ieq/L (western and northern Norway) to 20 to 21 (ieq/L (eastern and
 8    southern Norway). Only in mid-Norway, where average SC>42  concentration decreased by only 6 (ieq/L,
 9    did the organic acid anion concentration remain unchanged between 1986 and 1995 (Skjelkvale et al.,
10    1998).
11          Recent monitoring data have shown that DOC and organic acid anion concentrations in many lakes
12    and streams in the U.S. have increased in association with decreased S deposition. It is likely that a high
13    percentage  of this DOC originates from wetland soils within the monitored watersheds. This result
      August 2008
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 1    appears to be partly responsible for the limited lakewater ANC and pH recovery that has occurred at many
 2    locations. The response of surface waters to changes in acidic deposition has included a general increase
 3    in surface water DOC (Figure B-2). All regions of the eastern U.S. analyzed by Stoddard et al. (2003) that
 4    had sufficient DOC data for analysis exhibited increases in DOC concentrations during the  1990s. All
 5    regional trends were significant with the exception of the Northern Appalachian Plateau, the region with
 6    the lowest median DOC concentration. The median increase in DOC of 0.05 mg/L/yr reported by
 7    Stoddard et al. (2003) corresponds to an overall increase of about 10% across study regions, similar to
 8    trends reported elsewhere in the northern hemisphere (Evans and Monteith, 2001; Skjelkvale et al., 2001).
 9    This suggests a common cause. Both climate warming and decreasing acidic deposition are possible
10    causal agents.
      August 2008                                     B-21                   DRAFT-DO NOT QUOTE OR CITE

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      100
       90

       80

    I  7°
    5  60
    0_
    S  50
    I
    |  40

    o  30

       20

       10
              Regional Sulfate Trends in LTM Network
                                                                  Regional Nitrate Trends in LTM Network
• New England Lakes
 Adirondack Lakes
 Appalachian Streams
• Upper Midwest Lakes
• Ridge and Blue Ridge Streams
         -12
               -10
                     -8    -6    -4    -2
                      Slope of Trend (peq/Uyr)
New England Lakes
Adirondacks Lakes
Appalachian streams
Upper Midwest Lakes
Ridge/Blue Ridge Streams
                                                                        -2         -1          0
                                                                          Slope of Trend ((jeq/Uyr)
               Regional ANC Trends in LTM Network
                                  New England Lakes
                                  Adirondacks Lakes
                                  Appalachian Streams
                                  Upper Midwesl Lakes
                                  Ridge and Blueridge Streams
                                                               Regional Hydrogen Ion Trends in LTM Network
                  -2024
                      Slope of Trend (iJeq/Uyr)
                                                                         90
                                                                         80

                                                                      I  7°
                                                                      I  60
                                                                      .1  50
                                                                      E
                                                                      O
                                                                 - New England Lakes
                                                                  Adirondack Lakes
                                                                  Appalachian Streams
                                                                 • Upper Midwest Lakes
                                                                 - Ridge and Blue Ridge Streams
                                                                       -1.0        -0.5        0,0
                                                                          Slope of Trend ((jeq/Uyr)
                                                                                                                     0.5
            Regional [Ca2* + Mg2*] Trends in LTM Network
                                    New England Lakes
                                    Adirondack Lakes
                                    Appalachian Streams
                                    Upper Mid^sl Lakes
                                    Ridge/Blue Ridge Str
                                                                   Regional DOC Trends in LTM Network
                    -4-2024
                      Slope of Trend (Meq/Uyr)
                                                                                           New England Lakes
                                                                                           Adirondack Lakes
                                                                                           Appalachian Streams
                                                                                           Upper Midwesl Lakes
                                                                           -0.4
                                                                     -0.2      0.0      0.2       0,4
                                                                          Slope of Trend (mg/L/yr)
                                                                                                         Source: Stoddard et al. (2003).
Figure B-2. Cumlative frequency diagram (distribution) of slopes for SO/", N03~, 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.
August 2008
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      B.2. Factors That  Determine Ecosystem Sensitivity

            B.2.1. Transitional Ecosystems

      B.2.1.1. Wetlands and Peatlands
 1          Wetlands and peatlands often contain highly acidic soils. Their acidity is mainly attributable to the
 2    presence of large quantities of naturally occurring organic materials. Fulvic and humic acids, formed
 3    during the breakdown of organic matter, contribute substantial organic acidity to soil and surface waters
 4    in wetland and peatland environments. In the case of ombotrophic bogs and poor fens, there is also a
 5    scarcity of base cations, which would serve to buffer both organic and mineral acidity.
 6          Because wetland and peatland vegetative communities are adapted to high levels of natural organic
 7    acidity, it is unlikely that S or N deposition would cause any acidification-related effects at levels of
 8    acidic deposition commonly found in the U.S. Nevertheless, wetlands are closely tied to a number of
 9    important biogeochemical processes that regulate watershed response to acidic deposition. The major
10    interactions are described below.
11          High concentrations of DOC in brownwater lakes and streams are  often due to the influence of
12    wetlands on hydrography within the watershed. This presence of high concentrations (higher than about
13    500 (iM) of DOC can substantially reduce the pH and ANC of surface waters, buffer those waters against
14    pH changes in response to added mineral acidity, and form stable complexes with dissolved Al, thereby
15    reducing its toxicity to aquatic life. Therefore, the response of surface waters to acidic deposition is
16    strongly influenced by the extent of upstream and shoreline wetland development.
17          Wetlands also  serve as a (sometimes-temporary) sink for atmospheric S and N.  Chemical reduction
18    reactions and biological uptake contribute to S and N storage in wetland soils. Oxidation during drought
19    periods, when water levels recede, followed by flushing from wetland to  downstream  surface water
20    during subsequent storm flow, can cause substantial pulses of mineral acidity in downstream receiving
21    waters. On a chronic basis, the concentration of SO42 (and associated acidity) in surface water can be
22    substantially lower as a consequence of dissimulatory S reduction in upslope wetlands. On an episodic
23    basis, wetlands can contribute to wide fluctuations in downstream surface water acid-base chemistry.
24    Such fluctuations can include pulses of acidity that may be toxic to aquatic biota.
25          Wetlands provide anaerobic substrate for S-reducing bacteria. These bacteria are also partly
26    responsible for the increased rate of mercury (Hg) methylation that is known to occur in wetlands. As a
27    consequence, fish in lakes drained by wetlands often have much higher concentrations of tissue  methyl
28    Hg, as compared with fish in lakes that lack watershed wetlands (Driscoll et al., 2007).
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      B.2.1.2. Ponds
 1          The factors that determine the sensitivity of ponds to acidification from acidic deposition are
 2    generally similar to those that determine the sensitivity of lakes (discussed in the following section). In
 3    general, however, ponds and small lakes tend to exhibit low ANC and pH at a greater frequency than do
 4    larger lakes (Sullivan et al., 1990). This pattern is mainly a consequence of the higher concentrations of
 5    DOC frequently found in ponds as compared with larger lakes. In addition, because larger bodies of water
 6    tend to have larger watersheds, there is a greater likelihood that they will integrate conditions across a
 7    broader landscape, increasing the possibility of receiving at least a moderate level of base cation supply
 8    (Sullivan et al., 1990). Thus, where lakes are acid-sensitive, it is likely that ponds are also acid-sensitive.
 9    However, synoptic databases of pond acid-base chemistry are generally not available.


            B.2.2. Streams and Lakes
10          Acidic deposition that falls as precipitation directly on the lake surface may eventually be
11    neutralized by in-lake  reduction processes which are controlled in part by hydraulic residence time (Baker
12    and Brezonik, 1988). Natural hydrologic events also alter acidification and neutralization processes
13    during snowmelt and change flowpaths during extended droughts (Webster et al., 1990).
14          Leaching of base cations by acidic deposition can deplete the soil of exchangeable bases. The
15    importance of this response has recently been widely recognized because most watersheds are not
16    exhibiting much ANC and pH recovery of drainage water in response to recent large decreases in S
17    deposition. This limited recovery can be at least partially attributed to decreased base cation
18    concentrations in surface water. This understanding of the base cation response has developed slowly.
19    During the 1980s, the generally accepted paradigm of watershed response to acidic deposition was
20    analogous to a large-scale titration of ANC (Henriksen, 1984). Atmospheric inputs of acidic anions were
21    believed to result in movement of those anions through soils into drainage waters with near proportional
22    loss of surface water ANC. This view was modified by Henriksen (1984), who suggested that a modest
23    component of the added SO42  (up to a maximum of about 40%) could be charge-balanced by increased
24    mobilization of base cations from soils, and the remaining 60% to 100% of the added SC>42  resulted in
25    loss of ANC in surface waters. During the latter part of the 1980s, it became increasingly clear that a
26    larger component (> 40%) of the added SC>42 was in fact neutralized by base cation release in most cases
27    and the ANC (and therefore also pH) of surface waters typically did not change as much as was earlier
28    believed. This understanding developed in large part from paleoecological studies (e.g., Charles et al.,
29    1990; (Sullivan et al.,  1990), which indicated that past changes in lakewater pH and ANC had been small
30    relative to estimated increases in lakewater SC>42  concentrations since pre-industrial times (Sullivan,
31    2000). The belief that changes in acidic deposition were accompanied mainly by changes in ANC and pH

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 1    has been replaced by the realization that changes in SC>42  were accompanied mainly by changes in base
 2    cations. Thus, surface waters have not been acidified as much by historical deposition as was earlier
 3    believed. Furthermore, surface water ANC and pH should not be expected to show substantial chemical
 4    recovery upon reduced emissions and deposition of S and N. The magnitude of the base cation response
 5    has clearly limited the extent of surface water acidification caused by acidic deposition. However, this
 6    same response has contributed to base cation deficiencies in some soils, with associated adverse terrestrial
 7    effects.
            B.2.3. Other Types of Ecosystems
 8          There has been little work on the rates of atmospheric deposition to urban ecosystems despite
 9    extensive data on concentrations and chemical reactions of air pollutants in cities (U.S. Environmental
10    Protection Agency, 2004). Nevertheless, urban ecosystems are often subjected to large rates of deposition
11    of anthropogenic pollutants (Lovett et al., 2000a). Decades of research on urban air quality indicate that
12    cities are often important sources of emissions of NOX, SOX, and dust. Urban N deposition may affect
13    nutrient cycles and soil acid-base chemistry in vegetated areas in and around cities, but such possible
14    effects have not been studied sufficiently to draw conclusions about sensitivities or effects.
15          To determine the patterns of atmospheric deposition and throughfall in the vicinity of a large city,
16    Lovett et al. (2000a) measured bulk deposition, oak forest throughfall, and particulate dust at sites along a
17    transect within and to the north of New York City. They found that throughfall N was twice as high in the
18    urban areas compared with suburban and rural areas. Most of the urban dry deposition of NC>3  was from
19    gaseous NOx  Because there is limited biological uptake of throughfall N in an  urban setting, it is
20    believed that a relatively high (but unknown) percentage of N deposited to the urban landscape leaches to
21    surface waters. Aquatic effects associated with N leaching from urban environments would be expected to
22    be most pronounced near coastal cities. This is because atmospheric deposition  to near-coastal urban
23    environments can provide a substantial N load to estuaries and near shore oceanic environments, which
24    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
25          Coniferous forests, with soils that are naturally more acidic, generally have lower pH and base
26    saturation than soils in deciduous forests (Fernandez et al., 2003). In a paired watershed study at Bear
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 1    Brook Watershed in Maine, one watershed with mixed coniferous and deciduous species received
 2    (NH4)2SO4 corresponding to about 25 kg N/ha/yr and 29 kg S/ha/yr. After a decade of experimental
 3    acidification, the treated watershed had 66 kg/ha/yr less exchangeable Ca2+ and 27 kg/ha/yr less
 4    exchangeable Mg2+ than the untreated watershed (Fernandez et al., 2003). Soils under conifers (red
 5    spruce, balsam fir [Abies balsamea], hemlock [Tsuga canadensis]) appeared to be more sensitive to
 6    acidification than those under hardwoods (American beech [Fagus grandifolia], yellow birch [Betula
 1    alleghaniensis], sugar, and red maples [Acer rubrum]). The hardwoods demonstrated no significant
 8    effects from (NH4)2SO4 addition. Differences in response to acid treatment among vegetation covers were
 9    most pronounced in upper soil (O horizon and upper 5 cm of the B horizon). The study did not distinguish
10    between effects from NH4+ versus SO42 additions.
11          Kozlowski (1985) suggested that plants and soils act as sinks for SC>2 deposition at low exposures
12    of 1 to 4 (ig/m3, with no discernible effects on ecosystem structure at those levels. Shugart and
13    McLaughlin (1985)  cautioned that forest responses to SC>2 and other stressors are strongly controlled by
14    the successional dynamics of impacted forests. Thus, efforts to better understand and quantify forest
15    dynamics and development will be paramount to predicting chronic pollution  effects.
16          Results of N fertilization studies have been used to infer the response of forests to atmospheric N
17    deposition. Such studies were reviewed by Johnson (1991) and EPA (1993), illustrating that forests can
18    respond differently to periodic large pulsed fertilizer inputs, as compared with steady, low-level inputs
19    from atmospheric deposition. For example, multiple or continuous inputs of N may stimulate populations
20    of nitrifying bacteria (U.S. Environmental Protection Agency, 1993). This might be expected to modify
21    the competitive interactions between trees and microbes and affect both the forest growth response and
22    the extent of NO3 leaching and associated acidification.
23          In the southern Appalachian Mountains, acidification sensitivity has been evaluated for two
24    common tree species: red spruce (sensitive) and loblolly pine (Pinus taeda; insensitive). Dendro-
25    chronological analyses of tree cores collected for permanent plots in the Great Smoky Mountains National
26    Park (37 trees cores  from low elevation [-1500 m]; 35 tree cores from high elevation sites [-2000 m]),
27    demonstrated a positive correlation between temporal and spatial trends in red spruce growth and acidic
28    deposition, with a greater response in trees on ridges than in draws. Ridges are naturally more acidified,
29    receive higher levels of acidic deposition, and have shallower soils with lower base saturation (Webster
30    etal., 2004).
31          Loblolly pine seems to have low susceptibility to adverse  effects from acidic deposition. A
32    simulated acid addition experiment showed no significant effect of acidification on foliar nutrition in
33    loblolly pine seedlings, at application levels of 21 to 26 kg/ha SO4 -S and 8 to 10 kg/ha NOs -N (Baker
34    et al., 1994). Loblolly pines grown on old agricultural fields showed signs of N deficiency over 25 years
35    of growth despite atmospheric deposition of 5 to 10 kg N/ha/yr (Richter et al., 2000).
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 1          In the northeastern U.S., two species of coniferous tree (red spruce and red pine) have been shown
 2    to be sensitive to acidification. Aber et al. (2003) reported a decrease in C:N ratio from about 35 to about
 3    25 along an increasing N deposition gradient of 3 to 12 kg N/ha/yr across the Northeast. At the Harvard
 4    Forest LTER site, at chronic experimental N addition levels of 50 and 150 kg N/ha/yr, Magill et al. (2004)
 5    found 31% and 54% decreases, respectively, in red pine growth after 15 years of chronic N application.
 6    No additive effect of S was seen after 11 years of a combined N and S treatment, with an application of
 7    74 kg S/ha/yr and 50 kg N/ha/yr. There were no significant differences in baseline measurements between
 8    the low N and combined N and S treatments.
 9          Recent evidence indicates that mortality in red spruce in the southern Appalachian Mountains is not
10    abnormal when compared to historical rates, and that Fraser fir stands killed by the balsam woolly adelgid
11    (Adelges piceae) are largely being replaced by vigorous re-growth of young stands of that species (Van
12    Miegroet et al., 2007). To what extent spruce or fir mortality in the southern Appalachian Mountains will
13    be replaced with a species mix similar to that existing prior to the mortality remains to be seen.
14          At the Fernow Experimental Forest in West Virginia, (NH4)2SO4 inputs of 54 kg N/ha/yr and 61 kg
15    S/ha/yr (application plus ambient atmospheric deposition), each about three times the ambient deposition
16    level, were applied to  one watershed for 4 years. Few differences in soil and forest floor chemistry were
17    found in response to the N addition, although pH was significantly lower in the treatment watershed,
18    corresponding to increased base-flow concentrations  of NOs  and Ca2+ (Gilliam et al., 1996).
19          Deciduous forests show variable responses to acidification depending on the tree species present.
20    Along an increasing N deposition gradient in the northeastern U.S., from 4.2 to 11.1 kg N/ha/yr, Lovett
21    and Rueth (1999) found a twofold increase in mineralization in soils of sugar maple stands, but no
22    significant relationship between increased deposition and mineralization in American beech stands. This
23    difference might be attributable to the lower litter quality in beech stands. Thus, sugar maple appears to be
24    more susceptible to effects of increasing deposition and concomitant soil acidification from either direct
25    leaching of NO3  or enhanced nitrification. For northeastern hardwoods, Aber et al. (Aber et al., 2003)
26    found a decrease in C:N ratio  from 24 to 17 over a deposition gradient of 3 to 12 kg N/ha/yr. This
27    decrease was similar but less steep than the decrease  seen in conifers.
28          Across an 800 km pollution gradient (3 to 11 kg  SC>4 -S/ha/yr; 2 to 4 kg NC>3 -N/ha/yr) in northern
29    hardwood forests, with maples dominant, Pregitzer et al. (1992) found a 200 to  300 (ig/g increase in foliar
30    S, and litter fall S content ranged from 872 to 1356 (ig/g. While foliar N did not change across the
31    gradient, litterfall N was correlated with changing deposition. Pregitzer and Burton assert that their data
32    did not suggest a causal link between acid deposition and forest decline. Decline would be impossible to
33    document in the short  5-year time frame of their study. They did, however, assert that their results
34    supported the plausibility of altered tree nutrition across large geographic regions due to atmospheric
35    deposition.
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            B.3.2. Transitional  Ecosystems
 1          Wetlands are common in many areas that contain acid-sensitive surface waters. For example,
 2    wetlands constitute about 14% of the land surface in the Oswegatchie/Black River watershed in the
 3    southwestern Adirondack Mountains (Ito et al., 2005), one of the regions of the U.S. most affected by
 4    surface water acidification from acidic deposition. There are no studies, however, that have documented
 5    the extent or magnitude of acidification effects of S and N deposition on wetland ecosystems in the U.S.
 6          The topic of acidification effects on wetlands is not well represented in the literature, and therefore
 7    the distribution of ecosystem effects for these systems is not presented. Because levels of natural organic
 8    acidity tend to be high in wetland soils and water, it is not likely that such ecosystems are affected by the
 9    levels of acidic deposition commonly encountered in the U.S. It is more likely that atmospheric
10    deposition affects wetlands via nutrient N enrichment pathways. (See Discussion in ISA Sections 3.3.2.2
11    and 3.3.5.2) Gorham et al. (1987) hypothesized that acidic deposition to mineral-poor fens might cause
12    depletion of exchangeable base cations and decreased pH of soil water. This mechanism was suggested as
13    a possible cause of transition from mineral-poor fen to Sphagnum bog. Such an effect has not been
14    observed in response to acidic deposition at levels found in the U.S.
15          Synoptic surveys of ponded waters generally are restricted to lakes larger than 1 ha,  4 ha, or 10 ha.
16    Reasons for this limitation are varied and can include the perception that larger lakes are more important,
17    the failure of regional topographic map coverages to include the smaller lakes and ponds, and the fact that
18    smaller lakes tend to be much more numerous than larger lakes within the major lake districts of the U.S.
19    In general, if the larger lakes in a given region are sensitive to acidification, the smaller ponds would also
20    be expected to be sensitive. In most cases, data to demonstrate this are not available.
21          Ponds that have been observed to be sensitive to acidic deposition have been found in the
22    Adirondack Mountains in New York  (Kretser et al., 1989) and the Mount Zirkel Wilderness Area located
23    in the Colorado Rockies (Campbell et al., 2004). Acid-sensitive ponds are likely to be found elsewhere as
24    well.
            B.3.3. Aquatic Ecosystems
25          In most regions of the U.S., the majority of lakes and streams are not highly sensitive to existing
26    levels of ambient air pollution. In addition, air pollution levels are generally decreasing in many parts of
27    the country, especially in the eastern U.S., in response to federal and state air pollution control
28    regulations. Therefore, the highly sensitive, and acidified, systems tend to be restricted to a relatively
29    small percentage of the overall aquatic resource base. There are exceptions to this generalization, such as
30    for example in Monongahela National Forest, WV, where a high percentage of the streams are acid-
31    sensitive and highly acid-affected (cf Sullivan et al., 2002). Similarly, a high percentage of Adirondack

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 1    lakes (and presumably also streams) are acid-sensitive and have been acidified by atmospheric deposition
 2    ofSandN(Driscolletal., 1991).
 3          Studies to assess relationships between atmospheric deposition loading (N and S) and the estimated
 4    or expected extent, magnitude, and timing of aquatic acidification effects (U.S. Environmental Protection
 5    Agency, 1995; cf Van Sickle and Church, 1995; NAPAP, 1998) often employ a "weight of evidence"
 6    evaluation of the relationships between deposition and effects, as followed by NAPAP in the Integrated
 7    Assessment (IA) (NAPAP, 1991).
 8          Several kinds of evidence were used in the lAto assess the extent and magnitude of acidification in
 9    sensitive regions of the U.S. These included:
10          •   results of watershed simulation models that projected past or future chemical changes in
11              response to changes in S deposition

12          •   empirical biological dose/response models

13          •   improved relationships between surface water chemistry and ambient acidic deposition

14          •   trend analyses of long-term monitoring chemical data in regions that have experienced large
15              recent changes in acidic deposition levels

16          •   paleolimnological reconstructions of past water chemistry using fossil remains of algae
17              deposited in lake sediments

            •   results from whole-watershed or whole-lake acidification or deacidification field experiments
18          Evidence from  each type of study contributes to understanding of the quantitative importance of
19    acidification and neutralization processes and their effects on the chemistry and biology of affected
20    ecosystems.

      B.3.3.1. Status  of  Surface Waters - Regional Overview
21          In the NSS, DOC concentrations were much higher in lowland coastal streams, compared with
22    inland streams. National Stream Survey data also supported the hypothesis that atmospheric sources and
23    watershed retention of S control regional patterns in streamwater SC>42  concentrations. Most NSS
24    watersheds retained the vast majority of the total N loading from wet deposition. The 1986 data
25    suggested, however, that some atmospherically deposited N may have been reaching streams in the
26    northern Appalachians (Kaufmann et al., 1991).
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 1          In the 1990 NAPAP State of Science/Technology (SOS/T) summary, Baker et al. (1991a) identified
 2    six high interest subpopulations that accounted for most of the U.S. surface waters that were acidic with
 3    acidic deposition  having been identified as the likely source (Figure B-3; Table B-2):
          •   Southwestern Adirondacks
          •   New England Uplands
          •   Low Si Eastern Upper Midwest
          •   Forested  Mid-Atlantic Highlands
          •   Mid-Atlantic Coastal Plain
          •   Northern Florida Flighlands
 4          Stream data for the NSS was unavailable for three of these high-interest areas: the Adirondacks,
 5    New England, and Upper Midwest. The national WSA data indicated that acidic streams in the Upper
 6    Midwest are likely to be rare but there are acidic streams in the Adirondacks/New England region.
 7    Specific areas of interest within the other three high-interest regions are described below.
 8          In addition to the large water chemistry databases developed by the EPA, there are also some
 9    important supplemental databases in some regions. For example, based on results of lake surveys
10    conducted during the 1980s, about 70% of the known acidic lakes in Maine were either seepage type or
11    high elevation (Kahl et al., 1991). The Maine seepage lake dataset includes 120 of the estimated 150 lakes
12    in Maine that meet the following criteria: (1) located in sand and gravel mapped by the USGS or Maine
13    Geological Surveys; (2) depth at least 1 m; and (3) area at least 0.4 ha (1 ac). Sampling was conducted in
14    1986-87 and 1998-2000, and included at least one fall index sample for each lake. There were 87 lakes
15    with Gran ANC less than 100 (ieq/L.
16          The Maine high elevation lake dataset includes 90 lakes above 600 m elevation. Sampling was
17    conducted during the periods 1986-88 and 1997-99. The study included the vast majority of Maine lakes
18    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
19    100 (ieq/L.
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                                                        101 SILICA
                                                   [ASTERS UPPER
                                    NE*  ENGLAND
                                      UPLANDS
                                                                                    BIO-ATLANTIC
                                                                                   COASTAL PLAIN
                                                               FORESTED
                                                                 Aft ANT 1C
                                                               HIGHLANDS ''
                                                                             NORTHERN  FLORID*
                                                                                HIGHLANDS
                                                                                       Source: Baker etal. (1990b).
      Figure B-3. Location and percentage of acidic surface waters in U.S. high-interest subpopulations
      with respect to acidic deposition effects. Estimates are for the upstream reach ends in the NSS
      data. Population estimates in the Mid-Atlantic Highlands apply only to the forested watersheds,
      while estimates in the eastern Upper Midwest apply only to low-silica (> 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
 1          Surface water acid-base chemistry monitoring throughout the eastern U.S. occurs primarily in two
 2    EPA programs: the Temporally Integrated Monitoring of Ecosystems (TIME) project (Stoddard, 1990)
 3    and the Long-Term Monitoring (LTM) project (Ford et al., 1993; Stoddard et al., 1998). Both projects are
 4    operated in cooperation with numerous state agencies, academic institutions  and other federal agencies.
 5    Each is described below.
 6          The regions represented by the LTM and TIME monitoring programs (Annex A) are estimated to
 7    contain 95% of the lakes and 84% of the streams in the U.S. that have been anthropogenically acidified by
 8    acidic deposition. The Adirondacks had a large proportion of acidic surface waters (14%) in the NSWS;
 9    from 1984 to 1987, the ALSC sampled 1,469 Adirondack lakes greater than 0.5 ha in size and estimated
10    that many more (26%) were acidic (Driscoll et al., 1991). The higher percentage of acidic lakes in the
11    ALSC sample was due to inclusion of smaller lakes and ponds (1 to 4 ha in area), many of which were
12    acidic as a consequence of naturally occurring organic acids (Sullivan et al.,  1990). The proportions of
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 1    lakes estimated by NSWS to be acidic were smaller in New England and the Upper Midwest (5% and 3%,
 2    respectively), but because of the large numbers of lakes in these regions, there were several hundred
 3    acidic waters in each of these two regions.
 4          The Valley and Ridge Province and Northern Appalachian Plateau had 5% and 6% acidic sites,
 5    respectively. The only potentially acid-sensitive region in the eastern U.S. not assessed in the Stoddard
 6    et al. (2003) report was Florida, where the high proportion of naturally acidic lakes, and a lack of long-
 7    term monitoring data, make assessment of the effects of acidic deposition problematic (Stoddard et al.,
 8    2003).
 9          The TIME project is structured as a probability sampling. Each site is chosen statistically to be
10    representative of a target population. In the Northeast (New England and Adirondacks), this target
11    population consists of lakes with Gran ANC < 100 (ieq/L, which are those likely to be most responsive to
12    changes in acidic deposition. In the Mid-Atlantic, the target population is upland streams with ANC <
13    100 (ieq/L. Each lake or stream is sampled annually, and results are extrapolated to the target populations
14    (Larsen and Urquhart, 1993; Larsen et al., 1994; Stoddard et al.,  1996; Urquhart et al.,  1998). The TIME
15    project began sampling northeastern lakes in 1991. Data from 43 Adirondack lakes can be extrapolated to
16    the target population of about 1,000 lakes having ANC < 100 (ieq/L, out of a total population of 1,830
17    lakes with surface area > 1 ha. Data from 30 lakes representing about 1,500  lakes having ANC <
18    100 (ieq/L, out of a total population of 6,800 lakes, are included in the TIME program in New England.
19          As a compliment to lake and stream sampling in the statistical populations of lakes in TIME, the
20    LTM project samples a subset of sensitive lakes and  streams with long-term data, many dating back to the
21    early 1980s. Each LTM site is sampled 3 to 15 times per year, and the resulting data are used to
22    characterize the response of the most sensitive aquatic systems in each region to changing levels of acidic
23    deposition. In most regions, a small number of higher ANC (e.g., Gran ANC >  100 (ieq/L) sites  are also
24    sampled. Because of the long-term records at most LTM sites, their trends can also be placed in  a better
25    historical context than those of TIME sites,  where data are only available from the 1990s. Monitoring
26    results from the LTM project have  been widely published (Kahl et al., 1991, 1993b; Driscoll and Van
27    Dreason, 1993; Murdoch and Stoddard, 1993; Stoddard and Kellogg, 1993; Webster et al., 1993; DeWalle
28    and Swistock,  1994; Driscoll et al., 1995; Stoddard et al., 1998).  Overall results were summarized by
29    Stoddard and Kellog (1993).
30          Monitoring data from the  LTM and TIME projects were used to evaluate recent changes in lake and
31    stream chemistry from 1990 to 2000 in many of the sensitive areas of the eastern U.S. including New
32    England, the Adirondack Mountains, the Northern Appalachian Plateau, the  Ridge and Blue Ridge
33    provinces of Virginia, and the Upper Midwest (Stoddard et al., 2003). There are also substantial  numbers
34    of acid-sensitive streams in the Blue Ridge Province in North Carolina and portions of South Carolina
3 5    and Tennessee that have been affected by acidic deposition but that were not included in this analysis. In
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 1    general, the results of the TIME/LTM data analysis suggest that about one-quarter to one-third of the
 2    lakes and streams that were chronically acidic in the 1980s were no longer chronically acidic in the year
 3    2000. However many still had low ANC and were potentially susceptible to episodic acidification
 4    (Stoddard et al., 2003).
 5          Stoddard et al. (2003) found little evidence of regional change in the acidity status of lakes in New
 6    England or streams in the Ridge/Blue Ridge regions. Furthermore, none of the study regions showed an
 7    increase in the number of chronically acidic waters, even though there was a decline in base cation
 8    concentrations and a likely increase in natural organic acidity (Stoddard et al., 2003). An important caveat
 9    in this analysis is that changes in Gran ANC used in the analysis were based on the median change of all
10    sites in a region (Table B-4). However, as shown in (Table B-5), the rates of ANC increase were generally
11    more rapid in chronically acidic lakes with ANC less than 0 (ieq/L and streams with ANC between 0 and
12    25 (ieq/L. If acidic sites are recovering more rapidly than the population of sites as a whole, then the
13    estimates of change in the number of acidic  lakes and streams presented would be conservative.
14         While general estimates for large regions are useful in providing a broad picture of the extent and
15    status of surface water acidity, specific results from studies within those regions can help isolate trends
16    and determine the specific mechanisms that contribute to change. The following sections report on the
17    current status, past acidification, and potential future conditions for lakes and streams in acid sensitive
18    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
19         The Adirondacks and New England are two of the most acid sensitive and intensively studied
20    regions in the Northeast. The glaciated soils and location downwind from emissions  sources have made
21    these areas the subject of intense scientific study over the past four decades. Most of this research has
22    focused on lake ecosystems, though important stream studies have been undertaken at specific research
23    sites and more regional stream survey work  is being conducted. As discussed below, the surface water
24    chemistry in these areas integrates atmospheric deposition, local geology, and upland watershed
25    characteristics.
26         Available surface water datasets for Adirondack lakes include TIME, the Environmental
27    Monitoring and Assessment Program (EMAP), and ALSC, each of which is useful for documenting
28    chemical status and recent chemical changes. Population estimates from the TIME dataset suggest that
29    13.0% of Adirondack lakes (238 lakes) were chronically acidic in the early 1990s during baseflow

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 1    conditions in the summer. By applying an approximate rate of change in Gran ANC of+0.8 (ieq/L/yr to
 2    these estimates (based on trend slopes for TIME and LTM data, (Table B-6), Stoddard et al. (2003)
 3    projected that approximately 8.1% of the population (149 Adirondack lakes) remained chronically acidic
 4    in 2000. This finding suggests that roughly 38% of lakes in the Adirondacks that were chronically acidic
 5    in the early 1990s were not chronically acidic a decade later. Certain caveats need to be included with the
 6    results of this analysis, however. Summertime baseflow sampling reflects the least acidic conditions
 7    experienced throughout the year. In addition, LTM trends, which are based on year-round sampling, may
 8    not be representative of trends in the summer-only sampling of the TIME program, and the rate of change
 9    determined through the TIME program was not controlled for possible differences in flow conditions
10    between the two sample periods. Lastly, the ANC value of 0 (ieq/L/year used to define acidic waters has
11    been shown to be below the level needed to protect aquatic ecosystems in the Adirondack region (Baldigo
12    et al., 2007; Lawrence et al., 2007).
13          A study by Driscoll et al. (2001b) used EMAP data from 1991 to 1994 to evaluate the extent of
14    acidic lakes in the Adirondacks for that period. The EMAP survey is a probability based survey
15    representative of lakes with surface area greater than 1 ha (1,812 lakes). The survey was conducted during
16    low-flow summer conditions, and the results  therefore likely reflect the highest ANC values  for the year.
17    Results from the survey indicate that  10% of the population of Adirondack lakes were chronically acidic
18    (ANC values of less than 0) and 31% were sensitive to episodic acidification (ANC values between 0 and
19    50) during the study period (Driscoll et al., 200Ib).
20          The ALSC conducted a comprehensive survey of Adirondack lakes greater than 0.2 ha in surface
21    area between 1984 and 1987 (Kretser et al., 1989). Of the 1,489 lakes surveyed, 24% had summer pH
22    values below 5.0, 27% were chronically acidic (ANC < 0), and an additional 21% were probably
23    susceptible to episodic acidification (ANC between 0 and 50) (Driscoll et al., 2007).
24          For the New England region, the TIME population data indicates that 5.6% of the population (386
25    lakes) in New England exhibited Gran ANC < 0 (ieq/L during the period of 1991 to 1994. This result is
26    similar to the EMAP findings which indicate that  5% of lakes in New England and in the eastern Catskill
27    region of New York  had ANC values  less  than 0 (ieq/L. The EMAP analysis also estimated that an
28    additional 10% of the population had low ANC values, between 0 and 50 (ieq/L, and were probably
29    sensitive to episodic acidification (Driscoll et al.,  200Ib).
30          Both TIME and LTM data from the New England region indicate that only a small increase in Gran
31    ANC had occurred during the reported monitoring period (+0.3 (ieq/L/yr). As a result, it is estimated that
32    the proportion of chronically acidic lakes  decreased only 0.1% from 5.6% to 5.5% over the previous 10
33    years  (Table B-4) (Stoddard et al., 2003).
34          State surveys  within New England provide  additional information on the variation in lakewater
35    chemistry across the region. In Maine, approximately 100 clearwater lakes in that state have been
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 1    classified as acidic, based on surveys conducted by EPA and the University of Maine (Kahl et al., 1999).
 2    An estimated 13% of the high-elevation lakes in Maine are acidic, compared to less than 1% of Maine
 3    lakes (>4 ha) represented in EPA's Eastern Lakes Survey (ELS) (Linthurst et al., 1986a; Kahl et al., 1991).
 4    Most acidic lakes in Maine are either seepage lakes located in sand and gravel deposits, or high-elevation
 5    lakes located above 600 m elevation. Roughly 60% of the acidic lakes are seepage lakes. The acid-
 6    sensitive seepage lakes are located in mapped sand and gravel deposits, are at least 1 m deep, and are at
 7    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.,
 8    1999).
 9          Whereas lakes in the Adirondacks and New England have been intensively studied, there are no
10    published data which describe the status of streamwater acid-base chemistry at a regional scale, except for
11    the Catskill Mountains.
12          In the absence of regional streamwater studies, insights can be gained from site-specific long-term
13    studies in the region. The HBEF has one of the longest continuous records of precipitation and
14    streamwater chemistry in the U.S. Compared to model hindcast approximations, current conditions at
15    HBEF indicate that soil percent base saturation has decreased in response to acidic deposition and
16    because of accumulation of nutrient cations by forest vegetation. Further, acidic deposition has
17    contributed to a nearly fourfold increase in stream SC>42  concentration; a decrease in ANC from positive
18    to negative values; a decrease in stream pH to 5.0; and an increase in stream Al, largely occurring as the
19    inorganic form which has been shown by Lawrence at al. (2007) to be an unequivocal indication of the
20    effects of acidic deposition. Driscoll et al. (200 Ib) estimated that roughly 6% of lakes and streams in  the
21    Northeast are considered more sensitive to acidic deposition than the stream monitored at HBEF (Driscoll
22    etal.,2001b).

      Past Acidification
23          There are limited surface water data that directly document historic conditions and the response to
24    atmospheric deposition since the time of the Industrial Revolution ((Charles, 1991). To address this gap,
25    scientists use sediment cores from lakes and detailed computer models to try to reconstruct past
26    conditions as well as understand the mechanisms that contribute to changing conditions.

      Paleolimnological Studies
27          Paleolimnological studies use the remains of diatoms and other algae embedded in lake sediments
28    to reconstruct historical water chemistry. In the Adirondack Mountains and northern New England, both
29    diatom and chrysophyte algal remains in sediment cores have been used to evaluate patterns of past
30    acidification in a large number of lakes.
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 1          Major findings of the Paleoecological Investigation of Recent Lakewater Acidification (PIRLA)-I
 2    and PIRLA-II research programs in the Adirondack Mountains suggested that: (1) Adirondack lakes had
 3    not acidified as much since pre-industrial times as had been widely believed prior to 1990; (2) many
 4    Adirondack lakes with ambient pH greater than 6.0 had not acidified historically, whereas many of the
 5    lakes having pH less than about 6.0 had acidified; (3) many of the lakes having high pH and ANC had
 6    increased in pH and ANC since the previous century; and (4) the average F-factor for acid-sensitive
 7    Adirondack lakes was near 0.8 (Charles et al., 1990; (Sullivan et al., 1990). The results of these studies
 8    had major effects on scientific understanding of the extent to which lakes had acidified in response to
 9    acidic deposition. The view of surface water acidification as a large-scale titration of ANC (Henriksen,
10    1980, 1984) was  replaced by the realization that base cation  concentrations typically changed more than
11    ANC in response to acidic deposition. This realization also modified expectations for chemical recovery
12    of surface waters as acidic deposition levels have decreased  (Sullivan, 2000).
13          Diatom and chrysophyte reconstructions of pH and ANC for a statistically selected group  of
14    Adirondack lakes suggested that about 25% to 35% of the Adirondack lakes that are larger than 4 ha had
15    acidified since preindustrial time (Cumming et al., 1992). Low-ANC lakes of the southwestern
16    Adirondacks acidified the most, probably due to low initial buffering capacity and high rainfall and
17    deposition of S and N in that area.  Cumming et al. (1992) estimated that 80% of the Adirondack lakes that
18    had ambient pH > 5.2 had experienced large declines in pH and ANC since the previous century, and that
19    30% to 45% of the lakes with pH between 5.2 and 6.0 had also acidified.
20          Cumming  et al. (1994) reported the results of chrysophyte inferences of pH in recently deposited
21    lake  sediments to assess acidification timing for 20 low-ANC Adirondack lakes. Lakes that were inferred
22    to have acidified  since about 1900  tended to be small, high-elevation lakes with lower inferred pre-
23    industrial pH than the group of study lakes as a whole. These were probably the most acid-sensitive and
24    were the first to acidify with increasing acidic deposition. Husar and Sullivan (1991) estimated that S
25    deposition was about 4 kg S/ha/yr at that time. These lakes are located in the high peaks area and in the
26    southwestern portion of Adirondack Park. A second category of acidification response included high-
27    elevation lakes that were historically low  in pH (<5.5) but that acidified further beginning about 1900.
28    The third identified type of response  included lakes with pre-industrial pH in the range of about  5.7 to 6.3
29    that started to acidify around 1900  but showed their greatest pH change around 1930 to 1950. The final
30    category included lakes that were not inferred to have acidified. They had pre-industrial pH around 6.0
31    and are located at lower elevation.
32          Davis et al. (1994) conducted paleolimnological studies of 12 lakes in northern New England that
33    were low in pH and ANC. Past logging, forest fire, and vegetation composition in the watersheds were
34    estimated  from oral and written historical information, aerial photographs, and tree ring analyses. Lake
35    sediment cores were collected and  analyzed for pollen, diatoms, and chemistry to reconstruct past lake
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 1    conditions for several hundred years. All 12 lakes were historically low in pH and ANC, with diatom-
 2    inferred pre-industrial ANC of-12 to 31 (ieq/L. The inferred pH and ANC values of the lakes were
 3    relatively stable throughout the one to three centuries of sediment record prior to watershed disturbance
 4    by Euro-Americans. From the early  19th into the 20th century, however, all of the lakes showed increased
 5    diatom-inferred pH changes of about 0.05 to 0.6 pH units and increased diatom-inferred ANC of about 5
 6    to 40 (ieq/L.  Most of these inferred increases  in pH and ANC coincided with watershed logging. For all
 7    study lakes, recovery to pre-logging acid-base lake chemistry conditions was followed by continued
 8    decline in pH by 0.05 to 0.44 pH units and ANC by up to 26 (ieq/L, probably because of acidic
 9    deposition. The 12-lake mean inferred decreases in pH and ANC in response to acidic deposition were
10    0.24 pH units and 14 (ieq/L, respectively (Davis et al., 1994).

      Modeling Studies
11          The most extensive regional modeling  study that provides estimates of past acidification of
12    Adirondack lakes is that of Sullivan  et al. (2006a). They modeled past changes in the acid-base chemistry
13    of 70 Adirondack lake watersheds, including 44 that were statistically selected to be representative of the
14    approximately  1,320 lake watersheds in the Adirondacks that have lakes larger than 1  ha and deeper than
15    1m and that have ANC > 200 (ieq/L. Model hindcasts were constructed using both the MAGIC and
16    PnET-BGC models.
17          Based on MAGIC model outputs extrapolated to the regional Adirondack lake population,
18    maximum past acidification occurred by about 1980 or 1990, with median ANC of the population under
19    investigation of about 61 (ieq/L (reduced from a median of 92 (ieq/L estimated for the pre-industrial
20    period). By 1990, 10% of the population target lakes had decreased in ANC to below -16 (ieq/L and 25%
21    had ANC < 28  (ieq/L. Percentile values in 2000 illustrated  limited chemical recovery (3 to 5 (ieq/L)
22    compared with simulated values in 1980 and  1990.
23          The MAGIC model simulations suggest that none of the target lakes were chronically acidic (had
24    ANC < 0 (ieq/L) under pre-industrial conditions, but that by 1980 there were about 204 acidic Adirondack
25    lakes. That number decreased by an  estimated 14% between 1980 and 2000. Similarly, the MAGIC model
26    simulations suggested that there were no Adirondack lakes having ANC < 20 (ieq/L in 1850, but by 1990
27    there were 263 such lakes. Many lakes (N = 191) were estimated to have had pre-industrial ANC below
28    50 (ieq/L, and this estimate increased threefold by 1990, followed by a decrease to 399 lakes in 2000.
29          PnET-BGC model simulations generated output generally similar to results provided by MAGIC
30    model simulations. PnET-BGC simulations suggested  that lakewater SC>42  , NC>3 , and base cation
31    concentrations under pre-industrial conditions were much lower than current values. In 1850, simulated
32    SC>42  concentrations in all  study lakes were less than 25 (ieq/L, and the median value was about
33    15 (ieq/L. By 1980, the  median simulated SC>42  concentration had increased more than sixfold to about
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 1    100 (ieq/L. Simulated lake NC>3  concentrations also increased markedly during that time, with the
 2    median value increasing from about 4 (ieq/L in 1850 to 12 (ieq/L in 1980. Simulated increases in the sum
 3    of divalent base cation concentrations were less than for SC>42 concentrations, with the median value
 4    increasing from 93 (ieq/L in 1850 to 140 (ieq/L in 1980. This large change in SC>42  + NC>3  relative to the
 5    change in the sum of base cation concentrations was the major mechanism driving the decreases in ANC
 6    and pH associated with historical increases in acidic deposition.
 7          Simulated lakewater ANC and pH and soil base saturation decreased from pre-industrial conditions
 8    to recent times. Results from PnET-BGC suggested that the median Adirondack lake, from among the
 9    estimated 1,320 lakes in the population larger than 1 ha that had measured recent ANC < 200 (ieq/L, had
10    pre-industrial ANC near 80 (ieq/L; an estimated 10% of the lake population had pre-industrial ANC <
11    41 (ieq/L; and one-fourth had pre-industrial ANC < 64 (ieq/L. Percentiles for the year 2000 suggest
12    decreases in SC>42  , NC>3 , and sum of base cations, and small increases in ANC since 1990 for lower-
13    ANC lakes.
14          Results from PnET-BGC suggest that none of the lakes in the Adirondack population had pre-
15    industrial ANC below 20 (ieq/L. By 1990, there were 289 lakes having ANC < 20 (ieq/L and 217
16    chronically acidic  (ANC > 0 (ieq/L) lakes according to PnET-BGC simulations. There were 202 lakes in
17    the population simulated to have had pre-industrial ANC below 50 (ieq/L, and this number increased 2.8
18    times by 1980 under the PnET-BGC simulations.
19          PnET-BGC  has also been used to characterize past conditions at streams within the HBEF.
20    Gbondo-Tugbawa et al. (Gbondo-Tugbawa et al., 2002) used relationships between current emissions and
21    deposition, and estimates of past emissions to reconstruct historical deposition conditions. Their analysis
22    also considered land disturbances such as logging in 1918-1920 and hurricane damage in 1938. Using
23    this approach, they estimated that past soil base saturation in the mineral soil (circa 1850) was
24    approximately 20%, stream SO42 concentration was approximately 10 (ieq/L, stream ANC was about
25    40 (ieq/L, stream pH was approximately 6.3, and stream Al; concentration was below 1 (imol/L (Driscoll
26    etal., 2001b).

      Recent Trends
27          Sulfur deposition has contributed to chronic soil and surface water acidification in the eastern U.S.
28    to a much greater extent than has N deposition. Nitrate concentrations in acid-sensitive drainage waters in
29    the eastern U.S. are generally much lower than SO42 concentrations.
30          The concentration of SC>42 in precipitation has declined for about the past three decades
31    throughout the northeastern U.S., in response to decreased atmospheric emissions and deposition. EPA's
32    LTM Program has been collecting monitoring data since the early 1980s for many lakes and streams in
33    acid-sensitive areas of the U.S., including the Northeast. These data allow evaluation of trends and
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 1    variability in key components of lake and streamwater chemistry prior to, during, and subsequent to Title
 2    IV implementation. Throughout the northeastern U.S., the concentration of SC>42  in surface waters has
 3    decreased substantially (Figure B-4) in response to decreased emissions and atmospheric deposition of S.
 4    Decreased concentrations of SC>42  in lakes and streams of a third, or more, have been commonly
 5    observed.
 6          Lakewater SC>42  concentrations have decreased steadily in Adirondack lakes, at least since 1978
 7    (Driscoll et al., 1995; Stoddard et al., 2003). Initially, there was not a systematic increase in lakewater pH
 8    or ANC in response to the decreased SO42 concentrations. Rather, the decline in lakewater SO42 during
 9    the 1980s was charge-balanced by a nearly equivalent decrease in concentrations of base cations in many
10    of the low-ANC lakes (Driscoll et al.,  1995). Similar findings were reported by Stoddard and Kellog
11    (1993) for lakes in Vermont. F-factors for the nine ALTM lakes that showed significant declines in both
12    the sum of base cations (SBC) and (SC>42 + NO3 ) concentration ranged from 0.55 to greater than 1.0,
13    with a mean of 0.93 (Driscoll et al., 1995). These high F-factor values for chemical recovery from
14    acidification were similar to results of historical acidification obtained by Sullivan et al. (Sullivan et al.,
15    1990), based on diatom reconstructions of historical change  for 33 Adirondack lakes.
16          Trend analysis results for the period 1982 to 1994 were reported  by Stoddard et al. (1998) for 36
17    lakes in the northeastern U.S. having ANC > 100 (ieq/L. Trend statistics among sites were combined
18    through a meta-analytical technique to  determine whether the combined results from multiple sites had
19    more significance than the individual Seasonal Kendall Test statistics. All lakes showed significant
20    declining trends in SC>42 concentration (A SC>42 = -1.7 (ieq/L/yr; p > 0.001). Lakewater ANC responses
21    were regionally variable. Lakes in New England showed evidence of ANC recovery (A ANC =
22    0.8 (ieq/L/yr; p > 0.001), whereas Adirondack lakes exhibited either no trend or further acidification,
23    largely because of declines in base cation concentrations.
24          The observed changes in the  concentration of NO3  in some surface waters have likely been due to
25    a variety of factors, including climate. During the  1980s, NO3~concentration increased in many surface
26    waters in the Adirondack and Catskill Mountains in New York (Driscoll and Van Dreason, 1993; Murdoch
27    and Stoddard, 1993).  There was concern that northeastern forests were becoming N-saturated, leading to
28    increased NO3 leaching from forest soils throughout the region. Such a response could negate the
29    benefits of decreased SC>42 concentrations in lake and stream waters. However, this trend was reversed in
30    about 1990, and the reversal could not be attributed to  a change in N deposition. Nitrate leaching through
31    soils to drainage waters is the result of a complex set of biological and hydrological processes. Key
32    components include N uptake by plants and microbes,  transformations between the various forms of
33    inorganic and organic N, and local precipitation patterns. Most of the major processes are influenced by
34    climatic factors, including temperature, moisture, and snowpack development. Therefore, NO3
35    concentrations in surface waters respond to many factors in addition to  N deposition and can be difficult
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 1    to predict. It is likely that monitoring programs of several decades or longer will be needed to separate
 2    trends in NO3  leaching from climatic variability in forested watersheds (Driscoll et al., 1995).
 3          Monitoring data collected during the 1990s in the LTM and TIME projects illustrated that most
 4    regions included in the monitoring efforts showed large declines in SO42 concentrations in surface waters
 5    over the 10 years of monitoring, with rates of change ranging from 1.5 to 3 (ieq/L/yr (Figure B-4). These
 6    declines in lake and stream SO42  concentration were considered consistent with observed declines in S
 7    wet deposition. Surface water NO3  concentrations also decreased, but only in the two regions that had
 8    the highest ambient surface water NO3 concentrations (Adirondacks and Northern Appalachian Plateau),
 9    but were relatively unchanged in regions with lower concentrations. DOC increased in each region over
10    time. This finding suggests an increase in the contribution of natural organic acidity, which would
11    partially offset the expected chemical recovery from decreased acidic deposition.
12          ANC increased in the Adirondacks at a rate of about +1 (ieq/L/yr, despite a decline in surface water
13    base cation concentrations (Ca2+ + Mg2+; Figure B-4). The decline in base cations offset some of the
14    decline in SO42 , and thus limited the increase in ANC or pH that occurred in response to lower SO42
15    concentrations. Surface water ANC and pH increased significantly in the 1990s; Al; concentrations
16    declined slightly. Regional surface water ANC did not change significantly in New England (Stoddard
17    etal., 2003).
18          Moderate increases in surface water ANC during the 1990s reduced the estimated number of acidic
19    lakes and stream segments in the northeastern U.S. Stoddard et al. (2003) estimated that there were 150
20    Adirondack lakes in the year 2000 that had ANC less than 0 (8.1% of the lake population), compared to
21    13% (240 lakes) in the early 1990s.
22          Lakewater SO42  concentrations in the most acid-sensitive Maine lakes declined by about 12% to
23    22% during the period 1982 to 1998 (Kahl, 1999). Only in the seepage lakes, however, was there
24    evidence of a small decline in lakewater acidity during that period (Table B-7).
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         200
         100-
         200
         100-
         200
         100-
          0
           Uan81
                   Sulfate concentration in Lake and Stream Water (|Jeq/L)

                    Lake Rondaxe, NY                         Biscuit Brook, PA
      200
      100-
            Uan81    Uan86      Uan91     Uan96


                   Long Pond, ME	
       0
       26Nov81 22Aug84 19May8712Feb90 8Nov92 5Aug95

                Vandercook Lake, Wl
      200
      100-
            Uan81     Uan86     Uan91

                   Lake Elbert, CO
                                      Uan96
      200
         Uan81     Uan86     Uan91

                 Grout Pond, VT
                                                                                Uan96
      100-
                    Uan86
                             Uan91
                                     Uan96
       0
       Uan81
                                                             Uan86
                                                                      Uan91
                                                                               Uan96
Figure B-4. Measured concentration of S042~ 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 Long-Term
Monitoring (LTM) program.
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                                                      Regional Trends, 1990-2000
                                                          (in lakes and streams)
                 Sulfate {|jeq/L/yr)
                  Nitrate (|jeq/L/yr)
                    ANC (|jeq/L/yr)
           Hydrogen Ion (peq/L/yr)
           Base Cations (peq/L/yr)
                    DOC (mg/L/yr)
              Aluminum ((jeq/L/yr)
                                                  -2
          -1        0        1
           Slope of Trend
                                                             New England Lakes
                                                             Adirondack Lakes
                                                             Northern Appalachian Streams
                                                             Upper Midwest Lakes
                                                             Ridge and Blue Ridge Streams
                                                                                    Source: Stoddard et al. (2003).
      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.

 1         However, evidence for reductions in lakewater ANC in seepage lakes from the mid-1980s to 1998
 2    were based on a comparison of only two sampling points, which may have been influenced by climatic
 3    variation. Therefore, the conclusion of decreasing acidity of seepage lakes is considered preliminary. The
 4    seepage lakes are generally hydrologically isolated from their surrounding soil environment. They
 5    therefore did not show a clear decreasing trend in base cation concentrations, as has been found in
 6    drainage lakes throughout the Northeast. The high-elevation lakes, in contrast, showed small declines in
 7    lakewater acidity during the 1980s, but that trend slowed or reversed in the 1990s (Kahl, 1999). Both the
 8    seepage and high elevation lakes showed increased DOC concentrations of 10% to 20%, generally by
 9    about 0.5 to 1.0 mg/L. The increase in dissolved organic matter would be expected to limit the extent of
10    ANC and pH recovery that would otherwise accompany the observed decreases in SC>42  concentration.
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 1    Whereas NC>3  concentrations decreased during the 1990s in many lake chemistry datasets (cf. Stoddard
 2    et al., 2003), the high-elevation lakes in Maine continued to show high concentrations.
 3          The reference stream of the Bear Brook Watershed Study (East Bear Brook) has the longest
 4    continuous, high-frequency data record of stream chemistry in Maine. Sulfate and NC>3 concentration
 5    have both declined substantially since 1987. Base cations declined by an almost equivalent amount, and
 6    the increase in ANC has been limited (Kahl, 1999).
 7          Long-term stream water data from the HBEF reveal a number of changes that are consistent with
 8    trends in lakes and streams across Europe and eastern North America (Stoddard et al., 1999, 2003; Evans
 9    and Monteith, 2001). Stream water draining the HBEF reference watershed (Watershed 6) had a 32%
10    decline in annual volume-weighted concentrations of SO42  (-1.1 (ieq/L) between 1963 and 2000
11    (Driscoll et al., 2007). This decrease in stream SC>42  concentration corresponds to both decreases in
12    atmospheric emissions of SC>2 and to bulk precipitation concentrations of SC>42  (Likens et al., 2001). In
13    addition, there has been a long-term decrease in stream concentrations of NC>3 that is not correlated with
14    a commensurate change in emissions of NOx or in bulk deposition of NC>3 . The long-term declines in
15    stream concentrations of strong acids (SC>42 + NC>3 ; -1.9 (ieq/L/yr) have resulted in small but significant
16    increases in pH, from 4.8 to 5.0 (Driscoll et al., 2007). Streams at HBEF remain acidic compared to
17    background conditions, when stream pH was estimated to be approximately 6.0 (Driscoll et al., 2007).
18    The increase in stream pH has been limited because of marked concurrent decreases in the sum of base
19    cations (-1.6 (ieq/L/yr; Driscoll et al., 2001b).

      Future Projections
20          MAGIC model simulations were conducted for the NAPAPIA to forecast the response of lakes and
21    streams in the eastern U.S. to S deposition. Results were reported by NAPAP (1991), Sullivan et al.
22    (1992), and Turner et al. (1992). The projected median change in lakewater or streamwater ANC during
23    50-year simulations was similar among regions, except in the Southern Blue Ridge and Mid-Atlantic
24    Highlands, where acidification was delayed due to S adsorption on watershed soils. MAGIC projected
25    relatively small future loss of ANC in most northeastern watersheds under continued constant deposition.
26    These modeled changes were due to a simulated slight depletion of the supply of base cations from soils
27    (Turner etal., 1992).
28          On average, each kg/ha/yr change in S deposition was projected by MAGIC to cause a 3 to 4 (ieq/L
29    median change in surface water ANC. Such projected changes in ANC, while considerably smaller than
30    was generally thought to occur in the 1980s, nevertheless suggested widespread sensitivity of surface
31    water ANC to changes in S deposition throughout the northeastern U.S. (Sullivan, 2000).
32          Since 1990, adjustments have been made to  the MAGIC model and its application method in
33    response to model testing using paleolimnological  data (Sullivan et al., 1992,  1996a) and the results of
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 1    acidification and deacidification experiments (Norton et al., 1992; Cosby et al., 1995, 1996) and empirical
 2    studies (Sullivan and Cosby, 1998). The net effect has been that the model projects somewhat less
 3    sensitivity of Adirondack lakes to change in S deposition as compared with the version of MAGIC
 4    applied in 1990 (Sullivan and Cosby, 1998).
 5          Model projections of future acid-base chemistry under three scenarios of future atmospheric
 6    emissions controls were presented by Sullivan et al. (2006b) and Zhai et al. (2008) for lakes in the
 7    Adirondack Mountains to evaluate the extent to which lakes might be expected to continue to increase in
 8    ANC in the future. Estimated levels of S deposition at one representative watershed are shown in figure
 9    B-6 for the hindcast period and in the future under the three emissions control scenarios. Model
10    simulations for 44 statistically selected Adirondack lakes using the MAGIC and PnET-BGC models were
11    extrapolated to the regional lake population. Cumulative distribution frequencies of ANC response
12    projected by MAGIC are shown in figure B-7 for the past (1850), peak acidification period (1990), and
13    future (2100). Results for the future are given for each of the scenarios.
14          Results suggested that the ongoing trend of increasing lakewater ANC for the most acid-sensitive
15    lakes would not continue under future emissions and deposition levels anticipated as of 2003 (Base Case
16    Scenario). The numbers of Adirondack lakes having ANC below 20 and below 50 (ieq/L were projected to
17    increase between 2000 and 2100 under that scenario, and the number of chronically acidic Adirondack
18    lakes (i.e., ANC less than 0) was projected to stabilize at the level reached in 2000. This projected reversal
19    of chemical recovery of acid-sensitive lakes was due to a continuing decline in the simulated pool of
20    exchangeable base cations in watershed soils.
21          Simulations suggested that re-acidification might be prevented with further reductions in emissions
22    and deposition.
23          Chen and Driscoll (2005) applied the PnET-BGC model to 44 EMAP lake watersheds in the
24    Adirondacks. PnET-BGC was used to predict the acid-base chemistry of soils and surface waters, and to
25    assess the fisheries status during pre-industrial conditions (-1850) and under three future acidic
26    deposition scenarios.
27
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                                                                                Hindcast
                                                                                Base
                                                                                Mod
                                                                                Agg
       1850
1900
1950
2000
2050
2100
                                              Year
                                                                          Source: Sullivan et al. (Sullivan, 2003).
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. Future deposition estimates were
based on three emissions control scenarios (Base Case, Moderate Additional Controls, Aggressive
Additional Controls).
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                           MAGIC Model Estimates of ANC Distribution
                             Adirondack Lakes with ANC < 200 ueq/L
             1.0H
                                                                           1850
                                                                           1990
                                                                           2100-Aggresive
                                                                           2100-Base
                                                                           2100-Moderate
                                         50          100         150
                                             Predicted ANC (peq/L)
                             200
                    250
                                                                                  Source: Sullivan etal.(2006b).
     Figure B-7. Simulated cumulative frequency distributions of lakewater ANC at three points in time
     for the population of Adirondack lakes.

1         Model hindcasts using PnET-BGC indicated that acidic deposition has greatly altered surface
2    waters and soils in the Adirondacks over the past 150 years, and that some ecosystems are continuing to
3    acidify despite decreases in S deposition. The model was applied to three future emissions scenarios: base
4    case, moderate emissions reductions, and aggressive emissions reductions. A case study for Indian Lake
5    in the Adirondacks illustrated that larger reductions in deposition caused greater decreases in SC>42  and
6    base cation concentrations in stream water and greater recovery in pH and ANC. Within the full
7    population of lake-watersheds, some showed decreasing ANC and pH values from 1990 to 2050 even
8    under the moderate and aggressive reduction scenarios. By 2050 to 2100, however, nearly all lakes
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 1    experienced increasing ANC and pH. The rate of soil base saturation regeneration increased very slowly
 2    over the modeled time period, compared to changes in surface water chemistry. For 95% of the lake-
 3    watersheds studied, simulated soil base saturation remained below 20% in 2100 under all emissions
 4    scenarios.
 5          There are few streams in the northeastern U.S. for which future acid-base chemistry status has been
 6    modeled. One notable exception is the modeling conducted for streams at the HBEF and in the Catskill
 7    Mountains. Calculations performed by Driscoll et al. (2003c) using the PnET-BGC model suggested that
 8    "aggressive reductions in N emissions alone will not result in marked improvements in the acid-base
 9    status of forest streams." For example, in response to an aggressive utility emissions control scenario
10    (hypothesized 75% reduction in utility N emissions beyond the CAAA), the ANC values of Watershed 6
11    at HBEF in New Hampshire and Biscuit Brook in the Catskill Mountains in New York were only
12    projected to increase by 1 and 2 (ieq/L, respectively, by the year 2050 (Driscoll et al.,  2003c). Projected
13    changes in water chemistry in response to differing levels of N deposition were small  in comparison with
14    model projections of variations resulting from climatic factors (Aber and Driscoll, 1997; Driscoll et al.,
15    2003c).

      B.3.4.2. Southeastern  Surface Waters
16          The two regions in the  Southeast that were identified by Charles (Charles,  1991) as containing low-
17    ANC  surface waters are the Appalachian Mountains and Northern Florida. The Appalachian Mountain
18    region contains many streams that have low ANC, and it receives one of the highest rates of acidic
19    deposition in the U.S. (Herlihy et al., 1993). Streamwater acid-base chemistry has been extensively
20    studied in this region (e.g.,  Church et al., 1992; Herlihy et al.,  1993; Van Sickle and Church, 1995;
21    Sullivan et al., 2002, 2003).
22          Northern Florida contains the highest percentage of acidic lakes of any lake population in the U.S.
23    (Linthurst et al., 1986a,b). Most lakes in Florida are located in marine sands overlying carbonate bedrock
24    and the Floridan aquifer, a series of limestone and dolomite formations that underlies  most of Florida.
25    Most  of the acidic and low-ANC lakes are located in the Panhandle and north central  lake districts.
26          The current status, past acidification and recent trends in surface waters chemistry for both the
27    Appalachian Mountains and northern Florida are discussed below.

      Appalachian Mountains
      Current Status
28          One of the most important processes affecting watershed acid-neutralization throughout much of
29    the Southeast is S-adsorption  on soil. If S adsorption on soil is high, relatively high levels of S deposition
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 1    have little or no effect on stream acid-base chemistry, at least in the short-term. However, this
 2    S-adsorption capacity can become depleted over time under continued S deposition, and this causes a
 3    delayed acidification response.
 4          Sulfur-adsorption varies by physiographic province. It  is highest in the soils of the Southern Blue
 5    Ridge, where typically about half of the incoming S is retained. Adsorption is lower in the Valley and
 6    Ridge watersheds and especially in the Appalachian Plateau (Herlihy et al., 1993). In general, S
 7    adsorption is higher in the southern portions of the Appalachian Mountain region.
 8          The Mid-Atlantic Highlands consists of the portions of the Blue Ridge Mountains, Ridge and
 9    Valley, and Appalachian Plateau ecoregions between the Virginia-North Carolina border and the Catskill
10    Mountains in southeastern New York. Acid mine drainage (AMD) is a major source of acidity to streams
11    in the Mid-Atlantic Highlands but in many cases is easy to identify due to the high concentrations of
12    SC>42  in the streams that are influenced by AMD (Herlihy et al., 1991). Acidic and low-ANC streams
13    affected by AMD were removed before analyses of acid-base chemistry population statistics.
14          Streams in the Appalachian Mountain portion of the mid-Atlantic region receive some of the largest
15    acidic deposition loadings of any region of the U.S. A compilation of survey data from the mid-
16    Appalachians yields a consistent picture of the acid-base status of streams. Acidic streams, and streams
17    with very low ANC, are  almost all located in small (watershed area < 20 km2), upland, forested
18    catchments in areas of base-poor bedrock. Acidic surface waters in this region are nearly always found in
19    forested watersheds because the thin soils and steep slopes that make these watersheds unsuitable for
20    agriculture and other development also contribute to their sensitivity to acidic deposition (Baker et al.,
21    1991a).
22          In the subpopulation of upland forested streams, which comprises about half of the total stream
23    population in the mid-Appalachian area, data from various local surveys showed that 5% to 20% of the
24    streams were acidic, and about 25 to 50% had ANC < 50 (ieq/L (Herlihy et al.,  1993). NSS estimates for
25    the whole region showed that there were 2330 km of acidic streams and 7500 km of streams with ANC <
26    50 (ieq/L. In these forested reaches, 12%  of the upstream reach ends were acidic and 17% had pH > 5.5
27    (Table B-4). Sulfate  from atmospheric deposition was the dominant source of acid anions in acidic mid-
28    Appalachian streams. In these acidic streams, the low pH (median 4.9) and high levels of Al; (median
29    129 (ig/L) leached through soils by acidic deposition were considered to be sufficiently high to cause
30    damage to aquatic biota. Acidic streams in this subpopulation typically had low DOC (mean 1.5 mg/L).
31          Localized studies  have clearly shown that streamwater ANC is closely related to bedrock
32    mineralogy (Herlihy et al., 1993). Sullivan et al. (2007) delimited a high-interest area for streamwater
33    acidification sensitivity within the Southern Appalachian Mountain region (Virginia/West Virginia to
34    Georgia) based on geological classification and elevation. It covered only 28% of the region, and yet
35    included almost all known acidic and low ANC (<20  (ieq/L) streams, based on evaluation of about 1,000
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 1    streams for which water chemistry data were available. They found that the vast majority of low ANC
 2    sample streams were underlain by the siliceous geologic sensitivity class, which is represented by such
 3    lithologies as sandstone and quartzite. Low ANC streamwater throughout the region was also found to be
 4    associated with a number of watershed features in addition to lithology and elevation, including
 5    ecoregion, physiographic province, soil type, forest type and watershed area.
 6          Sulfate mass balance analyses indicated that, because of watershed SO42 retention, soils and
 7    surface waters of the region have not yet realized the full effects of elevated S deposition. On average,
 8    based on NSS data, sites in the Blue Ridge Mountains retain 35% of incoming SO42  from atmospheric
 9    deposition. The amount of SO42  retention was strongly related to ecoregion in the order, Piedmont > Blue
10    Ridge and Ridge & Valley  > Appalachian Plateau.
11          Herlihy et al.  (1993) believed that the observed differences were due to the effects of cumulative
12    loadings from atmospheric S deposition and not due to inherent ecoregional differences in the soils. They
13    also concluded that S retention will likely continue to decrease in the future, resulting in further losses of
14    stream ANC.
15          Both mineral  acids and organic acids play important roles in the acid-base status of streams in the
16    Mid-Atlantic Coastal Plain (Baker et al., 1991a). Acidic streams in the New Jersey Pine Barrens (Table B-
17    2) are largely inorganically dominated, but most likely they were naturally organically acidic in the past.
18    It is uncertain what effect the addition of inorganic acids from acidic deposition has had on these low
19    ionic strength colored systems. Over half the streams in the Pine Barrens included in the NSS were acidic,
20    and virtually all (96%) had ANC less than 50 (ieq/L. Human disturbances in the Pine Barrens often result
21    in the alkalinization of streams (increases in pH and ANC) that alter the natural Pine  Barrens aquatic
22    biota. Outside of the Pine Barrens in the NSS, the remainder of the acidic streams in the Coastal Plain
23    were all high DOC organically dominated systems. Low DOC  (<4 mg/L), acidic streams have been
24    observed, however, in other Mid-Atlantic Coastal Plain surveys.
25          The Virginia Trout Stream Sensitivity Study (VTSSS) surveyed streamwater chemistry for 344
26    (-80%) of the native brook trout (Salvelinus fontinalis) streams in western Virginia. About half of the
27    streams included in the VTSSS had ANC < 50 (ieq/L. In contrast, the NSS  (Kaufmann et al., 1988) data
28    for western Virginia suggested that only about 15% of the streams in the NSS target population had ANC
29    < 50 (ieq/L. These differences may reflect the smaller watershed size, more mountainous topography, and
30    generally more inert bedrock of the VTSSS watersheds, as compared with the overall NSS stream
31    population.
32          In the Appalachian Plateau of West Virginia there are two wilderness areas located in close
33    proximity in an area of base-poor bedrock ~ Dolly Sods and Otter Creek Wilderness  Areas. Most streams
34    draining these wilderness areas are acidic or low in ANC.
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 1          In the Great Smoky Mountains National Park in North Carolina and Tennessee, Cook et al. (1994)
 2    reported high NC>3 concentrations (-100 (ieq/L) in upland streams which were correlated with elevation
 3    and forest stand age. The old growth sites at higher elevation showed the highest NOs  concentrations.
 4    This pattern could have been due to the higher rates of N deposition and flashier hydrology at high
 5    elevation, and also decreased N uptake by trees in older forest stands. High N deposition at these sites has
 6    likely contributed to both chronic and episodic acidification of streamwater (Flum and Nodvin, 1995;
 7    Nodvin etal., 1995).
      Recent Trends
 9          Population estimates from TIME surveys of streams in the Northern Appalachian Plateau region
10    suggested that 5014 km of streams (11.8% of the stream length) were acidic in 1993-94, but that only
11    3600 km of streams (8.5% of the stream population) remained acidic in this region in 2000. The
12    approximate rate of estimated change in Gran ANC in the region (Table B-5) was +0.79 (ieq/L/yr. On this
13    basis, Stoddard et al. (2003) estimated that roughly 3600 km of stream (8.5%) remained acidic 10 years
14    later. This represents about a 28% decrease in acidic stream length over the preceding decade.

      Future Projections
15          Model projections of future changes in acid-base chemistry of streams in the southeastern U.S.
16    were presented by Sullivan et al. (2002, 2003, 2005). In the eight-state Southern Appalachian Mountains
17    region, Sullivan et al. (2005) modeled future effects of atmospheric S and N deposition on aquatic
18    resources. Modeling was conducted with the MAGIC model for 40 to 50 sites within each of three
19    physiographic provinces, stratified by stream water ANC class. Simulations were based on assumed
20    constant future atmospheric deposition at 1995 levels and on three regional strategies of emissions
21    controls provided by the Southern Appalachian Mountains Initiative (SAMI), based on the Urban to
22    Regional Multiscale One-Atmosphere model (Odman et al., 2002).
23          The NSS statistical frame (Kaufmann et al., 1991) was used to estimate the number and percentage
24    of stream reaches in the region that were projected to change their chemistry in response to the emissions
25    control strategies. There was a small decline in the estimated length of projected acidic (ANC > 0)
26    streams in 2040 from the least to the most restrictive emissions control strategy, but there was little
27    difference in projected stream length in the other ANC classes as a consequence of adopting one or
28    another strategy. However, projections of continued future acidification were substantially larger under a
29    scenario in which S and N deposition were held constant into the future at 1995 levels. Turner et al.
30    (1992) also reported MAGIC model simulation results that suggested substantial acidification
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 1    (~20 (ieq/L) of aquatic systems would occur in the southeastern U.S. if deposition remained constant at
 2    1985 levels. Those model analyses were conducted as part of the NAPAP (1991) IA.
 3          The SAMI emissions control strategies used in the modeling represented air regulatory
 4    requirements being implemented at the time of SAMFs formation, expected reductions under recent
 5    federal regulatory actions, and additional emissions controls applied to all emissions sectors in the eight
 6    SAMI states. The spatial variability of these emissions controls resulted in varying estimated future
 7    changes in S and N deposition at different locations within the SAMI region. The SAMI strategies were
 8    designated A2, B1, and B3. A2 is the reference strategy that represented SAMFs best estimates for air
 9    emission controls under regulations for which implementation strategies were relatively certain at the
10    time of the study (about the year 2000). Emissions reductions under the A2 strategy included the acid rain
11    controls under Title IV of the  1990 Amendments to the CAAA, the 1-h 63 standard, NOx reductions
12    required under EPA's call for revised State Implementation Plans (Sips), and several highway vehicle and
13    fuel reductions. The A2 strategy was applied for all eastern states and focused on the utility and highway
14    vehicle sectors. The Bl and B3 strategies assumed progressively larger emissions reductions, targeted
15    only to the eight SAMI states  but covering all emissions sectors.
16          Streams exhibited a broad range of response to the cumulative S deposition loadings received to
17    date and the large simulated decreases in S deposition in the future under the emissions control strategies
18    (Table B-8). Some  streams showed modeled stream water SC>42 concentrations increasing in the future,
19    even while S deposition was reduced by more than two-thirds. These were mostly sites that had relatively
20    low SC>42  concentrations in 1995 (> about 50 (ieq/L) because of S adsorption on soils. They generally
21    showed simulated future acidification, which was most pronounced for the A2 strategy. Other streams
22    were simulated to show relatively large decreases in future stream water SO42  concentrations and
23    concurrent increases in ANC in response to the strategies, with progressively larger changes from the A2
24    to the B3 strategy. These tended to be streams that had relatively high concentrations of SO42
25    (>50 (ieq/L) in 1995, suggesting that they were closer to steady state with respect to S inputs and outputs.
26    Some streams were projected  to exhibit future decreases in both SC>42  and NC>3  concentrations but
27    nevertheless to continue to acidify. This response was attributed by Sullivan et al. (2004) to large
28    simulated decreases in base cation concentrations at these sites due to soil base cation  depletion.
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               Acidification
               (25 ueq/L)
                Recovery
                (25 ueq/L)
                                                                                Source: Sullivan et al. (2004).
     Figure B-8. Map showing simulated changes in streamwater ANC from 1995 to 2040 in response to
     the SAMIA2 emmissions control strategy, representing existing emissions control regulations.

1         Most simulated changes in stream water ANC from 1995 to 2040 were rather modest (Table B-8),
2    given the very large estimates of decreased S deposition. Few modeled streams showed projected change
3    in ANC of more than about 20 (ieq/L. Some of the largest changes were simulated for some of the streams
4    that were most acidic in 1995. For such streams, however, even relatively large increases in ANC would
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 1    still result in negative ANC stream water, and therefore little biological benefit would be expected from
 2    the simulated improvement in chemistry. The model results suggested, however, that benefits would
 3    continue to accrue well beyond 2040 for all strategies, even if deposition was held constant at 2040 levels
 4    into the future.

      Florida Surface Waters
      Current Status
 5          According to the ELS survey conducted in 1984, 75% of the Florida Panhandle lakes were acidic at
 6    that time, as were 26% of the lakes in the northern peninsula. Most of the acidic lakes were clearwater
 7    (DOC < 400 (imol) seepage lakes in which the dominant acid anions were Cl and SO42  . Most of the
 8    acidic and low-ANC lakes are located in the Panhandle and north central lake districts. In these areas, the
 9    Floridan aquifer is separated from overlying sand deposits by a confining layer called the Hawthorne
10    formation. The major lake districts are located in karst terrain, where lakes formed through dissolution of
11    the underlying limestone followed by movement of surficial deposits into solution cavities (cf. Arlington
12    etal., 1987).
13          Flow of water from most of the lakes is downward, recharging the Floridan aquifer. Lake stage
14    varies in response to long-term trends in precipitation, and perhaps in response to groundwater
15    withdrawals. ANC generation in most lakes that have been studied appears to be due primarily to in-lake
16    SO42  and NO3  reduction (Baker et al., 1988; Pollman and Canfield,  1991). Retention of SO42  by
17    watershed soils  may also be important. Lakes can be highly alkaline where groundwater interacts with the
18    deeper aquifer. Lakes with hydrologic contributions from shallow  aquifers in highly weathered sands can
19    be quite acidic and  may be sensitive to acidic deposition.
20          DOC concentrations are high in many Florida lakes, but organic anions are less important than
21    SO42  in most low-ANC and acidic lakes (Pollman and Canfield, 1991). Aluminum concentrations tend to
22    be very low in Florida lakes despite the high lakewater acidity because most of the Aln+ is removed from
23    soil solution by  precipitation and ion exchange reactions within 75 cm depths (Grates et al., 1985), and
24    relatively little AF+ is transported in groundwater to lake waters.
25          Baker et al. (1988) reported that retention of inorganic N is nearly 100% in most Florida lakes.
26    ANC generation from SO42  retention may approach 100 (ieq/L in some Florida lakes (Pollman and
27    Canfield, 1991). These in-lake processes are important for generating ANC. Base cation deposition and
28    NH4+ assimilation can also influence the acid-base status of lakes in Florida.
29          The Northern Florida Highlands high interest area identified by Baker et al. (1991a) consists of the
30    northern portion (north of 29°N latitude) of the Central Lake District and the Florida Panhandle (Figure
31    B-3). Acidic streams were located in the Florida Panhandle and were mildly acidic (mean pH 5.0) and
32    extremely dilute, with very low sea  salt-corrected SBC (mean 21 (ieq/L) and sea salt corrected SO42

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 1    concentrations (mean 16 (ieq/L). One-fourth of these acidic Panhandle streams were organic-dominated
 2    but the remaining sites all had DOC < 2 mg/L and were inorganically dominated. Inorganic monomeric Al
 3    concentrations in these acidic streams were very low (mean 11 (ig/L). In these low DOC, low ANC
 4    Panhandle streams, it was suggested that the degree of SO42  and NOs retention was an important control
 5    on streamwater ANC (Baker et al., 1991a).

      Past Acidification
 6          Considerable research has been conducted on past acidification in Florida lakes. Historical analyses
 7    of lake chemistry (Battoe and Lowe,  1992), inferred historical deposition (Hendry and Brezonik, 1984;
 8    Husar and Sullivan, 1991), and paleolimnological reconstructions of lake pH (Sweets et al., 1990; Sweets,
 9    1992) suggest evidence that some Florida lakes have acidified in response to acidic deposition. However,
10    the role of acid deposition in lakewater acidification is not entirely clear (cf. Pollman and Canfield, 1991),
11    and the interpretation is complicated by regional and local changes in land use and hydrology (Sullivan,
12    2000).
13          An alternative explanation for the apparent acidification of some lakes is  the regional decline in the
14    potentiometric surface of the groundwater (Sweets et al., 1990). Large groundwater withdrawals of the
15    Floridan aquifer for residential and agricultural purposes may have reduced groundwater inflow of base
16    cations into seepage lakes, and therefore caused less buffering of acidity. Other  land use changes may
17    have increased lake pH by providing inputs of fertilizer, which would increase lake productivity.
18    Paleolimnological evidence of this effect was provided by Brenner and Binford (1988) and Deevey et al.
19    (1986).
20          It is likely that lake chemistry in Florida has been heavily effected by land use changes. More than
21    half of the Florida lakes included in the ELS showed evidence of disturbance based on deviations from
22    expected geochemistry (Pollman and Canfield, 1991). Such effects complicate efforts to determine the
23    role of acidic deposition  in controlling lakewater acid-base chemistry.
24          Diatom-inferred pH reconstructions of lakewater chemistry of six seepage lakes in Florida were
25    calculated as part of the PIRLA-I project and reported by Sweets et al. (1990). An additional 10 seepage
26    lakes were cored as part of PIRLA-II, and results of those analyses were reported by Sweets (1992).
27    Paleolimnological study  lakes are located in the Panhandle, the Trail Ridge Lake District, and Ocala
28    National Forest, generally in terraces of highly weathered loose sand that were deposited on top of the
29    clay-confining layer.
30          Of the six lakes analyzed in PIRLA-I, two (Barco and Suggs) were  inferred to have acidified since
31    1950 (Sweets et al., 1990). The timing of the onset of inferred acidification correlated with increases  in
32    SO2 emissions and S deposition, which increased consistently between about 1945 and 1985 (Husar and
33    Sullivan, 1991).
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 1          Of the 16 Florida seepage lakes studied in the PIRLA-II projects, 5 were located in or near the Trail
 2    Ridge Lake District, and all showed diatom-inferred acidification of at least 0.2 pH units (Sweets, 1992).
 3    Lakes located in the Panhandle region and Ocala National Forest generally did not show evidence of
 4    recent acidification. The  exception was Lake Five-O, which was inferred to have decreased by 2 pH units.
 5    However, the diatom data suggested that this pH decline was associated with a sudden change in
 6    chemistry, probably caused by a catastrophic disturbance such as sinkhole development, rather than by
 7    acidification from atmospheric deposition (Pollman and Sweets, 1990; Sweets, 1992).

      B.3.4.3. Upper Midwest
 8          The Upper Midwest contains numerous lakes created by glaciation. The region has little
 9    topographic relief and with its deep glacial overburden, it also has little or no exposed bedrock. Acid-
10    sensitive surface waters in the Upper Midwest are mainly seepage lakes (Eilers et al., 1983). Most
11    drainage lakes and some of the seepage lakes in the Upper Midwest region receive substantial inflow
12    from groundwater, which is generally high in base cation concentrations from dissolution of carbonate
13    and silicate minerals. Relatively high concentrations of base cations in these lakes make them insensitive
14    to acidification from acidic deposition. The seepage lakes that have low base cation concentrations, and
15    that are therefore more acid-sensitive, generally receive most of their water input from precipitation
16    directly on the lake surface (Baker et al., 1991b).

      Current Status
17          Based on the ELS survey, the Upper Midwest has a large  population of low ANC lakes, but
18    relatively few chronically acidic (ANC > 0) lakes (Linthurst et al., 1986a,b). Acidic lakes in the Upper
19    Midwest are primarily small, shallow, seepage lakes that have low concentrations of base cations and Al
20    and moderate SC>42  concentrations. Organic anions, estimated by both the Oliver et al. (1983) method
21    and the anion deficit, tend to be less than half the measured SO42 concentrations in the acidic lakes
22    (Eilers et al., 1988), but much higher in many of the drainage  lakes that are less sensitive to acidification
23    from acidic deposition.
24          Groundwater flow-through lakes in the Upper Midwest can be identified on the basis of having Si
25    concentration greater than about 1 mg/L (Baker et al., 1991b). They generally have high pH and ANC,
26    due to groundwater inputs of base cations (e.g., Baker et al., 1991b). Based on results from the ELS
27    survey, only 6% of these lakes had ANC > 50 (ieq/L and none were acidic. Groundwater recharge lakes
28    (those having Si concentration less than  1 mg/L) constituted 71% of the seepage lakes in the Upper
29    Midwest, and were more frequently low pH and ANC. Five percent were acidic and 9% had pH > 5.5.
30    Nearly 90% of Upper Midwestern lakes that had ANC > 50 (ieq/L were in this category (Baker et al.,
31    1991b). Such lakes tend to be susceptible to acidification from acidic deposition.

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 1          Sullivan (2000) summarized patterns in lakewater chemistry across the Upper Midwest from the
 2    ELS survey. Lakewater pH, ANC, base cations, and DOC all decreased from west to east across the
 3    region. Lakewater SO42  concentrations did not show a comparable change, despite a substantial increase
 4    in wet 864 deposition from Wisconsin to Michigan. Cook and Jager (1991) attributed the absence of a
 5    more pronounced gradient in lakewater SO42  concentration across the region to watershed sources of S in
 6    Minnesota and high anion retention in seepage lakes, which predominate in the eastern portion of the
 7    region. The retention of SO42  by dissimilatory reduction is generally high for seepage lakes because of
 8    their long hydraulic retention times (TW). For example, an Upper Midwestern seepage lake with mean
 9    depth of 3 m and hydraulic retention time of 7.5 years would be expected to lose about 50 (ieq/L of SO42
10    from the water column by this process (Cook and Jager, 1991).
11          Lakewater concentrations of inorganic N reported by the ELS were low throughout the Upper
12    Midwest. In addition, snowmelt would not be expected to provide any significant NC>3 influx to lakes in
13    the Upper Midwest because most snowmelt infiltrates the soil before reaching the drainage lakes, and
14    because snowmelt input of N into seepage lakes would be limited mainly to the snow on the lake surface
15    and immediate near-shore environment. Aluminum concentrations are far lower in the Upper Midwest
16    than in lakes of similar pH in the Northeast.
17          Wetlands are common throughout the Upper Midwest. They contribute to high production of
18    organic matter which is reflected in high DOC concentrations in many lakes. Despite the abundant
19    wetlands, SO42 is the dominant anion in the low-ANC (> 50 (ieq/L) groundwater recharge seepage lakes.
20          Base cation production is the dominant ion-enrichment process in most Upper Midwestern lakes.
21    Even in low-ANC groundwater-recharge seepage lakes, base cation production accounts for 72% to  86%
22    of total ANC production (Cook and Jager, 1991).

      Past Acidification
23          Space-for-time substitution analysis was used to infer the general levels of past change in lake
24    water acid-base chemistry in the Upper Midwest. Such an analysis assumes that study lakes were
25    generally similar in acid-base chemistry prior to the onset of acidic deposition and that the only
26    substantial driver of recent change in acid-base chemistry has been the level of acidic deposition. Across
27    an increasing S depositional gradient from eastern Minnesota eastward to eastern Michigan, ANC
28    expressed as (HCO3  - FT) decreased and the ratio SO42 to SBC increased in the groundwater recharge
29    seepage lakes. In Michigan and Wisconsin, many lakes had SO42  > SBC, indicating that the acidity was
30    due to high SO42  relative to SBC concentration. There were also many lakes that had high concentrations
31    of DOC, and organic acidity probably accounted for many of these lakes  having ANC < 0. The spatial
32    pattern in (HCOs  - FT) could not be attributed to DOC, which generally  showed a decreasing trend with
33    increasing acidic deposition.
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 1          The concentration of lakewater (Ca2+ + Mg2+) also decreased with increasing acidic deposition,
 2    probably due to lower levels of base cation deposition and greater amounts of precipitation in the eastern
 3    portion of the region. Atmospheric deposition is an important source of base cations for groundwater
 4    recharge seepage lakes because of minimal groundwater inputs. In the eastern portion of the region, such
 5    lakes are more sensitive to pH and ANC depression in response to either elevated SO42 or DOC. The
 6    spatial patterns for low ANC groundwater recharge lakes in the Upper Midwest are consistent with the
 7    following hypotheses (Sullivan et al., 1990)(Sullivan, 1990, 2000):
 8            1.  Sensitivity to mineral and organic acidity increased from west to east because of decreasing
 9               lakewater base cation concentrations, and this may have been due, in part, to changes in base
10               cation deposition and precipitation volume along this gradient.
11            2.  High concentrations of DOC were responsible for the acidic conditions in some of the lakes,
12               and DOC may have decreased in response to acidic deposition.
13            3.  Many of the lakes in eastern Michigan, and some in Wisconsin, were acidic because of high
14               SO42  relative to base cation concentration, and had probably been acidified by acidic
15               deposition.
16          Diatom-inferred pH reconstructions were completed for 15 lakes in the Upper Midwest region, and
17    summarized by Kingston et al. (1990) and Cook and Jager (1991). Four lakes, all of which had measured
18    pH < 5.7, showed a diatom-inferred pH decline of 0.2 to 0.5 pH units during the preceding 50 to 100
19    years. Diatom-inferred pH increased in one  lake by 0.2 pH units. No change was inferred for the other 10
20    lakes, including 4 lakes with pH > 6.0. No major, recent, regional acidification was indicated by the
21    diatom-inferred pH reconstructions. Inferred changes in most lakes were small, and were no greater
22    during the industrial period than during the pre-industrial period (Sullivan, 2000).
23          Although diatom data suggested that  some Upper Midwestern lakes may have acidified since pre-
24    industrial times, there is little paleolimnological evidence indicating substantial widespread acidification
25    in this region (Cook et al., 1990; Kingston et al., 1990). Land use changes and other human disturbances
26    of Upper Midwestern lakes and their watersheds have probably exerted more influence on the acid-base
27    chemistry of lakes than has acidic deposition (Kingston et al., 1990; (Sullivan et al., 1990)Sullivan,  1990,
28    2000). The portion of the region most likely to have experienced acidification from acidic deposition is
29    the Upper Peninsula of Michigan, where acidic seepage lakes are particularly numerous (Baker et al.,
30    1991a); acidic deposition is highest for the region, and the [SO42 ]/[SBC] ratio is commonly >1.0. The
31    percentage of acidic lakes in the eastern portion of the Upper Peninsula of Michigan (east of longitude
32    87°) was estimated to be 18% to 19% in 1984 (Schnoor et al., 1986;  Eilers et al., 1988).
33
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      Recent Trends
 1          Regional trend values for long-term monitoring lakes during the period 1990 to 2000 suggested
 2    that SC>42  declined in lakewater by 3.63 (ieq/L/yr, whereas lakewater NC>3  concentrations were relatively
 3    constant. The large decrease in SC>42 concentration was mainly balanced by a large decrease in base
 4    cation concentrations (-1.42 (ieq/L/yr) and an increase in ANC (+1.07 (ieq/L/yr). All of these trends were
 5    significant at p < 0.01 (Stoddard et al., 2003). In the Upper Midwest, an estimated 80 of 251 lakes that
 6    were acidic in the mid-1980s were no longer acidic in 2000. This change may be due to reduced levels of
 7    S deposition (Stoddard et al., 2003).

      B.3.4.4. West
 8          Portions of the mountainous West contain large areas of exposed bedrock, with little soil or
 9    vegetative cover to neutralize acidic inputs. This is particularly true of alpine regions of the Sierra
10    Nevada, northern Washington Cascades, the Idaho batholith, and portions of the Rocky Mountains in
11    Wyoming and Colorado. However, the percentage of exposed bedrock in a watershed does not always
12    indicate acid-sensitivity. If the bedrock contains even small deposits of calcareous minerals or if physical
13    weathering such as that caused by glaciers causes a high production of base cations within the watershed
14    (Drever and Hurcomb, 1986), surface waters may be alkaline, and are not sensitive to acidification from
15    acidic deposition.
16          The areas that are sensitive to adverse effects from acidic deposition in the western U.S. form two
17    nearly continuous ranges, the Sierra Nevada, which extends through most of the length of California, and
18    the Cascade Mountains, which extend from northern California to northern Washington (Source: Landers
19    etal. (1987).
20          Figure B-9). The sensitivity of the Rocky Mountains varies widely because the ranges are
21    discontinuous with highly variable geological composition. For that reason, assessments of the sensitivity
22    of Rocky Mountain aquatic resources to acidification should be specific to individual ranges (Turk and
23    Spahr, 1991).
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                         Cescadot
                Qfympic
                Ufltitt   S
Sallcirk    Cabinet
Mountains Mount fins
          fugtt
          Lowlorwis
             ts/ 8ilt«reoo!
             r'~ Range   (
                                                                          40
                                                      /}  Anncontlt'fintla'
                                                    /•'-Jj Mounwitis     Oenftoath
                                                    pf/ /  rt~6\ x^'"
                                                    ^f  .M«-«h
                                                                                          3Ml! Ufttltt
                                                                                        Sangre De Crtsio
                                                                                        l/jtfill
                                                                                        Source: Landers etal. (1987).
      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.

 1          The NAPAP SOS/T Reports and the IA (NAPAP, 1991) provided only a cursory treatment of
 2    aquatic effects issues in the West, largely because it was well known that atmospheric deposition of S and
 3    N were generally low compared to highly affected areas in the East (Sullivan,  2000) and because results
 4    from the WLS (Landers et al., 1987) indicated that there were virtually no acidic (ANC > 0)  lakes in the
 5    West. NAPAP (1991) indicated, however, that high-elevation areas of the West contained many of the
 6    watersheds most sensitive to the potential effects of acidic deposition.
 7          Because of the proximity of western urban and industrial pollution sources to individual mountain
 8    ranges, it is important to consider emissions in the immediate vicinity of sensitive resources as well as
 9    regional emissions. Atmospheric deposition in the far western ranges (i.e., Sierra Nevada, Cascade
10    Mountains) is largely influenced by local emissions, particularly emission sources to the west (upwind) of
11    sensitive resources. In the Rocky Mountains, deposition chemistry is often influenced by a more complex
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 1    collection of emission sources. For example, in the Mt. Zirkel Wilderness of northwestern Colorado,
 2    SC>42 and NC>3  in the snow appeared to originate largely from sources in the Yampa Valley, about 75 km
 3    to the west (Turk et al., 1992). Rocky Mountain National Park is affected by emissions from the Front
 4    Range to the southeast.
 5          The acid base chemistry of lake and stream waters in Rocky Mountain National Park appears to be
 6    primarily a function of the interactions among several key parameters and associated processes:
 7    atmospheric deposition, bedrock geology, the depth and composition of surficial deposits and associated
 8    hydrologic flowpaths, and the occurrence of soils, tundra, and forest vegetation (Sullivan, 2000). Potential
 9    biological effects of acidic deposition on lakes in the Rocky Mountains are primarily attributable to
10    acidification from high NO3  concentration. In general, such effects tend to be episodic, rather than
11    chronic. Flighest NC>3 concentrations in both precipitation and surface waters are found above timberline
12    in Colorado, where biological activity, and therefore NC>3 uptake, by terrestrial and aquatic biota is
13    lowest.

      Current Status
14          The available information on acid-base chemistry of surface waters in the West is based mostly on
15    synoptic  data from the WLS (Landers et al.,  1987) and some more localized studies. Acid anion
16    concentrations in most western lakes are low during fall, but can be higher during snowmelt (Williams
17    and Melack, 1991). Available data from intensive study sites in the West (e.g., Loch Vale, CO, Emerald
18    Lake Basin,  CA, and the Glacier Lakes Watershed, WY)  suggest that episodic depression of stream pH
19    may be more pronounced than for lakes. However, there are no available systematic regional stream
20    chemistry data with which to assess regional sensitivity of streams to acidic deposition.
21          In  most western lakes concentrations of SC>42  are low, although watershed sources of S are
22    substantial in some cases (Table B-9). Turk and Spahr (1991) presented a conceptual model for expected
23    SC>42  distributions in lakewaters in the West that can be used as an aid in identifying the proportion of
24    watersheds with significant watershed sources of S. Considering that atmospheric sources can account for
25    generally < 30 (ieq/L of 864 in the West, it appears that many lakes, particularly in  Colorado, receive
26    variable amounts of watershed S (Sullivan, 2000; Figure B-10).
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           Z 25
              0
                           A2
                                           50
                                                         B1
               -90    -60   -30    0    30   60     -90   -60   -30    0    30
                                                                                       B3
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             50
                                                                          25

               -90    -60   -30   0    30   60
                                             -90   -60    -30    0    30    60      .90   _eo   -30   0   30   60
                              Projected Percent Change in Total Deposition from 1995 to 2040
      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.

                                                                                         Source: Sullivan et al. (2004).
 1          Nitrate concentrations in most WLS lakes were near 0 during fall (Landers et al., 1987), although
 2    fall NO3  concentrations were high in some cases (Table B-10). For example, nearly one fourth of the
 3    lakes in northwest Wyoming had NO3  > 5 ^teq/L and almost 10% had NO3  > 10 Meq/L (Table B-10). In
 4    both the Sierra Nevada and Colorado Rockies subregions, about 10% of the lakes had fall NO3
 5    concentrations above 5 (ieq/L (Table B-10).
 6          It is important to note that even low to moderate concentrations of NO3 in western lakes might be
 7    significant in view of: (1) the low base cation concentrations in many lakes; (2) potential for continuing N
 8    deposition to eventually exhaust natural assimilative capabilities; and (3) the fact that these distributions
 9    are based on fall data. Time-intensive discharge and chemical data for two alpine streams in Loch Vale
10    watershed identified strong seasonal control on streamwater NO3  concentrations (Campbell et al., 1995).
11    In spite of the paucity of soil cover, the chemical composition of streams is regulated much as in typical
12    forested watersheds. Soils and other shallow groundwater matrices such as boulder fields are more
13    important in controlling surface water chemistry than their abundance would indicate. Spring streamwater
14    NO3 concentrations ranged to 40 (ieq/L, compared with summer minimum values near 10 (ieq/L. Elution
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 1    of acidic waters from snowpack along with dilution of base cations originating in shallow groundwater
 2    caused episodes of decreased ANC in alpine streams (Campbell et al., 1995). A subalpine stream in the
 3    same watershed similarly displayed decreased ANC and elevated monomeric Al concentrations during
 4    snowmelt over 2 years of intensive sampling, related to elevated concentrations of SC>42 , NOs , and DOC
 5    (Denning et al., 1991). Limited data collected during snowmelt suggest that spring concentrations could
 6    be several times higher than samples collected during the fall (e.g., Reuss et al., 1995).
 7          The surface water chemistry data for the West indicate that the Sierra Nevada and Cascade
 8    Mountains constitute the mountain ranges with the greatest number of sensitive resources (Table B-9 and
 9    Table B-10). Lakes in the Sierra Nevada are especially sensitive to  effects from acidic deposition because
10    of the predominance of granitic bedrock, thin acidic soils, large amounts of precipitation, coniferous
11    vegetation, and dilute nature of the lakes (Melack et al.,  1985; Melack and  Stoddard, 1991). Surface
12    waters in this region are among the poorly buffered surface waters in the U.S. (Landers et al., 1987;
13    Melack and Stoddard,  1991). The hydrologic cycle is dominated by the annual accumulation and melting
14    of a dilute, mildly acidic (pH about  5.5) snowpack.
15          During the 1980s, an Integrated Watershed Study (IWS) was conducted at seven  lakes in the Sierra
16    Nevada, including Emerald Lake and surrounding watersheds (-3,000 m elevation) to determine the
17    effects of acidification on surface waters (Tonnessen, 1991). Three  lakes (Lost, Pear, and Emerald) had
18    volume-weighted mean ANC in the range of 15 to 30 (ieq/L. Moderate ANC waters (Topaz, Spuller, and
19    Marble Fork) exhibited mean ANC in the range of 30 to 50 (ieq/L. Crystal and Ruby Lakes had mean
20    annual ANC > 50 (ieq/L.
21          Many Cascade and Rocky Mountain lakes are also highly sensitive to potential acidic deposition
22    effects (Nelson, 1991; Turk and Spahr, 1991). It does not appear that chronic acidification has occurred to
23    any significant degree, although episodic acidification has been reported for lakes in the Colorado Front
24    Range (Williams and Tonnessen, 2000). The data that would be needed for determining the extent and
25    magnitude of episodic acidification  have not been collected to a sufficient degree in acid-sensitive areas
26    of the West to support regional assessment of episodic acidification (Sullivan, 2000).
27          Along the eastern edge of the Continental Divide  in Colorado and southeastern Wyoming,
28    Musselman et al. (1996) conducted  a synoptic survey of surface water chemistry in the  mountainous areas
29    that are exposed to relatively high (by western standards) deposition of N. A total of 267 high-elevation
30    lakes situated in watersheds having  a high percentage of exposed bedrock or glaciated landscape were
31    selected for sampling. None of the lakes were chronically acidic (ANC < 0), although several had ANC <
32    10 (ieq/L, and more than 10% of the lakes had ANC < 50 (ieq/L. The WLS data for lakes in Colorado and
33    Wyoming demonstrate that surface waters in this area had fall concentrations of NOs in the range  of 10
34    to 30 (ieq/L, and likely had substantially higher NOs  concentrations during spring.
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 1          The weight of evidence suggests many high-elevation lakes in the West receive N deposition
 2    sufficiently high to cause chronic NC>3 leaching, and likely some degree of associated chronic and
 3    episodic acidification. However, existing data are insufficient to make a conclusive determination
 4    (Sullivan, 2000).

      Past Acidification
 5          The limited paleolimnological data available for lakes in the western U.S. suggest that widespread
 6    chronic acidification probably has not occurred. Some lakes may have experienced recent pH declines,
 7    but the magnitude of such changes has likely been small (Sullivan, 2000).
 8          In the Sierra Nevada, paleolimnological reconstructions of lakewater pH and ANC were calculated
 9    by Holmes et al. (1989) at 24 depth intervals at Emerald Lake, for the period 1825 to the present.
10    Significant trends were not found for either pH or ANC, and the authors concluded that Emerald Lake had
11    not been acidified by acidic deposition. Whiting et al. (1989) completed paleolimnological analyses of
12    three additional lakes in the Sierra Nevada. Eastern Book Lake (pH = 7.06) showed evidence of both
13    long-term alkalization (-0.3 pH units over the past 200 years) and pH fluctuations since 1970. Lake 45
14    (pH = 5.16) may have acidified slightly (-0.2 pH units) over the last 60 years. Lake Harriet (pH = 6.52)
15    showed no significant change.
16          In Rocky Mountain National Park, Colorado, Baron et al. (1986) investigated metal stratigraphy,
17    diatom stratigraphy, and inferred pH profiles of four subalpine lakes. They found no evidence of historical
18    influence on pH attributable to atmospheric deposition. Other paleolimnological studies of Rocky
19    Mountain lakes report similar results: metals (primarily lead) exhibit temporal dynamics related to the
20    increase and decline of precious metal mining in the region, but these are asynchronous with other metal
21    or biological indicators of acidification (Wolfe et al., 2003). Both the study by Wolfe et al. (2003) and a
22    study by Saros et al. (2003) showed no evidence of acidification of lake waters over time, but increasing
23    evidence of eutrophication from atmospheric N deposition (see Annex C).
24          DayCent-Chem, a model that simulates the daily dynamics of plant production, soil organic matter,
25    cation exchange, mineral weathering, elution, stream discharge, and stream solute concentrations, was
26    able to recreate daily stream chemistry dynamics over 13 years for an alpine watershed in the Colorado
27    Front Range (Hartman et al., 2007). Using the model to hindcast stream chemical dynamics back to 1900
28    revealed changes in simulated pH coincident with maximum SO2 emissions in the late 1960s and early
29    1970s. Model simulations suggested annual mean pH values decreased to 5.6 to 5.8 during the years of
30    maximum regional SC>2 emissions, and have since recovered to circumneutral values. Simulated ANC
31    responded to both SC>2 and NOx emissions, decreasing to annual values of 20 to 25 (ieq/L during years of
32    highest SC>2 or NOx emissions compared with current mean annual ANC values near 50 (ieq/L (Hartman
33    etal., 2007).
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      Recent Trends
 1          Limited monitoring data are available on recent trends in surface water chemistry in the western
 2    regions and are mostly limited to the recent past and a number of reconnaissance studies (Melack and
 3    Stoddard, 1991; Nelson, 1991; Turk and Spahr, 1991). Existing information on recent trends in surface
 4    water chemistry since the 1980s suggests that conditions vary widely across the West. Parts of Colorado,
 5    Wyoming and the western Cascades showed decreased ANC, while Emerald Lake experienced reduced
 6    NC>3  concentrations.
 7          Turk et al. (1993) reported the results of 5 years of monitoring for ten lakes in the Mt. Zirkel and
 8    Weminuche Wilderness areas in Colorado. Based on lake concentrations of SC>42 and Cl~ and on wet
 9    deposition concentrations of SC>42 , NOs , and H+, Turk and Spahr (1991) concluded that low-ANC lakes
10    had lost no more than 5 (ieq/L ANC in the Bitterroot Range of the Northern Rocky Mountains, 12 (ieq/L
11    ANC in the Wind River Range of Wyoming, and  10 (ieq/L ANC in the Front Range of Colorado. It is
12    likely that the actual ANC losses had been much less than these estimates (Sullivan, 2000).

      Future Projections
13          The DayCent-Chem model was used to project a timeline to acidification for an alpine watershed
14    of Rocky Mountain National Park (Hartman et al., 2007). At current levels of N deposition of 4 to 6 kg
15    N/ha/yr, acidification does not occur over 48 years of simulation, but increasing deposition amounts lead
16    to first episodic acidification over time at deposition of 7.0 to  7.5 kg N/ha/yr. MAGIC model simulation
17    results suggested that a sustained N deposition load  of 12.2 kg N/ha/yr would be required over a period of
18    50 years to cause chronic acidification of the Andrews Creek watershed in Rocky Mountain National Park
19    (Sullivan etal., 2005).

      B.3.4.5. Temporal Variability in Water Chemistry
20          Water chemistry changes on both intra-annual and inter-annual time scales in response to changes
21    in environmental conditions. Because of this variability, many years of data are required to establish the
22    existence of trends  in surface water chemistry. Assignment of causality to changes that are found to occur
23    is even more difficult.
24          Temporal variability in surface water and soil solution chemistry, and patterns in nutrient uptake by
25    terrestrial and aquatic biota,  influence acidification processes and pathways. Thus, conditions are
26    constantly changing in response to episodic, seasonal, and inter-annual cycles and processes. In particular,
27    climatic fluctuations that govern the amount and timing of precipitation inputs, snowmelt, vegetative
28    growth, depth to groundwater tables, and evapoconcentration of solutes influence soil and surface water
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 1    chemistry and the interactions between pollution stress and sensitive aquatic and terrestrial biological
 2    receptors.
 3          Decreases in pH with increases in flow are nearly ubiquitous in drainage waters throughout the
 4    U.S. (Wigington et al., 1991). Chemical changes during episodes are controlled in part by acidic
 5    deposition and in part by natural processes, including dilution of base cation concentrations, nitrification,
 6    flushing of organic acids from terrestrial to aquatic systems, and the neutral salt effect. Episodic
 7    acidification pulses may last for hours to weeks, and sometimes result in depletion of ANC in acid-
 8    sensitive streams and lakes to negative values and concomitant increases in Al; in solution to toxic levels.
 9          During episodes, which are driven by rainstorms and/or snowmelt events, both discharge
10    (streamflow volume per unit time) and water chemistry change, sometimes dramatically. This is important
11    because streams may in some cases exhibit chronic chemistry that is still suitable for aquatic biota, but
12    nevertheless experience occasional episodic acidification with lethal consequences (cf Wigington et al.,
13    1993).
14          The most important factor governing watershed sensitivity to episodic acidification is the pathways
15    followed by snowmelt water and stormflow water through the watershed. These pathways determine the
16    extent of acid neutralization provided by the soils and bedrock in that watershed. High-elevation
17    watersheds with steep topography, extensive areas of exposed bedrock, deep snowpack accumulation, and
18    shallow, base-poor soils tend to be most sensitive to episodic acidification.
19          Rainfall and snowmelt typically pass through the soil profile prior to reach a stream channel. The
20    typical soil profile in acid-sensitive watersheds has lowest pH in upper organic soil horizons, increasing
21    down the profile to higher pH at depth. Drainage water chemistry during baseflow conditions is generally
22    reflective of conditions in the lower soil horizons and the subsoil. During high flows during snowmelt or
23    rainfall events, however, flow-routing favors water flowpaths through upper horizons. During such
24    events, drainage water chemistry, therefore, typically reflects the lower pH, higher organic content, and
25    lower ANC of these upper soil horizons (Sullivan, 2000). As such, storm flow and snowmelt are often
26    associated with episodes of extreme surface water acidity due to an increase in the proportion of flow
27    derived from water that has moved laterally through the surface soil without infiltration to deeper
28    soil horizons (Wigington et al., 1991).
29          The routing of water as it flows through a watershed determines the degree of contact with
30    acidifying or neutralizing materials and therefore influences (along with soils and bedrock characteristics)
31    the amount of episodic acidification that occurs. In any given watershed, surface water ANC may vary in
32    time depending upon the proportion of the flow that has contact with deep versus shallow soil horizons;
33    the more subsurface contact, the higher the surface water ANC (Turner et al., 1991). This can be
34    attributed in part to higher base saturation and (in some watersheds) greater SC>42 adsorption capacity in
35    subsurface soils. It may also relate to the accumulation in the upper soil horizons of acidic material
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 1    derived from atmospheric deposition and decay processes (Lynch and Corbett, 1989; Turner et al., 1991).
 2    Episodic acidification is often the limiting condition for aquatic organisms in streams that can be suitable
 3    for aquatic life under baseflow conditions.
 4          Episodes are generally accompanied by changes in at least two or more of the following chemical
 5    parameters: ANC, pH, base cations, SC>42 , NOs , Aln+, organic acid anions, and DOC (Sullivan, 2000).
 6    The EPA's Episodic Response Project (ERP) confirmed the chemical and biological effects of episodic pH
 7    depressions in lakes and streams in parts of the U.S. (Wigington et al., 1993). The ERP illustrated that
 8    episodic processes are mostly natural, that SO42 and especially NO3  attributable to atmospheric
 9    deposition play important roles in the episodic acidification of some surface waters, and that the chemical
10    response that has the  greatest effect on biota is increased Al; concentration. Similar findings had been
11    reported elsewhere, especially in Europe, but the ERP helped to clarify the extent, causes, and magnitude
12    of episodic acidification in portions of the U.S. (Sullivan, 2000).
13          Water chemistry trends documented by long-term monitoring programs and reported here represent
14    recovery from chronic acidification. Most surface waters exhibit seasonally lower ANC and pH values
15    than would be captured by trend analysis that considers only chronic chemistry data. In many cases, sites
16    that are relatively low in ANC, but not chronically acidic, undergo short-term episodic acidification to
17    negative ANC values during spring snowmelt, or during intense rain events. Lawrence (2002) found that
18    16% of total stream reaches in the West Branch Neversink River, in the Catskill Mountains of New York,
19    were chronically acidic, whereas 66% of total stream reaches has a high likelihood of becoming acidic
20    during high flows.
21          Most research  on episodic processes has been conducted on stream systems, which tend to be more
22    susceptible to such effects than  lakes. Spatial variability can be considerable in lakes, and this complicates
23    efforts to quantify the magnitude of episodic effects (Gubala et al., 1991). Moreover, synoptic lake
24    surveys are typically  conducted during the autumn "index period," during which time lakewater chemistry
25    exhibits low temporal variability. Although autumn is an ideal time for surveying lakewater chemistry in
26    terms of minimizing variability, lakewater samples collected during autumn provide little relevant data on
27    episodic processes, and in particular on the dynamics or importance of N as an agent of acidification.
28    Nitrate concentrations in lakewater are elevated during the autumn season only in lakes having
29    watersheds that exhibit fairly advanced symptoms of N saturation (Stoddard, 1994).
30          Mixing zones have received little attention despite the fact that they can be acutely toxic to aquatic
31    biota. Whether an area of acidic water that comes in contact with non-acidic water is a safe haven or a
32    toxic zone depends on many parameters, one of the most important of which is the amount and form of Al
33    species produced at the boundaries. For example, Al hydroxide (A1(OH3)) can precipitate out of solution
34    if pH is suddenly increased within a mixing zone. This form of Al is acutely toxic to fish.
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 1          The mechanisms that produce acidic episodes can include dilution of base cations and flushing of
 2    NC>3 , SC>42  and/or organic acids from forest soils to drainage water (Kahl et al., 1992; Wigington et al.,
 3    1996; Wigington, 1999; Lawrence, 2002). Acidic deposition can contribute to episodic acidification of
 4    surface water both by supplying N which can produce pulses of NOs  during high flow periods,
 5    contributing hydrologically mobile SC>42 through dry deposition, and by lowering baseline pH and ANC,
 6    so that episodes are sufficient to produce biologically harmful conditions (Stoddard et al., 2003).
 7          Episodic acidification due to atmospheric deposition is most commonly associated with N
 8    deposition, and effects tend to be most pronounced during snowmelt. However, snowmelt can flush into
 9    surface waters  N that was deposited from the atmosphere to the snowpack and also N that was
10    mineralized within the soil  under the snowpack during winter. A substantial component of the NC>3  flux
11    may have been derived from mineralization of organic N (Ley et al., 2004). Much of the N released from
12    the  snowpack during the melting period is retained in underlying soils and only a component of that is
13    flushed to surface waters. Where soils are sparse, as in alpine regions of the western U.S., most snowpack
14    N is flushed to surface waters, and even though there is evidence through use of isotopic tracers that much
15    of the N was cycled  microbially, snowpack N has been reported to caused temporary acidification of
16    alpine streams  (Williams and Tonnessen, 2000; Campbell et al., 2002).
17          Episodic pH and ANC depressions during snowmelt are largely driven by base cation dilution and
18    NO3 enrichment in  most areas (cf. Wigington et al.,  1991, 1993; Campbell et al., 1995; Stoddard, 1995),
19    although Denning et al. (1991) found a significant decline of both pH and ANC associated with DOC
20    flushing from forest soils. Pulses of increased SC>42  during hydrological episodes are usually attributable
21    to S storage and release in soils (for example, in the southeastern U.S.) or wetlands. More commonly, lake
22    and streamwater concentrations of SO42  decrease or remain stable during snowmelt. This is probably
23    because most stream flow during episodes is derived from water previously stored in watershed soils that
24    is then forced into streams and lakes by the piston effect.
25          In the Northeast, the most severe acidification of surface waters generally occurs during spring
26    snowmelt (Charles,  1991).  Stoddard et al. (2003) found that on average, spring ANC values in New
27    England, the Adirondacks,  and the Northern Appalachian Plateau were about 30 (ieq/L lower than
28    summer values during the period 1990 to 2000 (Figure B-l 1). This implies that lakes and streams in these
29    regions would  need to recover to chronic Gran ANC values above about 30 (ieq/L before they could be
30    expected to not experience  acidic episodes (Stoddard et al., 2003). However, the estimate of 30 (ieq/L is
31    certain to be low because the comparison was made with non-episodic sampling in spring.
32          In the West, episodic acidification is an especially important issue for surface waters throughout
33    high-elevation  areas. A number of factors pre-dispose western systems to potential episodic effects
34    (Peterson et al., 1998; (Sullivan, 2000), including:
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 1          •  the abundance of dilute to ultradilute lakes which exhibit very low concentrations of base
 2             cations, and therefore ANC, throughout the year;

 3          •  large snowpack accumulations at the high-elevation sites, thus causing substantial episodic
 4             acidification via the natural process of base cation dilution; and

 5          •  short hydraulic retention times for many of the high-elevation drainage lakes, thus enabling
 6             snowmelt to rapidly flush lake basins with highly dilute meltwater.

 7          Based on measurements of microbial biomass, CC>2 flux through the snowpack, and soil N pools,
 8    Williams et al. (1996b) concluded N cycling under the snowpack in Colorado during the winter and
 9    spring was sufficient to supply the NC>3  measured in stream waters. Brooks et al. (1996) investigated soil
10    N dynamics throughout the snow-covered season on Niwot Ridge, CO. Sites with consistent snow cover
11    had a 3 to 8 cm layer of thawed soil  under the snowpack for several months before snowmelt began.
12    Nitrogen mineralization in this thawed layer contributed to Nr pools that were significantly larger than the
13    pool of N stored in the snowpack. As snowmelt began, soil inorganic N pools decreased sharply,
14    concurrent with a large increase in microbial biomass N. As snowmelt continued, both microbial N and
15    soil inorganic N decreased, presumably due to increased demand by growing vegetation (Brooks et al.,
16    1996).
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              200
                           New England Lakes
                       O  Adirondack Lakes
                       O  Appalachian Streams
              -50
                                               50             100
                                         Mean Summer ANC (peq/L)
                          150
                   200
      Figure B-11. 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).
 1
 2         In the Sierra Nevada, the hydrology of alpine and subalpine ecosystems is dominated by snowfall
 3    and snowmelt, with over 90% of the annual precipitation falling as snow. The relatively small loads of
 4    acidic deposition can supply relatively high concentrations of SC>42 and NOs to lakes and streams during
 5    the early phase of snowmelt (Stoddard, 1995) through the process of preferential elution (Johannessen
 6    and Henriksen, 1978).
 7         Lakewater pH and ANC in the Sierra Nevada generally decrease with increasing runoff, reaching
 8    minima near peak snowmelt discharge. Most other solutes exhibit temporal patterns that indicate dilution
 9    or a pulse of increased concentration followed by either dilution or biological uptake. Williams and
10    Melack (1991) and Williams et al. (1995) documented ionic pulses (2 to 10 days in duration) in meltwater
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 1    concentrations in the Emerald Lake watershed twofold to twelvefold greater than the snowpack average.
 2    Sulfate and NC>3  concentrations in meltwater decreased to below the initial bulk concentrations after
 3    about 30% of the snowpack had melted. The initial meltwater draining from the snowpack had
 4    concentrations of NC>3  and NH4+ as high as 28 (ieq/L, compared to bulk snowpack concentrations <
 5    5 (ieq/L (Williams et al., 1995). Streamwater NC>3 concentrations peaked during the early snowmelt
 6    period, with maximum streamwater concentrations of 18 (ieq/L. During summer, streamwater NC>3
 7    concentrations were always near or below detection limit.
 8          Stoddard (1995) reported results for two lakes in the Sierra Episodes Study, one of which (Treasure
 9    Lake) typified the response of the majority of high elevation lakes in the study and one whose response
10    was most extreme (High Lake). At Treasure Lake, ANC began to decline at the onset of snowmelt and
11    reached a minimum at peak runoff, corresponding with minimum base cation, NC>3 , and SC>42
12    concentrations. The lakewater did not become acidic. High Lake watershed contained a deeper snowpack,
13    and began melting later in the season. ANC fell to 0 and below twice during the first 10 days of
14    snowmelt. The ANC minimum corresponded with maximum concentrations of base cations, NC>3  and Al.
15    Nitrate concentrations increased to values greater than 40 (ieq/L, exceeding concurrent increases in base
16    cations and causing the lake to become acidic for brief periods. Stoddard (1995) concluded that High
17    Lake appeared to be representative of the most extreme conditions of episodic acid-sensitivity in the
18    Sierra Nevada.
19         Data regarding episodic variability in streamwater ANC for six intensively studied sites within
20    Shenandoah National Park for the period 1993 to 1999 are presented in Sullivan et al (2003); (Figure B-
21    12. The minimum measured ANC each year at each site (which generally is recorded during a large rain
22    or snowmelt episode) is plotted against the median spring ANC for that year at that site. Sites that
23    exhibited median spring ANC below about 20 (ieq/L (Paine Run, White Oak Run, Deep Run) generally
24    had minimum measured ANC about 10 (ieq/L lower than median spring ANC.
25         In contrast, at the high-ANC Piney River site (median spring ANC > 150 (ieq/L), the minimum
26    measured ANC was generally more than about 40 (ieq/L lower than the respective median  spring ANC. At
27    sites having intermediate ANC values, with median spring ANC in the range of about 30 to 90 (ieq/L, the
28    minimum ANC measured each year was generally about 20 to 30 (ieq/L lower than the respective median
29    spring ANC. Thus, there is a rather clear pattern of larger episodic ANC depressions in streams having
30    higher median ANC and smaller episodic ANC depressions  in streams having lower median ANC. The
31    two sites that had median spring ANC between about 0 and  10 (ieq/L consistently showed minimum
32    measured values below 0. Streams having low chronic ANC can be expected to experience relatively
33    small episodic ANC depressions. However, those depressions can result in minimum ANC values that are
34    associated with toxicity to aquatic biota.
35
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         C7
         0)
         o
         E
         =5
         E
              200
              150-
              100-
50-
               0 -
              -50-
          IZI  Paine Run
          A  White Oak Run
          O  Deep Run
          A  Staunton River
          •  NF Dry Run
          •  Piney River
                                           .1*
                 -50
                                50
100
150
200
                                              Spring ANC (peq/L)
                                                                                Source: Sullivan et al. (Sullivan, 2003).
      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.  Data are provided for all
      intensively studied streams within Shenandoah National Park during the period 1993-1999. A1:1
      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.
 2         A recent study by Deviney et al. (2006) used hourly ANC predictions over short time periods to
 3    compute recurrence intervals of annual water-year minimum ANC values for periods of 6, 24, 72, and 168
 4    h. They extrapolated the results to the rest of the Shenandoah National Park catchments using catchment
 5    geology and topography. On the basis of the models, they conclude that large number of Shenandoah
 6    National Park streams have 6- to 168-h periods of low ANC values, which may stress resident fish
 7    populations (Deviney et al., 2006). Specifically, on the basis of a 4-year recurrence interval,
 8    approximately 23% of the land area (44% of the catchments) can be expected to have conditions that are
 9    indeterminate (ANC 20 to 50), episodically acidic (ANC 0 to 20) or chronically acidic (ANC less than 0)
10    for 72 continuous hours. Many catchments are predicted to have successive years of low-ANC values
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 1    potentially sufficient to extirpate some species (Deviney et al., 2006). The authors of the study reported
 2    that smaller catchments are more vulnerable to episodic acidification than large catchments underlain by
 3    the same bedrock. Catchments with similar topography and size are more vulnerable if underlain by less
 4    basaltic and carbonate bedrock.
 5          There are several different mechanisms of episodic acidification in operation in the streams in
 6    Shenandoah National Park, depending at least in part on the bedrock geology of the stream. The most
 7    acidic conditions in Shenandoah National Park streams occur during high-flow periods, in conjunction
 8    with storm or snowmelt runoff. The general relationship between flow level and ANC is evident in
 9    Source: Sullivan et al. (Sullivan, 2003).
10          , which plots ANC measurements against flow for three intensively studied streams representing
11    the major bedrock types in the park. The response of all three streams is similar in that most of the lower
12    ANC values occur in the upper range of flow levels.
13          Consistent with observations by Eshleman (1988), the minimum ANC values that occur in response
14    to high flow are related to baseflow ANC values. Paine Run (siliciclastic bedrock) had a mean weekly
15    ANC value of about 6 (ieq/L and often had high-flow ANC values that were less than 0 (ieq/L. Staunton
16    River (granitic bedrock) had a mean weekly ANC value of about 82 (ieq/L and had only a few high-flow
17    ANC values less than 50 (ieq/L. Piney River (basaltic bedrock) had a mean weekly ANC value of
18    217 (ieq/L and no values as low as 50 (ieq/L.
19          Eshleman and Hyer (2000) estimated the contribution of each major ion to observed episodic ANC
20    depressions in Paine Run, Staunton River, and Piney River during a 3-year period. During the study, 33
21    discrete storm events were sampled and water chemistry values were compared between antecedent
22    baseflow and the point of minimum measured ANC (near peak discharge). The relative contribution of
23    each ion to the ANC depressions was estimated using the method of Molot et al. (1989), which
24    normalized the change in ion concentration by the overall change in ANC during the episode. At the low-
25    ANC (~0) Paine Run  site on siliciclastic bedrock,  increases in NO3 and SO42 , and to a lesser extent
26    organic acid anions, were the primary causes of episodic acidification. Base cations tended to compensate
27    for most of the increases in acid anion concentration. ANC declined by 3 to 21 (ieq/L (median 7  (ieq/L)
28    during the episodes studied.
29          At the intermediate-ANC (~60 to 120 (ieq/L) Staunton River site on granitic bedrock, increases in
30    SC>42  and organic acid anions, and to a lesser extent NC>3  , were the primary causes of episodic
31    acidification. Base cation increases compensated these changes to a large degree, and ANC declined by 2
32    to 68 (ieq/L during the episodes (median decrease in ANC was 21 (ieq/L).
33
34
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                                    Pine Run (siliciclastic bedrock class)
                                  0.001   0.010   0.100  1.000  10.00
                                   Staurton River (granitic bedrock class)
                            o
                            z
                              "
                            O
                            Z
150
125
100
  75
  50
  25


400
350
300
250
200
150
100
  50
                                     0.010      0.100      1.000
                                    Piney River (basaltic bedrock class)
                                  0.001   0.010    0.100  1.000
                                         Runoff (mm/hr)
                                                                            Source: Sullivan et al. (Sullivan, 2003).
Figure B-13. 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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 1         At the high-ANC (-150 to 200 (ieq/L) Piney River site on basaltic (69%) and granitic (31%)

 2    bedrock, base cation concentrations declined during episodes (in contrast with the other two sites where

 3    base cation concentrations increased). Sulfate and NOs usually increased. The change in ANC during the

 4    episodes studied ranged from 9 to 163 (ieq/L (median 57 (ieq/L; Eshleman and Hyer, 2000).

 5         Previous studies have shown that mobilization of dissolved Al during episodic acidification is a

 6    primary cause offish mortality in streams that have low ANC under baseflow conditions (Wigington

 7    et al., 1993). Streams with higher ANC during baseflow are less likely to become sufficiently acidic

 8    during episodes to bring much Al into solution.

 9         Figure B-14 provides an example of changes in ANC, pH, and total monomeric Al that occurred in

10    Paine Run, Staunton River, and Piney River during a high-flow episode in January 1995. Under baseflow

11    conditions, ANC at the Paine Run site was above 0 (ieq/L, pH was above 5.5, and Al concentration was

12    less than about  1 (iM. Discharge levels increased dramatically during the episode, resulting in depression

13    of ANC to less than 0 (ieq/L, pH values less than 5.5, and an increase in Al concentration to near 3

14    above the threshold for adverse effects on some species of aquatic biota.
                  Paine Run
          (siliciclastic bedrock class)
    Staunton River
(granitic bedrock class)
         Piney River
   (basaltic bedrock class)
                                       125
             Total Monomeric Aluminum
                                        75-
                                        50-
                                        25-
                                         0
                                        25
                                        20
                                        15
 Total Monomeric Aluminum
 (ug/L)
 Runoff
 (mm/day)
75-
50-
25-
 0
25
20
15.
 5-
 0
Total Monomeric Aluminum
(ug/L)
Runoff
(mm/day)
                                                   Time
      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. 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.
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                                                                                    Source: Sullivan et al. (Sullivan, 2003).
 1          The same episode also resulted in substantial declines in ANC in the granitic (Staunton River) and
 2    basaltic (Piney River) watersheds. However, ANC values at these two sites were relatively high prior to
 3    the episode (about 75 and 175 (ieq/L, respectively) and did not decline to below about 50 (ieq/L during
 4    the episode at either site, and pH values remained above 6.0 and 6.5, respectively (Figure B-14).
 5          In general, pre-episode ANC is a good predictor of minimum episodic ANC and also a reasonable
 6    predictor of episodic AANC. Higher values of pre-episode ANC lead to larger AANC values, but
 7    minimum ANC values of such streams are generally not especially low. Lowest minimum ANC values are
 8    reached in streams that have low pre-episode ANC, but the AANC values for such streams are generally
 9    small.
10          Webb et al. (1994) developed an approach to calibration of an episodic acidification model for
11    VTSSS long-term monitoring streams in western Virginia that was based on the regression method
12    described by Eshleman (1988). Median, spring quarter ANC concentrations for the period 1988 to 1993
13    were used to represent chronic ANC, from which episodic ANC was predicted. Regression results were
14    very similar for the four lowest ANC watershed classes, and they were therefore combined to yield a
15    single regression model to predict the minimum measured ANC from the chronic ANC. Extreme ANC
16    values were about 20% lower than chronic values, based on the regression equation:

                    ANCmin = 0.79 ANCchronic -  5.88 (r2= 0.97; se of slope = 0.02, p = 0.001)

17          Because the model was based on estimation of the minimum ANC measured in the quarterly
18    sampling program,  it is probable that the true minimum ANC values were actually somewhat lower than
19    20% below the measured chronic ANC. Nevertheless, regression approaches for estimation of the
20    minimum episodic ANC of surface waters, such as was employed by Webb et al. (1994) for western
21    Virginia, provide a  basis for predicting future episodic acidification. It must be recognized, however, that
22    future episodic behavior might vary from  current behavior if chronic conditions change dramatically.
23          The relative importance of the major processes that contribute to episodic acidification varies
24    among the streams  within Shenandoah National Park, in part as a function of bedrock geology and
25    baseflow streamwater ANC. Sulfur-driven acidification was an important contributor to episodic loss of
26    ANC at all three study sites, probably because S adsorption by soils occurs to a lesser extent during high-
27    flow periods. This is due, at least in part, to diminished contact between drainage water and potentially
28    adsorbing soils surfaces. Dilution of base  cation concentrations was most important at the high-ANC site.
29          The documented importance of NOs to episodic acidification was a relatively recent development,
30    attributed to the effects of gypsy moth (Lymantria dispar) infestation in many watersheds within
31    Shenandoah National Park (Webb et al., 1995). Consumption of foliage by the moth larvae converted
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 1    foliar N, which is normally tied up in long-term N cycling processes, into more labile N forms on the
 2    forest floor.
 3          Thus, episodic acidification of streams in Shenandoah National Park can be attributed to a number
 4    of causes, including dilution of base cations and increased concentrations of sulfuric, nitric, and organic
 5    acids (Eshleman et al., 1995; Hyer et al., 1995). For streams having low pre-episodic ANC, episodic
 6    decreases in pH and ANC and increases in toxic Al concentrations can have adverse effects on fish
 7    populations. Not all of the causes of episodic acidification are related to acidic deposition. Base-cation
 8    dilution and increase in organic acid anions during high-flow conditions are natural processes. The
 9    contribution of nitric acid, indicated by increased NO3 concentrations, has evidently been (at least for
10    streams in the park) related to  forest defoliation by the gypsy moth (Webb et al., 1995; Eshleman et al.,
11    1998). However, significant contributions of sulfuric acid, indicated by increased SC>42  concentrations
12    during episodes in some streams, is an effect of atmospheric deposition and the dynamics of S adsorption
13    on soils (Eshleman and Hyer, 2000).
      B.4.  Effects  on  Biota
14          Soil and surface water acidification involve changes in a number of chemical parameters, each of
15    which has the potential to influence the health and vigor of biological communities and the species that
16    comprise them. In most cases where biological effects of acidification have been documented, the most
17    important chemical parameters involved in those effects have been pH, Al;, and Ca2+. Less commonly, one
18    or more base cations other than Ca2+ (e.g., Mg2+, K+) or C  are also involved. This is true for both aquatic
19    and terrestrial effects.
20          A number of authors have examined the complex interactions between pH, Al, and Ca2+ that must
21    be considered when attempting to determine the effects of acidification on both aquatic and terrestrial
22    biota (e.g., Mount et al., 1988; Ingersoll et al., 1990b; Wood et al., 1990). Calcium concentration
23    significantly affects the distribution of species and their ability to survive in acidified environments.
24    Aluminum, leached by acid precipitation from soils in the  watershed, complicates the response
25    considerably because some forms of Al are highly toxic to both aquatic and terrestrial species. Aluminum
26    and hydrogen ions interact both synergistically and antagonistically depending on conditions (Havas,
27    1985; Rosseland and  Staurnes, 1994). In the presence of naturally occurring organic acids, Al toxicity can
28    be reduced or eliminated. A number of authors have examined the complex interactions between pH, Al,
29    and Ca2+ that must be considered when attempting to determine the effects of acidification on both
30    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
 1          Ecological effects occur at four levels of biological organization: (1) the individual, (2) the
 2    population, comprised of many individuals, (3) the biological community, composed of many species,
 3    (Billings, 1978), and (4) the ecosystem. Several metrics have been developed to describe the effects of
 4    acidification at each of these levels of organization. For the individual, effects are assessed in terms of
 5    sublethal effects on condition. At the population level, effects are measured by changes in  the population
 6    of a certain species. At the community level, species richness and community structure can be used to
 7    evaluate effects, and at the ecosystem level, changes in nutrient cycling and ecosystem processes are
 8    assessed. Most of these indices have been applied primarily to aquatic ecosystems. Each is discussed
 9    below.
10          Baker et al. (1990a) conducted a rigorous review of the effects of acidification on aquatic biota for
11    the  1990 NAPAP State of Science/Technology reports. They evaluated hundreds of laboratory, in situ
12    bioassay, field surveys, whole-system field experiments, and smaller mesocosm studies on the effects of
13    acidification on aquatic biota. Their 381-page report is the most exhaustive source summarizing the
14    aquatic biological effects of acidification from acidic deposition. The summaries provided here in sections
15    B.4 and B.6 rely heavily on this source.
16          In Shenandoah National Park, a statistically robust relationship between acid-base status of streams
17    and fish species richness was documented. The three-year Fish in Sensitive Habitats (FISH) study of
18    stream acidification in Shenandoah National Park demonstrated negative effects on fish from both chronic
19    and episodic acidification (Bulger et al., 1999). Biological differences in low- versus high-ANC streams
20    included species richness, population density, condition factor, age, size, and field bioassay survival. Of
21    particular note was that both episodic and chronic mortality occurred in young brook trout exposed in a
22    low-ANC stream, but not in a high-ANC stream (MacAvoy and Bulger, 1995), and that blacknose dace
23    (Rhinichthys atratulus) in low-ANC streams were in poor condition relative to blacknose dace in higher-
24    ANC streams (Dennis et al., 1995; Dennis and Bulger, 1995).

      B.4.1.1. Individual Condition Factor
25          Relatively little is known about changes in the condition offish or other aquatic biota resulting
26    from acidification. It is expected that sublethal effects will occur in acid-sensitive species well before the
27    species is eliminated from a particular lake, stream, or terrestrial habitat. For that reason, loss of an acid-
28    sensitive species is not necessarily an ideal indicator of acid stress. Clearly, stress begins to occur prior to
29    species elimination. Sublethal effects are more difficult to quantify, but are nevertheless important.
30          Condition factor is one measure of sublethal effect that has been used to quantify effects of
31    acidification on fish. Condition factor is an index to describe the relationship between fish weight and


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 1    length. Expressed as fish weight/length3, multiplied by a scaling constant, this index reflects potential
 2    depletion of stored energy reserves (Everhart and Youngs, 1981; Goede and Barton, 1990; Dennis and
 3    Bulger, 1995). Condition factor is interpreted as depletion of energy resources such as stored liver
 4    glycogen and body fat (Goede and Barton, 1990). Fish with higher condition factor are more robust than
 5    fish having low condition factor.
 6          Field studies have shown lower condition factor in fish found in more acidic streams (Dennis and
 7    Bulger, 1995). Condition factor has been developed and applied mainly for blacknose dace. This species
 8    is widely distributed in Appalachian Mountain streams and is moderately tolerant of low pH and ANC,
 9    relative to other fish species in the region. However, the concept is probably applicable to other species as
10    well. Condition factor may be a useful metric for many species in aquatic  ecosystems that are only
11    marginally affected by acidification.
12          Bulger et al. (1999) observed a positive relationship between condition factor and pH in streams in
13    Shenandoah National Park (Figure B-15). Dennis and Bulger (1995) found a reduction in the condition
14    factor for blacknose dace in waters near pH 6.0. The four populations shown in Figure B-15 with the
15    lowest condition factor have mean habitat pH values within or below the range of critical pH values at
16    which Baker and Christensen (1991)  estimated that negative population effects for blacknose dace are
17    likely for the species. The mean length-adjusted condition factor offish from the study stream with the
18    lowest ANC was about 20% lower than that of the fish in best condition. Comparisons with the work of
19    Schofield and Driscoll (Schofield and Driscoll, 1987) and Kretser et al. (1989) suggest that pH in the low-
20    pH Shenandoah National Park streams is near or below the limit of occurrence for blacknose dace
21    populations in the Adirondack region of New York (Sullivan et al., 2003).
22          Chronic sublethal stress caused by pH below about 6.0 may have serious effects on a variety of
23    wild fish populations. There is an energy cost in maintaining physiological homeostasis; the calories used
24    to respond to stress are a part of the fish's total energy budget and are unavailable for other functions,
25    such as growth and reproduction (Schreck, 1981, 1982; Wedemeyer et al., 1990).
26          Observed differences in condition factor may occur because maintenance of internal chemistry in
27    the more acidic streams would require energy that otherwise would be available for growth and weight
28    gain (Dennis and Bulger, 1999; Sullivan et al., 2003). The energy costs to  fish for active iono-
29    osmoregulation can be substantial (Farmer and Beamish, 1969; Bulger, 1986). Because of the steep
30    gradient in Na+ and Cl  concentrations between fish blood and freshwater, there is constant diffusional
31    loss of these ions, that must be replaced by energy-requiring active transport. Low pH increases the  rate
32    of passive loss of blood electrolytes (especially Na+ and Cl), and Al elevates losses of Na+ and Cl  above
33    the levels that occur due to acid stress alone (Wood, 1989).
34
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                                      y = 1.194x +1.519, r   =.777
       I
        re
        o
       S
       TJ
       O
             11
           10.5
             10
            9.5 -
              9
            8.5
             8  -
            7.5
                      O
               5.2     5,4      5.6     5.8      6      6-2     6.4

                                                 mean pH
                   6.6
     6.8
7.2
     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. 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 etal. (1999).
1         It is also possible that the loss of sensitive individuals or early life stages within species may reduce

2    competition for food among the survivors, resulting in better growth rates, survival, or condition.

3    Similarly, competitive release (increase in growth or abundance subsequent to removal of a competitor)

4    may result from the loss of a sensitive species, with positive effects on the density, growth, or survival of

5    competitor population(s) of other species (Baker et al., 1990b). However, in some cases where

6    acidification continued, transient positive effects on size of surviving fish were shortly followed by

7    extirpation (Bulger et al.,  1993).
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 1          Acid stress is at least partly responsible for the lower condition of blacknose dace populations in
 2    Shenandoah National Park, though reduced access to food or lower food quality (Baker et al., 1990b),
 3    either resulting from the nature of soft water streams or exacerbated by acidification, cannot be ruled out.
 4    Primary productivity is low in headwater streams and lower still in soft water headwaters, which are more
 5    likely to be acidified. Production of invertebrates is likely to be low  in such streams as well (Wallace
 6    et al., 1992). Thus, lower food availability cannot be discounted as a potential contributor to lowered
 7    condition in Shenandoah National Park blacknose dace populations  in low-pH streams. Nevertheless,
 8    reduced growth rates have  been attributed to acid stress in a number of other fish species, including
 9    Atlantic salmon (Salmo salaf), Chinook salmon (Oncorhynchus tshawytschd), lake trout (Salvelinus
10    namaycush), rainbow trout (Oncorhynchus mykiss), brook trout, brown trout (Salmo trutta), and Arctic
11    char (Salvelinus alpines).

      BAA.2. Species Composition
12          Species composition refers to the mix of species that are represented in a particular ecosystem.
13    Acidification alters species composition in aquatic ecosystems. There are a number of species common to
14    many oligotrophic waters that are sensitive to acidic deposition and that cannot survive, compete, or
15    reproduce in acidic waters. In response to small to moderate changes in acidity, acid-sensitive species are
16    often replaced by other more acid-tolerant species, resulting in changes in community composition, but
17    little or no change in total community abundance or biomass. The effects of acidification are continuous,
18    with more species being affected at higher degrees of acidification. Therefore, the degree of alteration of
19    surface water biological community composition increases as surface waters become more acidic. There
20    is a consistent pattern of lower community diversity with increased acidification.

      B.4.1.3. Taxonomic Richness
21          Taxonomic richness is a metric that is commonly used to quantify the effects of an environmental
22    stress such as acidification or eutrophication. The richness metric  can be applied at various taxonomic
23    levels. For example, the number offish species can be used as  an index of acidification (cf. Bulger et al.,
24    1999). Similarly, acidification effects on aquatic insects can be evaluated on the basis of the number of
25    families or genera of mayflies (order Ephemeroptera) (Sullivan et al., 2003).
26          Acidification results in the loss of acid-sensitive species, with more species lost with higher
27    degrees of acidification. A direct outcome of population loss caused by acidification is a decline in species
28    richness (the total number  of species in a stream or lake). This  is a highly predictable outcome of regional
29    acidification, although the  pattern and rate of species loss varies from region to region.
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 1          Decreases in ANC and pH and increases in Al; concentration contribute to declines in species
 2    richness and abundance of zooplankton, macroinvertebrates, and fish (Schindler et al., 1985; Keller and
 3    Gunn, 1995). Species richness is positively correlated with pH and ANC (Rago and Wiener, 1986; Kretser
 4    et al., 1989) because of the elimination of acid-sensitive species (Schindler et al., 1985). Knowledge of
 5    the spatial distribution of pH and other water quality variables is necessary to explain the  presence or
 6    absence of species within heterogeneous environments. Organisms that are mobile and can sense the pH
 7    of their environment can move to areas (called refugia) that have more favorable water chemistry.
 8    Although some species are favored by increased acidity, species diversity generally decreases as surface
 9    water acidity increases.
10          Decreases in species richness have been observed for all major trophic groups of aquatic organisms
11    (Baker et al., 1990a). Baker et al. (1990a) discussed 10 selected studies that documented this
12    phenomenon, with sample sizes ranging from 12 to nearly 3,000 lakes and streams analyzed per study.
13          Lake and stream size can be an important complicating factor in interpreting species richness data.
14    Larger lakes and streams in larger watersheds would generally be expected to contain more species than
15    smaller lakes or streams in smaller watersheds, irrespective of acid-base chemistry. Nevertheless, when
16    adjusted for lake size, lakes with pH less than approximately 6.0 contain significantly fewer species than
17    lakes with pH above 6.0 (Figure B-16)  (Frenette et al., 1986; Rago and Wiener, 1986; (Schofield and
18    Driscoll, 1987); Matuszek and Beggs, 1988).
19          Studies in the Adirondack Mountains demonstrated the effect of acidification on species richness;
20    of the 53 fish species recorded in Adirondack lakes by the ALSC, about half (26 species) were absent
21    from lakes with pH below 6.0. Those 26 species included important recreational species, such as Atlantic
22    salmon, tiger trout (Salmo trutta X Salvelinus fontinalis), redbreast sunfish (Lepomis auritus), bluegill
23    (Lepomis macrochirus), tiger musky (Esox masquinongy X Indus), walleye (Sander vitreus), alewife
24    (Alosapseudoharengus), and kokanee (Oncorhynchus nerkd) (Kretser et al., 1989), plus ecologically
25    important minnows that serve as forage for sport fish. Fully 346 of 1,469 lakes surveyed by the ALSC
26    supported no fish at all at the time of the survey. These lakes were significantly lower in pH, dissolved
27    Ca2+, and ANC, and had higher concentrations of Al; than lakes hosting one or more species offish
28    (Gallagher and Baker,  1990). Among lakes with fish, there was an unambiguous relationship between the
29    number offish species and lake pH, ranging from about one species per lake for lakes having pH less  than
30    4.5 to about six species per lake for  lakes having pH > 6.5 (Kretser et al., 1989; Driscoll et al., 2001a).
31    Figure B-17 shows the mean number offish species for pH classes from 4.0 to 8.0 in lakes in the
32    Adirondacks. It is important to note, however, that there are many possible causes offish  absence in
33    addition to acidification. These include lack of suitable habitat (especially for spawning),  winter kill,
34    blocked access, etc.
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         +2
      (0
      LU
      Q.
      o:
      LU
      CO
          -1
          -2
      U)
      LU
          -7
                 4.5
5.0
5.5
6.0
6.5
pH
7.0
7.5
8.0
8.5
                                                                                   Source: Matuszek an'
     Figure B-16. 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.

1          Sullivan et al. (2006a) developed a relationship between fish species richness and ANC class for
2    Adirondack lakes. Fish species richness observations, as a function of ANC (|ieq/L) class, were fit to a
3    logistic relationship by a non-linear regression analysis. Under chronically acidic conditions (summer
4    index or annual average ANC < 0 (ieq/L), Adirondack lakes are generally fishless. There was a marked
5    increase in mean species richness with increases in ANC up to values of approximately 100 (ieq/L. The
6    asymptote for the fish species equation was 5.7 species. This analysis suggests that there could be loss of
7    fish species with decreases in ANC below approximately 100 (ieq/L. It does not account, however, for the
8    possibility that lakes having  higher ANC are often larger, and therefore support more fish species because
9    of increased habitat diversity and complexity.
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                            V)
                            0)
                            'o
                            0)
                            Q.
                            0)
                            .Q
14

12 -

10 -

 8 -

 6 -

 4 -

 2 -

 0 -

-2 -
                               -4
                                -200  -100   0    100  200   300  400   500

                                              ANC(|jeq/L)
                         B
10
                            0)
                           'o
                            o
                            Q.
                           (O
                            tn
                            (D
                           -Q
                            E
                            3
 9 -

 8 -

 7 -

 6 -

 5 -

 4 -

 3 _

 2 -

 1 _
                               0
y = .024x + 2.076, r2 = .763


              D
                                 -50    0     50    100   150  200   250  300

                                           mean ANC(|jeq/L)


     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 peq/LANC categories, based on data collected by the Adirondack Lakes Survey

     Corporation.



1         As an element of the FISH project (Bulger, 1999), numbers offish species were compared among

2    13  Shenandoah National Park streams spanning a range of pH and ANC conditions. There was a highly

3    significant (p < 0.0001) relationship between stream acid-base status (during the 7-year period of record)

4    and fish species richness among the 13 streams. The streams with the lowest ANC hosted the fewest

5    species (see figure B-18).
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0)
'o
0>
V)
.c
LJ_
*
2
^p



a •
7 .




2_



-2

Vj
^
•"(•> x"
X / X
JH3 i
''if ':lMt 4 -'i> -!l4
v / v T Y1 T
/*\ A A JL Jk A^.
ff / ;• '•
is r T fif 'ih
VX '„;.' ',> V if
X x i X JX
f^ 1 Iff 1 t
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5 0 25 50 75 100 125 150 175 200 225 250
V
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, \
f
A
T
I
1
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1
275 30
                                            Average ANC (iieq/L)
                                                                               Source: Redrawn from Bulger et al. (1999).
      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. 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 peq/L had three or fewer species.
 1         Median stream ANC values and watershed areas are shown in Table B-l 1 for the 14 streams used
 2    by Bulger et al. (1999) to develop the relationship between ANC and fish species richness shown in
 3    Figure B-18. Despite the overall similarities, these study streams vary in watershed area by a factor of 10.
 4    The streams that have larger watershed areas generally have more fish species than the streams having
 5    smaller watershed areas. All of the "rivers" have watersheds larger than 10 km2 and ANC higher than
 6    75 (ieq/L. In contrast, the majority (but not all) of the "runs" have watershed area smaller than 10 km2 and
 7    ANC less than 20 (ieq/L. All of the streams that have watershed areas smaller than  10 km2 have three or
 8    fewer known species offish present. All of the streams having larger watersheds (>10 km2) have three or
 9    more known fish species; seven of nine have five or more species; and the average  number offish species
10    is six. There is no clear distinction between river and run, but it is clear that as small streams in
11    Shenandoah National Park combine and flow into larger streams and eventually to rivers, two things
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 1    happen: acid-sensitivity generally declines, and habitat generally becomes suitable for additional fish
 2    species (Sullivan et al., 2003).
 3          South of Shenandoah National Park the effects of surface water acidification on fish species
 4    richness have been studied in some detail in the St. Marys River in Virginia. Fish species richness was
 5    closely associated with surface water acid-base chemistry. Bugas et al. (1999) conducted electrofishing in
 6    the St. Marys River in 1976, and every 2 years from 1986 through  1998. Systemic stream acidification
 7    occurred during the study period. Sampling occurred at six sites between the downstream end of the St.
 8    Marys Wilderness and the headwaters over a distance of about 8 km. The number offish species in the St.
 9    Marys River within the wilderness declined from 12 in  1976 to 4 in 1998. Three of the four species
10    present in 1998 (brook trout, blacknose dace, fantail darter \Etheostoma flabellare\) are tolerant of low
11    pH and are typically the only fish species present in streams having similar levels of acidity in
12    Shenandoah National Park, which is also located in Virginia (Bulger et al., 1999). Bugas et al. (1999)
13    reported that successful brook trout reproduction in the  St. Marys River occurred only 1 year out of 4
14    during the period 1995 through 1998. Eight of the fish species recorded in one or more early years have
15    not been observed in more recent years. Several, including blacknose dace, rainbow trout,  and torrent
16    sucker (Thoburnia rhothoeca), showed a pattern of being progressively restricted over time to lower river
17    reaches, which generally have higher ANC. The number offish species decreased with decreasing
18    minimum ANC, from nine species at ANC of about 160 (ieq/Lto one to three species  at ANC near 0. The
19    best fit regression line suggested, on average, a loss of one species  for every 21 (ieq/L decline in annual
20    minimum recorded ANC value.
21          Dynamic water chemistry model projections have been combined with biological dose-response
22    relationships to estimate declines in fish species richness with acidification. A relationship derived from
23    the data in Figure B-18 was used by Sullivan et al. (2003) with stream ANC values predicted by the
24    MAGIC model to provide estimates of the expected number offish species in each of the modeled
25    streams for the past, present, and future chemical conditions simulated for each stream. The coupled
26    geochemical and biological model  predictions were evaluated by comparing the predicted  species
27    richness in each of the 13 streams with the observed number of species that occur in each stream. The
28    agreement between predicted and observed species numbers was good, with a root mean squared error
29    (RMSE)  in predicted number of species across the 13 streams of 1.2 species. The average error was 0.3
30    species, indicating that the coupled models were unbiased in their predictions. Model  reconstructions of
31    past species richness in the streams suggested that historical loss of species had been greatest in the
32    streams located on the most sensitive geological class (siliciclastic). The average number of species lost
33    from streams on the three bedrock types examined were estimated  as:  1.6 species on siliciclastic bedrock;
34    0.4 species on granitic bedrock; and 0.4 species on basaltic bedrock. In the case of the siliciclastic
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 1    streams, the projected past changes were much larger than the average error and RMSE of the coupled
 2    models, suggesting that the projections were reasonably robust.
 3          It appears that fish species richness is controlled by multiple factors, of which both acidification
 4    and watershed area can be  important. Watershed area might be important in this context because smaller
 5    watersheds may contain smaller streams having less diversity of habitat, more pronounced effects on fish
 6    from high-flow periods, or lower food availability. Such issues interact with other stresses, including
 7    acidification, to determine  overall habitat suitability.
 8          For Shenandoah National Park, Bulger et al. (1999) concluded that the most important cause of the
 9    observed decline in species richness with decreasing ANC was acid stress associated with acidification.
10    However, an additional causal factor may have been the decrease in the number of available aquatic
11    niches when moving from  downstream locations (which are seldom low in pH and ANC) to upstream
12    locations (which are often  low in pH and ANC in this region; Sullivan et al. 2003). The relative
13    importance of this latter factor, compared with the importance of acid stress, in determining this
14    relationship is unknown.
15          In the Adirondack region, Driscoll et al. (200Ib) concluded that high-elevation lakes are more
16    likely to be fishless than larger lakes at low elevation (Gallagher and Baker, 1990) because they have poor
17    access for fish immigration, poor fish spawning substrate, or low pH, or they may be susceptible to
18    periodic winter kills. Nevertheless, small, high-elevation Adirondack lakes with fish also had significantly
19    higher pH compared with fishless lakes; acidity is likely to play an important role in the absences  offish
20    from such lakes (Driscoll et al., 200Ib).

      B.4.1.4. Community Structure
21          Ecosystem response to pollutant deposition is a direct function of the ecosystem's ability to
22    ameliorate resulting changes in individual species (Strickland et al., 1993). In order to determine
23    ecosystem response and the possible effects on community structure, species responses must be scaled in
24    both time and space and be propagated from the individual to the more complex levels of community
25    interaction within an ecosystem.
26          Individuals within a population vary in their ability to withstand a stress. The response of each
27    individual is based on its genetic constitution (genotype),  its stage of growth at time of exposure to the
28    stress, and the microhabitat in which it lives (Levlin,  1998). The range within which individuals in the
29    population can exist and function determines the ability of the population to survive when exposed to a
30    chronic stress. Those individuals that are able to cope with the stress survive and reproduce. The same
31    kinds of pressures act on populations of different species.  Competition among species results in
32    community change over time and eventually produces ecosystems composed of populations of species
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 1    that have the capability to tolerate the stress (Guderian et al., 1985; Rapport and Whitford, 1999; U.S.
 2    Environmental Protection Agency, 2004).
 3          Work conducted on the biological effects of acidification has largely been focused on the response
 4    offish, especially salmonids (trout and salmon). This focus tends to be driven by the value people place
 5    on fish and fishing, rather than any ecological consideration. Other vertebrate, invertebrate, plant, and
 6    algal communities are also sensitive to acidification. In general, higher order trophic groups are more
 7    susceptible to acidification. Thus, in terms of changes in community structure in response to aquatic
 8    acidification, the general progression of sensitivity is as follows: fish > invertebrates (benthic and
 9    zooplankton) > algae > microbes (Baker et al., 1990a). Population-level fish response to acidification is
10    primarily through recruitment failure, a result of increased mortality of early life stages or indirect effects
11    through the food chain (loss of prey species). Al;, pH, and Ca2+ have been identified as the variables most
12    likely to have the greatest influence on fish community structure.

      B.4.1.5. Indices of Ecological Effects
13          The most widely used index of acidification effect is the Acid Stress Index (ASI) developed by
14    Baker et al. (1990a). This index uses fish bioassay survival data fitted to a maximum likelihood logistic
15    regression model as a function of exposure to pH, Al, and Ca2+ to predict the probability offish survival
16    expressed as a percent mortality. This approach can aid in determination of effects on species composition
17    by predicting the probability of occurrence of species of varying acid sensitivity. Separate ASI models
18    were developed for tolerant, intermediate, and sensitive fish species. Approximate ASI reference levels
19    were established for various species based on logistic regression offish presence as a function of the
20    sensitive, intermediate, and tolerant ASI values  for brown bullhead (Ameiurus nebulosus), brook trout,
21    lake trout, and common shiner (Luxilus cornutus). They are presented in Table B-12.
22          The ASI was deemed a useful index of stress by Baker et al. (1990a), even though the relationships
23    between ASIs and fish population status could not be quantified precisely because of confounding factors.
24    Such factors included the abundance and types of food species, competitors and predators present,
25    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
26          Episodic and chronic changes in the chemistry of surface waters can have different effects on
27    aquatic organisms and populations depending on species and the life history stages present. More is
28    known about the sensitivity to acidification of the life stages offish than is known for other aquatic
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 1    organisms. In general, early life stages are more sensitive to acidic conditions than the young-of-the-year,
 2    yearlings, and adults (Baker and Schofield, 1985; Johnson et al., 1987; Baker et al., 1990a). Also, small
 3    fish, especially swim-up fry, are probably less mobile and less able to avoid exposure to adverse chemical
 4    conditions than the relatively larger adults (Baker et al., 1996).
 5          There are a number of issues of acidification timing that are important to determination of the
 6    extent and magnitude of effects. One important issue concerns the timing of acidity exposure relative to
 7    life stage. For example, adult fish are generally more tolerant of acidity than early life stages such as eggs,
 8    fry and juveniles. There could be substantial differences in effect based on small differences in age  or
 9    timing of exposure to acidity. No definite pattern was observed by Baker et al. (1990a) across all studies
10    or species. This may reflect either differences in the test conditions or actual differences among species.
11          The presence of early life stages of brook trout, which are most sensitive to adverse effects from
12    acidification (Bulger et al., 2000), varies with season. For example, the most acid-sensitive stages of
13    brook trout development are present in Virginia streams throughout the cold season in general, and  the
14    winter in particular (Source: Sullivan et al. (Sullivan, 2003).
15          Figure B-19).
                     Acid-Sensitive Life Stages of the Brook Trout
                                                    sacfry in nest
                                   spawning       hatching      swim-up fry
                                  - >    — ^     - >
             l
              JUL   AUG   SEP   OCT  NOV  DEC  JAN   FEB  MAR  APR   MAY   JUN
                                                                                    Source: Sullivan et al. (Sullivan, 2003).
      Figure B-19. Life stages of brook trout.

16          The processes of oogenesis and fertilization in fish and aquatic invertebrates are especially
17    sensitive to low pH (Muniz, 1991; Havas et al., 1995). In fish, this sensitivity is most likely due to
18    adverse effects on the female spawner. For instance, Beamish et al. (1976) reported that reduced serum
19    and plasma Ca2+ in female fish in acidified Canadian lakes lead to a higher probability for failure in
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 1    producing viable eggs. A depletion of Ca2+ from bone and increased numbers of females with unshed eggs
 2    have also been linked to sensitivity at this life stage (cf Rosseland, 1986; Muniz, 1991).
 3          After fertilization, the embryo seems to be susceptible to acidic waters throughout the whole period
 4    of development. The periods shortly after fertilization and prior to hatching seem to be most critical
 5    (Rosseland, 1986). The susceptibility of the embryo can be the result of direct exposure to elevated H+
 6    concentrations and also to the toxic effects of Al; at intermediate pH-values. Low pH in the surrounding
 7    water also results in pH-depression inside the egg, leading to either a prolongation of the hatching or to a
 8    reduced hatching success (Rosseland, 1986).  Eggs lying in gravel on stream and lake beds are to some
 9    extent protected from exposure to rapid changes in pH (Gunn and Keller, 1984b; Lacroix, 1985b).
10    Nevertheless they can experience high mortality during periods of acid runoff, such as snowmelt (Gunn
11    and Keller, 1984a).
12          In fish, emergent alevins show susceptibility to the adverse effects of Al; and H+ that increases with
13    age (Baker and Schofield, 1982; Wood and McDonald, 1982). Rosseland (1986) indicated that this
14    increasing sensitivity results from changes that take place in the respiratory system. Shortly after hatch,
15    alevins still respire through their skin but gradually gills become the primary organ of gas and ion
16    exchange. Gills are the locus for interference of fT and Al; with iono-regulatory exchange. Woodward
17    et al. (1989) exposed cutthroat trout (Oncorhynchus clarki)  from the Snake River in Wyoming to pH
18    depressions from pH 4.5  to 6.5 in the laboratory. Fertilized egg, eyed embryo, alevin, and swim-up larval
19    stages were exposed to low pH for a period of seven days. Each life stage was monitored for mortality,
20    growth, and development for 40 days after hatching. Reductions in pH from 6.5 to 6.0 in Iow-Ca2+ water
21    (70 (ieq/L) did not affect survival, but reduced growth of swim-up larvae. The eggs, alevin, and swim-up
22    larval stages showed significantly higher mortality at pH 4.5 than at pH 6.5. Mortality was also higher at
23    pH 5.0 than at pH 6.5, but only statistically higher for eggs.
24          Woodward (1991) exposed greenback  cutthroat trout (Oncorhynchus clarki stomias) in the
25    laboratory to 7-day pH depressions. Low-Ca2+ (65 (ieq/L) water at pH 6.5 was experimentally reduced to
26    pH values of 6.0, 5.5, 5.0, and 4.5. Four life stages were exposed: freshly fertilized egg, eyed embryo,
27    alevin, and swim-up larva. Alevin survival was reduced at pH 5.0, whereas survival of eggs, embryos, and
28    swim-up larvae was reduced at pH 4.5.  Swim-up larvae showed feeding inhibitions at pH 4.5. The authors
29    concluded that the threshold for effects  of acidity on greenback cutthroat trout in the absence of Al was
30    pH 5.0 (Woodward,  1991).
31          Yellowstone cutthroat trout (O. c. bouveri) were exposed to 7-day pH depressions by Farag et al.
32    (1993). Of the four life stages studied, eggs were most sensitive to low pH. Eggs exposed for seven days
33    to pH 5.0 test water showed a statistically significant reduction in survival compared with eggs exposed
34    for seven days to pH 6.5  water. Survival of alevin and swim-up larvae were significantly reduced from
35    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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 1    survival compared with the control (6.5) but not by statistically significant amounts. Eyed embryos were
 2    not sensitive to any of the exposures.
 3          According to Bulger et al. (1999), adult brook trout in Shenandoah National Park streams are more
 4    tolerant of acidity than are adult blacknose dace. For both species, the early life stages are more sensitive
 5    than the adults, and brook trout young are actually more sensitive than blacknose dace adults (Bulger
 6    et al., 1999). Blacknose dace spawn during summer and the eggs and very young fry are therefore
 7    somewhat protected from the most acidic episodes, which typically occur during cold-season, high-flow
 8    conditions.

      B.4.2.2. Biological Effects of Episodes
 9          Episodic decreases in pH and ANC can produce chemical conditions in lakes, and especially in
10    streams, that are as harmful to  biota as chronic acidification (Baker et al., 1996). Adverse effects on biota
11    occur particularly when changes involve pH, Al;, or Ca2+ (Baker et al., 1990a). Aquatic biota vary greatly
12    in their sensitivity to episodic decreases in pH and increases in Al; in waters having low Ca2+
13    concentration. However, Baker et al. (1990a) concluded that episodes are most likely to affect biota if the
14    episode occurs in waters with pre-episode pH above 5.5 and minimum pH during the episode of less than
15    5.0.
16          Results from the ERP demonstrated that episodic acidification can have long-term adverse effects
17    on fish populations. Streams with suitable chemistry during low flow, but low pH and high Al; levels
18    during high flow, had substantially lower numbers and biomass of brook trout than in non-acidic streams
19    (Wigington et al.,  1996). Streams having acidic episodes showed significant mortality offish.
20          Some brook trout avoided exposure to stressful chemical conditions during episodes by moving
21    downstream or into areas with higher pH and lower Al;. This movement of brook trout only partially
22    mitigated the  adverse effects of episodic acidification, however, and was not sufficient to sustain fish
23    biomass or species composition at levels that would be  expected in the absence of acidic episodes. Just as
24    spatially heterogeneous environments or refugia enable some species to survive in otherwise unfavorable
25    conditions, temporal heterogeneity often has the opposite effect. These findings suggest that stream
26    assessments based solely on chemical measurements during low-flow conditions will not accurately
27    predict the status offish populations and communities in small mountain streams unless some adjustment
28    is made for episodic processes (Baker et al., 1990a, 1996; Wigington et al., 1996; (Sullivan,  2000).
29          In Shenandoah National Park, MacAvoy and Bulger (1995) used multiple bioassays over 3 years in
30    one of the low-ANC streams as part of the FISH project to determine the effect of stream baseflow and
31    acid episode stream chemistry on the survival of brook  trout eggs and fry. Simultaneous bioassays took
32    place in mid- and higher-ANC reference streams. Acid episodes (with associated low pH and elevated Al;
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 1    concentrations, and high streamwater discharge) induced rapid mortality in the low-ANC stream, while
 2    the test fish in the higher-ANC stream survived (Bulger et al., 1999).
 3          In the West, it has also been shown that native trout are sensitive to short-term increases in acidity.
 4    For example, Woodward et al. (1989) exposed native western cutthroat trout to pH depressions (pH 4.5 to
 5    6.5) in the laboratory. Reductions in pH from 6.5 to 6.0 in Iow-Ca2+ water (70 (ieq/L) did not affect
 6    survival, but did reduce growth of swim-up larvae. Eggs, alevins, and swim-up larvae showed
 7    significantly higher mortality at pH 4.5 as compared to pH 6.5. Mortality was also somewhat higher at pH
 8    5.0, but only statistically higher for eggs. Some species of aquatic biota in western aquatic ecosystems
 9    have been shown to be somewhat more sensitive to pH and ANC change than are cutthroat trout (Baker
10    etal., 1990a).
11          Multiple logistic regression models were used by Van Sickle et al. (1996) to relate fish bioassay
12    mortality rates to summary statistics of time-varying stream chemistry over the 20-day bioassay periods.
13    Higher mortality of all three test fish species (brook trout, dace, sculpin [Cottus spp.]) during the in situ
14    bioassays was clearly associated with increased Al;. In addition, individual bioassays conducted during
15    chronically or episodically acidified conditions had higher median mortality than did those during non
16    acidic conditions,  but no mortality differences were detected between chronically acidic and episodically
17    acidic conditions.  Time-weighted median Al; was the best single predictor of 20-day mortality for both
18    brook trout and sculpin, whereas the number of days with Al; > 200 (ig/L provided the best prediction of
19    blacknose dace mortality.
20          In the Northeast, Baker et al. (1996) studied the effects of episodic acidification on fish in 13 small
21    streams in the Adirondack and Catskill Mountains of New York and the Northern Appalachian Plateau in
22    Pennsylvania. They conducted in situ bioassays with brook trout and blacknose dace, mottled sculpin
23    (Cottus bairdf) or slimy sculpin (Cottus cognatus} depending on the region, to measure direct toxicity.
24    Movements of brook trout individuals in relation to stream chemistry were tracked using radiotelemetry.
25    Electrofishing surveys assessed fish community status and the abundance and biomass of brook trout in
26    each stream. Streams with suitable conditions during low flow, but moderate-to-severe episodic
27    acidification during high flow, had higher fish mortality in bioassays, higher net downstream movement
28    of brook trout during events, and lower brook abundance and biomass  compared to nonacidic streams.
29    These streams lacked the more acid-sensitive fish species (blacknose dace and sculpin). Movement of
30    trout into refugia (areas with higher pH and lower Al) during episodes  partially mitigated the adverse
31    effects of episodes.
32          Chemical measurements by ERP during high flow correlated with fish community status. In
33    general, reduced trout abundance occurred in ERP streams with median high flow pH < 5.0 and Al; > 100
34    to 200 (ig/L. Acid -sensitive fish species were absent from streams with median high flow pH < 5.2 and
35    Al; > 100 (ig/L. More recently, Baldigo et al. (2007) found that mortality of brook trout young of the year
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 1    occurred at concentrations as low as 54 (ig/L .Al; was the single best predictor offish mortality in ERP
 2    bioassays (Van Sickle et al., 1996) and has been identified as an important toxic factor in other bioassays
 3    and field studies (Mount et al., 1988; Ingersoll et al., 1990b; Rosseland et al., 1990). The relationships
 4    between pH and Al; or ANC and Al; vary among streams (Wigington et al., 1996), and therefore
 5    predictions of potential effects on fish based solely on pH or ANC may be misleading. High Al;
 6    concentrations during episodes are probably the dominant cause of adverse effects on fish during episodic
 7    acidity events.

      Biological  Effects of Chronic Acidification
 8          Changes in surface water acid-base chemistry, including pH, ANC, Al;, and Ca2+, can affect in-
 9    stream and in-lake biota. Adverse biological effects may be seen at pH less than about 6.0 to 6.5 and Al;
10    greater than about 30 to 50 (ig/L (1 to 2 (JVI). It tends to increase with decreasing pH, and reaches
11    potentially toxic concentrations (> ~2 (iM) in surface drainage waters having pH less than about 5.5.
12    Effects vary substantially by organism, life stage, and the concentration of DOC. Inorganic Al in solution
13    is also toxic to plants.
14          Calcium can ameliorate the toxic effects of acidity and Al on biota. Most organisms can tolerate
15    lower pH and higher Al; at higher Ca2+ concentrations, but in natural environments, elevated
16    concentrations of Al; are only found in Ca2+-depleted systems. This effect is most important at low Ca2+
17    levels. Overall biological effects noted with decreasing pH are described in Table B-13 (Baker et al.,
18    1990a). The organisms  most likely to respond to such changes in water chemistry include fish, aquatic
19    insects, zooplankton, and diatoms. In some cases, amphibians are also important sensitive biological
20    receptors. Most available data are for fish response.
21          In most stream or lake survey areas, direct quantification of biological responses to surface water
22    acidification is not possible, given the scarcity or absence of biological long-term monitoring and dose-
23    response data. Few biological long-term monitoring studies have been conducted. Much of the available
24    in situ dose-response data have been generated from studies of streams in Virginia and Pennsylvania and
25    lakes in New York. Data with which to evaluate acidification relationships have been scarce in most other
26    regions.

      Lakes
27          Fish status assessments for the eastern and upper midwestern U.S. were conducted by Baker et al.
28    (1990a), by region, using a variety of assessment methods. For the northeast region, two water chemistry
29    models were linked to fish response models: the Integrated Lake-Watershed Acidification Study (ILWAS)
30    model and MAGIC. For the Adirondack subregion, three process models were used: ILWAS,  MAGIC,
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 1    and Regional MAGIC. For other areas in the eastern U.S. and for the Upper Midwest, analysis offish
 2    status was limited to application of the sensitive, intermediate, and tolerant toxicity models.
 3          Assessment results reported by Baker et al. (1991a) for the Adirondack region are presented in
 4    Table B-14 showing results based on the ASI. Table B-15 shows the estimated percentage of Adirondack
 5    lakes with acid-base chemistry unsuitable for fish population survival according to various assessment
 6    models based on responses for brook trout, lake trout, and common shiner. Assessment results for the
 7    Northeast region are presented in Table 16 and Table 17.
 8          In acid-sensitive lakes in the western U.S., the focus is often mainly on native cutthroat trout. It is
 9    important to note, however, that many high-elevation western lakes and streams were historically fishless.
10    The top predators in such aquatic ecosystems were often amphibians or crustaceans. Thus, even though
11    cutthroat trout might be considered native to the region, they are not necessarily native to a particular lake
12    or stream.

      Streams
13          In streams, the major organisms of concern with respect to water acidification are fish, amphibians,
14    benthic macroinvertebrates, and periphyton (attached algae). All of these groups have shown adverse
15    effects in response to acidification (see Annex B-6). Most available data are for fish and aquatic insects,
16    mainly in the southeastern U.S. Streams affected by acidic deposition tend to occur at high elevation.
17    They are often high-gradient and flow through base-poor geology.
18          Baker et al. (1991a) presented assessment results for the mid-Appalachian region as the distribution
19    (percent) of NSS lower node, upper node, and total streams classified in various ASI values (Table B-18)
20    (Baker et al., 1991a). Most of the streams were classified in the lowest ASI category (Table B-18).
21    Assessment results for the interior Southeast region were similar (Table B-19).
22          Some fish response research has also been conducted for streams in the Catskill Mountains. Baker
23    and Christensen (1991) estimated that the fish species found in the Neversink River Basin in the Catskill
24    Mountains are typically lost when  pH decreases to the range of 4.7 to 5.2  (brook trout), 5.5 to 5.9 (slimy
25    sculpin), 4.7 to 5.7 (brown trout), 5.6 to 6.2 (blacknose dace), and 4.9 to 5.3 (Atlantic salmon).
26          The Shenandoah National Park FISH Project evaluated the effects of streamwater acidification on
27    fish populations and communities in streams in Shenandoah National Park. Fish species richness,
28    population density, condition factor, age distribution, size, and bioassay survival were all lower in streams
29    having low-ANC compared to intermediate-ANC and high-ANC streams  (Bulger et al., 1995; Dennis
30    et al., 1995; Dennis and Bulger, 1995; MacAvoy and Bulger, 1995).
31          Bulger et al. (2000) developed model-based projections using the MAGIC model to evaluate the
32    potential effect of reductions in S deposition of 40% and 70% from 1991 levels using data from VTSSS
33    and SWAS. Projections were based on four brook trout stream categories: Suitable, ANC > 50 (ieq/L;
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 1    Indeterminate, ANC 20 to 50 (ieq/L; Marginal, ANC 0 to 20 (ieq/L; and Unsuitable, ANC < 0 jieq/L.
 2    Three scenarios of future acidic deposition were modeled: constant deposition at 1991 levels, 40%
 3    reduction from 1991 deposition levels, and 70% reduction from 1991 deposition levels. Based on
 4    observed 1991 ANC values, approximately 30% of all trout streams in Virginia were marginal or
 5    unsuitable for brook trout because they were either episodically (24%) or chronically (6%) acidic. In
 6    addition, another 20% of the streams were classified as indeterminate, and brook trout in these streams
 7    may or may not have been affected. Based on the model simulations, 82% of these streams would not
 8    have been acidic prior to the onset of acidic deposition and would likely have been suitable for brook
 9    trout.
10          The model projections suggested that neither the 40% nor the 70% reductions in acidic deposition
11    would be expected to increase the number of streams that were suitable for brook trout above the ambient
12    50%. In fact, the results suggested that a 70% reduction in deposition would be needed in the long-term
13    just to maintain the number of streams that were considered suitable for brook trout. Because of the length
14    of time required to restore buffering capacity in watershed soils, most of the marginal or unsuitable
15    streams were expected to remain marginal or unsuitable for the foreseeable future.
16          To develop projections of probable past and future responses of aquatic biota to changing S
17    deposition in Shenandoah National Park, the MAGIC model was coupled by Sullivan et al. (2003) with
18    several empirical models that linked biological response to past and future model projections of water
19    quality. Unlike MAGIC, which is a geochemical, process-based model, the biological effects estimates
20    were based on observed empirical relationships rooted in correlation and expressed as linear relationships.
21    Correlation does not necessarily imply cause, but an observed pattern of co-variation between variables
22    does provide a context for analysis of a possible relationship. In this case, the projections did not require
23    extrapolation and are, therefore, statistically robust. To the extent that the observed empirical relationships
24    used in the coupled models do in  fact reflect the effects of acid stress on aquatic biota, the projections
25    were also biologically robust.
26          The geochemical and biological response models also differ in that MAGIC is a dynamic model
27    and explicitly predicts the time course of changing water quality, whereas the empirical relationships used
28    for estimating biological response were static. These relationships reflected a point in time (when the
29    observations were made) and provided no information concerning the dynamics of biological response.
30    That is, the empirical models predicted a new biological status for a new water chemistry, but gave no
31    indication of the time required to  achieve the biological status once the water quality change had
32    occurred.
33          There are thus two considerations that must be kept in mind when interpreting the biological
34    responses predicted using a combination of process-based and empirical modeling approaches: the
35    causality of the relationship between water quality and response, and the dynamics of biological response.
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 1    With respect to the issue of causality, acidification is a disturbance and disturbance usually lowers species
 2    richness. In turn, loss of species usually lowers ecosystem stability. Biodiversity loss is a predictable and
 3    proven consequence of acidification, and there are abundant examples of this in North America and
 4    Europe (cf Bulger et al., 2000). With respect to the timing of biological response, it can be variable and
 5    difficult to predict.

      B.4.2.3. Timing of Recovery from  Acidification
 6          Lakes and streams show spatial and temporal variability in response to a host of biotic and abiotic
 7    factors. Against this background of variability, it is difficult to detect changes in biological communities
 8    in response to changes in an individual environmental stressor without long-term biological data
 9    (Schindler, 1990; Lancaster et al.,  1996).  Long-term data sets are rare, and there are few well-documented
10    instances of temporal changes in biological communities in response to changes in water chemistry.
11    Regardless, it is known that surface water acidification affects virtually all trophic levels (e.g., Flower and
12    Battarbee, 1983; 0kland and 0kland, 1986; Rundle and Hildrew, 1990; St. Louis et al., 1990; Ormerod
13    and Tyler, 1991; Siminon et al., 1993; Lancaster et al., 1996; (Sullivan, 2000).
14          Biological recovery can occur only if chemical recovery is sufficient to allow survival and
15    reproduction of acid-sensitive plants and  animals. The time required for biological recovery is uncertain.
16    For terrestrial ecosystems, it may be decades after soil chemistry is restored because of the long life of
17    many plant species and the complex interactions of soil, roots, microbes, and soil biota. For aquatic
18    systems, research suggests that stream macroinvertebrate populations may recover relatively rapidly
19    (within approximately 3 years), whereas lake populations of zooplankton recover more slowly (Gunn and
20    Mills, 1998).
21          The timing offish recovery  is highly uncertain, and probably will depend heavily on dispersal
22    opportunities. Stocking could accelerate fish population recovery (Driscoll et al.,  200Ib). Fish populations
23    have recovered in acidified lakes when the pH and ANC have been raised through liming or reduction of
24    acidic deposition (Hultberg and Andersson, 1982; Beggs and Gunn, 1986; Dillon et al., 1986; Keller and
25    Pitblado, 1986; Raddum et al.,  1986; Gunn et al., 1988; Kelso and Jeffries, 1988).
26          Studies in Canada have improved understanding of the feasibility and complexity of biological
27    recovery in response to chemical recovery from acidification. Biological recovery of previously acidified
28    lakes is expected to be a slower process than chemical recovery.  Sometimes there are other environmental
29    stresses in addition to acidity, such as metal contamination (Gundersen and Rasmussen, 1995; Havas
30    et al., 1995; Jackson and Harvey, 1995; McNicol et al., 1995; Yan et al.,  1996b). Barriers can be imposed
31    by water drainage patterns between lakes that hinder re-colonization by some fish species (Jackson and
32    Harvey, 1995). Predation by non-acid-sensitive fish species can affect the recovery of zooplankton and
33    macroinvertebrate communities (McNicol et al., 1995). Finally, tributary-spawned fish can be preyed

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 1    upon when they move downstream into lakes inhabited by predatory fish and birds (Schofield and
 2    Keleher, 1996).
 3          Changes in surface water chemistry as a direct response to changes in S and N deposition are
 4    difficult to predict. Both chemical and biological effects of changing deposition can lag as the ecosystem
 5    comes into equilibrium with the modified deposition inputs. Soils or wetlands may continue to release S
 6    at a high rate for many years subsequent to a decrease in S deposition. As a result, surface water SC>42
 7    concentrations may decrease in the future as a consequence of deposition changes that have already
 8    occurred. If soil base cations have become depleted, base cation concentrations in some surface waters
 9    could decrease in the future irrespective of any further changes in SO42  concentrations. This  would be
10    expected to contribute to additional acidification.
11          Studies in the U.S., Canada, and Europe have illustrated the feasibility and complexity of biological
12    recovery in response to decreased surface water acidity. There is currently no theoretical basis on which to
13    predict the paths of biological recovery. At some scale, each stream or river is unique. The null hypothesis
14    is that recovery will proceed in the same fashion as acidification, only backwards. Thus, for example, the
15    last species lost (the most acid-tolerant) would be the first to return. However, time lags are expected to
16    differ widely among species and among water bodies. Biological recovery of previously acidified lakes or
17    streams can lag behind chemical recovery because of such factors as (a) limits on dispersal and
18    recolonization; (b) barriers imposed by water drainage patterns (Jackson and Harvey,  1995); (c) the
19    influence of predation (McNicol et al.,  1995); and (d) other environmental stresses (Gunn et al., 1995;
20    Havas et al., 1995; Jackson and Harvey, 1995; McNicol et al., 1995; Yan et al., 1996a,b).
21          Limitations on dispersal and recolonization can hamper biological recovery from chronic and
22    episodic acidification. If fish move into refugia areas during low pH and then return, behavioral
23    avoidance would reduce the overall effect of acidification on fish populations. However, if fish move out
24    of the stream system in response to sublethal episodes, as suggested by Baker et al. (1996), and do  not
25    return or return in smaller numbers, then the population level effects of episodic acidification would be
26    greater than predicted based on mortality tests alone.
27          Stream macroinvertebrate communities are often dominated by immature life stages of flying
28    insects, such as mayflies, dragonflies, and stoneflies. Such species have rather rapid colonization times,
29    such that a functional stream macroinvertebrate community may return in only a few years in response to
30    improved chemistry. However, fish community recovery is expected to be quite variable, depending on
31    sources of colonists. In streams, fish could be introduced as soon as the water quality becomes suitable or
32    the macroinvertebrate community becomes established. In streams that had simple fish communities in
33    the past, a fish community might become rapidly established. It might take decades for complex
34    communities without species introductions.
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 1          The Sudbury region of Ontario, Canada has been important for studying the chemical and
 2    biological effects of S deposition. Mining and smelting of copper-nickel ore began in the 1880s. By the
 3    1950s and 1960s, 862 emissions from the mining and smelting operations peaked at over 5,000 tons/day
 4    and extensive acidification of nearby surface waters was documented (Beamish and Harvey, 1972).
 5    Emissions of 862 then decreased during the 1970s to less than one-third of the peak values. This region
 6    has been the focus of extensive chemical and  biological effects work since the 1980s  (Keller, 1992).
 7    Sulfur emission reductions resulted in improved water quality in many lakes (Keller and Pitblado,  1986;
 8    Keller et al., 1986), and some fisheries recovery was also documented (Gunn and Keller, 1990; Keller and
 9    Yan, 1991). Griffiths and Keller (1992)  found changes in the occurrence and abundance of benthic
10    invertebrates that were consistent with a direct effect of reduced lakewater acidity. A more recent
11    assessment of recovery of ecosystems in Canada provided further evidence of biological recovery,  but
12    also showed that the spatial extent of recovery was limited to lakes that had  been severely acidified by the
13    Sudbury smelter (Jeffries et al., 2003).
14          Whitepine Lake, located 90 km north of Sudbury, had low pH (5.4) and ANC (1 (ieq/L) in 1980
15    and its fish populations displayed symptoms of acid stress. Acid-tolerant yellow perch (Percaflavescens)
16    were abundant, but the more acid-sensitive species lake trout and white sucker (Catostomus commersoni)
17    were rare and not reproducing. Fish populations were studied by Gunn and Keller (1990) from 1978
18    through 1987, and zooplankton were sampled at least monthly during the open-water periods of 1980
19    through 1988. During the period between 1980 and 1988, pH increased to 5.9 and ANC increased to
20    11 (ieq/L. Young lake trout first reappeared in 1982 and became increasingly abundant throughout the
21    study. The number of benthic invertebrate taxa increased from 39 in 1982/83 to 72 in 1988, and the
22    relative abundance of many of the invertebrates found  in 1982 changed along with the changes in water
23    chemistry (Gunn and Keller, 1990). Research at Sudbury clearly documented that chemical recovery of
24    lakes was possible upon reduced emissions and deposition of S, and also that biological recovery,
25    involving multiple trophic levels, would soon follow.
26          Baker et al. (1990a) used field-based models to test the potential for biological recovery. The
27    models were calibrated from the observed among-lake or among-stream associations  between fish  status
28    and the chemical and physical characteristics  measured in the surface water. The models were generally
29    calibrated using chemistry data collected in conjunction with surveys offish status. It was assumed that
30    the systems surveyed were at steady state and that the observed status of the fish community was
31    determined by the observed chemical and physical conditions in the lake or  stream. For each species
32    considered, the current presence or absence of the species  was analyzed as a function of the water quality
33    variables associated with acidification (e.g., pH, Al, Ca2+, ANC, and DOC) using maximum likelihood
34    logistic regression (Reckhow et al., 1987). Models developed from data from the ELS and the ALSC were
35    calibrated against data  from Ontario lakes.
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1         The results from the various models were compared to their prediction of the change in the number

2    of Adirondack lakes with unsuitable acid-base chemistry, given a 50% decrease or a 30% increase in S

3    deposition relative to the existing conditions. All the models provided similar results (Figure B-20) with

4    the exception of those that relied on the pCa/pH term to predict fish status. Those models seemed to

5    overestimate the effect of Ca2+, and thus underestimate predicted fish response to changes in acidic

6    deposition.
                       Brook Trout Models
                                       10    20
                                                           B
              Tolerant
              ASI > 10

              Bayesian
       LAP Framework
              (Case 1)

       Field-Based: pH


               pCa/pH


       pCa/ph, AI/DOC
                   Lake Trout Models
                -20    10     0    10    20
     Intermediate
        ASI > BO

  Field-Based: pH


          pCa/pH


              Al
                     -20   -10    0     10    20
                     Change in Percent Unsuitable
                -20   -10    0    10    20
                Change in Percent Unsuitable

                Common Shiner Models

                -30 -20 -10  0   10  20  30
                          | -50% scenario

                          +30% scenario
        Sensitive
        ASI > 80

  Field-Based: ph


          pCa/pH
                                                               -30  -20 -10  0  10  20  30
                                                               Change in Percent Unsuitable
                                                                                 Source: Baker etal. (1990a).
     Figure B-20. Example model application. Projected changes in the percentage of Adirondack lakes
     (Direct/Delayed Response project target population) with acid-base chemistry unsuitable for the
     survival offish 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 offish response, given a 50% decrease in
     deposition or a 30% increase in deposition, (a) brook trout, (b) lake trout, (c) common shiner.
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 1          An important consideration for measuring the success of S and N emissions controls is the
 2    development of appropriate expectations for the magnitude of potential chemical recovery. Most lakes
 3    inferred to have been measurably acidified by atmospheric deposition were already marginally acidic,
 4    typically with pH less than about 6, before anthropogenic atmospheric pollution began prior to 1900.
 5    Therefore, full recovery of currently acidic lakes would not be expected to yield neutral pH. Nevertheless,
 6    increases in ANC may allow recovery offish populations even if pH remains relatively low (Stoddard
 7    etal.,2003).


            B.4.3. Effects by Ecosystem  Type

      B.4.3.1. Terrestrial Ecosystems
 8          Due to a strong dependency on atmospheric deposition and exposure to gaseous compounds as the
 9    major sources of nutrients, lichens are affected by changes in these conditions. Vulnerability of lichens to
10    increased N input is generally greater than that of vascular plants (Fremstad et al., 2005). Even in the
11    Pacific Northwest, which receives uniformly low levels of N deposition, changes from acid-sensitive and
12    N-sensitive to pollution-tolerant and nitrophilic lichen taxa are occurring in some areas (Fenn et al.,
13    2003). In eastern North America and  central Europe, areas experiencing relatively high levels of acidic
14    deposition have experienced noticeable reductions in cyanolichen abundance on both coniferous and
15    deciduous trees (Richardson and Cameron, 2004). Effects on lichen species biodiversity are also likely
16    (McCune, 1988; Van Haluwyn and van Herk, 2002).
17          Fenn et al. (2007)  speculated that large, pollution-sensitive macrolichens, including epiphytic
18    cyanolichens, will be replaced by N-tolerant species in areas where development expands in western
19    Oregon and Washington  into N-limited Coast Range forests.  Currently, in the Pacific Northwest,
20    nitrophilic lichen species are common in and around Seattle, Portland, Spokane, the Tri-cities, Salem,
21    Oregon's agricultural lands in the northeast and southwest, and the Willamette Valley (Fenn et al., 2007).
22    The USDA Forest Service website contains information about lichen species pollution tolerance,
23    diversity, and preferred habitat in relation to exposure to N (http://www.nacse.org/lichenair).
24          In London, epiphyte diversity,  including a majority of the lichen taxa, declined in areas where NO2
25    surpassed 40 (ig/m3 and NOx surpassed 70 (ig/m3. Lichens remaining in areas affected by these levels of
26    exposure contained almost exclusively families Candelariaceae, Physciaceae and Teloschistaceae
27    (Davies et al., 2007).
28          Progressive decline in ectomycorrhizal fungal (EMF) community structure and species richness
29    was observed at five Alaskan coniferous forest sites (white spruce [Picea glauca] dominant) along  an
30    N deposition gradient (1  to 20 kg N/ha/yr) downwind from a large industrial complex on the Kenai
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 1    Peninsula. The effects were attributed to both acidification and fertilization processes (Lilleskov et al.,
 2    2002). EMF communities are important in tree nutrition and C balance, and EMF trees tend to be
 3    dominant in N-limited forest ecosystems. A shift in EMF community structure could result in changes in
 4    tree species.
 5          Westman et al. (1985)  summarized the literature of negative effects of SO2 on native plants,
 6    including decreased pollen germination and tube elongation in both angiosperms and gymnosperms. It is
 7    often difficult to separate the effects of SO2 exposure on plants from the effects of S deposition. This is
 8    because areas that experience high SO2 exposure generally also receive high S deposition. Kozlowski
 9    (1985) summarized relative susceptibility of different trees, lichens, and bryophytes to  SO2.
10          Available information  is not sufficient to draw conclusions regarding the increased likelihood of
11    future effects on the condition of hardwood forests in the Southern Appalachian Mountain region
12    (Sullivan et al., 2002). Certainly, such effects are less likely for hardwood forests than for spruce-fir
13    forests. Red oak seedlings grown in a greenhouse in deciduous forest soils exhibited no response to
14    acidified soil (pH 4.0 from 9:1 H2SO4:HNO3) or to high or low SO42 inputs (12.8 to 24.8 mg/L).  The
15    lack of response suggested that red oak seedlings are not sensitive indicators of acidification effects from
16    S deposition (McClenahen, 1987).

      Grasslands and Alpine Tundra
17          Alpine communities are considered very sensitive to changes in N deposition, but documented
18    effects in the scientific literature have been attributed to nutrient enrichment, rather than acidification
19    (Seastedt et al., 2004; Bowman et al., 2006). Lower-elevation grasslands, especially those  in semi-arid
20    environments, would be expected to be even less sensitive to acidification because of low water leaching
21    potential and the common presence of base-rich Mollisol and Aridisol soils. However, some effects of
22    acidification may be manifested in mesic grasslands.
23          In a review of SO2 effects on grasses in the United Kingdom, Bell (1985) suggested that damage
24    can occur at levels as low as  150 (ig/m3. However, he asserted that any ubiquitous critical load value must
25    be modified to include variations due to environmental conditions and combined effects with other
26    pollutants. He also suggested that many grass species exhibit a tolerance to SO2, resulting from more
27    intraspecific competition in agricultural grasslands. Westman et al. (1985) also provided evidence  of the
28    evolution of a tolerant grass species, Bromus rubens, in southern California coastal sage scrub, influenced
29    by an average of 3.7 (imol/m3 of SO2 over 25 years.
30          Studies of SO2 effects  on timothy grass (Phleum pratense) showed diminished leaf production and
31    increased leaf senescence in  seedlings exposed to 0.120 ppm SO2 for 35  days (Mansfield and Jones,
32    1985). In another experiment, Mansfield and Jones (1985) reported that exposure to 0.120 ppm SO2 in
33    seedlings over 40 days resulted in a 62% reduction in the dry weight of roots and 51% reduction in the
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 1    dry weight of shoots, as well as a significant decline in leaf-area ratio (LAR) and specific leaf area (SLA)
 2    by the end of the experiment. They suggested that decreased growth and shifts in LAR and SLA could
 3    lead to decreased hardiness and increased susceptibility to water stress.
 4          In a 5-year exposure of native mixed prairie grassland in Montana, Lauenroth and Milchunas
 5    (1985) exposed grasses to a control (-20 (ig/m3) and three elevated levels of SO2 (-60, 106, 184 (ig/m3).
 6    Year-to-year S accumulation did not appear to occur over the 5-year course of the treatment, though
 7    progressive increases in root and rhizome S concentrations were observed seasonally. No significant
 8    negative effects on either above-ground net primary productivity or below-ground biomass dynamics in
 9    grasses were observed, except a decrease in biomass for Bromus japonicus. However,  lichen cover
10    declined after 1-year of exposure at the low treatment level. Though no biomass or cover effects were
11    observed at the community level, there were minor population changes. These results are  consistent with
12    the nature of semi-arid grasslands that typically adjust well to perturbations (Lauenroth and Milchunas,
13    1985).

      Arid Lands
14          At the time of the previous AQCD, it was believed that arid and semi-arid ecosystems were not as
15    susceptible to soil acidification  and high NO3  leaching as are forested ecosystems. This is because of a
16    scarcity of water for NO3  leaching, except on an episodic basis, and because arid soils tend to be more
17    alkaline than soils in more humid environments. No new research has altered that conclusion. Arid lands
18    in the U.S. generally receive low levels of S deposition. However, N deposition can be quite high,
19    especially in southern California in the vicinity of the Los Angeles Basin. Little work has been done on
20    the effects of acidification on arid land ecosystems. As reviewed by Fenn et al. (2003), acidification
21    effects have not been demonstrated at the Central Arizona-Phoenix LTER site, despite the almost 30 kg
22    N/ha/yr of deposition received.  Nevertheless, N deposition has the potential to increase plant growth and
23    denitrification and alter community composition in arid environments (Egerton-Warburton and Allen,
24    2000; Allen et al., 2005). Such changes could alter key ecosystem processes and, as such, merit
25    consideration. There has been little research to examine these issues and, therefore, the state of knowledge
26    is similar to what it was in 1993.
27
28
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      B.4.3.2. Aquatic Ecosystem
      Chronic Effects
      Sulfate
 1          The study of Clow and Mast (1999) is unique in that SC>42  trends were evaluated with both raw
 2    data and data adjusted for existing trends in flow. From 1967 to 1983, Clow and Mast (1999) showed a
 3    decreasing trend in SC>42 concentrations in a Catskill river, no trend in three rivers in Maine,
 4    Pennsylvania, and Virginia, and an increasing trend in a river in Ohio. The Maine river did show a
 5    decreasing trend before flow adjustment of the data. From 1984 to 1996, however, Clow and Mast (1999)
 6    found decreasing trends in SO42  concentrations in all five rivers, both with and without flow adjustment.
 7    The rivers in Pennsylvania, Ohio, and Virginia were south of the maximum southern extent of glaciation,
 8    and therefore were more likely to be subject to the  effects of SO42 adsorption in soils of their watersheds.
 9    In such streams, decreasing S-adsorption on soils would be expected to counteract the effects of
10    decreasing S deposition in terms of effects on stream SO42 concentration.
11          Surface waters in other unglaciated regions exhibited decreasing trends in SO42  by the 1980s.
12    Concentrations of SO42 in 130 northeastern lakes in 1984 were compared to those in the same lakes in
13    2001 (Warby et al., 2005). Median concentrations in each subregion were lower in 2001 than 1984, and in
14    the  region as a whole, the overall median decrease  was 1.53 (ieq/L/yr. A decrease in SO42  concentrations
15    that averaged 2.16 (ieq/L/yr was also observed in 47 of 48 Adirondack lakes from 1992 to 2004, and a
16    similar decrease of 2.09 (ieq/L/yr was observed in a subset of these lakes from 1982 to 2004 (Driscoll
17    etal.,2007).
18          The pattern of increasing concentrations of SO42 in surface waters before the year of peak S
19    emissions in 1973, followed by widespread decreasing trends in SO42 concentrations after the peak (with
20    the  only exception being the Blue Ridge Mountain region in Virginia), provides convincing evidence of
21    the  link between S emissions and SO42  concentrations in surface waters. A similar link has been shown in
22    Europe (Stoddard et al., 1999). On this basis, continued decreases in S emissions would be expected to
23    result in further decreases in SO42 concentrations in surface  waters, although the rate of response is
24    uncertain due to an incomplete knowledge of S retention mechanisms in terrestrial systems. Also, in a
25    detailed analysis of flow effects on SO42 trends, Murdoch and Shanley (2006) found SO42~that higher
26    concentrations of SO42  occurred at corresponding  high, medium  and low flows in 2000 to 2002 than in
27    1997 to 1999 in two of the rivers studied by Clow and Mast (1999), and at high and medium flows in a
28    third river. Continued monitoring of surface waters will be needed to verify a future link between
29    emissions and SO42  concentrations in surface waters.
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      Nitrate
 1          Driscoll et al. (1985) found that NO3 concentrations in 20 lakes in the early 1980s in the
 2    Adirondack region of New York averaged 12% of SC>42 concentrations, whereas Lovett et al. (2000b)
 3    found that baseflow NO3  concentrations in 1994-97 were an average of 37% of SC>42 concentrations in
 4    39 streams in the Catskill region of New York. Average concentrations of NO3 in most southeastern
 5    streams also tend to be considerably less than SC>42  concentrations (Webb et al., 2004).
 6          High-frequency sampling in the study of Murdoch and Stoddard (1993) demonstrated the
 7    importance of NO3  during high-flow conditions in Catskill streams in which concentrations periodically
 8    equaled or exceeded SC>42 concentrations. This study also reported increasing trends in NO3
 9    concentrations during the period of 1970 to 1990 in all 16 Catskill streams for which data were available.
10    A similar increase in NO3 concentrations was reported for Adirondack lakes in the 1980s (Stoddard et al.,
11    1999). These  increasing trends in NO3 concentrations were attributed to N saturation in  response to
12    atmospheric deposition (Aber et al., 1998).
13          The relationship between N deposition and surface water NO3  concentrations up through the 1980s
14    suggested that continued N deposition would further the accumulation of N in terrestrial ecosystems and
15    drive continued increases in surface water NO3 concentrations. However, more recent information on
16    NO3 concentrations have been less consistent with the concept of N saturation. Goodale et al. (2003)
17    resampled New Hampshire streams in 1996-97 that had been previously sampled in  1973-74 and found
18    substantially lower NO3  concentrations  in the more recent sampling, despite two decades of relatively
19    stable levels of deposition to otherwise undisturbed forests. The lower NO3  concentrations could not be
20    accounted for by differences in flow or forest succession, but interannual climate variation was proposed
21    as a possible cause. The long-term record of dissolved  inorganic N  (which is largely NO3 ) concentrations
22    at the HBEF showed a similar pattern; high concentrations in the late 1960s and  1970s, followed by
23    decreases to minimum values in the mid-1990s (Aber et al., 2002).  These authors attributed this pattern to
24    a combination of environmental factors, but did not identify a single most important control variable.  A
25    reversal from increasing trends in NO3  concentrations in the 1980s to decreasing trends  in the 1990s was
26    also observed in Adirondack lakes (Driscoll et al., 2003a). A small decrease in atmospheric deposition of
27    N also occurred in this region through the 1990s, but was not considered sufficient by these authors to
28    explain the decreasing trend in lakewater NO3 concentrations. Rather, they proposed that increased
29    concentrations of atmospheric CO2 may have resulted in a fertilization effect that increased
30    N assimilation (Driscoll et al., 2007).
31          In general, trends in surface water NO3 concentrations during the 1990s were much smaller than
32    trends in SC>42 , with the only ecologically significant changes  occurring in the two regions with the
33    highest ambient NO3 concentrations (Figure B-21). Lakes in the Adirondacks and streams in the
34    Northern Appalachian Plateau both exhibited small but significant downward trends in NO3 in the 1990s

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 1    (Figure B-21). Both of these regions are central to the debate over whether N saturation is a legitimate
 2    threat to the health of forests and surface waters (Stoddard, 1994; Aber et al., 1998). While declining NO3
 3    concentrations in these regions is a positive development for these ecosystems, we clearly do not know if
 4    these trends will continue, especially because they do not appear to reflect changes  in N emissions or
 5    deposition. The presence of strong upward trends in NO3 in these same regions in the 1980s (Murdoch
 6    and Stoddard, 1992; Stoddard,  1994) suggests that trends measured on the scale of a single decade may
 7    reflect variability in long-term patterns of changing NO3 leakage from forested watersheds. Such patterns
 8    are controlled by factors that may take many years of additional research to determine. While great
 9    uncertainty exists and the time  scales of N saturation may be longer than previously considered (e.g.,
10    centuries rather than decades), the  long-term retention of N deposition in forested regions is unlikely to
11    continue indefinitely (Aber et al., 2003).
12          In New England and the Upper Midwest, where ambient NO3  concentrations are much lower than
13    in the Adirondacks and Northern Appalachian Plateau (Source: Stoddard et al. (2003).
14          Figure B-21), NO3  concentrations in surface waters were unchanged during the 1990s. The
15    Ridge/Blue Ridge province registered a small, but significant, decrease in NO3 during the  1990s, but
16    interpretation of trends for NO3 in this region is complicated by an outbreak of gypsy moths that also
17    occurred during this period. Forest defoliation by gypsy moths was the most likely cause of a pulse in
18    NO3 export from many streams in this region in the mid-1990s (Eshleman et al., 1998).
19          Some evidence of climate effects on long-term trends in NO3  concentrations in surface waters was
20    provided by studies of Mitchell et al. (1996) and Murdoch et al. (1998). A synchronous pattern in NO3
21    concentrations was observed from 1983 to 1993 in four small watersheds in New York, New Hampshire,
22    and Maine, which included anomalously high concentrations during the snowmelt period of 1990. The
23    region-wide spike in NO3 concentrations followed an unusually cold December that may have disrupted
24    soil N cycling processes (Mitchell  et al., 1996). Murdoch et al. (1998) also found that mean annual air
25    temperatures were strongly related to average annual NO3 concentrations in most years in a Catskill
26    watershed with elevated NO3 concentrations in stream water. Those relationships were explained by
27    microbial control of N release in watersheds that were considered to be N-saturated.
28          Efforts to explain the decreasing trends in NO3 concentrations under conditions of reasonably
29    stable atmospheric N deposition have  focused on terrestrial N cycling and N-saturation theory. However,
30    processes within lakes may have also played a role in the trends in Adirondack lakes.  In a study of 30 of
31    the 48 lakes studied by Driscoll et  al. (2003a; 2007), Momen et al. (2006) found that concentrations  of
32    NO3 were inversely correlated with concentrations of chlorophyll a in 11 lakes, and that chlorophyll a
33    was increasing in concentration in 9 lakes. The increase in pH observed in most of these lakes may have
34    stimulated productivity so that N assimilation by plankton increased (Momen et al., 2006).
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1          Thus, there is little or no apparent relationship between recent trends in N deposition and trends of
2    NO3 concentrations in surface waters, in sharp contrast to S deposition and SC>42  concentrations. Rather
3    than disprove the concept of N-saturation; however, these studies more likely reflect the complexities of
4    N utilization within terrestrial and aquatic ecosystems. These complexities create considerable uncertainty
5    with regard to how future trends in NO3  concentrations in surface waters will respond to changing levels
6    of deposition.
7
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                     Salmon Pond (New England)
                                                                       Darts Lake(Adirondacks)
         i«:
                       "v/V
                  , .-v^--	"-x.••••«.*•,.•
            1/1/82 1/1./84 1/1/86 1/1/88 1/1/90 1/1/92 1/1/94 1/1/96 1/1/98 1/1/00 1/1/02
                East Branch Neversink River (Appalachian Plateau)
                       A*
                                                             20 -
                                                              0
                                                                                     •
                                                             20 ^
                                                              '  ^


                                                              '
                                                              a 4	
           65 -
           60
           5.5 - <
           5.0 - '
           4.5
           4.0 f-



                                                             1/1/82 1/1/84 1/1/86 1/1/88 1/1/90 1/1/92 1/1/94 1/1/96 1/1/93 1/1/00 1/1/02
                                                                     Vandercook Lake (Upper Midwest)
            1/1/82 1/1/84 1/1/86 1/1/88 1/1/90 1/1/92 1/1/94 1H/96 U1/98 1,'UOO 1/1/02
                                                             1/1/82 1/1/84 1/1/86 1/1/BS 1/1/90 1/1/92 1,'1,'94 1/1/96 1/1/98 1/1,'00 1..'1,'02
                                                                                        Source: Stoddard etal. (2003).
Figure B-21. Time series data for S042", N03", 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
 1          The earliest trends of base cation concentrations in acid-sensitive surface waters of the U.S. were
 2    presented by Stoddard (1991) for 12 streams in the Catskill region. In 5 of 12 streams, concentrations of
 3    (Ca2+ + Mg2+) increased from 1915-22 to 1945, but decreased from 1945-46 to 1990. In the remaining
 4    seven streams, concentrations increased during both periods, but at a lower rate in the more recent period
 5    in five of the seven streams. In streams that showed an increase in concentrations during both periods, the
 6    average rate of increase from 1915 to 1922 was 2.8 (ieq/L, whereas the average rate of increase from
 7    1945 to 1990 was 1.2 (ieq/L. Data on SC>42  trends were not available for the early period, but the trends
 8    in (Ca2+ + Mg2+) concentrations were consistent with the expected pattern of high rates of cation leaching
 9    during the early stages of acidification from S deposition.
10          Clow and Mast (1999) observed trends in (Ca2+ + Mg2+) concentrations that were generally
11    consistent with SC>42 trends in five eastern rivers from 1968 to  1983. Decreasing trends in concentrations
12    of (Ca2+ + Mg2+) and SO42 concentrations were observed in a Maine river, and increasing trends in (Ca2+
13    + Mg2+) and SC>42 concentrations were observed in an Ohio river. None of the three other rivers showed a
14    decrease in concentrations of (Ca2+ + Mg2+), and only one showed a decreasing trend in SO42
15    concentrations. For the period 1984 to 1996, the trend in SC>42 concentrations was negative in the Ohio
16    River and the concentrations of (Ca2+ + Mg2+) showed no trend. Also, a negative trend in (Ca2+ + Mg2+)
17    concentrations in a Virginia river was coupled with a negative trend in SO42 concentrations. Relations for
18    the other three rivers were similar to the earlier period of 1984 to 1996.
19          The study of Likens et al. (1996) evaluated trends in base cations in relation to trends in (SO42  +
20    NO3~) in the long-term record for the HBEF. This record showed an approximately linear, increasing
21    relationship between concentrations of base cations and (SO42 + NO3  ) from 1964 to  1969, then a
22    reversal in 1970 to a decreasing trend up to  1994. The slope of the phase with increasing anion
23    concentrations was steeper than the slope for the phase with decreasing anion concentrations. This
24    indicates lower base cation leaching per equivalent of mobile anion, and therefore suggests depletion of
25    base cations stored in soil. The study of Lawrence et al. (1999b) showed decreased concentrations of base
26    cations at a rate that exceeded decreases in (SO42 + NO3 ) in Catskill streams from 1984 to 1997. In
27    streams within western Virginia and in Shenandoah National Park, concentrations of base cations did not
28    exhibit significant trends from 1988 to 2001, perhaps due to the influence of S adsorption on streamwater
29    SO42  concentrations.
30          Regional declines in base cation concentrations were measured in the LTM Program from 1990 to
31    2000 in New England lakes, Adirondack lakes, Appalachian streams, and upper Midwest lakes (Stoddard
32    et al., 2003). These results were consistent with decreased Ca2+ concentrations measured by Warby et al.
33    (2005) in 130 acid-sensitive lakes in the Northeast between 1984 and 2001. The rate of decrease
34    identified by Warby et al. (2005) for base cations (1.73 (ieq/L) was somewhat less than the rate of

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 1    decrease in SC>42 concentrations (1.53 (ieq/L). Driscoll et al. (2007), also documented decreasing trends
 2    in base cation concentrations in 16 Adirondack Lakes from 1982 to 2004, and similar rates of decrease in
 3    48 lakes (including the 16) from 1992 to 2004.
 4          In summary, decreases in base cation concentrations over the past two to three decades are
 5    ubiquitous and closely tied to trends in SC>42  concentrations in acid-sensitive regions of the U.S. Reports
 6    of increases in concentrations of base cations in acid-sensitive regions were not found in the literature. In
 7    most regions, rates of decrease for base cations have been similar to those for SO42 and NO3~, with the
 8    exception of streams in Shenandoah National Park. Decreasing trends of base cation concentrations do
 9    not necessarily indicate further acidification or recovery of surface waters, but do indicate lower leaching
10    rates in  soils, a prerequisite for recovery of soil base saturation. However, decreased concentrations of
11    base cations, particularly Ca2+, would also be expected to lower productivity in oligotrophic surface
12    waters.

      Acid Cations
13          Measurements of pH (sometimes expressed as fT) have been routinely collected in surface waters
14    in the U.S. where effects of acidic deposition have been monitored, but a long-standing reliance on
15    titrated ANC rather than pH as the primary chemical measurement has limited the amount of pH data
16    published. The longest continuous record of pH in surface waters dates back to 1963 at the HBEF
17    (Driscoll et al., 2001b). This record shows an overall increasing trend from 1963 to 1994, although most
18    of the increase occurred after 1980. In Adirondack lakes, 12 of 16 monitored from 1982 to 2004 showed
19    an increase in pH, but the rates of change among lakes were highly variable, and one lake showed a
20    decrease in pH (Driscoll et al., 2007). When expressed as fT concentration, the average increase for the
21    12 lakes was 0.18 (ieq/L/yr. In this same region, pH also increased in 31 of 48 lakes (including the 16
22    lakes monitored from 1982) from 1992 to 2004. Two lakes showed increases in pH over the 12 years.
23          Comparison of pH measurements of 130 lakes in 1984 and in 2001, in the northeastern U.S.,
24    showed an overall average increase in pH of 0.002 units (Warby et al., 2005). However, in this
25    assessment, lakes in the Adirondack region did not show a significant increase, nor did lakes in central
26    New England, or Maine. The Catskill/Poconos region of New York and Pennsylvania showed an average
27    increase of 0.008 pH units per year, and southern New England showed an average increase of 0.002 pH
28    units per year. Through continuous monitoring from 1990 to 2000, Stoddard et al. (2003) found a decrease
29    in H+ (0.19 (ieq/L/yr) similar to that observed in the same Adirondack lakes by Driscoll et al. (2007) from
30    1992 to 2004 (0.18 (ieq/L), and an increase in Appalachian streams (0.08 (ieq/L/yr) and Midwest lakes
31    (0.01 (ieq/L/yr). No trends were found in New England lakes or Blue Ridge streams in Virginia in this
32    study. Stream monitoring in the Adirondack region from 1991 to 2001 showed an increase in ET in one
33    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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 1    (Lawrence et al., 2004). In summary, decreasing trends in pH in surface waters are common through the
 2    1990s up to 2004, but many exceptions occur, and overall, the rates of change have been small.
 3          The discovery that Al; was toxic to aquatic life resulted in a considerable amount of data on Al
 4    concentrations in surface waters in the 1980s, but most of this sampling was done either once or for a
 5    limited period of time (Johnson et al., 1981; Driscoll and Newton, 1985; Driscoll et al., 1987; Lawrence
 6    et al., 1987; Cronan et al., 1990). Monitoring of Al; concentrations was begun in 16 Adirondack lakes in
 7    1982 and expanded to 48 lakes in 1990. From 1982 to 2004, 5 of the original 16 Adirondack monitoring
 8    lakes showed decreasing trends in Al; concentrations at rates that ranged from 0.02 (iM/yr to 0.18 (iM/yr
 9    (Driscoll et al., 2007). From 1992 to 2004, 24 of the 48 lakes showed decreasing trends in Al;
10    concentrations (Driscoll et al., 2007). The analysis of Stoddard et al. (2003) also observed an average
11    decrease in Al; concentrations from 1990 to 2000 in the same group of Adirondack lakes reported on by
12    Driscoll et al. (2007), but observed no trend for this period in New England lakes, Appalachian streams,
13    or Midwest lakes.
14          Monthly stream chemistry monitoring at the HBEF showed decreases in Al; concentrations at four
15    locations along the reference stream for the experimental forest from 1982 to 2000, but no trends at two
16    other locations along this stream (Palmer et al., 2004). These data also showed a surprising decrease in pH
17    at two of the locations where Al; decreased, and no pH trend at the other two locations where Al;
18    decreased (Palmer et al., 2004). Comparison of total Al concentrations in 130 lakes in the northeastern
19    U.S. in 1984 with those measured in 2001 showed lower average concentrations in 2001 in the
20    Adirondack region, the Catskill/Pocono region, central New England, southern New England, and Maine
21    (Warby et al., 2005). Because these measurements are of total Al, they are not directly comparable to Al;.
22    Most recently, Lawrence et al. (in review) found that 49 of 195 streams  (25%) during August base flow in
23    the western Adirondack region had Al; concentrations above 2.0 (JVI, the level above which toxic effects
24    on biota have been shown (Driscoll et al., 200Ib; Baldigo et al., 2007).

      Acid Neutralizing Capacity
25          In response to reduced levels of acidic deposition required by the CAA and other emissions control
26    legislation, Stoddard et al. (2003) found trends during the 1990s toward increasing Gran ANC (Figure B-
27    21)  in all of the glaciated regions of the eastern U.S. (i.e., New England, Adirondacks, Northern
28    Appalachian Plateau) and Upper Midwest, and decreasing Gran ANC in the Ridge/Blue Ridge province.
29    Changes were relatively modest compared with observed reductions in SO42  concentrations. Only the
30    regional increases in the Adirondacks, Northern Appalachian Plateau, and Upper Midwest were
31    statistically significant (Figure B-21). Median increases of about +1 (ieq/L/yr in the Northern Appalachian
32    Plateau, Adirondacks and Upper Midwest represent significant movement towards ecological recovery
33    from acidification (Stoddard et al., 2003).
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 1          It has been hypothesized that decreases in acidic deposition will yield the most chemical recovery
 2    in lakes and streams that have experienced the most severe acidification. Using data from all of the sites
 3    in regions where decreases in surface water SC>42  and NO3 have occurred, Stoddard et al. (2003) found
 4    that acidic lakes and streams exhibited a highly significant median increase in Gran ANC of
 5    +1.3 (ieq/L/yr during the 1990s. Low-ANC sites (0 to 25 (ieq/L) showed a smaller significant median
 6    ANC increase of+0.8 (ieq/L/yr. Moderate ANC sites, those with mean ANC values greater than 25 (ieq/L,
 7    showed no significant change in Gran ANC (Figure B-21).
 8          All of the glaciated regions in the eastern U.S. showed declines in base cation (Ca2+ + Mg2+)
 9    concentrations during the 1990s, with the average changes in the range of-1.5 to -3.4 (ieq/L/yr. All of
10    the regional trends were highly significant (Figure B-21). Across the eastern U.S., surface water SO42  has
11    decreased at a rate of about -2.5 (ieq/L/yr (the mean of regional median slopes), and NO3  at a rate of
12    -0.5 (ieq/L/yr, in surface waters on glaciated terrain during the  1990s. These  rates of change set an upper
13    limit to our expectation of ANC recovery of+3 (ieq/L/yr (i.e., the sum of SC>42 and NO3  trend
14    magnitudes). The Gran ANC increase reported by Stoddard et al. (2003) was  actually about one-third of
15    this maximum, +1 (ieq/L/yr. The difference between the observed Gran ANC trend and the maximum
16    trend estimated from rates of acid anion change can largely be explained by the average regional median
17    decline in (Ca2+ + Mg2+ concentrations, which was about -2.0 (ieq/L/yr (Stoddard et al., 2003).

      Episodic Effects
18          Episodic acidification can result naturally from the mobilization of organic acids and from dilution
19    of base cation concentrations, but decreases in pH and ANC associated with increases in SC>42 and NO3
20    are largely attributable to acidic deposition (Wigington et al.,  1996). Episodic acidification is most
21    common in the early spring and late fall as a result of snowmelt and rainstorms, and is least common in
22    summer, when high flows tend to be infrequent. Seasonal variations in stream flow also result in seasonal
23    patterns of surface water chemistry at base flow. Lakes and streams at base flow tend to be more acidic in
24    early spring than at other times of the  year and low flows in late summer tend to be least acidic (Lawrence
25    et al., 2007).
26          The transient nature of high flows makes episodic acidification difficult to measure. Therefore,
27    assessments have generally estimated the number of lakes and streams prone to episodic acidification by
28    combining episode information from a few sites with base flow values of ANC determined in large
29    surveys (Eshleman et al., 1995; Bulger et al., 2000; Driscoll et al., 2001b). Inclusion of episodically
30    acidified water bodies in regional assessments substantially increases estimates of the extent of surface
31    water acidification. For example, baseflow samples collected from 1991 to 1994 through the EPA TIME
32    Program indicated that 10% of the 1,812 lakes (>1 ha surface area) in the Adirondack region of New York
33    could be considered chronically acidic on the  basis of ANC values less than 0 (ieq/L, but that an
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 1    additional 31% of these lakes had baseflow ANC values less than 50 (ieq/L and were, therefore, estimated
 2    to be susceptible to episodic acidification (Driscoll et al., 200Ib). Lawrence (2002) also estimated the
 3    extent of episodically acidified stream reaches in a Catskill, NY watershed (area = 85 km2) through the
 4    use of an index site at the base of the watershed that became episodically acidified at high flows.
 5    Upstream  sites with a lower base flow ANC than the index site at the same date and time were found to
 6    have a high likelihood of becoming episodically acidified. Base flow sampling of 122 upstream sites
 7    indicated that approximately 16% of the total upstream reaches were chronically acidified (ANC <
 8    10 (ieq/L), but that 66% of the stream reaches became episodically acidified.
 9          Stoddard et al. (2003) compared seasonal data from New England lakes, Adirondack lakes and
10    Northern Appalachian streams, collected monthly to quarterly, to evaluate the difference between the
11    chemistry  of surface waters in the summer and in the  spring. Results indicated that spring values of ANC
12    were an average of 30 (ieq/L lower than summer ANC. This study referred to samples collected in spring
13    as "episodic samples," although sampling was done independent of flow. Therefore, the 30 (ieq/L
14    difference should be considered a seasonal effect rather than an episodic effect.
15          The most thorough characterization of episodic variations in stream chemistry was conducted
16    through the ERP, in  which 13 low-order streams (watershed areas less than 24 km2) in the Adirondack and
17    Catskill regions of New York,  and the Appalachian Plateau in Pennsylvania were monitored from 1988 to
18    1990 (Wigington et  al., 1996). Acid episodes with chemical concentrations within the 90th percentile
19    involved decreases in ANC of up to 200 (ieq/L, decreases in pH of up to one unit, and increases in
20    concentrations of Al; of up to 15 (iM (Wigington et al., 1996). Results also showed that acid episodes
21    reduced the size offish populations and eliminated acid-sensitive species if median high-flow pH was less
22    than 5.2 and Al; concentrations exceeded 3.7 (iM, despite the relatively short duration of episodes (Baker
23    et al., 1996). Baker et al. (1996) concluded that effect on biota from episodic acidification were  likely to
24    be similar to those from chronic acidification. Elimination of an annual age class can result from an
25    episode that occurred in the presence of a sensitive life stage. Largely on the basis  of this study,  the EPA
26    concluded that reversal of effects from episodic acidification could be used as a key ecological endpoint
27    for an acid deposition standard for protection of the environment (U.S. Environmental Protection Agency,
28    1995).
29          Despite the significance of the findings of ERP, little assessment or monitoring of episodes was
30    done in the 1990s. One exception was the work of Hyer et al. (1995) in three watersheds of differing
31    geology in Shenandoah National Park.  Results suggested that episodic acidification was occurring
32    throughout the park  on all bedrock types, although acidification was not sufficient to cause elevated Al
33    concentrations. Lawrence (2002) also documented severe episodic acidification in August 1998 in a
34    tributary of an ERP  stream, where Al; concentrations increased from 1.6 to 7.3 (iM in 6.5 h.
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 1         In the first large-scale study designed to sample streams during high-flow conditions, Lawrence
 2    et al. (2007) found that 124 out of 188 (66%) western Adirondack streams were prone to acidification to
 3    the level at which Al; becomes mobilized. Only streams accessible with less than a 60-min hike were
 4    sampled in this study. The March, 2004 survey was chosen to represent episodic conditions, and a survey
 5    conducted August 16-18, 2004 was chosen to represent base flow conditions. Based on this comparison,
 6    35% of the streams were chronically acidified, 30% of the streams were episodically acidified, and 34%
 7    were not acidified. Survey results were also used to estimate that 718 km of stream reaches were prone to
 8    acidification, although 3085 km of stream reaches within the study region could not be assessed because
 9    of inaccessibility.
10         There have been no studies in the U.S. that determine if either the severity or frequency of episodic
11    acidification has lessened. In a study of two streams in Nova Scotia (Laudon et al., 2002), trends in ANC
12    in four phases of storm hydrographs from 1983 to 1998, were not detected other than in the peak-flow
13    phase of one stream (an increase of 0.87 (ieq/L). In Sweden, the anthropogenic contribution to episodic
14    decreases in ANC were estimated to range from 40 to 80% in five streams from 1990 to 1999 (Laudon
15    etal.,2002).
      B.5.  Effects  on Watersheds and Landscapes

            B.5.1. Interactions among Terrestrial, Transitional, and Aquatic
            Ecosystems
16         Acidification has pronounced effects on nutrient cycling in terrestrial, transitional, and aquatic
17    ecosystems. Of particular importance in this regard is the role of N deposition in influencing N cycling.
18    This topic is discussed in detail in Section 5. Also important are the influences of acidification on the
19    availability of Ca2+ and other nutrient base cations (Mg2+, K+).
20         In general, decomposition, nutrient cycling, productivity, and other system-level processes in
21    surface waters are not as sensitive as species composition and richness to relatively small amounts of
22    acidification. Such effects only seem to occur at high levels of acidification (e.g., pH < 5). This is because
23    acid-sensitive species are often replaced by more acid-tolerant species that perform the same function
24    until acidification becomes severe. For example, whereas changes in microbial composition and
25    abundance have been observed with acidification, they appear to have minimal effect on overall microbial
26    respiration and nutrient cycling. At extreme levels of acidity, however, these system-level functions may
27    also decrease. Thus, system-level functions are not generally good indicators of light to moderate levels
28    of acidification.
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 1          Integrating the effects of atmospheric deposition across spatial scales is difficult. The response of a
 2    single plant, or small group of plants, cannot be easily scaled up to examine effects on plant communities,
 3    ecosystems, watersheds, or geographic regions. Integration typically requires a combination of
 4    approaches, including ecosystem modeling, experimental manipulation studies, surveys across pollution
 5    deposition gradients, and long-term monitoring studies. Similarly, aquatic effects at the population level
 6    can be readily quantified, but extrapolation to the community or aquatic ecosystem level is problematic.
 7    Linking research results across scales will be an important component of future research. Measurements
 8    that correlate with ecosystem processes, such as foliar N concentration, leaf area index, or spectral
 9    reflectance, can in some cases be remotely sensed. They offer great promise for future assessment of
10    terrestrial effects across spatial scales.
11          Effects of atmospheric deposition of acidifying substances on soil, vegetation, and surface water
12    are manifested in specific processes, affecting energy, water and nutrient flow, intra- and inter-species
13    competitive interactions, and ecosystem primary production. Therefore, effects on sensitive species (only
14    some of which have been documented) have the potential to cascade throughout the ecosystem and
15    become manifested at a variety of scales. Such ecosystem- to landscape-scale effects from atmospheric
16    deposition of acidifying substances are known to occur, but the results of these interacting processes have
17    not been conclusively demonstrated.
18          It is also evident that acidification from natural and human-caused disturbances, including climatic
19    stressors (temperature, moisture availability, wind), insect infestation, disease, fire, and timber harvest,
20    can affect the severity of effect of atmospheric deposition of SOx and NOY. Although it is clear that such
21    interactions can occur, there are  no studies that have clearly documented that acidic deposition at levels
22    that commonly occur across broad landscapes in the U.S. has conclusively altered ecosystem structure or
23    function. Similarly, although it is widely believed that acidic atmospheric deposition can make plants
24    more susceptible to the adverse effects of other natural and human-caused stressors, such effects have not
25    been conclusively demonstrated in more than a few cases. The data demonstrating and quantifying the
26    extent to which SOx and NOY deposition are altering natural terrestrial ecosystems via acidification
27    pathways are sparse. In particular, effects of soil and soil water acidification on soil ecosystem processes
28    and nutrient cycling are poorly known. Even less is known about effects on soil microorganisms and food
29    webs, or how such effects interact with the above-ground vegetation community.


             B.5.2. Interactions with  Land Use and Disturbance
30          The prevailing scientific consensus during the 1980s held that the majority of lakes in eastern
31    North America that had pH less than about 5.5 to 6.0 had been acidified by acidic deposition. Reports that
32    acidic lakes and streams were rare or absent in similar areas not receiving acidic deposition were used as
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 1    evidence of acidification by acidic deposition in many regions (e.g., Neary and Dillon, 1988; Sullivan
 2    et al., 1988; Baker et al., 1991a). An alternative hypothesis had been advanced by Rosenquist (1978),
 3    Krug (1989, 1991), and Krug and Frink (1983) that land use changes could explain recent lake
 4    acidification in southern Norway and the northeastern U.S. According to this hypothesis, natural soil
 5    processes and changes in vegetation can generate more acidity than is received from atmospheric
 6    deposition. For example, an increase in acidic humus formation in response to decreased upland
 7    agriculture was suggested as being responsible for regional acidification in southern Norway, rather than
 8    acidic deposition (Rosenqvist, 1978). Subsequent acidic deposition effects research in some cases seemed
 9    to be designed to refute this hypothesis rather than to explore the relationships between land use and acid-
10    base chemistry (cf Havas et al.,  1984; Birks et al., 1990). Research intended to discriminate between
11    acidic deposition and land use as the major cause of acidification generally concluded that acidic
12    deposition was the principal cause of regional acidification in certain areas of North America and Europe.
13    Perhaps more appropriate research questions might have focused on quantifying the relative importance
14    of land use activities or landscape change in exacerbating or ameliorating acidic deposition  effects. The
15    importance of acidic deposition as an agent of acidification does not preclude the importance of land use
16    and landscape changes which, in some cases, may actually be more important than acidic deposition
17    (Sullivan et al., 1996b).
18          There has not been a regional evaluation of land use changes in areas of the U.S. susceptible to
19    surface water acidification from acidic deposition. It has therefore not been possible to quantify the extent
20    or magnitude of land use effects on acidification. It is clear, however, that such changes can have
21    important effects on acid-base status (Sullivan, 2000), especially as influenced by N deposition (Goodale
22    and Aber, 2001).
23          Changes in human land use activity, and associated changes in vegetative structure, influence
24    ecosystem response to external stressors such as acidic deposition, exposure to O3, natural disturbance
25    factors such as wind  and fire, and climatic changes. Some activities contribute to the acidification of soil
26    and surface waters; other activities decrease acidity (Sullivan et al.,  1996b) (Table B-21).
27          Forest  management practices, especially those that have occurred over many generations of trees,
28    can have important effects on soil erosion, nutrient supplies, and organic material. Such effects can
29    influence the  availability of base cations for acid neutralization and/or aspects of N cycling.
30          Forests are efficient at scavenging S and N from the atmosphere. Differences in forest canopy,
31    particularly between deciduous and coniferous trees, can cause large differences in dry deposition, and
32    therefore total deposition of S and N. In regions that receive high  levels of acidic deposition, the presence
33    of forest vegetation, especially coniferous trees, enhances total deposition of acid-forming precursors
34    (Rustad et al., 1994). In addition to the enhanced deposition caused by the presence of large trees, there
35    are also differences in nutrient uptake. In particular, younger trees take up larger quantities of N and other
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 1    nutrients than do trees in older forests. Therefore, changes in the occurrence and age or species
 2    composition of the forest can influence the rates of atmospheric deposition to the site as well as the fate of
 3    atmospherically deposited substances.
 4          Landscape processes and watershed disturbance can influence soil and water acidification in many
 5    ways. Land use practices and vegetation patterns have been changing in various parts of the U.S. for
 6    decades to centuries. These changes in human activity can influence the response of forested ecosystems
 7    to external stressors, including atmospheric deposition of S or N, natural disturbance factors such as wind
 8    and fire, and climatic changes. Some processes  contribute to the acidification of soil and surface waters or
 9    reduce the base saturation of the soils thereby increasing their sensitivity to acidic deposition. Other
10    processes cause decreased acidity (Sullivan et al., 1996b; (Sullivan, 2000).
11          Watershed disturbance from logging, blowdown, and fire disrupts the normal flow of water and can
12    cause increased contact between runoff water and soil surfaces, leading to increased base cation
13    concentration and ANC in drainage water. Recovery from disturbance can cause a decrease in drainage
14    water ANC as the system returns to pre-disturbance conditions. In particular, soil loss through erosion can
15    reduce the base cation pool size, thereby limiting the capacity of soils to neutralize atmospheric acidity. In
16    addition, forest harvesting has an important effect on forest N-demand, thereby reducing the likelihood of
17    future N-saturation in response to high N deposition. Forest management practices, especially those that
18    have occurred over many generations, have had important effects on soil chemistry, nutrient supplies, and
19    organic material.
20          Watershed disturbances, including road building, agriculture, mining, urbanization, logging,
21    blowdown, and fire can alter various aspects of ecosystems biogeochemistry. Such disturbances can
22    influence the water budget, base cation mobilization, routing of drainage water, nutrient input, and S and
23    N cycling in ways that affect the acid-base chemistry and nutrient dynamics of soils and drainage waters
24    (Sullivan et al., 1996b). The effects of such disturbances can greatly modify the response of a given
25    watershed to atmospheric inputs of S and N.

      B.5.2.1.  Timber Harvest
26          Removal of the forest affects drainage  water quality in several ways. Deposition of S and N are
27    reduced; leaching of NC>3  increases and, in some cases, causes a pulse of surface water acidification.
28    Base cations tied up in wood are lost when wood is transported off-site. Regrowth of the forest may
29    further affect drainage water quality through vegetative uptake of N and base cations. Trees accumulate
30    base cations to a greater degree than anions. To  balance the charge discrepancy, roots release an
31    equivalent amount of protons and acidify the  soil. Base cation accumulation by trees is age-dependent.
32    Young forests grow faster and are therefore more acidifying than older forests (Nilsson et al.,  1982;
33    Nilsson, 1993). They also retain greater amounts of N.

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 1          Most forests in the northeastern U.S. are recovering from extensive human disturbance that
 2    occurred over a period of about 200 years. Landscapes were mainly forested during pre-colonial times,
 3    logged or cleared for agriculture in the mid to late 19th century, and are now largely early to mid-serai
 4    stage regenerating forests (Niering,  1998).
 5          In some areas that experience relatively high levels of acidic deposition, there is growing concern
 6    about sustainable timber productivity (cf. Adams, 1999). Harvest-induced leaching losses have been
 7    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+
 8    (Federer et al., 1989). Timber harvesting also increases leaching losses from the site because of the
 9    reduction in transpirational water loss. The increased water flux after tree removal increases the
10    opportunity to leach base cations from the soil.
11          If base cations sequestered in tree wood are removed from the site by tree harvesting, the result is a
12    decrease in the available base cation pool on the site. Physical disturbances to forest soils during logging
13    operations and increased soil temperature that results from exposure of the forest floor to sunlight may
14    also cause a short-term increase in the rates of N mineralization and nitrification (Joslin et al., 1992). The
15    resulting increase in NC>3 production and leaching further depletes base cations from the soil pool.
16          Johnson et al. (199 la) measured short-term (3 years) effects of logging  at HBEF in New
17    Hampshire on soil acid-base chemistry. Base saturation of the mineral soil Bh horizon decreased from  14
18    to 11 % and pH decreased by 0.24 pH units.
19          Likens et al. (2002) reported results of a 34-year study of the biogeochemistry  of forest ecosystems
20    at HBEF. Part of the study evaluated the effects of tree removal on S cycling and related biogeochemical
21    processes. Vegetation removal resulted in increased decomposition of organic matter and nitrification.
22    These changes, in turn, lowered soil water pH, enhanced SO42 adsorption on mineral soil, and therefore
23    decreased the flux of SO42 in stream water. With subsequent vegetation regrowth, the adsorbed SO42
24    was released from the soil to drainage water, and streamwater SO42 concentrations increased.
25          Baldigo et al. (2005) compared the effects of clear-cut and timber-stand improvement (TSI)
26    harvests on water chemistry and mortality of caged brook trout in three Catskill Mountain streams.
27    Harvests removed 73% of tree basal area from a clearcut subbasin, 5% basal area from a TSI subbasin,
28    and 14% basal area at a site below the confluence of both streams (the combined effect of the two harvest
29    methods). Water quality and trout mortality were affected only in the clearcut stream.  Acidity and
30    concentrations of NC>3 and Al; increased sharply during high flows after the first growing season (1997).
31    Acid-Al; episodes were severe during this period and decreased steadily in magnitude and duration
32    thereafter. All trout at the clearcut site died within 7 days during spring 1998, and 85% died during spring
33    1999. Only background mortality was observed in other years at this site and at the other three sites
34    during all tests. The effects of tree harvests on fish communities are of concern because they might
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 1    interact with effects of acidic deposition and produce more substantial effects on biota than either stress
 2    factor on its own.

      B.5.2.2. Insect Infestation
 3          Effects of insect-caused defoliation on the N cycle can be pronounced. The foliar N consumed by
 4    insects is deposited on the forest floor as insect feces (frass), greenfall, and insect biomass. Some of this
 5    deposited N is subsequently taken up by tree roots and soil microbes, with little effect on the nutritional
 6    condition of the trees or the site. Where a sizable component of this N is leached in drainage water, the
 7    nutritional consequences can be more significant. There are also various feedback mechanisms. For
 8    example, low N supply can slow the population growth of defoliating insects (Mason et al., 1992) and
 9    enhance the tree's chemical defenses against insects (Hunter and Schultz, 1995). The amount of N
10    leaching loss is generally small, relative to atmospheric deposition inputs and relative to the amount of N
11    transferred to the forest floor with the defoliation (Lovett and Ruesink, 1995; Lovett et al., 2002).
12    Nevertheless, it can be high enough to contribute to base cation depletion of soils and effects  on
13    downstream receiving waters. The extent of NC>3 leaching may be partly related to the extent of
14    defoliation and tree mortality that occurs and also the amount of precipitation that occurs immediately
15    after the defoliation (Lovett et al., 2002).
16          Forest insect infestation can have profound effects on the acid-base and nutrient chemistry of soils
17    and drainage waters. Effects of a gypsy moth infestation in Shenandoah National Park provide a good
18    example. Between the mid-1980s and the early 1990s, the southward expanding range of the European
19    gypsy moth traversed Shenandoah National Park and affected all of the University of Virginia's SWAS
20    study watersheds (Webb,  1999). Some areas of the park were heavily defoliated 2 to 3 years in a row. The
21    White Oak Run watershed, for example, was more than 90% defoliated in both  1991 and 1992. This
22    insect infestation of forest ecosystems in Shenandoah National  Park resulted in substantial effects on
23    streamwater chemistry. The most notable effects of the defoliation on park streams were dramatic
24    increases in the concentration and export of N and base cations in streamwater. Following defoliation,
25    NO3 export increased to previously unobserved levels and remained high for over 6 years before
26    returning to predefoliation levels. The very low levels of pre-disturbance NC>3  export in park streams
27    were consistent with expectations for N-limited,  regenerating forests (Aber et al., 1989; Stoddard, 1994).
28    Release of NC>3  to surface waters following defoliation was likewise consistent with previous
29    observations of increased N export due to forest disturbance (Likens et al., 1970; Swank, 1988). The exact
30    mechanisms have not been determined, but it is evident that the repeated consumption and processing of
31    foliage by the gypsy moth larva disrupted the ordinarily tight cycling of N in Shenandoah National Park
32    forests.
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 1          Although N is thought to play an important role in the chronic acidification of surface waters in
 2    some areas (cf. Sullivan et al., 1997), the elevated concentrations of NC>3  in Shenandoah National Park
 3    streams following defoliation did not appear to contribute to baseflow acidification in White Oak Run.
 4    This was due to a concurrent increase in concentrations of base cations in streamwater (Webb et al.,
 5    1995). Both NOs and base cation concentrations increased during high-runoff conditions, although the
 6    increase in base cations did not fully compensate for the episodic increase in NOs . Episodic acidification
 7    following defoliation thus became more frequent and more extreme in terms of observed minimum ANC
 8    (Webb etal., 1995).
 9          The full effect of the gypsy moth on aquatic resources in Shenandoah National Park is not well
10    understood. One consequence may be a reduction in the supply of available soil base cations and
11    associated effects on streamwater ANC.  Repeated periods of defoliation would probably increase the
12    effect of episodic acidification on sensitive aquatic fauna and may determine the conditions under which
13    some species are lost. Ultimately such effects may depend upon both the severity of future gypsy moth or
14    other insect outbreaks and possibly on the amount of atmospheric N deposition. Gypsy moth populations
15    typically display a pattern of periodic outbreaks and collapse (Cambell, 1981). It remains to be seen what
16    the long-term pattern will be (Sullivan et al., 2003).
17          Webb et al. (1994) compared pre- and post-defoliation streamwater chemistry for 23 VTSSS
18    watersheds. Nitrate concentrations, measured quarterly, increased in most of the streams in response to
19    defoliation, typically by 10 to 20 (ieq/L or more. The increased streamwater NOs  concentration was
20    probably derived from the N content of the foliage that had been consumed by the gypsy moth larvae and
21    converted to feces on the forest floor. Sulfate concentrations and ANC also decreased in streamwater.
22    Although the mechanism for decreased SO42 was not totally clear, Webb et al. (1994) hypothesized that
23    increased nitrification in response to the increased soil N pool may have caused soil acidification, which
24    would be expected to have increased soil S adsorption (cf. Johnson and Cole, 1980). Decreased S
25    deposition during the comparison period may also have contributed to the SO42 response.
26          Eshleman et al. (1998) reported NOs outputs from five small (<15 km2) forested watersheds in
27    Virginia and Maryland from 1988 to 1995. The study watersheds varied in geology, vegetation, and acid
28    sensitivity, with baseflow ANC typically in the range of 0 to 10 (ieq/L in Paine Run to the range of 150 to
29    350 (ieq/L in Piney River. Oak species (Quercus spp.), which are a preferred food source of gypsy moth
30    larvae, occupied about 60% to 100% of the study watersheds. Nitrate concentrations increased in at least
31    three of the watersheds in association with intense defoliation by the gypsy moth larva during the late
32    1980s to early 1990s, to peak annual average NOs  concentrations of about 30 to 55 (ieq/L. Most of the
33    increased NOs  leaching occurred during storm flow conditions.
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 1          A number of other studies have been conducted that examined the effects of gypsy moth, or other
 2    forest insect pests, on watershed biogeochemistry. Defoliation of poplars (Populus sp.) by gypsy moth
 3    larvae in southwestern Michigan did not result in appreciable NOs  leaching (Russell et al., 2004).
 4          Other pest species can have similar effects. For example, spruce-fir forests throughout the southern
 5    Appalachian Mountains have been subjected to significant disturbance, especially from the balsam wooly
 6    adelgid, a European pest which has infested Fraser fir since about the 1960s. Severe fir mortality has
 7    occurred in many areas. This disturbance factor has the potential to interact with acidic deposition and
 8    other ecosystem stresses, and contribute to multiple-stress tree mortality and to changes in
 9    biogeochemical cycling.
10          Defoliation by the elm spanworm (Ennomos subsignarius Hiibner) larvae in old-growth hemlock-
11    hardwood forests on the Allegheny High Plateau of northwestern Pennsylvania increased streamwater
12    NC>3 concentrations from pre-defoliation levels of about 29 (ieq/Lto peak values the summer after
13    defoliation of about 100 (ieq/L (Lewis and Likens, 2007).
            B.5.3.  Wind or Ice Storm Damage
14          Forest blowdown might affect surface water acid-base chemistry by changing the pathway
15    followed by drainage water through watershed soils (Dobson et al., 1990). Pipes formed in the soil by
16    decaying tree roots can alter hydrologic flow so that less water enters the soil matrix, where neutralization
17    processes buffer the acidity of rainwater and snowmelt. Pipes tend to occur most commonly in near-
18    surface soil horizons where most tree rooting occurs. Contact between drainage water and mineral soil is
19    reduced when runoff is routed through them. If enhanced pipeflow, resulting from sudden extensive tree
20    mortality, affects a large portion of a watershed, runoff water may have less opportunity for acid
21    neutralization than would be the case in the absence of such pipeflow.
22          Severe canopy damage occurred in 1998 in response to an ice storm at HBEF and surrounding
23    areas in the White Mountains. Houlton et al. (2003) reported effects of this disturbance on N cycling and
24    leaching losses. Subsequent to the ice storm, drainage water NOs  concentrations increased sevenfold to
25    tenfold. Peak streamwater NOs" concentrations during spring months reached or exceeded 50 (ieq/L at
26    many sites. There were no significant differences, however, in N mineralization, nitrification, or
27    denitrification rates between damaged and undamaged areas. Houlton et al. (2003) interpreted these
28    results as an indication that increased NO3 leaching was probably due to decreased root uptake rather
29    than accelerated N cycling by soil microbes. The amount of NO3  leaching loss was estimated to be more
30    than half of the entire year's worth of atmospheric N deposition.
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      B.5.3.1. Fire
 1          Fire can increase concentrations of NO3  and SO42 in soils and drainage water (cf. Chorover et al.,
 2    1994; Riggan et al., 1994). Fenn and Poth (1998) hypothesized that successful fire suppression efforts
 3    may have contributed to the development of N-saturation in fire-adapted ecosystems in southern
 4    California by allowing N to accumulate in soil and in the forest floor, and by maintaining dense mature
 5    stands with reduced N demand.
 6          The effects of fire on NC>3  leaching in chaparral stands in the San Gabriel Mountains, CA that
 7    received high atmospheric N deposition were investigated by Riggan et al. (1994). Study watersheds were
 8    burned with fires of different intensity and, after rainfall, NC>3 and NH4+ were measured in watershed
 9    streams. Nitrogen release was up to 40 times greater in burned watersheds than in unburned watersheds,
10    and the  amount and concentration of N release were found to be related to fire intensity.
11          Chorover et al. (1994) evaluated the effects of fire on soil and stream water chemistry in Sequoia
12    National Park, CA. Burning increased concentrations of NC>3  and SCV in soil water and stream water.
13    Sulfate concentrations increased 100 fold. Nitrate concentrations also increased and remained higher in
14    soils and stream water for about 3 years. These results suggest that successful fire suppression may have
15    contributed to the development of N saturation in fire-adapted ecosystems in southern California by
16    allowing N to accumulate in the soil and forest floor, and by maintaining dense mature stands with
17    reduced N demand (Fenn and Poth, 1998).

      B.5.3.2. Multiple Stress Response
18          Acidification-related effects of S and N deposition do not occur in isolation; they interact with
19    disturbances of various types, both natural and human-caused. They also influence a range of
20    biogeochemical processes that may be difficult to predict. Overall, the interactions between disturbance
21    and ecosystem acidification as a consequence of acidic deposition are not well understood.
22          It is believed that high rates of N deposition cause increased susceptibility of forests to other
23    stressors, including reducing the resistance of some tree species to frost, insect damage, or drought. The
24    effects of acidic deposition can interact with a variety of stressors, both natural and human-caused. The
25    end result might include adverse effects that would not occur solely in response to acidic deposition, or in
26    response to any one of the other stressors.
27          Watershed disturbance might also effect Hg cycling and its relationship to  S deposition. For
28    example, Garcia and Carignan (2000), in a study of 20 watersheds in Quebec, Canada, found that the
29    average Hg concentration in 560-mm northern pike (Esox Indus) was significantly higher in lakes whose
30    watersheds had recently (1995) been logged (3.4 (ig/g), as compared with reference lake watersheds
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 1    (1.9 (ig/g), that had remained undisturbed for at least 40 years. Fish tissue Hg concentration also increased
 2    with increasing DOC and lakewater SC>42 concentration, and with decreasing pH.
      B.6.  Ecological indicators of acidification
            B.6.1.  Biological Indicators
 3          Surface water acidification from acidic deposition causes effects on organisms at all trophic levels.
 4    Early studies focused on the loss offish populations, especially salmonids. Later studies also reported that
 5    many species of phytoplankton, zooplankton, insect larvae, crayfish, snails, and freshwater mussels are
 6    sensitive and are often reduced or absent from acidified lakes and streams (Havas, 1986; Baker et al.,
 7    1990a). Similarly, many species of microrhizal fungi and lichens have been reported to be particularly
 8    sensitive to acidic deposition in terrestrial ecosystems.
 9          Effects of acidification on aquatic biota have been demonstrated in laboratory and field bioassays
10    (e.g., Baker et al., 1996), whole-ecosystem acidification experiments (e.g., Schindler et al., 1985), and
11    field surveys (e.g., Baker and Schofield, 1982; Gallagher and Baker, 1990). Many of the species that
12    commonly occur in acid-sensitive surface waters susceptible to acidic deposition cannot reproduce or
13    survive if the water is acidic. Some sensitive species offish, invertebrates, and algae cannot  survive at
14    moderate levels of acidity. For example, some zooplankton predators, sensitive mayfly species, and
15    sensitive fish species are affected at pH values below the range of 5.6 to 6.0 (Baker and Christensen,
16    1991). Such pH values generally equate to ANC below about 25 to 50 (ieq/L.
17          There are few published examples of long-term monitoring data for biological assemblages in acid-
18    sensitive surface waters, and none in the U.S. Therefore, conclusions about the effect of acidic deposition
19    on the distribution of sensitive species are based on other kinds of data (Stoddard et al.,  2003). For
20    example, the number offish species increases with increasing pH and ANC when evaluated  for multiple
21    water bodies across the landscape. This result has been shown for streams in Virginia, lakes  in the
22    Adirondacks, and both high-elevation and seepage lakes in Maine (Figure B-17).
23          Given the available data, it is clear that acidification from acidic deposition limits the  distribution
24    of acid-sensitive fish, benthic invertebrate, phytoplankton, and  zooplankton species, but a lack of
25    adequate data makes it difficult to quantify the magnitude of change in biota from historical  condition or
26    in response to recent (past two to three decades) decreases in acidic deposition in individual  lakes or
27    streams. Studies in Canada and Europe have illustrated the feasibility and complexity of biological
28    recovery in response to decreased acidity.
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 1          Threshold pH levels for adverse biological effects have been summarized for a variety of aquatic
 2    organisms (Haines and Baker, 1986; Baker et al., 1990a). The effects of low pH are specific to the
 3    organism, and perhaps region, under consideration and depend also upon the concentrations of other
 4    chemical constituents  in the water, notably Al; and Ca2+. In general, populations of salmonid fish are not
 5    found at pH levels less than 5.0, and smallmouth bass (Micropterus dolomieu) populations are usually not
 6    foundatpH values less than 5.2 to 5.5 (Haines and Baker, 1986). A number of synoptic surveys indicate
 7    loss of species diversity and absence of many other fish species in the pH range of 5.0 to 5.5 (Haines and
 8    Baker, 1986). Levels of pH less than 6.0 to 6.5 have been associated with adverse effects on populations
 9    of dace, minnows, and shiners (family Cyprinidae), and bioassays suggest that given sufficient Al
10    concentrations, pH less than 6.5 can lead to increased egg and larval mortality in blueback herring (Alosa
11    aestivalis) and striped bass (Morone saxatilis) (Hall,  1987; Klauda et al., 1987).
12          Mycorrhizal fungi have been suggested as possible biological indicators of atmospheric deposition
13    effects by L0kke et al. (1996) because they are intimately associated with tree roots, depend on plant
14    assimilates, and play essential roles in plant nutrient uptake. Thus, mycorrhizal fungi can influence the
15    ability of their host plants to tolerate different anthropogenically generated stresses. Mycorrhizae and
16    associated fine roots have short lifespans and their turnover appears to be controlled by environmental
17    factors. Changes in mycorrhizal species composition, or the loss of dominant mycorrhizal species in areas
18    where diversity is already low, may cause  increased susceptibility of plants to stress (L0kke et al., 1996).
19          Mycorrhizal fungi are dependent for their nutrition on the supply of assimilates from the host plant.
20    Stresses that shift the allocation of C reserves to the production of new leaves at the expense of supporting
21    tissues will be reflected rapidly in decreased fine root and mycorrhizal biomass (Winner and Atkinson,
22    1986). Decreased C allocation to roots could also affect soil carbon and rhizosphere organisms. For
23    example, earthworms  are believed to decrease in abundance, and in species number, in acidified soils
24    (L0kke et al., 1996). Soil dwelling animals, including earthworms, are important for decomposition, soil
25    aeration, and nutrient redistribution in the  soil. They contribute to decomposition and nutrient availability,
26    mainly by increasing the accessibility of dead plant material to microorganisms.

      B.6.1.1. Phytoplankton
27          Phytoplankton are the small microscopic plants or plant-like organisms that live suspended in the
28    water column of lakes and large rivers. Acidification results in decreased species richness and diversity of
29    phytoplankton communities. There is also a shift in the composition of dominant taxa, but species
30    composition shifts cannot be accurately predicted (though it is clear that community restructuring occurs
31    with acidification). This effect is most prevalent in the pH 5 to 6 range (Baker et al., 1990a). Acidification
32    has also been found to cause decreases in food web complexity (indicated by the number of trophic links
33    or species) in the Adirondack Mountains (Havens and Carlson, 1998). Both Al toxicity and P limitation

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 1    may also be responsible for shifts in phytoplankton community composition. Neither grazing pressure nor
 2    changes in water clarity associated with acidification seem to have a major effect on phytoplankton
 3    community structure. There is no consistent pattern of acidification effects on phytoplankton biomass.
 4    Various lakes have shown increases, decreases, or no change in phytoplankton biomass with acidification
 5    (Baker et al.,  1990a). Leavitt et al. (1999) suggested that the complex interactions between pH, DOC, and
 6    light explain the high variability in the algal biomass-acidification relationship. In most lakes,
 7    acidification has a negligible effect on primary productivity.
 8          Diatoms, which comprise an important component of the phytoplankton, are excellent indicators of
 9    environmental change in aquatic ecosystems, including acidity, nutrient status, salinity, and climatic
10    features (Sullivan and Charles, 1994; Stoermer and Smol, 1999). There are thousands of different species,
11    many of which have rather narrow ecological tolerance ranges. Diatoms have been widely used as
12    indicators of past lake acidification. Inference based on diatom fossil remains preserved in lake sediments
13    is an excellent approach for quantifying historical chemical change (Charles and Norton,  1986).
14          Paleolimnological reconstructions of past lakewater chemistry are based on transfer functions
15    derived from  relationships between current lakewater chemistry and diatom (or, in some cases,
16    chrysophyte)  algal remains in surface sediments. Predictive relationships are  developed using regional
17    lake datasets, and are then applied to diatom assemblage data collected from horizontal slices of lake
18    sediment cores to infer past lakewater conditions (Charles et al.,  1989; Husar and Sullivan,  1991).
19          Periphyton are the small microscopic plants (or plant-like organisms) that live on submerged
20    substrates in aquatic systems (e.g., stream or lake bottoms). As seen in phytoplankton communities,
21    acidification results in decreased species richness, community alteration, and emergence of new dominant
22    species in periphyton communities. Many diatom and blue-green bacterial periphyton species cannot
23    tolerate acidic conditions. On the other hand, green algae, particularly the filamentous Zygnemataceae,
24    increase in relative abundance at lower pH (Baker et al., 1990a). Unlike for phytoplankton,  there is
25    evidence that the biomass of attached periphyton increases at lower pH.
26          Studies of phytoplankton recovery from acidification indicate that there is an increase in
27    phytoplankton species richness and diversity as pH increases. In the Experimental Lakes area of Ontario,
28    previously acidified lakes have been experimentally de-acidified. In Lake 223, there was little increase in
29    phytoplankton diversity as pH changed from 5.0 to  5.8 but a strong recovery  of diversity at pH above 6
30    (Findlay and Kasian,  1996). In Lake 302S, profound change began at pH 5.5; phytoplankton assemblages
31    at pH below 5.5 resembled acidified lakes. Cyanobacteria were among the first to recover at pH 5.5 to 5.8
32    (Findlay et al., 1999). In the Killarney Park area of Ontario, Findlay (2003) reported that lakes that were
33    previously low in pH (5.0 to  5.5) and are now above pH 6 have shifted towards phytoplankton
3 4    assemblages typical of circumneutral environments.
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      B.6.1.2. Zooplankton
 1          Field survey and experimental lake studies both indicate that acidification reduces zooplankton
 2    species richness. Effects  of acidification on community biomass and abundance, however, were not
 3    definitive. Some studies indicated a lower biomass under low pH conditions, whereas other studies
 4    showed no consistent pattern in the biomass-pH relationship. Limited data indicated that acidification
 5    does not alter zooplankton community grazing rates. Zooplankton species that have been shown to be
 6    sensitive to low pH include Diaptomus sicilis, Epischura lacustris, Tropocyclops parsinus mexicanus,
 7    Daphnia galeata mendotae, Daphnia rosea, Diaphanosoma birgei, Leptodora kindtii, Asplanchna
 8    priodonta, and Conochilus unicornis. In North America, species reported to have increased dominance in
 9    acidic lakes (acid-tolerants) include Keratella taurocephala, Bosmina longirostris, and Diaptomus
10    minutus. Possible mechanisms for zooplankton sensitivity to low pH include ion regulation failure,
11    reduced oxygen uptake, inability to reproduce, and Al toxicity. Indirect effects of acidification on
12    zooplankton communities are also possible due to pH-induced shifts in higher trophic level zooplankton
13    predators. This mechanism is probably of less importance than the direct effects of low pH. It is also
14    probable that under acidic conditions, zooplankton communities are less able to ameliorate nutrient
15    additions or control algal densities (Baker et al., 1990a).
16          Reported pH thresholds for zooplankton community alteration range from 5 to 6. Holt and Yan
17    (2003) reported a threshold of community change at pH 6 for lakes in southern Ontario. Locke and
18    Sprules (1994) reported that acidification below pH 5 in the 1970s overcame the resistance stability of the
19    zooplankton community  in Ontario Precambrian Shield lakes. The subset of study lakes that showed pH
20    recovery from acidification 20 years later in 1990 also showed recovery in the stability of the zooplankton
21    community. Holt and Yan (2003) also noted recovery in zooplankton community composition (based on
22    similarity to neutral lakes) in the subset of Killarney Park (Ontario) lakes in which the pH increased to
23    over 6 during the 1971 to 2000 study period. They did not, however, note any time  trend of increasing
24    species richness between recovering lakes and non-recovering lakes.
25          Recovery in experimentally acidified Lake 223 back to pH 6.1 was  studied by Malley and Chang
26    (1995). They reported that the zooplankton community was still in a state of flux. Species diversity that
27    had been reduced during the acidification phase had partially returned to preacidification levels. Rotifers
28    had recovered less than crustaceans. One decade after cessation of the experimental acidification of Little
29    Rock Lake in Wisconsin, recovery of the zooplankton community was complete (Frost et al., 2006).
30    Recovery did not follow  the same trajectory as the initial  acidification, however, indicating a substantial
31    hysteresis in zooplankton community recovery. About 40% of the zooplankton species in the lake
32    exhibited a lag of 1 to 6 years to recover to levels noted in the neutral reference basin.
33          In situ enclosure studies were conducted for 35 days at Emerald Lake in the Sierra Nevada by
34    Barmuta et al. (1990). The lake sediments were included within the experimental enclosures. This allowed

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 1    the investigators to document the response of zoobenthos as well as zooplankton. Treatments included a
 2    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
 3    zooplankton were sensitive to acidification but zoobenthos were unaffected by the experimental
 4    treatment. Daphnia rosea and Diaptomus signicauda decreased in abundance below the range of pH 5.5
 5    to 5.8 and were eliminated below about pH 5.0. Bosnia longirostris and Keratella taurocephala generally
 6    became more abundant with decreasing pH.  Barmuta et al. (1990) concluded that even slight acidification
 7    of high-elevation lakes in the Sierra Nevada might alter the structure of the zooplankton community.
 8          Sullivan et al. (2006a) found that zooplankton taxonomic richness varied with ANC in Adirondack
 9    lakes (Table B-22). Taxonomic richness expressed as number of species of crustaceans, rotifers, and total
10    zooplankton, increased with increasing ANC. In general, lakewater ANC explained nearly half of the
11    variation in total zooplankton and crustacean taxonomic richness, but less for rotifer richness. These
12    results (Table B-22) provided the basis for estimating changes in  zooplankton richness in response to past
13    or future changes in lakewater ANC. Several zooplankton species found in lakes in the Sierra Nevada are
14    also  known to be sensitive to acidity status (Gerritsen et al., 1998).

      B.6.1.3.  Benthic Invertebrates
15          Within stream systems, macroinvertebrate communities are among the most sensitive life forms to
16    disturbances, including those associated with atmospheric deposition (Cairns and Pratt, 1993). In
17    addition, they are relatively easy to sample in the field (Plafkin et al., 1989; Resh et al., 1995; Karr and
18    Chu, 1999).
19          Acidification results in the loss of acid-sensitive benthic invertebrates and decreases in pH of one
20    unit  or more typically result in species loss. Invertebrate taxa that are most sensitive to acidification are
21    mayflies, amphipods,  snails, and clams. At low levels of acidification (pH 5.5 to 6.0),  acid-sensitive
22    species are replaced by more acid-tolerant species, yielding little  or no change in total community species
23    richness, diversity, density, or biomass. If pH decreases are  larger, more species will be lost without
24    replacement, resulting in decreased richness and diversity. Many  sites also note decreases in  invertebrate
25    biomass and productivity (more so in streams than lakes). High levels of acidification (pH <  5) were
26    found to virtually eliminate all mayflies, crustaceans and mollusks from French streams (Guerold et al.,
27    2000). Examples of sensitive benthic invertebrate species include Baetis rhodani, Gammarus lacustris,
28    Hyalella azteca, Asellus aquations, Orconectes rusticus, and O. propinquous. Stoneflies are generally
29    more acid-tolerant than mayflies and caddisflies.
30          Possible mechanisms for acidification effects on invertebrates include direct toxicity of FT and Al,
31    disruption of ion regulation, and reproductive failure. Indirect effects due to acidification-induced changes
32    to invertebrate predator populations are also possible (Baker et al., 1990a). Acidic episodes in streams  can
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 1    cause increased downstream drift of acid-sensitive species, particularly Baetis (Kratz et al., 1994; Smock
 2    and Gazzera, 1996).
 3          It has been well documented that low streamwater pH can be associated with reductions in
 4    invertebrate species richness or diversity (Townsend et al., 1983; Raddum and Fjellheim, 1984; Burton
 5    et al., 1985; Kimmel et al., 1985; Hall and Ide, 1987; Peterson and Van Eechhaute, 1992; Rosemond et al.,
 6    1992; Sullivan et al., 2003), and sometimes density (Hall et al., 1980; Townsend et al., 1983; Burton et al.,
 7    1985; Kimmel et al., 1985). Effects on invertebrate density are not universal; a number of studies have
 8    found no density effects (Harriman and Morrison, 1982; Simpson et al., 1985; Ormerod and Tyler, 1987;
 9    Winterbourn and Collier, 1987). However, a decrease in species richness with decreasing pH has been
10    found in almost all such studies (Rosemond et al., 1992), and this finding has been especially pronounced
11    in streams for order Ephemeroptera (mayflies).
12          The Ephemeroptera-Plecoptera-Tricoptera (EPT) Index is a common measure of stream
13    macroinvertebrate community integrity. The EPT metric is the total number of families present in those
14    three insect orders (mayflies, stoneflies, and caddisflies, respectively). The total number of families is
15    generally lower at acidified sites because species within those families tend to exhibit varying acid
16    sensitivity (cf SAMAB, 1996). Mayflies tend to be most sensitive of the three, and stoneflies tend to be
17    least sensitive (Peterson and Van Eechhaute,  1992).
18          There has been some recovery in benthic invertebrate communities in surface waters exhibiting
19    chemical recovery from acidification. In Scotland, Soulsby et al. (1995) reported an increase in acid-
20    sensitive mayflies in some  streams that showed recent ANC increases. However, no increases in
21    invertebrates were observed in the most acidic streams despite observed increases in ANC. They
22    suggested that further acidic deposition reductions and sufficient time for reversal of soil acidification
23    may be required before biotic recovery can occur. Tipping et al. (2002) noted increases of invertebrate
24    richness and diversity in the English Lake District in their study streams that had pH increases of 0.3 to
25    0.5 units since about 1970.
26          Responses of aquatic macroinvertebrates to acidification were evaluated by Kratz et al. (1994) in
27    12 streamside channels in Sequoia National Park, CA. Replicated treatments included a control (pH 6.5 to
28    6.7) and experimental exposure at pH levels of 5.1 to 5.2 and 4.4 to 4.6. Invertebrate drift was monitored
29    continuously and benthic densities were determined before and after acidification. Single 8-hr acid pulses
30    increased the drift of sensitive taxa, and benthic densities were reduced. Baetis showed reduced density
31    post-treatment to less than  25% of control densities in both pH reduction treatments (5.2, 4.6) and two
32    different experimental exposures. Densities of Paraleptophlebia appeared to be reduced by the
33    acidification, but most treatment effects were  not statistically significant. Kratz et al. (1994) suggested
34    that the effects of acid inputs on benthic species densities depended on microhabitat preferences. Baetis
35    nymphs are epibenthic and active. They are often found on the upper surfaces of rocks where they are
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 1    directly exposed to acidified water. This may have been responsible for their greater response to
 2    acidification.

      B.6.1.4. Fish
 3          By 1990 it was well established that pH in the range of 4.0-6.5 could cause significant adverse
 4    biological effects on fish. Low pH was one of the most important factors resulting in adverse effects. The
 5    toxicity of pH was, in most cases, the result of impaired body salt regulation. Decreased pH in the water
 6    inhibited the active uptake of Na+ and Cl~ and stimulated the passive loss of these ions (Baker et al.,
 7    1990a).
 8          The response to acidification was not uniform, however. Some species and life stages experienced
 9    significant mortality in bioassays at relatively high pH (e.g., pH 6.0-6.5 for eggs and fry of striped bass
10    and fathead minnow) (Buckler et al., 1987; McCormick et al., 1989), whereas others were able to persist
11    at quite low pH without adverse effect (Mudminnow; [Umbra spp.] at pH 4.0 and Umbra pygmaea at pH
12    3.5) (Dederen, 1987). Many minnows and dace (Cyprinidae) are sensitive to acidity (threshold effects at
13    pH < 5.5 to 6.0), but some common game species such as brook trout, largemouth bass, and small mouth
14    bass are relatively insensitive (threshold effects at pH < 5.0 to 5.5). A summary of studies that
15    demonstrated the difference among species is shown in Table B-23. Table B-24 summarizes the results
16    from a variety of studies that determined the threshold values of pH for various taxa and kinds of effects.
17          The effect of acidification on aquatic organisms, especially fish, is due in large part to the toxic
18    effect of Al; that is released from watershed soils. A number of studies reviewed by Baker et al. (1990a)
19    reported threshold values of Al; for various species and effects. Those results are presented in Table B-25.
20    The effects of low pH and high Al; can be ameliorated to an extent in the presence of increased Ca2+
21    concentration. A summary of the effect of increasing Ca2+ concentration is presented in Table B-26.
22          Fish populations in acidified streams and lakes of Europe and North America have declined, and
23    some have become extinct as a result of atmospheric deposition of acids and the resulting changes in
24    water quality (Baker et al., 1990a). A variety of factors, including Al;, DOC, and Ca2+, along with the
25    timing and magnitude of episodic fluctuations in toxic acid and Al; concentrations, are related to the
26    degree to which surface water acidification influences fish  survival in natural systems (Baker et al.,
27    1990a; Gagen et al., 1993; Siminon et al., 1993; Van Sickle et al., 1996; Baldigo and Murdoch, 1997).
28    Aluminum fractionation and Al; concentration are directly dependent upon pH levels (Driscoll et al.,
29    1985).
30          Fish communities of acid-sensitive streams and lakes may contain a variety of species, but are
31    often dominated by trout. Across the  eastern U.S., brook trout is often selected as an indicator of
32    acidification effects on aquatic biota  because it is native to  many eastern streams and lakes and because
33    residents place great recreational and aesthetic value on this species. It must be emphasized, however, that

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 1    brook trout is a relatively acid-tolerant species. Many other fish species, including rainbow and brown
 2    trout, as well as a variety of other fish species, are more acid-sensitive than brook trout. In many
 3    Appalachian Mountain streams that have been acidified by acidic deposition, brook trout is the last
 4    species to disappear; it is generally lost at pH near 5.0 (MacAvoy and Bulger,  1995), which usually
 5    corresponds in these streams with ANC near 0 (Sullivan et al., 2003).
 6          Although there are known differences in acid sensitivity among fish species, experimentally
 7    determined acid sensitivities are available for only a minority of freshwater fish species. Baker and
 8    Christensen (1991) reported critical pH values for 25  species offish. They defined critical pH as the
 9    threshold for significant adverse effects on fish populations. The reported range of pH values represents
10    the authors' estimate of the uncertainty of this threshold. The range of response within species depends on
11    differences in sensitivity among life stages, and on different exposure concentrations of Ca2+ and Al. To
12    cite a few examples, blacknose dace is regarded as very sensitive to acid stress, because population loss
13    due to  acidification has been documented in this species at pH values as high as 6.1; in field bioassays,
14    embryo mortality has been attributed to acid stress at pH values as high as 5.9. Embryo mortality has
15    occurred in common shiner at pH values as high as 6.0. Although the critical pH range for rainbow trout
16    is designated as 4.9-5.6, adult  and juvenile mortality have occurred at pH values as high as 5.9. Brown
17    trout population loss has occurred over the pH range of 4.8-6.0, and brook trout fry mortality has
18    occurred over the range of 4.8-5.9  (Baker and Christensen, 1991). Relative sensitivities can be suggested
19    by regional surveys as well, although interpretation of such data is complicated by factors that correlate
20    with elevation.  Such factors, including habitat complexity and refugia from high-flow conditions, often
21    vary with elevation in parallel  with acid sensitivity. It is noteworthy, however that about half of the 53 fish
22    species found in Adirondack Mountain waters in New York never occur at pH values below 6.0 (Kretser
23    et al., 1989; Driscoll et al., 2001b); for those species whose acid tolerances are unknown, it is probable
24    that acid sensitivity is responsible for at least some of these absences. It is the difference in acid tolerance
25    among species that produces a gradual decline in species richness as acidification progresses, with the
26    most sensitive species lost first.
27          Effects on biota can be assessed as effects on a particular sensitive species or species perceived to
28    be important, or as effects on the richness or diversity offish or other potentially sensitive life form. For
29    example, Bulger et al. (2000) developed ANC thresholds for brook trout in Virginia, which are presented
30    in (Table B-27). These values were based on annual average stream water chemistry, and therefore
31    represent chronic exposure conditions. The likelihood of additional episodic stress is incorporated into the
32    response categories in the manner in which they are interpreted. For example, the episodically acidic
33    response category, which has chronic ANC in the range of 0 to 20 (ieq/L, represents streams that are
34    expected to acidify to ANC near or below 0 during rainfall or snowmelt episodes. In such streams,
35    sublethal and/or lethal effects on brook trout are possible (Bulger et al., 2000; Sullivan et al., 2003).
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 1          Fish species richness, population density, condition factor, age distribution, size, and bioassay
 2    survival have all been shown to be reduced in low-ANC streams as compared to intermediate-ANC and
 3    high-ANC streams (Bulger et al., 1995; Dennis et al., 1995; Dennis and Bulger, 1995; MacAvoy and
 4    Bulger, 1995). Fish species richness is a good indicator of acidification response. Lakes or streams having
 5    pH below about 5.0 or ANC below about 0 generally do not support fish. Depending on the region, waters
 6    having pH above about 6.5 and ANC above about 50 (ieq/L support large, but variable, numbers of
 7    species. There is often a positive relationship between pH and number offish species, at least for pH
 8    values between about 5.0 and 6.5, or ANC values between about 0 and 50 to 100 (ieq/L (Bulger et al.,
 9    1999; Sullivan et al.,  2006a). Such observed relationships are complicated, however, by the tendency for
10    smaller lakes and streams, having smaller watersheds, to also support fewer fish species, irrespective of
11    acid-base chemistry. This pattern may be due to a decrease in the number of available niches as stream or
12    lake size decreases. Nevertheless, fish species richness is one of the most useful indicators of biological
13    effects of surface water acidification.
14          Acidification and the associated elevated concentrations of Al; in surface waters have adversely
15    affected fish populations and communities in parts of the Adirondack Mountains of northern New York
16    (Baker and Schofield, 1982; Johnson et al., 1987; (Schofield and Driscoll, 1987)Kretser et al., 1989;
17    Siminon et al., 1993) and in acid-sensitive  streams of the Catskill Mountains of southeastern New York
18    (Stoddard and Murdoch, 1991) and the Appalachian Mountains from Pennsylvania to Tennessee and
19    South Carolina (SAMAB, 1996; Bulger et al., 1999, 2000).
20          Adverse effects of low pH and high Al; concentration on fish include increased mortality, decreased
21    growth, decreased reproductive potential, and ionoregulatory impairment. A partial list of studies
22    demonstrating such effects is provided in Table B-28 from Baker et al. (1990a). It has been shown,
23    however, that there is marked variability among species and among life stages within species in the
24    specific levels of pH  and Al; that produce measurable responses.
25          Surface-water  acidification can affect fish populations by a number of mechanisms ranging from
26    increased morality and emigration to decreased food supplies (Baker et al., 1990a). The primary reason
27    for population decline and extinction, however, is usually the failure of a species to successfully recruit
28    young-of-the-year fish (Mills et al., 1987; Brezonik et al., 1993). The response of aquatic communities to
29    acidification, therefore, should appear first as changes in age distribution and decreased health of
30    individual fish (growth  and condition), then as decreased biomass and density in populations of acid-
31    intolerant fish species, and finally as elimination of sensitive species (Baker et al., 1990a).
32          The primary mechanism for the toxic effects of low pH and elevated Al on fish involves disruption
33    of normal ion regulation at the gill surface resulting in increased rates of ion loss and inhibition of ion
34    uptake (McWilliams and Potts, 1978; Leivestad, 1982; Wood and McDonald, 1987; Bergman et al.,
35    1988). Additional effects might include disruption of Ca2+ metabolism (Peterson and Martin-Robichaud,
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 1    1986; Gunn and Noakes, 1987; Reader et al., 1988), and decreased hatching success (Runn et al., 1977;
 2    Peterson et al., 1980; Haya and Waiwood, 1981; Waiwood and Haya, 1983).
 3          Prominent physiological disturbance for fish exposed to acid waters are iono- and osmoregulatory
 4    failure, acid-base regulatory failure, and respiratory and circulatory failure. Most of these effects can be
 5    directly attributed to effects on gill function or structure. The acute toxicity of low pH in acidic waters
 6    results in the loss of Ca2+ from important binding sites in the gill epithelium, which reduces the ability of
 7    the gill to control membrane permeability (McDonald, 1983; Havas,  1986; Exley and Phillips, 1988).
 8          The energy costs to fish for active iono-osmoregulation can be substantial (Farmer and Beamish,
 9    1969; Bulger, 1986). The concentrations of serum electrolytes (such as Na+ and Cl~) are many times
10    higher (often 100-fold higher) in fish blood than in the fresh waters in which they live. The active uptake
11    of these ions occurs at the gills. Because of the steep gradient in Na+ and Cl  concentrations between the
12    blood and fresh water, there is constant diffusional loss of these ions, which must be replaced by energy-
13    requiring active transport. Low pH increases the rate of passive loss of blood electrolytes (especially Na+
14    and Cl); and Al elevates losses of Na+ and Cl above the levels due to acid stress alone (Wood, 1989).
15    For example, dace in an acidified stream maintain whole-body Na+ at levels similar to dace in a high-
16    ANC stream (Dennis and Bulger, 1995), despite probable higher gill losses of electrolytes due to acid/Al
17    stress. Therefore, the homeostatic mechanisms at the gill responsible for maintaining blood electrolyte
18    levels must work harder and use more energy to maintain these levels for dace in the acidified stream.
19          Whole lake experiments and artificial stream channel experiments have shown that acidification
20    can lead to loss offish species. A summary of the work on Lake 223 in the Experimental Lakes Area in
21    Canada is provided in Table B-29. Work at Little Rock Lake in Wisconsin suggested that rock bass
22    suffered recruitment failure at pH 5.6 or below. Artificial channel studies showed poor survival and
23    reproductive success for fathead minnow at pH 5.9 to 6.0.
24          ANC criteria have been used for evaluation of potential acidification effects on fish communities.
25    The utility of these criteria lies in the association between ANC and the surface  water constituents that
26    directly contribute to or ameliorate acidity-related stress, in particular pH, Ca2+, and Al. Bulger et al.
27    (2000) developed ANC thresholds for brook trout response to acidification in forested headwater
28    catchments in western Virginia (See Table B-27). Note that because brook trout are comparatively acid
29    tolerant, adverse effects on many other fish species should be expected at relatively higher ANC values.
30          Streams with chronic ANC greater than about 50 (ieq/L are generally considered suitable for brook
31    trout in  southeastern U.S. streams because they have a large enough buffering capacity that persistent
32    acidification poses no threat to this species, and there is little likelihood of storm-induced acidic episodes
33    lethal to brook trout. In such streams, reproducing brook trout populations are expected if the habitat is
34    otherwise suitable (Bulger et al., 2000), although some streams may periodically experience episodic
35    chemistry that affects species more sensitive than brook trout. Streams having annual average ANC from
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 1    20 to 50 (ieq/L may or may not experience episodic acidification during storms that can be lethal to
 2    juvenile brook trout, as well as other fish. Streams that are designated as episodically acidic (chronic ANC
 3    from 0 to 20 (ieq/L) are considered marginal for brook trout because acidic episodes are likely (Hyer
 4    et al., 1995), although the frequency and magnitude of episodes vary. Streams that are chronically acidic
 5    (chronic ANC less than 0 (ieq/L) are not expected to support healthy brook trout populations (Bulger
 6    etal.,2000).
 7          Field surveys provided a regional context for fish response to acidification. Although there were
 8    some variations, the results of field surveys generally confirmed the results of bioassays, field
 9    experiments, and other intensive field studies. The results of many field surveys were summarized in
10    Baker et al. (1990a) and are compiled in (Table B-30).
11          It is important to note, however, that the absence offish from a given lake or stream in an area that
12    experiences surface water acidification does not necessarily imply that acidification is responsible for the
13    absence offish. For example, results of fisheries research in the Adirondacks has indicated that many
14    Adirondack lakes always had marginal spawning habitat for brook trout (Schofield, 1993), and some of
15    the currently fishless acidic lakes probably never supported fish.
16          Many of the data for the assessment offish status in the Adirondack region of New York come
17    from the reports by Kretser et al. (1989) and Baker et al.  (1990a). The status offish and of the presence of
18    individual species were related to a variety of lake characteristics. Of the lakes without fish, 42% had high
19    organic acid content that may have caused the observed low pH, 13% were bog lakes of high acidity and
20    naturally poor fish habitat, 9% had pH > 5.5 suggesting other factors were likely responsible for the lack
21    offish, and 3% were small high-elevation lakes that were unlikely to have fish regardless of acid-base
22    chemistry. However, 34% of the lakes surveyed (112 lakes) that had no fish at the time of survey had low
23    pH that was most likely the result of acid deposition and  no other obvious explanation for the lack offish.
24          Multivariate regression of the presence/absence of brook trout in Adirondack waters produced a
25    ranking of factors that appeared to influence the presence of brook trout when biological factors were
26    excluded from the analysis (stocking, presence of associated species, and presence of competitors).
27    Among contributing factors,  including SiC>2, ANC, DOC, substrate, and distance to the nearest road, pH
28    ranked first as a predictor of brook trout presence (Christensen et al., 1990). The results of this analysis
29    supported the hypothesis that 1990 levels of pH and related variables restricted the distribution of some
30    fish in Adirondack waters.
31          Fish toxicity models have been developed as mathematical regression functions fit to observations
32    offish mortality when exposed to constant levels of pH, Al;, and Ca2+ in laboratory toxicity tests. These
33    models had the  advantage that they dealt directly with the interaction effects of pH, Al, and Ca2+, but they
34    did not account for the effects of variations in other aspects of surface water quality, and they could not be
3 5    directly interpreted in terms of population-level response.
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 1          The many bioassays conducted of pH effects were screened by Baker et al. (1990a) to provide data
 2    most suitable for model development. Bioassays selected for inclusion were those that measured the
 3    mortality of early life stages, those that incorporated different combinations of pH, Al, and Ca2+, and those
 4    that used fish of varying sensitivity (Bergman et al., 1988).
 5          Acidity and Al toxicity are not the only stress factors that influence the distribution of fish in acid-
 6    sensitive streams. Other habitat characteristics, including water temperature and stream channel
 7    morphology, can be important (Sullivan et al., 2003). In addition, it is probable that some trout
 8    populations have been affected by competition with other introduced species (cf. Larson and Moore,
 9    1985).

      B.6.1.5. Amphibians
10          Some species of amphibians are considered to be highly sensitive to changes in environmental
11    conditions and some species have probably been adversely effected by acidic deposition in some areas.
12    Furthermore, several species of amphibian have exhibited marked declines in abundance throughout the
13    western U.S. in recent decades and there has been much speculation concerning the cause(s) of these
14    declines in abundance.
15          Populations of many species of amphibians have declined or become eradicated throughout the
16    world in recent decades (Barinaga, 1990; Wake,  1991). The causes have not been evident and some of the
17    declines have occurred in remote pristine areas. For example, in the Sierra Nevada, at least two of five
18    species of aquatic-breeding amphibians, Rana muscosa (mountain yellow-legged frog) and Bufo canorus
19    (Yosemite toad) have been declining (Phillips, 1990). A number of hypotheses have been proposed for
20    amphibian decline, including acidic deposition. In the western U.S., however, acidic deposition has been
21    discounted as the primary cause of the decline of R. muscosa and B. canorus in the  Sierra Nevada and of
22    R. pipiens and B. boreas in the Rocky Mountains (Corn et al., 1989; Bradford et al., 1992). Grant et al.
23    (2005) reported little relationship between streamwater ANC and the adjacent salamander community in
24    Shenandoah National Park.
25          In some  cases,  population fragmentation as a consequence  offish predation may be a more likely
26    cause (Bradford et al., 1993). It is generally recognized that R. muscosa was eliminated by introduced fish
27    early in the 20th century in many lakes and streams in Sequoia and Kings Canyon National Parks. The
28    amphibians have been eliminated from nearly all waters inhabited by fish, presumably by predation on
29    tadpoles. Prior to 1870, virtually all of the high-elevation (>2500  m) lakes in the Sierra Nevada were
30    barren offish, but have since been stocked with fish. Fish introductions may have contributed to recent
31    amphibian declines because  amphibian populations are now more isolated from each other than formerly.
32    The role of atmospheric deposition as an additional stressor is not clear.
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 1          The acidification sensitivity of temporary ponds, where many amphibians live or reproduce, have
 2    not been well studied. These ponds tend to fill directly from rain or snowmelt and thus can be more acidic
 3    than surrounding lakes and streams. There is a correlation between pond acidity and amphibian
 4    abundance.
 5          There are both acid-sensitive and acid-tolerant amphibians. Examples of acid-sensitive amphibians
 6    include the spotted salamander (Ambystoma maculatum) and Jefferson salamander (Ambystoma
 1    jeffersonianum). Embryos of acid-sensitive species are killed by water with pH less than about 4.5. Acid-
 8    tolerant embryos may survive  at a pH of 3.7. Toxicity is not solely a matter of pH, but is also influenced
 9    by Ca2+, Al;, and DOC concentrations. It is also dependent on the life stages present and water
10    temperature (Baker et al., 1990a). Large-scale amphibian extinctions in any geographic region due to
11    acidic deposition have not been detected.
12          Although acidic deposition may play a role in some areas, there is no evidence to suggest that it is a
13    primary factor. Other issues, including fish introductions, are probably more important as stressors on
14    amphibian populations across  broad regional to national scales.

      B.6.1.6. Fish-Eating Birds
15          Relative to other trophic groups, there are few studies assessing acidification effects on fish-eating
16    birds. Limited data suggest that fish-eating birds are adversely affected by acidification. Acidification
17    effects on birds may be indirect, related to changes in the quantity and quality of food.  Other potential
18    causal pathways include delayed egg laying, lighter/thinner egg shells, and reduced chick growth in acidic
19    waters (Tyler and Ormerod, 1992). There is also concern about increased metal and Hg concentrations in
20    fish-eating birds associated with bioaccumulation from contaminated fish in known areas of acidification
21    (Baker etal.,1990a).
22          Fish-eating birds can serve as biological indicators of lakes affected by acidic deposition (McNicol,
23    2002). Lack of prey resources, decreased food quality, and elevated lake water methylmercury (MeHg)
24    concentrations that could be associated with acidification may negatively effect foraging, breeding, and/or
25    reproduction for the common loon (Gavia immef), common merganser (Mergus merganser), belted
26    kingfisher (Ceryle alcyori), osprey (Pandion haliatus), American black duck (Anas rubripes), ring-necked
27    duck (Aythya collaris), eastern kingbird (Tyrannus tyrannus), and tree swallow (Tachycineta bicolor)
28    (Table B-31) (Longcore and Gill, 1993). Breeding distribution for the common goldeneye (Bucephala
29    clangula), an insectivorous bird, may be positively effected by acidic deposition (Longcore and Gill,
30    1993). Reduced prey diversity and quantity have been observed to  create feeding problems for nesting
31    pairs of loons on low-pH lakes in the Adirondacks (Parker, 1988).
32          Since the mid 1980s, a statistically significant increase in fish-eating birds  has been observed in the
33    Sudbury region of Ontario, Canada, which has corresponded with a decreasing abundance of common

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 1    goldeneye (McNicol, 2002). This interaction has been attributed to an increase in prey for pisciverous
 2    birds and a decrease in available prey for insectivorous birds as a result of stricter S emissions controls in
 3    the U.S. and Canada (McNicol, 2002). Logistic regression modeling with measured pH and species
 4    occurrence data for acid-sensitive lakes in the Algoma region of Ontario showed that the occurrence of
 5    fish, common loons, and common mergansers is positively related to lake water pH (McNicol, 2002).
 6    Predictions of common loon and merganser recovery for this area were made using the Waterfowl
 7    Acidification Response Modeling System (WARMS) under varying S emissions control scenarios
 8    targeted for 2010 (McNicol, 2002). The modeled emissions scenarios include:
 9          "SI: sulfate emissions equal to those in the early 1980's (base case)

10          •   S2: sulfate emissions equal to that in 1994 (full Canadian emissions reductions based on the
11              1991 Canada/U.S. Air Quality Agreement)

12          •   S3: expected sulfate emissions in 2010 (full implementation of U.S. emissions reductions
13              based on the 1991 agreement)

14          •   S4: a hypothetical 50% reduction in expected 2010 sulfate emissions

15          •   S5: a hypothetical 75% reduction in expected 2010 sulfate emissions

16          The number of lakes projected to be suitable for supporting breeding pairs and broods increased
17    with lake pH and stricter emissions controls (Table B-32) (McNicol, 2002).
18          Marginal improvements to fish-eating bird habitat were predicted to occur by 2010 (S3), with more
19    significant improvements expected under hypothetical S emissions reductions of 50% and 75% (S4 and
20    S5) for lakes with pH below 6.5 (McNicol, 2002). Fundamental to the predicted improvement of these
21    fish-eating bird populations is the expected increase in food availability with lake pH recovery.
22          Elevated MeHg accumulation in fish-eating birds in Wisconsin and the northeastern U.S. has been
23    linked to lake acidification (Meyer et al., 1995; Hrabik and Watras, 2002; Evers et al., 2007). This form of
24    Hg is toxic, bioavailable, and accumulates in top predators to levels of concern for both human health and
25    the environment (Table B-33) (Evers et al., 2007).
26          Acidic deposition might contribute to Hg toxicity in fish-eating birds because SC>42 addition to
27    wetland environments could stimulate the production of MeHg, thereby increasing lake water
28    concentrations of MeHg (Jeremiason et al., 2006). Kramar et al. (2005) determined that the extent of
29    wetland located in close proximity (less than 150 m) to loon territory was positively correlated with Hg
30    concentrations in loon blood. Wetland MeHg production is discussed in greater detail in Section 6.3.
31          Accumulation of MeHg in fish-eating birds can result in damage to nervous, excretory, and
32    reproductive systems (Wolfe et al., 1998). Table B-34 (Wolfe et al.,  1998) lists several studies indicating

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1    effects related to mercury bioaccumulation in avian eggs and tissues. Reproduction is considered one of
2    the most sensitive endpoints to chronic low-level MeHg exposure for fish-eating birds (Wolfe et al.,
3    1998). Reduced clutch size, increased number of eggs laid outside the nest, eggshell thinning, and
4    increased embryo mortality have all been documented (Wolfe et al., 1998).
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Table B-1. N-saturated forests in North America,  including estimated N  inputs and outputs.
Location
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
Forest Type
Northern hardwoods or
hardwood/conifer mix
Mainly hardwood; some
eastern hemlock
Sugar maple and yellow
birch
Red spruce
Mixed hardwood
American beech
Red spruce
Elevation (m)
396-661
335-675
350-400
1,650
735-870
1,600
1,800
N Input
(kg/ha/yr)
9.3'
10.2'
7.0-7.7
(as throughfall)
32C
15-20
3.1d
10.3d
N Output
(kg/ha/yr)
Stage 1 N lossb
Stage 1 and 2 N
lossb
17.9-23.6
47C
6.1
2.9
19.2
Reference
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)
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
Red spruce
Alpine tundra, subalpine
conifer

Chaparral and grasslands
Mixed conifer
                            Forest Type
                            Western coniferous
Red alder
   1,740



3,000-4,000


 580-1,080


   1,600



  Elevation

    (m)

    NA


    220
     26.6



    7.5-8.0


     23.3e


      30



    N Input

   (kg/ha/yr)

Mainly geologicg
                                                 4.7 plus > 100 as
                                                     N, fixation
   20.3        Johnson et al. (1991c)
   7.5        Williams et al. (1996)
0.04-19.4     Riggan et al. (1985)
                                                                        7-26
                                                                                    Fenn et al. (1996)
                                                                       N Output      Reference
                                                                      (kg/ha/yr)

                                                                         NAg        Dahlgren (1994)
                                                                         38.9
                                                     Johnson and Lindberg
                                                     (1992)
' 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 high-elevation 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.
' 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 long-term average. N output includes N export in stream water and to groundwater.
9 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).
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Table B-2. Summary of measured ANC, pH, and Al concentrations compared with reference values
in the six high-interest areas.
Area
ADIRONDACKS
Southwest lakes
Other lakes
NEW ENGLAND
Seaboard Lowlands lakes
Highland lakes
MID-ATLANTIC HIGHLANDS
Forested lakes
Other lakes
Forested streams
Other streams
ATLANTIC COASTAL PLAIN
Northeast lakes
Pine Barrens streams
Other streams
FLORIDA
Northern Highland lakes
Northern Highland streams
EASTERN UPPER MIDWEST
Low silica lakes
High silica lakes
n*

52
84

94
354

91
52
78
69

22
12
31

32
18

155
125
N*

450
707

848
3,574

433
791
11,631
10,172

187
675
7,452

522
669

1,254
1,673
Percent of Population with
ANC > 0

38
0

8
2

10
0
12
0

11
56
10

63
28

16
3
pH > 5.5

51
3

11
5

9
0
17
2

15
92
24

53
55

19
4
All > 100 ug/L

36
0

0
2

1
0
8
0

7
56
15

10
0

1
2
* n = sample size, N = estimated number of lakes or upstream reach ends in population.
Source: Baker et al. (1990b).
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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.
Source of Data and Region
No. of Sites1
Size of Population2
Percent Acidic in 1980s3

Statistical Surveys
New England Lakes4
Adirondack Lakes4
Appalachian Plateau Streams
Sensitive Surface Waters
New England Lakes
Adirondack Lakes
Northern Appalachian Streams
Upper Midwest Lakes
Ridge/Blue Ridge Streams

30
43
31
24
48
9
38
69

4,327 lakes
1,290 lakes
72,000 stream miles
N.A.
N.A.
N.A.
N.A.
N.A.

5%
14%
6%
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 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.
Results of Regional Survey
Region
New England
Adirondacks.
No.
Appalachians
Ridge/Blue
Ridge
Upper
Midwest
Population
Size
6,834 lakes
1830 lakes
42,426 km
32,687 km
8,574 lakes
Number
Acidic1
386 lakes
238 lakes
5014 km
1634 km
251 lakes
o/o
Acidic2
5.6%
13.0%
11.8%
5.0%
2.9%
Time Period
of Estimate
1991-94
1991-94
1993-94
1987
1984
Rate of ANC
change3
+0.3
+0.8
+0.7
-0.0
+ 1.0
Results of Monitoring during 1990s
Estimated
Number Acidic in
2000
374 lakes
149 lakes
3600 km
1634 km
80 lakes
°/o Acidic
in 2000
5.5%
8.1%
8.5%
5.0%
0.9%
°/o Change in
Number of Acidic
Systems
-2%
-38%
-28%
0%
-68%
Source: Stoddard et al. (2003)
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Table B-5. Regional trend results for long-term monitoring sites for the period 1990 through 2000.
Region
New England Lakes
Adirondack Lakes
Appalachian Streams
Upper Midwest Lakes
Ridge/Blue Ridge
Streams
so42-
(Meq/L/yr)
-1.77**
-2.26**
-2.27*
-3.36**
+0.29**
NO3~
(Meq/L/yr)
+0.01ns
-0.47**
-1.37**
+0.02ns
-0.07**
Base Cations
[Ca2+ + Mg2+]
(Meq/L/yr)
-1.48**
-2.29**
-3.40**
-1.42**
-0.01ns
Gran ANC
(Meq/L/yr)
+O.llns
+ 1.03**
+0.79*
+ 1.07**
-0.07ns
Hydrogen
(Meq/L/yr)
-0.01ns
-0.19**
-0.08*
-0.01*
+0.01ns
DOC
(mg/L/yr)
+0.03*
+0.06**
+0.03ns
+0.06**
NA
Aluminum
(ug/L/yr)
+0.09ns
-1.12**
+0.56ns
-0.06ns
NA
ns regional trend not significant (p > 0.05)
* p < 0.05
** p < 0.01
NA insufficient data
Note: Values are median slopes for the group of sites in each region.
Source: Stoddard et al. (2003).
Table B-6. Slopes of trends in Gran ANC in acidic, low ANC and moderate ANC lakes and streams
for the period 1990-2000.
ANC Class
Acidic (ANC < 0 ueq/L)
Low ANC (0 < ANC < 25 ueq/L)
Moderate ANC (25 < ANC < 200 ueq/L)
Number of Sites
26
51
43
Change in Gran ANC (Meq/L/yr)
+ 1.29**
+0.84**
+0.32 ns
ns trend not significant (p > 0.05)
** p < 0.01
Note: Analysis includes all sites in New England, Adirondacks, Appalachian Plateau, and Upper Midwest; Ridge and Blue Ridge sites excluded.
Source: Stoddard et al. (2003)
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Table B-7. Changes in key chemical characteristics during periods of record in aquatic systems in
Maine.


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 |jeq/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 ANCa
-7
-1
-2
-1
-8
7
-6
-12
ANC
0
1
-2
-1
-2
7
-4
-4
DOCb
-2
1
1
0
4
4
1
2
* Calculated ANC = [change in base cations] minus [change in (sulfate + nitrate)]
B DOC (ueq/L) = DOC in mg/l * 4 (e.g., Kahl et al., 1999).
Source: Kahl et al. (1999)
Table B-8. Projected changes (peq/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
A Sulfate
A Nitrate
A SBC
AANC

A2 STRATEGY'
Virginia and West Virginia
Blue Ridge
Valley and Ridge
Appalachian Plateau
North Carolina, Tennessee, South Carolina,
Blue Ridge
Appalachian Plateau
Virginia and West Virginia
Blue Ridge
Valley and Ridge
Appalachian Plateau
North Carolina, Tennessee, South Carolina,
Blue Ridge
Appalachian Plateau
B3 STRATEGY'
Virginia and West Virginia
Blue Ridge
Valley and Ridge
Appalachian Plateau


16
41
34
Georgia, and Alabama
33
6

16
41
34
Georgia, and Alabama
33
6

16
41
34


1.8
-0.45
-31.2

8.8
15.3

-2.7
-5.6
-36.3

5.6
11.6

-7.4
-13.8
-40.4


0.03
0.02
-3.5

0.15
0.15

-0.04
-0.37
-4.9

-0.48
-0.23

-0.09
-0.36
-5.8


-2.2
-6.8
-33.8

1.0
-1.2

-3.0
-8.2
-38.4

-0.60
-1.9

-5.1
-10.3
-39.3


-4.0
-6.6
-4.4

-8.0
-15.9

-1.0
-4.7
-1.4

-5.4
-13.3

2.9
-0.83
2.6
North Carolina, Tennessee, South Carolina, Georgia, and Alabama
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Physiographic Province                      Number of Sites          A Sulfate        A Nitrate       A SBC      AANC

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 strategies were based on existing regulations (A2), moderate additional controls (Bl), and more aggressive additional controls (B3)
Source: Sullivan et al. (2004)
Table B-9. Population estimates of water chemistry percentiles for selected lake populations in the
western U.S.3
Population
Sierra Nevada
Cascades
Idaho Batholith
NW Wyoming
Colorado Rockies
n
114
146
88
38
121
N
2,119
1,473
937
648
1,173
PH
PI
5.84
5.95
6.34
6.56
6.02
P5
6.31
6.25
6.42
6.56
6.65
ANC
(ueq/L)
PI
15
11
21
38
25
P5
16
18
33
38
42
SBC
(ueq/L)
PI
21
20
30
64
58
P5
26
31
45
66
80
S042-
(ueq/L)
P95
90
60
30
41
915
P99
386
97
43
2,909
2,212
NO3~
(ueq/L)
P95
8
3
3
13
10
P99
10
6
4
32
13
DOC
(mg/L)
P50
0.8
1.3
1.2
1.0
1.3
P99
2.7
2.6
2.4
4.8
5.7
"Data from Landers et al. (1987).
bExcluding Fern Lake (4D3-017) which is naturally acidic
Note: The 1st and 5th percentiles (PI, P5) are presented for pH, ANC (ueq/L), and SBC (ueq/L) and the 95th and 99th (P95, P99) percentiles are
shown for SO42- (ueq/L) and NO3~ (ueq/L). The median (P50) and 90th percentiles are shown for DOC (mg/L).
Table B-10. Population estimates of the percentage of lakes in selected subregions of the West with
ANC and N03~ within defined ranges.
ANC (ueq/L)

Sierra Nevada
Cascades
Idaho Batholith
NY Wyominga
Colorado Rockies
<0
0
0
0
0
0
<25
8.7
10.2
2.0
2.3
0.9
<50
39.3
22.4
23.6
12.8
5.5
>5
10.6
1.5
4.6
22.8
9.8
NC-3- (ueq/L)
>10
1.5
0.0
3.9
8.9
1.8
a Excluding Fern Lake (4D3-017) which is a naturally acidic lake
Source: Landers et al. (1987)
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Table B-11. Median streamwater ANC and watershed area of streams in Shenandoah National Park
that have water chemistry and fish species richness data.
Site ID
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
Watershed Area (km2)

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
Median ANC (ueq/L)

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
Number of
Fish Species

2
N.D."
3
2
1

3
5
7
3
6
7
9
6
8
' No data were available regarding the number offish species in Deep Run
Source: Sullivan (2003)
Table B-12. Reference levels for the Acidic Stress Index (ASI) based on logistic regression offish
presence as a function of the sensitive intermediate and tolerant ASI values for brown bullhead,
brook trout, lake trout, and common shiner.
Reference Acid Stress Index
                                                                  Fish Response
Lakes
Tolerant ASI > 30
Tolerant ASI > 10
Intermediate ASI > 80
Sensitive ASI > 80
Streams
Intermediate ASI > 30
Sensitive ASI > 30

Sensitive ASI > 10
Loss of all fish species
Loss of brook trout
Loss of other sport fish, such as smallmouth bass and lake trout
Loss of acid-sensitive species, such as minnows.
Source: Baker et al. (1990a)
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Table B-13. General summary of biological changes anticipated with surface water acidification,
expressed as a decrease in surface water pH.
PH
Decrease
                                                        General Biological Effects
6.0 to 5.5
5.5 to 5.0
6.5 to 6.0    Small decrease in species richness of plankton and benthic invertebrate communities resulting from the loss of a few highly acid-
            sensitive species, but no measurable change in total community abundance or production.

            Some adverse effects (decreased reproductive success) may occur for highly acid-sensitive 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  near-shore 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 near-shore 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 whole-system 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 acid-sensitive  species of  amphibians, such as spotted salamanders, Jefferson salamanders, and the leopard
            frog.

Source: Baker et al. (1990a)
5.0 to 4.5
5.0 to 4.5
(confd)
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 (pre-industrial)
chemistry and present-day measured and inferred acid-base chemistry.
DDRP Target Population
ASI Reference Level

Tolerant ASI > 30
Tolerant ASI > 10
Intermediate ASI > 80
Sensitive ASI > 80
Diatom Inferred
Historical
0.0
0.0
7.3
28.5
Current
3.6
9.1
21.8
41.2
Net Change
+3.6
+9.1
+ 14.5
+ 12.7
Measured

1.8
10.9
21.8
32.7
ELS/NSWS Target Population Measured

2.2
6.5
15.2
20.0
Source: Baker et al. (1990a)
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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.
Fish Species
Model9

Brook Trout
Bayesian
LAP framework
PH
pCa/pH
pCa/pH, AI/DOC
Lake Trout
PH
pCa/pH
Inorg. Al
Common Shiner
PH
pCa/pH
DDRP Target Population11
Diatom-Inferredc
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
ELS/ NWS
Target Populal
Measured

10.1
15.8
9.3
10.3
13.9

14.4
18.9
17.1

21.3
29.1
h ALSC
tionb
Measured

21.8
24.6
22.2
23.0
23.5

30.9
-
-

42.3
-
' All models, except the brook trout Bayesian model (Section 3.5) and LAP framework (Section 3.4), are field-based acidification response models as
defined in Section 3.3.3.
b ELS/NSWS target population in Subregion 1A, defined in Section 3.1 (N = 1,290 lakes); a subset of these lakes was considered for the DDRP and
sediment diatom analyses, for example, excluding lakes with ANC > 400 ueq/L and with site depths < 1.5 m (N = 675 lakes). See Sullivan (1990) for
further details.
c Estimates of acid-base chemistry inferred from sediment diatom analysis; methods and water chemistry described in Sullivan (1990).
- Analysis not conducted.
Source: Baker et al. (1990a)
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
definedinTableC-12.
ASI Reference Level
                                  Subregion 1A
                          Northeast Region
                                 Upper Midwest Region
Tolerant ASI > 30

Tolerant ASI > 10

Intermediate ASI > 80

Sensitive ASI > 80
2.2

6.5

15.2

20.0
1.0

2.4

5.7

8.6
0.5

1.0

2.0

3.1
Source: Baker et al. (1990a)
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Table B-17. Estimated percentage of lakes in the Northeast, ELS/NSWS target populations with acid-
base chemistry unsuitable for fish population survival.
Fish Species/Model
Subregion 1A
Entire Northeast

Brook trout
Bayesian
LAP Framework
PH
pCa/pH
pCa/pH, AI/DOC
Lake trout
PH
pCa/pH
Inorganic Al
Common shiner
PH
pCa/pH

10.1
15.8
9.3
10.3
13.9

14.4
18.9
17.1

21.3
29.1

3.7
8.9
3.5
4.4
7.0

5.8
8.9
6.3

9.5
19.7
Source: Baker et al. (1990a)
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Table B-1 8. Distribution
mid-Appalachian region

of acidic

Lower Node
Sensitive ASI
> 10
10-30
30-50
50-80
>80
Intermediate ASI
> 10
10-30
30-50
50-80
>80
Tolerant ASI
> 10
10-30
30-50
50-80
>80

84.6
10.1
1.4
1.8
2.0

97.8
0.2
0.6
0.1
1.3

99.4
0.6
0.0
0.0
0.0
stress index values among the NSS-1
Number (°/o)
Upper Node

66.7
18.9
1.9
2.6
9.8

89.3
2.5
1.3
1.4
5.4

97.1
1.4
0.6
0.9
0.0
Target populations for the
Total Length (°/o)

76.1
14.0
1.9
1.6
6.4

88.9
0.7
0.4
0.4
1.9

98.1
0.9
0.3
0.7
0.0
Source: Baker et al. (Baker, 1990)
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Table B-19. Distribution of acidic stress index values among the NSS-1 target populations for the
interior Southeast region.


Sensitive ASI
> 10
10-30
30-50
50-80
>80
Intermediate ASI
> 10
10-30
30-50
50-80
>80
Tolerant ASI
> 10
10-30
30-50
50-80
>80

Lower Node
79.4
18.8
0.0
1.7
0.0
100.0
0.0
0.0
0.0
0.0
100.0
0.0
0.0
0.0
0.0
Number (°/o)
Upper Node
70.1
21.1
1.7
5.2
1.7
98.3
0.0
0.0
0.0
1.7
100.0
0.0
0.0
0.0
0.0
Total Length (°/o)

75.9
18.8
2.0
2.5
0.7
99.3
0.0
0.0
0.0
0.7
100.0
0.0
0.0
0.0
0.0
Source: Baker et al. (Baker, 1990)
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. In many cases, adverse
impacts were observed at the lowest Al treatment level. Hence, the actual threshold Ca/AI ratio may
be higher than that reported. Results are from a variety of studies reported in the literature.
Study
           Solution  soluKon  Fo|jarCa  Fo|jarA|  ^
                                                                                           Al
  Al
(umol/L)    (M)
  RootCa   RootAI  g*  Type of Study  Response  Ana|ysjs
                          (mg/km)  (mg/km)
  (mg/km)  (mg/kg)
                                                                     Experiment3
Variable11   Used in
          Ratio0
Norway spruce
Godbold et al. 100
(1988)
Matzneretal. 100+
(1989)
Stienen and 1500
Bauch (1988)
Schroder et 2000
al. (1988)

1.3
0.3 to 1.8
0.66 1470 32
1

H
F
31 770 1890 0.28 H
H

N
N
N,B
N

All
Alt
Alt
AN
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Solution soiution Fo|jarCa Fo|jarA| Foliar
Study Al Ca/AI Ca/AI
(umol/L) (M) (m«"km> (m«"km> (M)
Red spruce
Thornton et 250 1 1100 65 11.4
al. (1987)
Hutchinson et 185 2.2
al. (1986)
Joslin and 200 nd ~3000
Wolfe (1988)
Schier (1985) 1850 1.35 12.9
Ohnoetal. 250 0.8 14
(1988)
Joslin and 0.45
Wolfe (1992)
White spruce
Noskoetal. 50 0.2
(1988)
Red oak
Joslin and 300 4.05 11.9
Wolfe (1989)
DeWald et al. 115 4.48 3630 75 32.4
(1990)
McCormick 7405 0.54
and Steiner
(1978)
Honeylocust
Thornton et 50 1.1 8.4
al. (1986b,c)
Sucoffetal. 100 1.4
(1990)
Wolfe and 100 4.3
Joslin (1989)
Sugar maple
Thornton et 100-600 0.42 to ~2000 ~190 9.9
al. (1986a) 2.5
Loblolly pine
Cronan et al. 500-3000 0.5 900 260 2.3
(1989)
Thornton
(unpubl.)
American beech
Cronan etal. 500-3000 0.5 2670 69 26.1
(1989)
European beech
Asp and 300 0.35 3.8
Berggren
(1990)
Cronan et al. 500 0.5
(1989)
Peach
Edwards and 222 10.8
Morton (1977)
Scotch pine
RootCa RootAI g* Type of Study Response Ana*j,sjs
(mg/km) (mg/kg) (M) Experiment* Variableb Usedjn
650 6000 0.07 H B AN
S B Alt
~2000 S B AN
0.43 H B,N All
S N Ala in soil
paste
F B Alt
H B Alt
0.06 S B Alt in SrCI2
3630 6415 0.38 S B Alt
H B Alt
0.21 to H B AN
0.32
0.35 S B AN
0.71 S B AN
~1500 ~2700 0.4 H B,N AN
3700 7770 0.32 H N AN
1140 7930 0.1 H N AN
0.2 H N AN
H N AN
0.008 H N AN
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Study
Solution   So,u/t;on   ^^   Fo|jarA|   ^.iar  Root Cg    RootA|   Root   Type ofstudy  Respo

(umol/L)     (M)     (m9/km)  (mg/km)   (M)    (mg/km)  (mg/kg)   (M)     c	:	«   Variable"
                                                                                                  Al
                                                                                               Analysis
                                                                                                Used in
                                                                                                Ratio0
Ilvesniemi      185       nd
(1992)

McCormick     2960      1.35
and Steiner
(1978)

Virginia pine

McCormick     2960      1.35
and Steiner
(1978)

Pitch pine

McCormick     2960      1.35
and Steiner
(1978)

Cumming and  50        20
Weinstein
(1990)

Birches: gray, paper and yellow

McCormick     4444      0.9
and Steiner
(1978)

European birch
                                   400        300
                                                        0.9
Goransson     1000
and Eldhuset
(1987)

Radiata pine

Truman et al.   17
(1986)

Douglas-fir

Keltjens and    370
Van Loenen
(1989)

Larch

Keltjens and    555
Van Loenen
(1989)
                        0.02
10.5
0.54
0.36
                                                  2300             S


                                                                   H
          2120       800
          2300       300
          1800       250
                                                                                    0.17     H
                                1.8     1320       1850      0.48    H
                               5.2
                               4.9
                                                                                             N,B


                                                                                             B
                                                                                   N,B
                                                                                                                       Alt
                                                                                              Alt
                                                                                              Alt
                                                                                              Alt
                                                                                              Alt
                                                                                              Alt
                                                                                                                       AN
                                                                                              Alt
                                                                                              Alt
                                                                                              Alt
' 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 AN (All), 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, AN, and labile Al are very comparable.
Source: Cronan and Grigal (Cronan, 1995)
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Table B-21. Overview of selected major processes by which landscape change can alter drainage
water acid-base chemistry

Landscape Change                                               Effect on Acid-Base Chemistry
Logging, blowdown                   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
Road building and construction          More base cation neutralization, less acidity initially
                                  Depletion of base cation reserves in soils, more acidity long-term
Drainage of wetlands                  Re-oxidation of stored S, pulses of acidity with increased discharge
Drought                            Reduced groundwater inputs to seepage lakes with consequent increased acidity
                                  Increased relative baseflow to drainage waters with consequent decreased acidity
Lake shore development               Decreased acidity
Insect damage                       Pulse of NO3- acidity initially
Source: Sullivan (2000)
Table B-22. Observed relationships between zooplankton species richness (R) and lakewater ANC.

Taxonomic Group                                             Equation                         r2            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 B-23. Threshold response of increased mortality of fish to low pH listed from least sensitive to
most sensitive.
Study
Johnson et al. (1987)
Holtze and Hutchinson
(1989)

Johansson et al. (1977)


Swenson et al. (1989)

Mills et al. (1987)



Species
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
Increased Mortality
Threshold, pH
5.9-6.0
4.8- 5.1
5.4-6.0
5.1- 5.2
4.8
5.0
4.5- 5.0
4.5
5.5
5.0
4.5
5.9
5.6- 5.9
5.6
5.1
5.0- 5.1
Study Conditions
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.

Whole-lake treatment (fish population recruitment
failure)



Source: Baker et al. (1990a)
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Table B-24. Threshold values of pH for various taxa and effects.
Reference
Buckler et al. (1987)
McCormicketal. (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 Martin-Robichaud
(1986)
Powell and McKeown (1986)
Type of Study
Lab bioassay
Lab bioassay
Whole-lake
experiment
Lab bioassay
Lab bioassay
Lab bioassay
Lab bioassay
Whole-Lake
experiment
Field survey
Lab bioassay
Lab bioassay
Lab bioassay
Lab bioassay
Lab bioassay
Lab experiment
Lab bioassay
Taxa
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
PH
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
Observed Effects
>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 B-25. Threshold values of Al for various species and effects (form of A not specified for most
studies).
Reference
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 etal. (1987)
Type of Study
LB
Field survey
LB
Field mesocosm
experiment
LB
LB
LB
LB
LB
Field study
Whole-stream
experiment
Taxa
Brown trout
Broen trout
Walleye
Atlantic salmon
Blueback herring
Atlantic salmon
White sucker
Eel
Brown trout
Brook trout
Atlantic salmon and
brown trout
PH
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
Al
(ug/L)
30
40
50
75
100
130
200
230
250
286
350
Observed Effect (at similar pH without added Al)
Significant reduction in fish growth
Fish absent or rare in streams in Wales and England
>50% mortality of embryos to 4— d post-hatch
>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 Al
(even after accounting for pH effects).
>50% mortality of young-of-the-year.
Source: Baker et al. (1990a)
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Table B-26. The effects of increasing Ca2+ to ameliorate low pH and high Al.
Reference
Brown (1982, 1983)
Wright and Snekvik
(1978)
Brown (1983)
McDonald (1983)
Edwards etal.
(1987)
Freda and McDonald
(1988)
Mount etal. (1988)
Sadler and Lynam
(Sadler, 1988)
Type of
Study
LB
Field
survey
LB
LB
LB
LB
LB
LB
Taxa
Brown trout
Brown trout
Brown trout
Rainbow trout
Brown trout
Common shiner,
rainbow trout
Brook trout
Brown trout
pH (ug/L)
4.5- —
5.1
4.5- —
7.5
4.5- 0-500
5.4
4.3 —
4.2 —
4.0 —
5.0- 0-500
6.3
5.2 0-80
Ca2+ Range
(ueq/L)
12-400
20-200
12-100
69-223
100-5600
70-1000
25-400
8-800
Observed Response to Increasing Ca2+
Increased embryo survival and hatch
Trouot status significantly correlated with log Ca2+ 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 B-27. Brook trout acidification response categories developed by Bulger et al. (XXXXXXXXXX)
for streams in Virginia (2000).
Response Category
Chronic ANC Range (ueq/L)
                      Expected Response
Suitable
Indeterminate
Episodically acidic
Chronically acidic
          >50
        20 to 50
         Oto20
           <0
Reproducing brook trout expected if other habitat features are also suitable
Brook trout response expected to be variable
Sub-lethal and/or lethal effects on brook trout are possible
Lethal effects on brook trout probable
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Table B-28. Partial listing of bioassays demonstrating
and (or) elevated aluminum.
Reference
Johansson and Kihlstrbm (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)

Species
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
decreased fish survival in waters with low pH
Life Stage
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 young-of-year



Egg and fry
Juvenile
Egg
Juvenile

Egg and fry
Egg and fry





Egg and fry

Lab/Field
Lab
Lab, field

Lab

Lab
Lab
Lab
Lab

Lab
Field

Field



Lab
Field
Lab
Lab
Field



Lab
Lab
Field
Lab

Lab, field
Lab





Lab

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Table B-29. Mills et al., 1987. Shows effect of various pH on fish forage fish and lake trout.
Biota
Forage
fish
Lake
trout
1977
pH6.13

Increase in
condition
i.e., "fatter"
1978
pH 5.93
Fathead
minnow
experience
recruitment
failure


1979
pH 5.64
Fathead minnow
near extinction;
slimy sculpin
decline in
abundance
Increased
abundance of
young-of-the-year
1980
pH 5.59
Increase in
abundance of pearl
dace, Suckers very
abundant.
Lake trout
recruitment failure;
condition similar to
preacidification
1981
pH 5.02
White sucker
recruitment failure;
no effect on adult
growth and survival
Recruitment failure;
no effect on adult
growth and survival
1982
pH 5.09
Recruitment failure
for all species
Lake trout condition
poor; recruitment
failure; reduced
adult survival
1983
pH 5.13
Recruitment failure
for all species
Lake trout condition
very poor;
recruitment failure;
reduced adult
survival
Source: (Baker, 1990)
Table B-30. Range of minimum pH offish 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
Salmonidae
Brook trout
Lake trout
Brown trout
Atlantic salmon
Centrarchidae
Smallmouth bass
Largemouth bass
Pumpkinseed
Bluegill
Rock bass
Black crappie
Percidae
Yellow perch
Walleye

6.6
6.3
6.8
5.9
5.9
6.2
5.5
5.9
5.6
5.2
5.0
6.3
7.0
5.0
6.6
4.5
6.2
5.6

5.8
6.9

5.6
5.1
5.6
4.7
4.7
4.9
4.5
4.6
4.6
4.9
4.6
5.3
4.9
4.6
4.6
4.5
4.6
5.6

4.4
5.2
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Family and Species
Johnny darter
Iowa darter
Esocidae
Northern pike
Catastomidae
White sucker
Ictaluridae
Brown bullhead
Umbridae
Central mudminnow
Gasterosteidae
Brook stickleback
High Minimum pH
6.2
6.2
5.9
5.5
5.6
4.5
5.4
Low Minimum pH
4.9
4.6
4.0
4.6
4.5
4.2
4.6
                                                Source: Baker et al. (1990a)
Table B-31. Studies3 that either did (yes) or did not (no) yield evidence that acidic deposition
affected certain species of birds
Species
Common loon
Common merganser
Belted kingfisher
Osprey
Black duck
Common goldeneye
Ring-necked duck
Eurasian dipper
Eastern kingbird
Tree swallow
Diet/ Foraging
Yes No
X


X
X

X
X

X
Breeding Distribution
Yes No
X X
X
X
X
X
xb

X
X
X
Reproductive Measures
Yes No
X X
X

X
xb

X
X
X
X
Reference9
1-3, 19,20
19
4
5,6
7-9
8
10,11
12-14
15
16-18
"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).
'The effect was beneficial
Source: Longcore and Gill (1993)
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Table B-32. Predicted habitat suitability for lakes in the Algona Model Dataset
Group
Total Model     Current
   Lakes    Suitable Lakes
      Number of Lakes with Suitable Habitat Under Each Emission Scenario

    Current pH < 6           Current pH 6—6.5            Current pH > 6.5

SI   S2  S3   S4   S5   SI   S2   S3   S4    S5   SI    S2   S3   S4   S5
Fish
Common loon
pairs
Common loon
broods
Common
merganser pairs
Common
merganser broods
526
433
433
433
433
338
100
36
52
31
29
12
2
6
6
34
14
2
12
10
40
14
2
27
18
77
16
2
68
69
100
17
2
86
89
97
22
6
14
14
100
22
6
17
15
107
23
7
20
16
124
24
7
33
21
133
24
7
44
29
196
66
28
31
12
196
66
28
31
11
197
66
28
32
10
197
67
28
34
10
197
67
28
35
10
Results are expressed as the number of lakes with suitable habitat for fish, common loons (pairs and broods) and common mergansers (pairs and
broods) under each emission scenario (SI, S2, S3, S4, S5) according to current pH classes (<6, 6-6.5, >6.5). Habitat suitability is calculated by
probability of presence at time t from WARMS output (# of suitable lakes at time t/total number of lakes), for fish [n = 526], and for loons and
mergansers [n = 433].
Table B-33. Summary statistics of biological data layers for mercury (Hg) concentrations in fish and
wildlife (pg/g) in the northeastern U.S. and southeastern Canada.
Hg Concentrations
Category/Species



Human health
Yellow perch'
Largemouth bassb
Ecological health
Brook trout
Yellow perchc
Common loone
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 fresh-weight basis
"Fillet Hg in yellow perch is based on individuals with a standardized length of 20 cm.
bFillet Hg in largemouth bass is based on individuals with a standardized length of 36 cm.
cWhole-fish Hg in yellow perch is based on individuals with a standardized length of 13 cm. Whole-fish Hg for yellow perch was converted to fillet Hg.
'The sample population of 841 yellow perch examined for whole-fish Hg is included with the 4089 fillets (i.e., the total number of all biotic data layers
does not double-count yellow perch).
eEgg Hg for the common loon was converted to the adult blood equivalent
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Table B-34. Mercury
Tissue
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
Concen. (ppm)
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
concentrations in avian eggs and tissues and related effects.
Wet (w)
or Dry (d)
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
Endpoint
No effect
Abnormal feather loss in juveniles
Conservative threshold for major toxic effects
Increased disease and emaciation
Nesting success
Hatching success
Neurologic effects
Death
LD33"
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
Species
Common tern
Common tern
Water birds
Common tern
Common tern
Common tern
Osprey
Common loon
European starling
Gannet
European starling
Red-winged blackbird
Black-footed
albatross
Black duck
Zebra finch
Merlin
Pheasant
Common loon
Common tern
Variety of water birds
Herring gull
Common tern
Black-footed
albatross
Crackle
Red-winged blackbird
European starling
Reference
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)
     "LD33 = lethal dose, 33%
     Source: Wolfe et al. (1998)
1
2
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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
 1        Some N fertilization experiments suggest that increasing N deposition drives an increase in
 2   production of dissolved organic nitrogen (DON) in soil (e.g., Seely and Lajtha, 1997; McDowell et al.,
 3   2004), but there is little evidence that elevated N deposition increases the export and loss of DON from
 4   terrestrial ecosystems.  Essentially all of the increase in N export across gradients of N deposition occurs
 5   as an increase in NO3 rather than DON export. The latter is typically less than 2 kg N/ha/yr from most
 6   northeastern-forested watersheds (Campbell et al., 2000; Goodale et al., 2000; Lovett et al., 2000; Aber
 7   etal., 2003).

     C.1.1.2. Interactions between snow melt and nitrate leaching
 8        Changes in other environmental parameters can also be important. Measurement of nutrient
 9   concentrations in Emerald Lake (Sierra Nevadas) over a period of 19 years suggested that NO3
10   concentration declined between 1983 and 1995. This was likely caused by changes in the snow regime
11   induced by a drought during the period 1987 to 1992 (Sickman et al., 2003a). Years that had shallow and
12   early melting snowpacks generally had lower snowmelt NO3 concentration. In addition, declines in
13   NO3  concentration during the growing season even in the wet years of 1993 through 2000 were likely the
14   result of increased P loading to Emerald Lake and the consequent release of phytoplankton from P
15   limitation (Sickman et al., 2003a).

     C.1.1.3. Denitrification: NO and N20 Flux
16        Davidson et al. (2000) described N gas loss from terrestrial ecosystems using a conceptual model
17   called "hole-in-the-pipe." In this model, production of NO, N2O, and N2 gas are functions of the general
18   rate of N cycling processes through soil (i.e., the N flux "flowing through the pipe"), combined with
19   information on soil water content, a key determinant of the ratio of NO:N2O (relative "hole size" for NO

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 1    and N2O gas "leakage"). The model formulation has been supported by a range of field measurements in
 2    temperate and especially tropical ecosystems (Davidson et al., 2000), and suggests that processes that
 3    increase the rate of N cycling through soils should also increase the rate of N gas loss from these systems.
 4    Production of NO and N2O tend to be lower in temperate than in tropical ecosystems, largely because of
 5    colder temperatures and slower rates of N cycling in temperate systems, and the frequency of P rather
 6    than N limitation in tropical systems.  However, increased availability of N through fertilization can
 7    increase the rate of NO  and N2O gas loss from temperate forests.
 8          Early studies of N gas emission in response to N fertilization experiments at the Harvard Forest,
 9    MA, found small increases in N2O production in response to the highest N treatment (150 kg N/ha/yr) to a
10    red pine (Pinus resinosa) stand, but N2O losses accounted for <0.4% of N additions (Magill et al., 2000).
11    However, later studies found that NO  emission rates can be more than an order of magnitude greater than
12    N2O emissions, with NO emissions amounting to 3-4% to 8% (4 to 5 kg N/ha/yr) of the N additions to the
13    fertilized pine stands (Venterea et al.,  2003, 2004).  Emissions of NO and N2O increased with fertilization
14    rate (0, 50, and 150 kg N/ha/yr) in both the red pine and a nearby red oak (Quercus rubra)/red maple
15    (Acer rubrum) stand (Venterea et al., 2003, 2004). A study of the response of Scots pine (Pinus
16    sylvestris) stands across a gradient of N deposition in Germany  found a threefold to fourfold increase in
17    the rate of NO and N2O production as N deposition increased from 15 to 22 kg N/ha/yr. In these forests,
18    both gases were produced in roughly equal amounts, although as N deposition increased, the rate of NO
19    production increased more steeply than did the rate of N2O production (Butterbach-Bahl et al., 2002a).
20          At Hoglwald, a German site receiving 20  to 30 kg N/ha/yr in throughfall, Butterbach-Bahl et al.
21    (2002b) reported higher emissions of NO than N2O in both a spruce and a beech stand, with N oxide
22    emissions totaling 4.5 to 6.8 kg N/ha/yr. Intensive laboratory studies suggested additional emissions of
23    N2 gas amounting to 7.2 and 12.4 kg N/ha/yr in the spruce and beech stands, respectively. This is the
24    only known forest site for which a complete (NO, NO2, N2O, and N2) N gas budget has been estimated,
25    and in total, these measurements suggest that soil emissions may balance 46% to 78% of the N received
26    in throughfall at this site.  This result suggests somewhat higher rates of N gas loss than might be inferred
27    from a series  of 15N tracer studies in conifer stands across Europe, which spanned a range of rates of N
28    input from atmospheric and experimental sources of 3 to 91 kg N/ha/yr. Across all sites, total recovery of
29    added 15N in soil, vegetation, and lysimeter leachate after 9 to 21 months amounted to 65% to 105% of
30    added 15N (Tietema et al., 1998), providing a broad constraint on N gas emissions of no more than 35% of
31    added 15N. The lowest rates of 15N recovery (65% to 67%) occurred at Speuld and Ysselsteyn, two sites
32    in The Netherlands with the highest rates of chronic throughfall N input (35 to 53 kg N/ha/yr). Although
33    much more work is needed on complete N gas budgets, several  lines of evidence suggest that trace gas
34    emissions of N may constitute an increasing pathway of N loss with increasing rates of N deposition.
35
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      C.1.1.4. Climate and N20 Interactions
 1          Rainfall events are an important feature controlling N2O produced via denitrification. Rainfall
 2    increases soil moisture. This inhibits oxygen diffusion creating anoxic conditions, which increases rates
 3    of denitrification.  A study of a spruce forest sites under ambient and elevated N deposition (20 and 30 kg
 4    N/ha/yr, respectively) indicated through most of the study period N2O emission was equivalent between
 5    the sites (Mohn et al., 2000).  However, after rainfall events the maximum rate of N2O emission was
 6    much higher for the +N plots, especially when rainfall caused low soil redox potential (an indicator of
 7    anoxic conditions). Another study of mixed spruce, pine and birch forest (100 yrs old) under well- and
 8    poorly drained soil moisture conditions indicated poorly drained soils produced 1/3 more N2O (118 g N2O
 9    N ha/yr) than well drained soils. In this study, N deposition was increased in the poorly drained soil from
10    ambient (12 kg N/ha/yr) to elevated (42 kg N/ha/yr), N2O emissions increased by a factor of more than 2
11    (254 kg N/ha/yr) (Klemedtsson et al.,  1997).
12          In addition to soil moisture, temperature also influences denitrification. The PnET model
13    (Photosynthesis-Evapotranspiartion-Model) is coupled with Denitrification-Decomposition (DNDC)
14    model, and an N module that are further described in Li et al. (1992, 1996, 2000), Li (2000), and Stange
15    et al. (2000) to form the PnET-N-DNDC model. The PnET-N-DNDC model is designed to simulate and
16    predict soil C and N biogeochemistry in temperate forest ecosystems and to simulate the emissions of
17    N2O and NO from forest soils. Denitrification is described in the model as a series of sequential
18    reductions driven by microorganisms using N oxides as electron acceptors under anaerobic conditions.
19    As intermediates of the processes, NO and N2O are tightly controlled by the kinetics of each step in the
20    sequential reactions. The capacity of this model to simulate N trace gas emissions from forest soils was
21    tested by comparing model results with results form field measurements at 19 different field sites across
22    Europe and 1 site in the United States  (Kesik et al., 2005). Possible feedbacks of temperature and
23    precipitationchange on forest soil NO  and N2O emissions in Europe were investigated using PnET-N-
24    DNDC (Kesik et al., 2006).  The model results indicated decreasing precipitation and increasing
25    temperature in areas with light texture soils (below 15%) resulted in decreased soil moisture values; in
26    turn, N2O production by denitrification decreases.  Under these same environmental conditions, NO
27    production by nitrification increases. Most laboratory studies show increasing temperature increases N2O
28    production, however if water filled pore space (WFPS) increases to 70-80%, then N2 rather than N2O is
29    the main product of denitrification and N2O emissions go down. This illustrates how N2O emissions
30    increase with increasing soil moisture  until soil moisture become more conducive to N2 emission.
31
32
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            C.1.2. N Cycling in Transitional  Ecosystems

      C.1.2.1. Denitrification: Measurement Techniques
 1          There are a variety of methods for measuring denitrification rates in wetland, freshwater and
 2    marine sediments, including measurements of NO3  loss, N2 production, N2O accumulation in response to
 3    acetylene inhibition of N2O reduction, isotopic methods, and N2:argon (Ar) measurement by membrane
 4    inlet mass spectrometry (MIMS) (Smith et al., 2006).  The majority of direct measurements of
 5    denitrification have measured only rates of production of N2O, or used the "acetylene block" technique of
 6    inhibiting transformation of N2O to N2 and monitoring the accumulation of N2O as a surrogate of the sum
 7    of N2O and N2. The acetylene block method is highly problematic, however, as it also inhibits rates of
 8    nitrification, and so denitrification rates are strongly underestimated where nitrification and denitrification
 9    processes are coupled closely in space or time (Groffman et al., 2006).
10          Techniques can be based on laboratory incubation of sediment cores or in situ studies.  Each
11    method has advantages and disadvantages and many studies have been conducted to compare results
12    among the various methods (e.g., Seitzinger, 1988; Seitzinger et al., 1993, 2002; Bernot et al., 2003;
13    Groffman et al., 2006; Smith et al., 2006).  Kana et al. (1998) described the MIMS method to measure
14    small changes in dissolved N2 caused by denitrification in sediments. This technique allows measurement
15    of N2 flux in unperturbed sediment cores with high temporal resolution (Kana et al., 1998). This is
16    especially useful during summer conditions when NO3 concentration in the water is typically low, but
17    the high temperature can support high rates of denitrification.  Under such conditions, it is  likely that a
18    coupled sequence of nitrification and denitrification accounts for substantial N loss from estuarine
19    sediments (Kemp et al., 1990). Constraints regarding  field and analytical methods have seriously limited
20    understanding of the magnitude and controls on denitrification (Groffman et al., 2006).
21
22

      C.1.2.2. N Deposition Effects on Methane
23          Increased N loading to transitional ecosystems can affect both methane (CH4)-producing and CHr
24    oxidizing microbial activity. The difference between the CH4 production and oxidation determines the
25    magnitude of CH4 emission from soils. There is evidence to support that ammonium compounds reduce
26    CH4 oxidation (Steudler et al., 1989; King and Schnell, 1994; Gulledge et al., 1997), but ammonium
27    compounds have also been observed to increase methanotropic bacterial activity (Bodelier et al., 2000). In
28    general CH4 emissions from saturated soils have been observed to increase with N addition (Granberg
29    et al., 2001; Saarnio et al., 2003; Zhang et al., 2007). The prevailing hypothesis for explaining this effect
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 1    is that increases in vegetative cover caused by N addition increase C availability through root exudates,
 2    which in turn stimulates methanogenic bacteria and CH4 emissions (Granberg et al., 2001; Saarnio et al.,
 3    2003; Zhang etal., 2007).
 4          Saarnio et al. (2003) observed moderate increases in QrU emissions from boreal wetland soils with
 5    N fertilization rates of 30 kg N/ha/yr as ammonium nitrate (NH4NO3).  Comparable N application rates
 6    and effects on CH4 emissions were also observed by Granberg et al. (2001) in a similar ecosystem type.
 7    Zhang et al. (2007) observed elevated Clr^ emissions from freshwater wetland soils with experimental N
 8    additions of 240 kg N/ha/yr. They postulated that additional N increased abundance ofDeyeucia
 9    angustifolia which increased Clr^ emissions by supplying methanogenic bacteria with additional substrate
10    in the form of root exudates. Other studies have shown that N addition had little or no  effect on Clr^
11    emissions across a variety of ecosystem types (Saarnio et al., 2000; Silvola et al., 2003; Ambus and
12    Robertson, 2006).  Note that the N enrichment rates employed in all of the above reported studies related
13    to N effects on soil CH4 emissions were greater (30 to 240 kg N/ha/yr) than atmospheric N inputs in most
14    areas of the United States that are heavily effected by elevated atmospheric N deposition.  See ISA
15    section 3.3 for a discussion of methane flux from terrestrial, transition and aquatic ecosystems.
16
            C.1.3. N Cycling in Estuarine Ecosystems

      C.1.3.1. Denitrification and Anammox in Estuarine Ecosystems
17          Denitrification is a major factor governing the loss of N from estuarine ecosystems. Denitrification
18    by microbes found in estuarine and marine sediments releases much of the added N inputs back into the
19    atmosphere (Vitousek et al., 1997a; Arrigo, 2005).  Collection of quantitative data on this process has
20    been hampered, however, by the complexity of environmental controls on the denitrification process and
21    difficulties in measuring denitrification rates (Kana et al., 1998). Major environmental controls include
22    temperature and the availability of NO3~, O2, and organic materials (Seitzinger, 1988; Rysgaard et al.,
23    1994).
24          Marine microbial ecology is highly complex and poorly understood.  Relatively new knowledge
25    about anammox bacteria has completely altered scientific understanding of N cycling in the oceans.
26    Although it was previously believed that denitrification was responsible for virtually all of the transfer of
27    Nr in the ocean to the atmosphere as N2 gas, it now appears that anaerobic ammonium oxidation
28    (anammox) may account for up to 50% of the N2 production in the oceans (Devol, 2003; Ward, 2003;
29    Dalsgaard et al., 2005;  Kuypers et al., 2005). This reaction uses NO2" as the primary electron acceptor and
30    is catalyzed by planctomycete bacteria of the genera Brocadia, Kuenenia, and Scalindua. That
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 1    could be oxidized under anoxic conditions was theorized several decades ago based on calculations of the
 2    ratios among N, P, and C in marine ecosystems.  Nevertheless, the process was not experimentally
 3    documented until the 1990s (van de Graaf et al.,  1995). More recently, anammox has been detected in a
 4    variety of freshwater, estuarine, and marine waters and sediments (Devol, 2003; Jetten et al., 2003; Ward,
 5    2003; Rysgaard et al., 2004; Dalsgaard et al., 2005; Engstrom et al., 2005; Jetten et al., 2005; Kuypers
 6    et al., 2005; Pilcher, 2005; Op Den Camp et al., 2006).

      C.1.3.2. N Budgets
 7         The greatest uncertainty in the development of detailed N budgets for coastal ecosystems is
 8    quantifying how much of the N deposited on the watershed is transferred through the terrestrial watershed
 9    to the estuary. The difficulty stems from (1) multiple agricultural and mobile and stationary fuel
10    combustion emission sources in an estuary watershed, (2) quantifying dry deposition to the estuary
11    surface and to the watershed,  (3) measuring gaseous losses of NH3 and NOX compounds to the
12    atmosphere, and (4) complex N flow pathways through the watershed (NRC, 2000).  Published estimates
13    of the contribution of atmospheric deposition to estuary N load exhibit wide variability. Such estimates
14    for a specific estuary may differ. Some examples are summarized in Table  C-l. Despite the variability, it
15    appears that atmospheric sources of N loading to estuaries in the U.S. can be quantitatively important.
16    The major sources of N to estuaries and near-coastal marine waters in the U.S., in addition to atmospheric
17    deposition, include wastewater effluent derived mainly from food imports and consumption, fertilizer
18    application, livestock feed imports, and N-fixing crops (Boyer et al., 2002; Driscoll et al., 2003a).
19         Boyer et al. (2002) estimated that atmospheric deposition averaged 31% of total N inputs over the
20    combined area of the 16 northeastern river basins.  Contributions from atmospheric deposition ranged
21    from 60% of N inputs for the basins in northern Maine to  15 to 20% for the Schuylkill and Potomac River
22    Basins, the latter of which had large agricultural N inputs. Across all basins, estimated riverine export of
23    N amounted to 25% of total N inputs, and ranged from 11% to 40%. This result is consistent with a
24    similar analysis by Howarth et al. (1996), who found that  basins draining to the North Atlantic exported
25    approximately 25% of anthropogenic N inputs on average.
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      Table C-1. Estimated percent of total N load to Delaware Bay and Hudson River/Raritan Bay
      contributed by atmospheric deposition.
      Reference

      Paerl (1985)
      Hinga etal. (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)
      Land-based
      Sparrow model
      Castro and Driscoll (2002)
      Castro et al. (2003)
                                                     Percent of N Load Contributed by Atmospheric Deposition
Delaware Bay
            44
             15
            44
            22
             16
             25
             20
             23
Hudson River/Raritan Bay

               33

               68
               68
               26
               10

               10
               27
               17
               18
  1
 2          Turner et al. (2001) found a strong correlation between population density (persons/ km2) and the
 3    total N loading from watershed to estuary (r2 = 0.78) for coastal watersheds in the United States.  This
 4    finding is likely due to the prevalence of automobiles in heavily populated areas, along with their
 5    associated N emissions and deposition, plus the myriad non-atmospheric sources of N from human
 6    activities, particularly sewage releases.  They also determined that direct atmospheric deposition becomes
 7    increasingly more important as a contributor to the total N loading to an estuary as the water surface area
 8    increases relative to total watershed area (terrestrial plus water surfaces). Turner et al. (2001) found that,
 9    on average, direct atmospheric deposition of N accounted for more than an estimated 25% of the estuarine
10    N load when the estuary surface occupied 20% or more of the overall watershed area. Few of the
11    estuaries in the eastern U.S. comprise such a large percentage of their watershed (Castro et al., 2001).
12          The estimates of the effect of direct atmospheric deposition to estuary surfaces are hampered by
13    uncertainties in dry deposition rates. Many published studies have assumed that dry N deposition is equal
14    to measured wet deposition (e.g., Fisher and Oppenheimer, 1991; Hinga et al., 1991;  Scudlark and
15    Church,  1993), and this is probably biased high (cf Baker,  1991). However, other studies have assumed
16    dry N deposition rates for estuarine and near coastal areas are equal to 40% of wet (Jaworski et al., 1997)
17    or 67% of wet (Meyers et al., 2001). Of particular importance, the rate of dry deposition to open water
18    surfaces  is much lower than the rate of dry deposition to vegetated terrestrial surfaces. Paerl et al. (2001)
19    estimated that dry deposition to open estuarine surfaces is three to five times lower than to vegetated
20    surfaces. This difference is seldom considered in N-budgeting studies, and can have a substantial effect
      August 2008
          C-7
     DRAFT-DO NOT QUOTE OR CITE

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 1    on estimates of direct atmospheric loading to estuary surfaces, which is especially important for estuaries
 2    having low watershed area to estuary surface area ratio.
 3          A number of empirical approaches have been developed to quantify N fluxes to the coastal zone
 4    which rely on estimates of N sources within the watershed and characteristics of the landscape.
 5    Alexander et al. (2002) compared several of these empirical methods, the most accurate and least biased
 6    of which was that of Howarth et al. (1996). A modified version of the Howarth et al. (1996) methodology
 7    was published by Boyer et al. (2006). More mechanistic approaches include those of Bouwman et al.
 8    (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
 9          Interannual changes in N cycling can be reflected in changes in streamwater chemistry. NO3
10    leaching from terrestrial ecosystems throughout the 1980s was observed in many of the original lakes in
11    EPA's Adirondack Long Term Monitoring (ALTM) program (cf Driscoll and Van Dreason, 1993),
12    which was followed by a decline during the 1990s.  As a consequence of this subsequent decline, Driscoll
13    (2003b) reported an overall significant (p < 0.1) decrease in NO3 concentration for the period 1982 to
14    2000 for 8 of the 16 original ALTM monitoring sites. Only the one mounded seepage lake in the study
15    (Little Echo Pond) had a small, but statistically significant,  increase in NO3  concentration (0.01  (ieq/L/yr,
16    p < 0.06).  It is not clear why many Adirondack watershed soils leached NO3 to a lesser extent during the
17    1990s than they did during the 1980s (Driscoll et al., 2003b).  Decreasing stream NO3  concentrations
18    during the 1990s was also observed in the Catskill Mountains (Stoddard et al., 2003) and in New
19    Hampshire (Goodale et al., 2003). There was not a substantial change in N emissions or deposition in the
20    Northeast region over that period. Climatic factors, insect defoliation, increases in atmospheric CO2, and
21    interactions with increasing availability of DOC have been  proposed as possible contributing factors to
22    regional decreases in NO3 leaching (cf. Mitchell et al.,  1996; Aber et al., 2002; Driscoll et al., 2003b;
23    Goodale et al., 2003, 2005), but the driver of this decadal scale pattern remains uncertain.

      C.1.4.2. Episodic Change
24          Nutrient enrichment effects of N deposition are controlled to a large degree by biological and
25    hydrological processes that operate on  episodic (hours to days), seasonal, and interannual time scales.
26    Nitrogen uptake and transformation reactions and processes vary greatly with season and with climatic
27    factors. In particular, N export from terrestrial and transitional ecosystems to aquatic ecosystems is
28    governed by seasonal fluctuations in temperature and biological uptake, and episodic fluctuations in water

      August 2008                                     C-8                   DRAFT-DO NOT QUOTE OR CITE

-------
 1    movement associated with rainstorms and snowmelt.  The role of N in driving biotic change in stream
 2    ecosystems due to episodic pulses of NO3 associated with spring snowmelt are discussed in detail in ISA
 3    section 3.2.
 4

      C.1.4.3. Reversibility of Impacts
 5          Although there are relatively few studies of the reversibility of the biogeochemical effects of
 6    elevated N deposition, the few to date suggest the possibility of recovery. Experimental studies in Europe
 7    suggest that some ecosystem processes and characteristics are likely to recover rapidly following a
 8    reduction in N deposition. In a study in northern Sweden, high levels of fertilization (90 kg N/ha/yr) over
 9    20 yrs induced substantial soil acidification, including loss of over half of the base cations in the mineral
10    soil, a decrease in pH, and an increase in soluble Al (Hogberg et al., 2006). However, 10 yrs after this
11    treatment was stopped, the pH of the mineral soil had increased, and extractable NO3 was no higher than
12    in the control plot. Stem volume growth did not substantially increase relative to the acidification period.
13    "Clean roof experiments that prevent N deposition inputs at sites receiving >40 kg N/ha/yr ambient
14    atmospheric N deposition in The Netherlands increased wood and root production soon after the roof was
15    installed (Boxman et al., 1998a), and NO3 exports below the rooting zone were reduced dramatically
16    within 2 yrs (Bredemeier et al., 1998).
17          A study of alpine lake sediment cores in Rocky Mountain National Park, CO (Wolfe et al., 2003)
18    suggested the possible reversibility of N enrichment effects on lake biota. Although increased dominance
19    of mesotrophic diatom species was correlated with increased N inputs during the 20th century, it did not
20    appear that any of the oligotrophic species had been totally lost from study lakes. Thus, reduced future N
21    loading may allow renewed dominance by oligotrophic diatom species.
22          It is not necessarily true, however, that nutrient enrichment effects of N deposition will, in all
23    cases, be easily reversible. For example, it has been suggested that vegetation conversion in the coastal
24    sage scrub community in California has altered hydrologic function to an extent that may be difficult to
25    reverse. The depth of rainwater percolation into soil has been reduced as a result of invasion of non-
26    native annual grasses. This hydrologic change inhibits the growth of deep-rooting native shrubs (Wood
27    etal., 2006).


            C.1.5. Tables Supporting Cross Ecosystem  Evaluation of N20,  CH4
            and C02 Flux
28          Table C-2 summarizes key information from the experiments included in the meta- analysis
29    presented in ISA section 3.3.4.

      August 2008                                     C-9                   DRAFT-DO NOT QUOTE OR CITE

-------
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, CH4 uptake, CH4 emission and N20
emission meta analyses.
Site
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
experimental
condition
(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
ecosystem

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
N form

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

N addition
(kg ha/yr)

320
320
32
64
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

Reference



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 etal. 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
Leggettetal. 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;
2000
Pregitzer et al. 2008;
2000
Pregitzer et al. 2008;
2000
Pregitzer et al. 2008;
2000
Shan et al. 2001
BURTON et al.
BURTON et al.
BURTON et al.
BURTON et al.

August 2008
C-10
DRAFT-DO NOT QUOTE OR CITE

-------
Site
CH4 emission
Netherland
Netherland
Polish
Netherland
Swiss FACE
Swiss FACE
Minnesota
Minnesota
Niwot Ridge, CO
Finland
Finland
Salmisuo, Finland
Salmisuo, Finland
Sanjiang mire, China
CH4 uptake
Quebec, Canada
Quebec, Canada
Michigan
Michigan
Michigan
Michigan
Michigan
Soiling, Germany
Soiling, Germany
Soiling, Germany
Soiling, Germany
Bousson, PA
Perridge Forest, UK
Perridge Forest, UK
MT Ascutney, VT
Bousson, PA
Abisko, Sweden
Finland
Finland
Finland
Villingen, Germany
LTER Alaska
LTER Alaska
LTER Alaska
LTER Alaska
Swiss FACE
Swiss FACE
experimental
condition

incubation
incubation
incubation
incubation
field
field
incubation
incubation
field
incubation
incubation
field
field
field

incubation
incubation
field
field
field
field
field
field
field
field
field
field
incubation
incubation
field
field
field
field
field
field
field
field
field
field
field
field
field
ecosystem

wetland
wetland
wetland
wetland
grassland
grassland
wetland
wetland
grassland
wetland
wetland
wetland
wetland
wetland

coniferous
coniferous
coniferous
coniferous
deciduous
deciduous
grassland
coniferous
coniferous
coniferous
coniferous
deciduous
deciduous
deciduous
coniferous
deciduous
tundra
wetland
wetland
wetland
coniferous
deciduous
coniferous
deciduous
coniferous
grassland
grassland
N form

NH4
NH4
NH4
NH4
NH4NO3
NH4NO3
NH4
NH4
urea
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3

NH4
NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NO3
NO3
NH4
NH4NO3
NH4NO3
NH4
NO3
urea
NH4
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
N addition
(kg ha/yr)





84
84
20
60
250
30
100
30
30
240



10
30
10
30
10
18.5
24.2
18.5
24.2
100


31.4
100
100
100
100
100
150
66.7
12.3
171.4
142.9
560
560
Reference

Aerts and Caluwe 1999
Aerts and Caluwe 1999
Aerts and Caluwe 1999
Aerts and Toet 1997
Baggs and Blum 2004
Baggs and Blum 2004
Keller et al 2005
Keller et al 2005
Neef etal. 1994
Nykanen et al. 2002
Nykanen et al. 2002
Sarrnio and Silvola 1999
Sarrnio and Silvola 1999
Zhang et al. 2007

Adamsen and King 1993
Adamsen and King 1993
Ambus and Robertson 2006
Ambus and Robertson 2006
Ambus and Robertson 2006
Ambus and Robertson 2006
Ambus and Robertson 2006
Borken et al. 2002
Borken et al. 2002
Borken et al. 2002
Borken et al. 2002
Bowen et al. 2000
Bradford et al. 2001
Bradford et al. 2001
Castro et al. 1992
Chan et al. 2005
Christensen et al. 1997
Crilletal. (Crill, 1994)
Crilletal. (Crill, 1994)
Crill et al. (Crill, 1994)
Gulledge and Schimel 2000
Gulledge and Schimel 2000
Gulledge and Schimel 2000
Gulledge and Schimel 2000
Gulledge and Schimel 2000
Ineson et al. 1998
Ineson et al. 1998
August 2008
C-11
DRAFT-DO NOT QUOTE OR CITE

-------
Site
Scotland
Scotland
Scotland
Finland
Swale, CO
Midslope, CO
Passture, CO
Niwot Ridge, CO
Villingen, Germany
Finland
Gjovelandsnesset, Norway
Gjovelandsnesset, Norway
Harvard forest, MA
Harvard forest, MA
Harvard forest, MA
Harvard forest, MA
Belgium
Belgium
Costa Rica
Costa Rica
hkO emission
Michigan biological Station
Michigan biological Station
Michigan biological Station
Michigan biological Station
Michigan
Michigan
Michigan
Michigan
Michigan
Hyytiala
Speulderbos
San Rossore
Glencorse
Nyirjes
Achenkirch
Hbglwald
Sor0
Bosco negri
Schottenwald
Hyytiala
Speulderbos
San Rossore
experimental
condition
incubation
incubation
incubation
field
field
field
field
field
field
field
incubation
incubation
field
field
field
field
incubation
incubation
field
field
incubation
incubation
incubation
incubation
field
field
field
field
field
incubation
incubation
incubation
incubation
incubation
incubation
incubation
incubation
incubation
incubation
incubation
incubation
incubation
ecosystem
heathland
heathland
heathland
coniferous
grassland
grassland
grassland
grassland
coniferous
wetland
coniferous
coniferous
coniferous
deciduous
coniferous
deciduous
wetland
wetland
tropical forest
tropical forest
deciduous
deciduous
deciduous
deciduous
coniferous
deciduous
grassland
coniferous
deciduous
coniferous
coniferous
coniferous
coniferous
coniferous
coniferous
coniferous
deciduous
deciduous
deciduous
coniferous
coniferous
coniferous
N form
NH4
NH4NO3
NO3
NH4NO3
urea
urea
urea
urea
NH4
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4
NH4


NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4
NH4
NH4
NH4
NH4
NH4
NH4
NH4
NH4
NH4
NO3
NO3
NO3
N addition
(kg ha/yr)
40
40
40
200
450
450
450
250
150
30
30
90
37
37
120
120


65
65




10
10
10
30
30













Reference
Macdonald et al. 1997
Macdonald et al. 1997
Macdonald et al. 1997
Maljane et al. 2006
Mosier et al 1991
Mosier et al 1991
Mosier et al 1991
Neef etal. 1994
Papen et. al 2001
Saarnio et al. 2003
Sitaula et al. 1995
Sitaula et al. 1995
Steudler et al. 1989
Steudler et al. 1989
Steudler et al. 1989
Steudler et al. 1989
VanderNat et al. 1997
VanderNat et al. 1997
Weitz et al (Weitz, 1999)
Weitzetal(Weitz, 1999)
Ambus and Robertson 1999
Ambus and Robertson 1999
Ambus and Robertson 1999
Ambus and Robertson 1999
Ambus and Robertson 2006
Ambus and Robertson 2006
Ambus and Robertson 2006
Ambus and Robertson 2006
Ambus and Robertson 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
August 2008
C-12
DRAFT-DO NOT QUOTE OR CITE

-------
Site
Glencorse
Nyirjes
Achenkirch
Hbglwald
Sor0
Bosco negri
Schottenwald
Swiss FACE
Swiss FACE
Swiss FACE
Swiss FACE
Swiss FACE
Swiss FACE
Swiss FACE
Swiss FACE
Soiling, Germany
Soiling, Germany
Soiling, Germany
Soiling, Germany
Harvard forest
Harvard forest
Harvard forest
Harvard forest
Soiling, Germany
Gullane
Gullane
Ascutney , VI
Allt, UK
Allt, UK
Afon Gwy, UK
Afon Gwy, UK
Afon Gwy, UK
River Etherow, UK
River Etherow, UK
St. James Parish, LO
St. James Parish, LO
Saratoga
Puerto Rico
Jasper Ridge , CA
Jasper Ridge , CA
Swiss FACE
Swiss FACE
Manaus, Brazil
Manaus, Brazil
experimental
condition
incubation
incubation
incubation
incubation
incubation
incubation
incubation
field
field
field
field
field
field
field
field
field
field
field
field
field
field
field
field
field
incubation
incubation
field
field
field
field
field
field
field
field
field
field
field
field
incubation
incubation
field
field
field
field
ecosystem
coniferous
coniferous
coniferous
coniferous
deciduous
deciduous
deciduous
grassland
grassland
grassland
grassland
grassland
grassland
grassland
grassland
coniferous
coniferous
coniferous
coniferous
deciduous
coniferous
deciduous
coniferous
deciduous
deciduous
deciduous
coniferous
heathland
heathland
grassland
grassland
grassland
heathland
heathland
wetland
wetland
grassland
tropical forest
grassland
grassland
grassland
grassland
tropical forest
tropical forest
N form
NO3
NO3
NO3
NO3
NO3
NO3
NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4
NH4
NO3
NH4
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NO3
NH4
NH4NO3
urea
urea
urea
NH4NO3
NH4NO3
NO3
NO3
N addition
(kg ha/yr)







84
84
420
420
420
420
420
420
18.5
24.2
18.5
24.2
37
37
120
120
140


31.4
20
20
20
20
20
20
20
100
100
168
300
200
200
560
560


Reference
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Ambus et al. 2006
Baggs and Blum 2004
Baggs and Blum 2004
Baggs et al 2003
Baggs et al 2003
Baggs et al 2003
Baggs et al 2003
Baggs et al 2003
Baggs et al 2003
Borken et al. 2002
Borken et al. 2002
Borken et al. 2002
Borken et al. 2002
bowden et al. 1991
bowden et al. 1991
bowden et al. 1991
bowden et al. 1991
Brumme and Beese 1992
Castaldi and Smith 1998
Castaldi and Smith 1998
Castro et al. 1993
Curitis et al 2006
Curitis et al 2006
Curitis et al 2006
Curitis et al 2006
Curitis et al 2006
Curitis et al 2006
Curitis et al 2006
Delaune et al. 1998
Delaune et al. 1998
Delgado et al. 1996
Erickson et al. 2001
Hungate et al. 1997
Hungate et al. 1997
Ineson et al. 1998
Ineson et al. 1998
Keller et al. (Keller, 1988)
Keller et al. (Keller, 1988)
August 2008
C-13
DRAFT-DO NOT QUOTE OR CITE

-------
Site
Manaus, Brazil
Manaus, Brazil
Manaus, Brazil
Manaus, Brazil
Manaus, Brazil
Manaus, Brazil
gardsjon watershed
gardsjon watershed
Avoyelles, LO
Avoyelles, LO
Avoyelles, LO
Avoyelles, LO
Harvard forest
Finland
Swale, CO
Midslope, CO
Passture, CO
Puerto Rico
Puerto Rico
Puerto Rico
Puerto Rico
Puerto Rico
Puerto Rico
Puerto Rico
Puerto Rico
Puerto Rico
Niwot Ridge, CO
Niwot Ridge, CO
Villingen
Duke FACE
Duke FACE
Finland
Finland
Finland
Mojave
Gjovelandsnesset, Sweden
Gjovelandsnesset, Sweden
Gjovelandsnesset, Sweden
Gjovelandsnesset, Sweden
Gjovelandsnesset, Sweden
Gjovelandsnesset, Sweden
Scotland
Deepsyke forest
Deepsyke forest
experimental
condition
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
incubation
incubation
field
field
field
incubation
field
field
field
field
field
field
field
field
field
ecosystem
tropical forest
tropical forest
tropical forest
tropical forest
tropical forest
tropical forest
coniferous
coniferous
wetland
wetland
wetland
wetland
coniferous
coniferous
grassland
grassland
grassland
wetland
wetland
wetland
wetland
wetland
wetland
wetland
wetland
wetland
grassland
grassland
coniferous
coniferous
coniferous
wetland
wetland
wetland
desert
coniferous
coniferous
coniferous
coniferous
coniferous
coniferous
coniferous
coniferous
coniferous
N form
NO3
NO3
NH4
NH4
NH4
NH4
NH4NO3
NH4NO3
NH4
NH4
NO3
NO3
NH4NO3

urea
urea
urea
NH4
NH4
NH4
NO3
NO3
NO3
NH4
NH4
NO3
urea
urea
NH4
NO3
NO3
NO3
NH4
urea
NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
NH4NO3
N addition
(kg ha/yr)






35
35
100
300
100
300
113
200
450
450
450
15.4
130.2
266
15.4
130.2
266
15.4
266
15.4
250
250
150


100
100
100

30
90
30
90
30
90
48
48
96
Reference
Keller et al. (Keller, 1988)
Keller et al. (Keller, 1988)
Keller et al. (Keller, 1988)
Keller et al. (Keller, 1988)
Keller et al. (Keller, 1988)
Keller et al. (Keller, 1988)
Klemedtsson et. al 1997
Klemedtsson et. al 1997
Lindau et al. 1994
Lindau et al. 1994
Lindau et al. 1994
Lindau et al. 1994
Magill et al. 1997
Maljane et al. 2006
Mosier et al 1991
Mosier et al 1991
Mosier et al 1991
Munoz-Hincapie et al. 2002
Munoz-Hincapie et al. 2002
Munoz-Hincapie et al. 2002
Munoz-Hincapie et al. 2002
Munoz-Hincapie et al. 2002
Munoz-Hincapie et al. 2002
Munoz-Hincapie et al. 2002
Munoz-Hincapie et al. 2002
Munoz-Hincapie et al. 2002
Neef etal. 1994
Neef etal. 1994
Papen et. al (2001)
Phillips etal. 2001
Phillips etal. 2001
Regina et al. 1998
Regina et al. 1998
Regina et al. 1998
Schaeffer et al. 2003
Sitaula et al. 1995
Sitaula et al. 1995
Sitaula et al. 1995
Sitaula et al. 1995
Sitaula et al. 1995
Sitaula et al. 1995
Skiba et al. 1998
Skiba et al. 1999
Skiba et al. 1999
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Site
Costa Rica
Costa Rica
Sanhuabg Mire, China
experimental
condition
field
field
field
ecosystem N form
tropical forest
tropical forest
wetland NH4NO3
N addition
(kg ha/yr)
65
65
240
Reference
Weitz et al (Weitz, 1999)
Weitzetal(Weitz, 1999)
Zhang et al. 2007
      C.2. Terrestrial  Ecosystems
 1          The following sections are organized by ecosystem type and combine information that is
 2    supplemental to section 3.3 of the ISA.
            C.2.1. General C cyling
 3          C cycling is a complex process that includes C capture from the atmosphere by autotrophic biota,
 4    the primary producers of the ecosystem, and respiration (autotrophic + heterotrophic).  In general,
 5    atmospheric nutrient (e.g., N) deposition on an ecosystem that is deficient in that nutrient will often cause
 6    an increase in growth, at least initially, especially of the primary producers.  If that same nutrient is
 7    deposited on an ecosystem that has an adequate supply of that nutrient, there may be no appreciable
 8    nutrient enrichment effect, at least up to a point. Nutrient input that is greatly in excess of biological
 9    demand will often cause toxicity, reduced growth, or problems other than those associated with nutrient
10    enrichment (i.e., N-saturation, acidification, base cation depletion) (Figure C-l).


            C.2.2. Forest Growth Interactions with Herbivores
11          Light availability, nutrient balance, and C:N:P stoichiometry are closely relate, and affect the
12    composition of autotrophic species that will occupy a particular habitat.  The resulting stoichiometric
13    balance of C:N:P in the autotrophic community can have additional  feedbacks on nutrient cycling by
14    herbivores, detritivores, and decomposers (Sterner and Elser, 2002). Such effects also extend to
15    herbivores, and likely other members of the food web. Forkner and  Hunter (2000) altered plant growth of
16    oak (Quercus prinus and Q. rubra) saplings through fertilizer (N, P, K) addition and then censused the
17    densities of insect herbivore guilds and predaceous anthropods on experimental and control trees.  In
18    general, leaf chewers, phloem feeders, and leaf miners were more common on fertilized, as compared
19    with non-fertilized, trees. Predaceous arthropods were also more abundant on fertilized trees and their
20    densities were correlated with herbivore densities.
21
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                              Deficiency
              2
             o
             +•>
              c
              (0
                                                         Sufficiency
                   Toxic
                  Nutrient Supply
                                                                                         Source: EPA (1993).
      Figure C-1. Schematic representation of the response of vegetation to nutrient addition.
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
      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
15         Wet N deposition is generally low throughout the region, in the range of 1 to 3 kg N/ha/yr.
16    However, dry deposition is highly variable, but ranges up to about 30 kg N/ha/yr or more (Bytnerowicz
17    and Fenn, 1996; Fenn and Bytnerowicz, 1997; Takemoto et al., 2001). Available data (e.g., Minnich
      August 2008
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 1    et al., 1995) suggest progression toward less needle retention, higher shootroot biomass ratios, increasing
 2    depth of litter, and high NO3 in soil solution in response to high N deposition. These changes may
 3    eventually lead to replacement of pine species with nitrophilous and O3-tolerant species such as fir and
 4    cedar (Takemoto et al., 2001).
 5          Streamwater NO3 concentrations in montane watersheds that are downwind of the greater Los
 6    Angeles area are the highest in North America. Some streams in the San Gabriel and the San Bernardino
 7    Mountains have been documented to have levels of NO3 in stream water with peaks as high as 370 (ieq/L
 8    (Fenn and Poth, 1999), reflecting very high N deposition and N-saturation of the terrestrial ecosystem. In
 9    contrast, N leaching is low in most watersheds in the Sierra Nevada, and NO3 concentrations in streams
10    are usually below 1 (ieq/L. Nevertheless, some of the higher elevation watersheds in the Sierra Nevada
11    export appreciable NO3 from the terrestrial environment, particularly  during the early phases of
12    snowmelt.  Fenn et al. (2002) reported springtime peaks of NO3 concentration in lakewater up to 38
13    (ieq/L at high elevation and for watersheds dominated by talus. At lower elevation areas, however, most
14    of the inorganic N deposition loading is retained within the watersheds and concentrations of NO3 in
15    stream and lake waters are low  (Fenn et al., 2003a).  Surface water NO3 concentrations in these areas
16    provide an index reflecting the general levels of N deposition.  For example, where surface water NO3
17    concentrations are high, N deposition to the terrestrial watershed is also high.


            C.2.5.  Boreal forests
18          The boreal  forest represents the largest terrestrial biome on Earth, and as such can have a large
19    influence on global cycling of N and other nutrients. Plant growth in the boreal  forest is limited mainly
20    by N availability,  in part because of slow mineralization of organic materials in the harsh climate
21    (Vitousek and Howarth, 1991). Conceptual models of N cycling in the boreal forest have typically
22    assumed that mineralization of organic N is required for plant uptake of N (Nasholm et al., 1998).
23    However, it has been demonstrated  in laboratory studies (Chapin et al., 1993) and field studies (Nasholm
24    et al., 1998) that some boreal plants are capable of directly taking up amino acids from the soil, and
25    therefore bypassing the need for prior mineralization. The interactions among soil abiotic processes,
26    mycorrhizal associations, microbes, and plants are complex and poorly understood. Nevertheless, these
27    interactions are important to global N cycling and to boreal plant species composition because organic N
28    concentrations are typically high in  the soil of boreal forests.  It appears that atmospheric N deposition
29    and climate warming have the potential to alter boreal forest plant communities by shifting nutritional
30    processes from organic to inorganic N uptake (Nasholm et al., 1998).
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            C.2.6.  Alpine
 1          The Western U.S. contains extensive land areas that receive low levels of atmospheric N
 2    deposition, interspersed with hot spots of relatively higher N deposition downwind of large metropolitan
 3    centers and agricultural areas (Fenn, 2003). Alpine plant communities occur in some of the areas that
 4    receive moderately elevated atmospheric N deposition such as those located in the Sierra Nevada in
 5    southern California, the Front Range in Colorado, and the Cascade Mountains in Washington (Figure C2).
 6          Alpine plant species are typically adapted to low nutrient availability and their soil-forming
 7    processes are poorly developed, therefore they are often sensitive to effects from N enrichment (Bowman
 8    et al. 2006) including changes in species composition (Bowman et al., 1995; Seastedt and Vaccaro, 2001).
 9    Other reasons alpine tundra are sensitive to N enrichment include factors such as low rates of primary
10    production, short growing season, low temperature, and a wide variation in moisture availability
11    (Bowman and Fisk, 2001).
12          N cycling in alpine environments is strongly tied to variations in moisture regime (Bowman et al.,
13    1993; Bowman, 1994; Fisk et al.,  1998). Blowing snow is transported across alpine landscapes by wind
14    and tends to  accumulate in certain depression areas. These areas receive much higher levels of moisture
15    and winter season N deposition than other more wind-swept portions of the alpine environment
16    (Bowman, 1992).  Fenn et al. (2003a) suggested that as much as 10 kg N/ha/yr may leach through the
17    snow during the initial phases of snowmelt in some of the alpine areas in Colorado that accumulate
18    substantial snowpack. It is these moist meadow areas that may be most affected by N deposition and are
19    also the areas most likely to show changes in plant species composition and impacts on N cycling
20    (Bowman and Steltzer, 1998).
21          N deposition to the alpine tundra of Niwot Ridge in the Colorado Front Range altered N cycling
22    and provided the potential for replacement of some native plant species by more competitive, faster-
23    growing native species (Bowman and Steltzer,  1998; Baron et al., 2000; Bowman, 2000).  Many plants
24    that grow in  alpine tundra, as is true of plants growing in other low resource environments (e.g., infertile
25    soil, desert), tend to have some similar characteristics, including slow growth rate, low photosynthetic
26    rate, low capacity for nutrient uptake, and low soil microbial activity (Bowman and Steltzer, 1998;
27    Bowman, 2000). Such plants generally continue to grow slowly when provided with an optimal supply
28    and balance of resources  (Pearcy et al., 1987; Chapin, 1991).
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                                (c)
                                      Source: Vegetative distribution data were taken from the national map LANDFIRE (September 2006) (http://qisdata.usqs.net/website/landfire/).
     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.
2    In addition, plants adapted to cold, moist environments grow more leaves than roots as the relative
3    availability of N increases.  These patterns of vegetative development and their response to added N
4    affect plant capacity to respond to variation in available resources and to environmental stresses such as
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 1    frost, high winds, and drought. Vegetation in the southern Rocky Mountains responds to increased N
 2    supply by increasing plant productivity for some species, but this increase in productivity is also
 3    accompanied by changes in species composition and abundance (Bowman et al., 1993). Many of the
 4    dominant plant species do not respond to additional N supply with increased production.  Rather, many
 5    subdominant species, primarily grasses and some forbs, increase in abundance when the N supply is
 6    increased (Fenn et al., 2003a).
 7           In alpine ecosystems, changes in plant species composition due to N deposition can result in
 8    increased leaching of NO3 from the soils because the plant species favored by higher N supply are often
 9    associated with greater rates of N mineralization and nitrification than the pre-existing species (Bowman
10    et al., 1993, 2006; Steltzer and Bowman, 1998; Suding et al., 2006).
11          Total organic N pools in the soils of dry alpine meadows are large compared to pools of NH4+ and
12    NO3 (Fisk and Schmidt, 1996). However, positive response to inorganic N fertilization has been
13    demonstrated,  and thus some plant species appear to be  restricted in their ability to take up organic N
14    from the  soil and are growth-limited by the availability of inorganic N (Bowman et al., 1993, 1995;
15    Theodose and  Bowman, 1997). Miller and Bowman (2002) analyzed patterns of foliar 15N, NO3
16    reductase activity, and mycorrhizal infection compared with N uptake quantified by stable isotope tracer
17    additions in the greenhouse.  13C enrichment subsequent to 13C, 15N-glycine addition indicated that all of
18    the 11 genera studied were able to take-up labeled glycine to some extent. Glycine uptake ranged from
19    about 35% to more than 100 % of NH4+  uptake.  Only Festuca (fescue grass)  showed glycine uptake
20    exceeding both NH4+ and NO3 uptake (Miller and Bowman, 2002).


            C.2.7. Arctic  Tundra
21          Soluble  N in tundra soil solution is dominated by organic N, including free amino acids, rather than
22    NFl4+ or NO3 (Kielland, 1995). Tundra plants appear to exhibit a range of interspecific differences that
23    allow coexistence under conditions that  reflect a single limiting element. Species differ in rooting depth,
24    phenology, and uptake preferences for organic and inorganic forms of N (Shaver and Billings, 1975;
25    Chapin et al., 1993; Kielland, 1994; McKane et  al., 2002). McKane et al. (2002) demonstrated, based on
26    15N field  experiments, that arctic tundra  plant species were differentiated in timing, depth, and chemical
27    form of N utilization. Furthermore, the  species that exhibited greatest productivity were those that
28    efficiently used the most abundant N forms.
29          Ericoid mycorrhizae provide host plants with the  capacity to take up N in the form of amino acids
30    (Stribley and Read,  1980; Bajwa and Read, 1985). This is important in arctic plant communities that
31    occur on  acidic organic soils because amino acids are typically readily available in such soils, and N
32    availability generally limits primary productivity.
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 1          Future climate warming could have important effects on N cycling in arctic tundra ecosystems. In
 2    the past, organic materials have accumulated in tundra soils, largely because decomposition has been
 3    slower than plant growth. Climate warming may increase the decomposition of soil organic matter,
 4    thereby increasing the availability of stored N (Weintraub and Schimel, 2005). The distributions of
 5    woody plant species are also increasing in response to warming, with likely feedbacks on C and N
 6    cycling. For example, the dominant shrub  species in the arctic tundra in Alaska, Betula nana, is
 7    expanding its distribution in tussock vegetation communities (Weintraub and Schimel, 2005).
 8          Poor soil aeration is caused by permafrost, resulting in poor water drainage and the development of
 9    anaerobic conditions. Vegetation composition and primary productivity vary in response to differences in
10    soil moisture and aeration (Everett and Brown, 1982; Gebauer et al., 1995).  Reduced soil O2 can limit
11    nutrient availability. For example, under anaerobic conditions, N mineralization and nitrification rates
12    decrease while denitrification increases (Ponnamperuma, 1972; Gebauer et al., 1995).
13


            C.2.8.  Arid Land
14          From 1989 to 2004 in the Chihuahuan desert, Baez et al. (2007) observed a 43% increase in
15    ambientN deposition, from 1.71 to 2.45 kg N/ha/yr, resulting in an additional 5.88 kg N/ha/yr deposition
16    over that time period. They suggest that these deposition trends may result in significant plant community
17    changes, as indicated by fertilization studies of blue gramma (Bouteloua gracilis) and black gramma (B.
18    eriopoda). In a field addition with additions of 20 kg N/ha/yr in one season, blue gramma was  favored
19    over black gramma, the current dominant species (Baez et al., 2007).


            C.2.9.  Lichens
20          There are several potential uses of lichens for air pollution and deposition monitoring. These
21    include measurement of tissue lichen concentrations of specific pollutants (i.e., lichens as passive
22    monitors), determination of changes in species composition or the presence/absence of sensitive species,
23    and identification of areas having relatively high levels of air pollution, where monitoring instrumentation
24    could be installed to more quantitatively measure pollution levels. Assessment of long-term change in the
25    epiphytic lichen community can be especially valuable to provide an early indication of either improving
26    or deteriorating air quality and atmospheric deposition. Such monitoring was incorporated in 1994 into
27    the USFS Forest Inventory and Analysis (FIA) Program (See Annex A).
28          Lichen communities in the Pacific Northwest show signs of air pollution damage under current air
29    pollution levels. Symptons include decreases in the occurrences of sensitive taxa and replacement by
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  1    pollution-tolerant and nitrophilous taxa (Fenn et al., 2003a; Geiser and Neitlich, 2007). Indicators of
 2    clean sites and polluted sites (Table C-3) were used by Geiser and Neitlich (2007) to create six lichen
 3    zones of air quality within the region, from worst (all sensitive species absent) to best (all sensitive
 4    species present). Air pollution was associated with effects on community composition of lichens, rather
 5    than species richness. The most widely observed effects included paucity of sensitive, endemic species,
 6    and enhancement of nitrophilous and non-native species (Geiser and Neitlich, 2007). The strongest
 7    relationship was with wet NH4+ deposition, consistent with findings in California (Jovan and McCune,
 8    2005) and Europe (van Dobben et al., 2001).  The zone of worst air quality was associated with absence
 9    of sensitive lichens, enhancement of nitrophyllous lichens, mean wet NH4+  deposition > 0.06 mg N/L,
10    lichen tissue N and S concentrations > 0.6% and 0.07 %, and SO2 levels harmful to sensitive lichens.
11          Jovan and McCune  (2005) constructed a model based on non-metric multidimensional scaling
12    ordination to analyze lichen species distribution from 98 FIA plots in the greater Central Valley of
13    California. The model used epiphytic macrolichen community data to reflect air quality and climate in
14    forested areas. Some species respond negatively to NOX and SOx deposition (McCune, 1988; Gauslaa,
15    1995; van Haluwyn and van Herk, 2002).  Other species respond positively to NHY deposition (de
16    Bakker, 1989; van Dobben and de Bakker, 1996; van Herk, 1999, 2001; Jovan and McCune, 2005).
17          Similarly, Jovan and McCune (2006) developed a model of NH3 exposure to epiphytic
18    macrolichens  in the Sierra Nevada region. They found that lichens provide a relatively inexpensive tool
19    for estimating fine-scale distributions of NH3  exposure to terrestrial  ecosystems.  Because NH3 has a high
20    deposition velocity (Asman and van Jaarsveld, 1992), dry deposition of reduced N exhibits high spatial
21    variability. Monitoring of species composition of epiphytic lichen communities can therefore help
22    quantify spatially variable  eutrophication risk to forest health in the  Sierra Nevada region (Jovan and
23    McCune, 2006).
      Table C-3. Principal Air Quality Indicator Lichen Species in Oregon and Washington

      Group                            Sub-Group                             Indicator Species
      Clean Air               Regional distribution                    Bryoria capillaris, Lobaria oregana, Sphaerophorus globosus, Usnea
                                                            filipendula, Usnea scabrata
                           Sub-regional 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).
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     C.3.  Transitional Ecosystems
1         The sensitivity of wetlands is particularly important given that they contain a disproportionately
2    high number of rare plant species (Figure C-3) (Moore et al., 1989).  EPA reported that, of the 130 plant
3    species from the conterminous U.S. that were listed as threatened or endangered in 1987, 14% occurred
4    principally in wetlands (U.S. Environmental Protection Agency, 1993). Bedford and Godwin (2003)
5    indicated that a disproportionately high number of rare plant species occur in fens relative to their percent
6    land cover (Table C-4) (Bedford and Godwin, 2003). For example, fens comprise only 0.01% of
7    northeastern Iowa but contain 12% of the region's rare plant species and 17% of the listed endangered,
8    threatened, and species of concern (Table C-4).
             8-1
                                    200       300        400       500
                                         Standing crop (g/.25 m2)
                              600
                   700
                                                                                   Source: Moore etal. (1989).
     Figure C-3. Number of nationally rare species versus standing crop in each of 401 quadrants from
     wetlands in Ontario, Quebec, and Nova Scotia.
     Table C-4. Contribution of fens to support of plant species diversity in selected states
Number of
Vascular Species
Found in Fens (# 1
Native)
Found in Fens (°/<
Native)
Non-Vascular
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
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Colorado
Idaho
Iowa

Montana
New
Hampshire
Calcareous
fens
Open fens
New Jersey
Calcareous
fens
Acidic
seeps
New York

North
Carolina
Number of Percent of State
Vascular Species Vascular Flora
Found in Fens (# Found in Fens (°/o
Native) Native)
~500 ~14
327
320 18
(307) (17.2)
174
340 17.2





245

169

440 13.8
(397) (19.0)


Non-Vascular .. Num
SPteFSeFn°sUnd %Ł
20
20 35
134

60 40
91 52




96
60

13 36

77 55

77

a'p Percent of State Percent of
on & Kare uncommon & Rare State Area
i-ouna in Species Found in Fens in Fens
3.3 0.08-0.15
12.0
12.0 0.01

0.0015
13.7 0.078

0.0058

0.0726
13.5 0.00733
0.0073

0.00003

7.0 0.07

11.0 0.0023

      Source: Bedford and Godwin (2003).D
      C.4.  Aquatic Ecosystems
 1         Aquatic systems can be subdivided into major types based on hydrology. At the broadest level,
 2    freshwater aquatic ecosystems can be classified as riverine, lacustrine, and palustrine systems. Riverine
 3    systems can be identified at varying scales, including valley segment, river reach, and channel unit.
 4    Lacustrine systems include deepwater habitats associated with lakes and reservoirs. Palustrine systems
 5    include small, shallow, or intermittent water bodies, including ponds. Each type of aquatic ecosystem is
 6    potentially sensitive to nutrient enrichment effects from N deposition. Nevertheless, available data
 7    documenting such effects are limited.
 8         The dose-response data for aquatic organisms such as those cited here are generally expressed in
 9    concentration units, as mg/L or (imol/L of N, for example.  Such exposure concentration data cannot be
10    directly related to ecosystem exposure, which is generally expressed in such units as kg/ha. This is
11    because a given N deposition exposure can result in widely varying concentrations of N compounds
12    (especially NO3 ) in water. For convenience, a concentration of 1 mg/L of N (as, for example, in the case
13    of NCV-N or NFL.+-N) is equal to 71.4 (imol/L or 71.4 (ieq/L of NO3 or NFL.+.
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            C.4.1. History of Evaluating N Enrichment in Freshwater Aquatic
            Ecosystems
 1          The role of N deposition in freshwater eutrophication and acidification processes has been
 2    considered secondary to P and S, and only within the past 20 years have there been studies questioning
 3    the established science and showing N-limitation in some fresh waters, N excess in some terrestrial
 4    systems, and N-caused acidification in poorly buffered fresh waters. A number of things have conspired
 5    to prevent extensive evaluation of the effects of atmospheric N deposition on aquatic organisms via
 6    nutrient-enrichment pathways.  These include assumptions, or prevailing paradigms, that have channeled
 7    scientific thought in one direction and away from others.
 8          First were the assumptions for many years that atmospheric deposition was caused primarily by
 9    sulfur (S) emissions and that effects on aquatic  ecosystems were primarily caused by acidification
10    processes. Only after S emissions began to decrease substantially in response to the Clean Air Act
11    amendments did the role of NOX, and still later, NH3, emissions become recognized as potential agents of
12    environmental change. And even then, that role was assumed to be restricted mainly to acidification from
13    NO3~, a strong acid anion, not eutrophication (Reuss and Johnson,  1985). Second, because N is the
14    nutrient most limiting to primary production in  most ecosystems, it was assumed until fairly recently that
15    N was tightly cycled in terrestrial systems, and that excess NO3  leaching rarely occurred in natural
16    environments (Vitousek and Howarth, 1991). Finally, the attention of aquatic biologists has been
17    strongly focused on the role of P in eutrophication of freshwaters for the past 40 years, largely due to the
18    demonstrated role of P in causing large increases in algal productivity worldwide (Schindler et al., 1971;
19    Schindler, 1974). P is an essential, and often limiting, nutrient to aquatic organisms. A large number of
20    highly influential studies in the  1960s and 1970s exposed the role of wastewater, in particular phosphate
21    detergents, in causing excessive algal production and anoxia in Lake Mendota (Wisconsin), Lake
22    Washington (Washington), Lake Erie, and many other locations (Hasler, 1947; Vollenweider, 1968;
23    Edmondson, 1969, 1991). Because of the emphasis on P as a major cause of fresh water eutrophication,
24    Downing and McCauley (1992) wrote as recently as 1992: "opinions differ on the role of N as a limiting
25    nutrient in lakes."
            C.4.2. Interactions between  N and P loading
26          Results from surveys, paleolimnological reconstructions of past conditions, experimental results,
27    and meta-analyses of hundreds of studies all consistently show N-limitation to be common in fresh
28    waters, especially in remote areas, and there is a nearly universal eutrophication response to N-enrichment
29    in lakes and streams that are N-limited. Surveys of lake N concentrations and trophic status along
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 1    gradients of N deposition show increased inorganic N and increased productivity to be strongly related to
 2    atmospheric N deposition.  Where N-enrichment has occurred, P limitation, N+P colimitation, and a few
 3    instances of Si depletion have been reported. Paleolimnological records show increases in productivity
 4    and changes in algal assemblages in the recent past (since 1950) that are correlated with increased societal
 5    use of synthetic N fertilizers and human. The paleolimnological evidence is strongest in regions with the
 6    highest N deposition, and is weaker where N deposition is lower (Wolfe et al., 2001, 2003, 2006; Saros
 7    et al., 2003).  In additions to changes in productivity, algal community reorganization has been observed
 8    in the paleolimnological record, experiments, and observations of N-enriched lakes, especially those
 9    where enrichment has come from N deposition.  A summary of additional studies addressing N-limitation
10    is given in Table C-5.
11          It is generally believed that the Laurentian Great Lakes are P-Limited (Schelske, 1991; Downing
12    and McCauley,  1992; Rose and Axler,  1998).  Water quality in the open waters of these lakes has been
13    improving in  recent years in response to controls on point sources of P (Nicholls et al., 2001). Work by
14    Levine et al. (1997), however, suggested a more complicated pattern of response to nutrient addition for
15    Lake Champlain.  They added nutrients to in situ enclosures and measured indicators of P status,
16    including alkaline phosphatase activity and orthophosphate turnover time.  Although P appeared to be the
17    principal limiting nutrient during  summer, N addition also resulted in algal growth stimulation. During
18    spring, phytoplankton growth was not limited by P, N, or silica  (Si), but perhaps by light or temperature
19    (Levine etal., 1997).
20          Data from 28 Sierra Nevada lakes sampled in 1985 and again in 1999 suggested that NO3
21    concentrations decreased during that period and total P concentrations increased in more than 70% of the
22    lakes  sampled.  Sickman et al. (2003a)  concluded that lakes throughout the Sierra Nevada appear to be
23    experiencing  measurable eutrophication in response to atmospheric deposition of nutrients, but N
24    deposition is only part of the process. Based on the evidence of increased P loading throughout the Sierra
25    Nevada, Sickman et al. (2003a) concluded that site-specific P sources were unlikely to be the cause of
26    observed trends. They proposed that atmospheric deposition and accelerated internal cycling of P in
27    response to changes in climatic factors  were the most likely sources of increased P loading to the Sierra
28    Nevada Lakes, but it is not known why atmospheric deposition of P to these lakes has increased over
29    time.  Possibilities include use of organo-phosphate pesticides and aeolian transport of soils and dust that
30    are high in P from the San Joaquin Valley to the Sierra Nevada Mountains (Bergametti et al., 1992;
31    Lesack and Melack, 1996; Sickman et al., 2003a).
32          Data from a survey of 44 lakes east and west of the Continental Divide in Colorado indicated that
33    lakes  on the western side of the Continental Divide averaged 6.6 (ieq/L of NO3~, whereas lakes on the
34    eastern side of the Continental Divide averaged  10.5 (ieq/L of NO3  concentration. In the Colorado Front
35    Range, NO3 concentrations in lakes above 15 (ieq/L have commonly been measured, suggesting some
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 1    degree of N-saturation (Baron, 1992). A meta-analysis of 42 regions in Europe and North America
 2    suggested that a majority of lakes in the northern hemisphere were limited by N in their natural state.
 3    While many of these lakes now receive sufficient N from deposition that they are no longer N-limited,
 4    some lakes in remote regions still maintain their original oligotrophic or ultra-oligotrophic status.
      Table C-5. Summary of additional evidence for N limitation on productivity of freshwater
      ecosystems.
Region
Sweden
Endpoint
N
limitation
Observation
a consistent pattern of nutrient limitation showing N limitation for deposition below
approximately 2.5 kg N/ha/yr, co-limitation 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.
Ecosystem
Type
lakes
Reference
Bergstrbm et
al. (2005)
      Rocky Mountains  N       Review: the author concluded that the effects of atmospheric N deposition were                 Burns (2004)
      of Colorado and  limitation  uncertain and that a widespread shift from N to P limitation had not been clearly
      Wyoming               demonstrated.
      Texas         N       some instances of seasonsal N-limitation, and other instances of year-round N-limitation  rivers      Stanley et al.,
                   limitation                                                                         1990
      Rocky Mountains  N       Review: Author stated that recent studies did suggest a change in diatom species                Burns (2004)
      of Colorado and  limitation  dominance in the 1950s, but widespread species changes across lakes in the region and
      Wyoming               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.
             CAS. Aquatic Species Affected
 5          The following section contains studies in which the amount of N added was less than 10 mg NO3
 6    N/L, or 714 (JVI, and most studies tested the effects of 5 mg NO3 -N/L or less.  Many of these studies are
 7    summarized in Table C.6. Overall, several major effects were reported on biota treated with N
 8    enrichment: (1) the effects on algae included growth stimulation, increased cell densities, decline or
 9    stimulation of individual taxa, and decline in diversity; (2) the amount of N required to stimulate growth
10    in phytoplankton is extremely low: 3 (iM or less; and (3) animal responses included no response,
11    decreased reproductive capability, declines in growth rate and biomass, mortality, and in one case,
12    increased fitness because NO3 was detrimental to a fungal parasite.
13
14

      C.4.3.1. Phytoplankton and Plants
15          Two species of diatom, Asterionella formosa and Fragilaria crotonensis, now dominate the flora of
16    at least several alpine and montane Rocky Mountain lakes (Interlandi and Kilham, 1998; Baron et al.,
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 1    2000; Wolfe et al., 2001, 2003; Saros, 2003; Saros, 2005). These species are opportunistic algae that have
 2    been observed to respond rapidly to disturbance and slight nutrient enrichment in many parts of the world.
 3    They were among the first diatoms to increase in abundance following watershed settlement and
 4    agricultural development in European lake watersheds in the 12th and  13th centuries (Anderson et al.,
 5    1995; Letter, 1998), and North American settlements in the 18th and 19th centuries (Christie and Smol,
 6    1993; Hall et al., 1999). In these studies, as well as in a Swedish lake influenced by acidic deposition,
 7    these two diatom species expanded following initial disturbance, and were later replaced by other species
 8    more tolerant of either acidification or eutrophication (Renberg et al., 1993; Hall et al.,  1999). Moreover,
 9    the growth of A. formosa has been stimulated with N amendments during in situ incubations, using
10    bioassays and mesocosms (6.4 to 1616 (iM N/L; McKnight et al., 1990) (76 (iM N/L; Lafrancois, 2004)
11    (18 (JVI N/L; Saros, 2005).
12          It may seem obvious that additions of N stimulate cell growth, but not all species of diatoms or
13    other algae are equally responsive to N supply. A. formosa and F. crotonensis have extremely low
14    resource requirements for P, enabling them to outcompete other algae for resources and such differences
15    in resource requirements allow some species to gain a competitive edge over others upon nutrient
16    addition, and as a consequence, shifts in assemblages have been observed (Wolfe et al., 2001, 2003;
17    Lafrancois, 2004; Saros, 2005). This is in keeping with findings of Interlandi and Kilham (2001), who
18    demonstrated that maximum species diversity was maintained when N levels were extremely low (<3 (iM
19    N) in lakes in the Yellowstone National Park (Wyoming, Montana) region. The implication is that species
20    diversity declines with increasing availability of N, and this finding complements the results of terrestrial
21    studies that also showed a negative relationship between species diversity and N availability (Stevens,
22    2004; Suding et al., 2005; Gilliam, 2006).
23          P limitation and co-limitation of both N and P are reported for fresh waters in the literature,
24    particularly during summer (Morris and Lewis, 1988; Elser, 1990) Downing and McCauley, 1992;
25    Sickman et al., 2003b). Because diatoms in northern temperate freshwaters respond rapidly and favorably
26    to N enrichment and also have relatively high Si requirements, Si can be depleted, at least seasonally,
27    from waters that are relatively high in N and P.
28          Silica depletion due to nutrient enrichment has been reported for the Great Lakes (Conley et al.,
29    1993). Increased growth of silicate-utilizing diatoms as a result of NO3 and phosphate (PO/f )-induced
30    eutrophication, and subsequent removal of fixed biogenic Si via sedimentation has brought about changes
31    in the ratios of nutrient elements Si, N, and P. In turn, such changes can cause shifts from diatoms to non-
32    siliceous phytoplankton in large  rivers and coastal marine regions (Ittekot, 2003). Reduction in  dissolved
33    Si in lakewater corresponded to phytoplankton blooms under ice and large numbers of diatoms  during
34    spring in Loch Vale Watershed, Rocky Mountain National Park (Campbell et al., 1995). This is a
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 1    potential seasonal issue in water bodies underlain by aluminosilicate rocks because mineral weathering
 2    can replenish the Si supply.
            C.4.4. Seasonal N Input and Cyanobacteria
 3          Some ecosystems are seasonally enhanced with N from atmospheric deposition, either from
 4    snowmelt flushing of accumulated N in winter snow, or from flushing during dormancy of terrestrial
 5    vegetation (Stoddard, 1994). While many eutrophic and hypereutrophic freshwater ecosystems have
 6    seasonal or perennial cynaobacteria that fix atmospheric N, obviating the need for an external source of N
 7    (Wetzel, 2001), we found only one study of an oligotrophic lake with obligate N-fixing bacteria (Reuter
 8    et al., 1985). N-fixation is energy expensive and sometimes limited by trace metal availability, so obligate
 9    N-fixing cyanobacteria (formerly called blue-green algae) are rarely found in ultra-oligotrophic waters
10    (McKnight et al., 1990; Vitousek and Howarth, 1991). Because of this, oligotrophic and ultraoligotrophic
11    waters are extremely sensitive to even low inputs of N from atmospheric deposition. Anabaena circinalis,
12    an obligate N-fixing cyanobacterium, was suppressed with additions  of 500 (iM/L N (Higley et al., 2001),
13    and DIN levels >~200 (iM/L N completely inhibited N fixation in Castle Lake, CA (Reuter et al., 1985).
            0.4.5. Nitrate Toxicity: Invertebrates
14          Toxic responses to N exposure by aquatic invertebrates have been identified in a number of studies.
15    Toxic response thresholds are typically much higher than the levels of N in surface waters that could be
16    attributable to N deposition in the U.S. Safe Concentrations (SC), or threshold values of N, were
17    determined by Camargo and Ward (1995) for several aquatic insects at different life stages. Early instars
18    are generally more sensitive to N in solution than later or adult stages. The SC for late instars of
19    Hydropsyche occidentalis, a caddis fly, was found to be 171 (iM/L, and concentrations greater than this
20    value induced mortality. The SC was 100 (iM/L for early instars of the same species  (Camargo and Ward,
21    1995). Another caddis fly, Cheumatopsyche pettiti, tolerated higher concentrations, with safe
22    concentrations of 171 and 250 (iM N/L, respectively for early and late instars (Camargo and Ward, 1995).
23    Two species of amphipod did not survive after 120-hr exposure to NO3 concentrations of 200 (iM N/L
24    for one species, and 314 (iM N/L for the other (Camargo et al., 2005). No observable effect
25    concentrations above which Ceriodaphinia dubia exhibited reduced reproductive capability ranged
26    broadly in laboratory experiments, but some effects were seen at concentrations greater than 507 (iM N/L
27    (Scott and Crunkilton, 2000). A decline in Daphnia spp. was observed in mesocosm nutrient enrichment
28    experiments where 75 (iM N/L was added, but this was attributed to lower food quality of the  algal
29    assemblage that replaced the original species as a result of fertilization (Lafrancois, 2004). Thus, toxic
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 1    responses seem to occur at N concentrations that are much higher than the concentrations required to
 2    elicit a response in competitive interactions.
 3          A whole-ecosystem experiment at the Bear Brook watershed, ME simulated the effects of N and S
 4    deposition by means of experimental (NH4)2SO4 addition over a period of 10 years. Researchers found
 5    that elevated N inputs had minimal effect on stream detritus processing (Chadwick and Huryn, 2003).
 6    They also found that N additions had no significant effect on stream macroinvertebrate secondary
 7    production or varying production by functional feeding groups. They concluded that climate-related
 8    variables such as flow duration and litter inputs controlled secondary production when N was not limiting
 9    (Chadwick and Huryn, 2005).
10          Changes to aquatic food webs have not been as thoroughly explored as changes to algal
11    assemblages, but a few studies have shown declines in zooplankton biomass (Paul et al., 1995;
12    Lafrancois, 2004) in response to N-related shifts  in phytoplankton biomass toward less palatable taxa with
13    higher C:P ratios (Elser et al., 2001). N enrichment of arctic streams not only increased periphyton
14    biomass and productivity, but also stimulated the entire ecosystem, increasing decomposition rates, fungal
15    biomass, and invertebrates (Benstead et al., 2005).
            C.4.6.  Nitrate Toxicity: Amphibians and Fish
16          A summary of studies the effects of nitrate on amphibians and fish is given by Table C-6. It
17    appears that very high NO3 concentrations in surface water are required to elicit a toxic response in
18    amphibian populations. Concentrations that caused no observed effects and no observed adverse effects
19    ranged from 357 to 714 (iM N/L for frogs, salamanders, and the American toad (Bufo americanus)
20    (Hecnar,  1995; Laposata and Dunson, 1998; Johansson et al., 2001; Romansic et al., 2006). In one
21    experiment, the red-legged frog (Rana aurora) exhibited a decreased susceptibility to Saprolegnia mold
22    when exposed to elevated NO3  concentrations (Romansic et al., 2006).
23          According to one review, adverse direct effects of N deposition on fish due to nutrient enrichment
24    are probably minimal (Burns, 2004). N concentrations alone are not high enough to influence fish
25    metabolism, and the extent of eutrophication is insufficient (due to induced P limitation in oligotrophic
26    waters) to cause O2 depletion.
27          Other research suggests that the eggs and fry of rainbow trout (Oncorhyncus mykiss; including
28    steelhead), cutthroat trout (O. clarki), and chinook salmon (O.  tshawytscha) are susceptible to elevated
29    concentrations of NO3~, with rainbow trout mortality occurring after 30 day incubations in concentrations
30    >79 (iM N/L (Kincheloe  et al., 1979). There were no observed effects reported below this concentration.
31    Chinook salmon and cutthroat trout eggs and fry responded to slightly higher concentrations; no observed
32    effects occurred below 164 (iM N/L, but mortality occurred at higher concentrations (Kincheloe et al.,
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1 1979). Lake whitefish (Coregonus clupeaformis) and lake trout (Salvelinus namaycush) embryos
2 displayed developmental delays at concentrations greater than 446 and 114 LiM N/L, respectively
3 (McGurk et al., 2006). All of these toxic threshold concentrations are much higher than the concentrations
4 of NO3 in surface water that would routinely be expected to occur solely in response to atmospheric N
5 deposition in the U.S. Nevertheless, such high concentrations of streamwater NO3 have been measured in
6 the Great Smoky Mountains, NC (Cook, 1994) and in mixed conifer forests in southern California (Fenn
7 and Poth, 1999).
Table C-6. Summary of effects of N enrichment on aquatic biota in freshwater ecosystems.
Species
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
Common Name _.
Stage


diatoms
diatoms
diatoms
diatoms
diatoms
diatoms
diatoms
diatoms
diatoms
benthic diatoms
phytoplankton
assemblages
phytoplankton
assemblages
phytoplankton
assemblages
phytoplankton
assemblages
crysophytes
epilimnetic algae
N Concentration
(mg NO3" -N/L)


0.252 mg/L
6.4 umol/L
1.06 mg/L
5.7 x 10-4(>0.041
uM) (high light)
0.252 mg/L
1.06 mg/L
3.5 x 10-4(>0.028
uM) (high light)
8.4 x 10-6 (>0.006
uM) (med light)
8.4 x 10-6 (>0.006
uM) (med light)
1.7 x 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
Observed Effects


stimulated growth
stimulated growth
(low ambient N-deposition): increase in chlorophyll-
a 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 chlorophyll-
a 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 chlorophyll-a and cell
density when NO3 combined with acid and P
increased growth rate (measured at half the
maximum growth rate)
Reference


Saros et al.
(Saros, 2005)
McKnight et al.
(1990)
Lafrancois et al.
(Lafrancois,
2004)
Michel etal.
(2006)
Saros et al.
(Saros, 2005)
Lafrancois et al.
(Lafrancois,
2004)
Michel etal.
(2006)
Michel etal.
(2006)
Michel etal.
(2006)
Michel etal.
(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)
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Species
Multiple species
Periphyton


not identified
not identified
not identified
not identified
not identified
not identified
not identified
not identified
not identified
not identified
Cyanobacteria
Anabaena circinalis
Microcycstis sp.
Invertebrates
Hydropsyche
occidental

Cheumatopsyche pettiti

Echinogammarus
echinosetosus
Eulimnogammarus
toletanus
Ceriodaphnia dubia
Daphnia pulex
Common Name
hypolimnetic
algae

attached benthic
algae
attached benthic
algae
attached benthic
algae
attached benthic
algae
attached benthic
algae
attached benthic
algae
epilithic
attached benthic
algae
attached benthic
algae
sublittoral epilithic
algae
eulittoral epilithic
algae
epipelic algae

N fixing
cyanobacteria
non-N-fixing
cyanobacteria

caddis fly

caddis fly

amphipod
amphipod
water
flea/cladoceran
Water flea
Life
Stage


















early
instaar
late
instaar
early
instaar
late
instaar
adult
adult
adult
adult
N Concentration
(mg NO3" -N/L)
0.050 mg/L

0.5 M NaNO3 in
2%agar
2.5 M
0.5 M NaNO3 in
3%agar
0.5 M NaNO3 in
3%agar
0.036 mg/L
0.5 M NaNO3 in
2%agar
~700 ug NO3-N
0.16 mg/L
0.32 mg/L
0.259 mg/L
0.126 mg/L
0.713 mg/L

0.5 M NaNO3 in
2%agar
0.28 mg/L

1.4 (SC)
2.2 (SC)
2.4 (SC)
3.5 (SC)
2.8 (120-h LC0.01)
4.4 (120-h LC0.01)
7.1-56.5 (7-d NOEC)
1.06
Observed Effects
increased growth rate (measured at half the
maximum growth rate)

biomass increased in response to N and N&P
additions during period of seasonal N-limitation
(July-August)
NO3 stimulated stream algal growth during
seasonal N-limitation
NO3 alone stimulated stream algal growth during
seasonal N-limitation, while N&P co-limited
growth in other times
NO3 alone stimulated stream algal growth during
seasonal N-limitation, while N&P co-limited
growth in other times
stimulated NO3 uptake
no growth response
suppressed N2-fixation
increased summer growth rate (measured at half
the maximum growth rate)
increased winter 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)

decreased abundance
increased growth rate; increased mycrocystin and
anatoxin-a concentrations

mortality
mortality
mortality
mortality
mortality
mortality
decreased reproductive ability; fewer neonates
produced per female
(low ambient N-deposition): decreased biomass in
Reference
Priscu et al.
(1985)

Smith and Lee
(2006)
Bushong and
Bachmann
(1989)
Wold and
Hershey (1999)
Allen and
Hershey (1996)
Axler and Reuter
(1996)
Higleyetal.
(2001)
Reuter et al.
(1985)
Reuter et al.
(1985)
Reuter et al.
(1986)
Reuter and Axler
(1992)
Reuter and Axler
(1992)
Reuter and Axler
(1992)

Higleyetal.
(2001)
Gobler et al.
(2007)

Camargo and
Ward (1995)
Camargo and
Ward (1995)
Camargo and
Ward (1995)
Camargo and
Ward (1995)
Camargo et al.
(2005)
Camargo et al.
(2005)
Scott and
Crunkilton (2000)
Lafrancois et al.
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Species
Daphnia schoedleri
Common Name
Water flea
Life
Stage
adult
N Concentration
(mg NO3" -N/L)
1.06
Observed Effects
(low ambient N-deposition): decreased biomass in
response to NO3
Reference
Lafrancois et al.
(Lafrancois,
2004)
Vertebrates
Amphibians
Rana temporaria
Rana sylvatica
Ambystoma
jeffersonianum
Ambystoma maculatum
Ambystoma gracile
Rana aurora
Myla regilla
Pseudacris triseriata
Rana pipiens
Bufo americanus
Fish
Oncorynchus mykiss
(anadromous)
Oncorynchus mykiss
(nonanadromous)
Oncorynchus mykiss
(nonanadromous)
Oncorynchus
tshawytscha
Salmo clarki
Salmo clarki
Coregonus
clupeaformis
Miscellaneous
Saprolegnia spp.

common frog
wood frog
Jefferson's
salamander
spotted
salamander
northwestern
salamander
red-legged frog
Pacific tree frog
striped chorus
frog
northern leopard
frog
American toad

steelhead
rainbow trout
rainbow trout
Chinook salmon
cutthroat trout
(Lahontan)
cutthroat trout
(Lahontan)
lake whitefish

pathogenic water
mold

larvae
fertilized
eggs
fertilized
eggs
fertilized
eggs
larvae
larvae
larvae
tadpole
tadpole
fertilized
eggs

eggs
eggs
fry
fry
eggs
fry
embryo



5 (70-d NOEC)
9 (NOAEL)
9 (NOAEL)
9 (NOAEL)
5-20
5-20
5-20
10 (100-d LOEC)
10 (100-d LOEC)
9.0 (NOAEL)

1.1 (30-d NOEC)
1.1 (30-d NOEC)
1.1 (30-d NOEC)
2.3 (30-d NOEC)
2.3 (30-d NOEC)
4.5 (30-d NOEC)
6.25 (~120-d NOEC)

5-20 mg NO3/L

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

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

decreased ability to infect and kill the larvae of the
red-legged frog, Rana aurora

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)

Kincheloe et al.
(1979)
Kincheloe etal.
(1979)
Kincheloe etal.
(1979)
Kincheloe etal.
(1979)
Kincheloe etal.
(1979)
Kincheloe etal.
(1979)
McGurk et al.
(2006)

Romansic et al.
(2006)
     NOEC = No-observed-effect concentration; NOAEL = No-observed-adverse-effect 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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 1    sources, including both atmospheric and non-atmospheric sources. Other key elements include the
 2    dilution capacity of the watershed, which reflects the volume of water available to dilute added N, and
 3    flushing rate, which reflects the time required for inflowing water to replace estuary volume, (Bricker
 4    et al., 1999; NRC, 2000). Other potentially important factors can include the following (NRC, 2000):
 5          •   Physiography (geomorphology, dominant biological communities, biogeographic province);

 6          •   Type of primary production base (i.e., seagrasses, phytoplankton, coral, attached intertidal
 7              algae, etc.);

 8          •   Stratification and extent to which phytoplankton occupy the nutrient-rich photic zone; and

 9          •   Allochthonous inputs of organic matter.

10          A number of factors control the N loading rates to estuaries and the potential effects of N
11    deposition on nutrient loading and algal blooms. Estuaries communicate with fresh water on the upstream
12    side and with the ocean on the downstream side. The flushing of fresh river water through the system and
13    the movement and mixing of salt water from the ocean are complicated and are always changing in
14    response to weather and tidal cycles. The surface area, volume, and depth of the estuary are also critical
15    factors governing the sensitivity of an estuary to N inputs. Decreases in grazer, filter-feeder, and higher
16    trophic level populations offish and shellfish  exacerbate problems associated with nutrient over-
17    enrichment (Jackson et al., 2001).
18          At the upstream end of an estuary, the water is primarily fresh much of the time. Discharge of N
19    from the land surface, only a part of which is of atmospheric origin (mainly as deposition to the land that
20    subsequently leached to the river water), dominates new N inputs. Further downstream within the estuary,
21    where fresh water is more thoroughly mixed with saltwater,  much of the terrestrial N load is assimilated
22    by phytoplankton and benthic flora or removed by microbes in the process of denitrification (Paerl, 2002).
23    The importance of atmospheric N as a contributor to the total N load beyond this zone probably increases,
24    but there are no data to evaluate that.
25          The principal watershed features that control the amount of increased N flux to estuaries in the U.S.
26    include human population, agricultural production, and the size of the estuary relative to its drainage
27    basin (Peierls et al., 1991; Caddy, 1993;  Fisher et al., 2006). Dense human populations generate large
28    volumes of nutrient-rich wastewater. Tertiary sewage treatment can reduce effluent N concentrations to
29    less than 35 (iM, but these technologies have not been promoted as aggressively in the U.S. as elsewhere
30    (Conley et al., 2002; EPA,  2003). Agricultural production is heavily dependent on fertilizer application to
31    generate high yields from small areas. Fertilizer application has dramatically increased NO3
32    concentrations in ground water in many agricultural areas (Bohlke and Denver, 1995), which can leach to
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 1    surface waters. Large terrestrial drainage basins that drain into small estuaries tend to have high nutrient
 2    flux if the land is heavily populated or used for agriculture.
 3         In addition to estuaries, coastal marine ecosystems are highly susceptible to nutrient enrichment,
 4    especially from N. Land clearing, agricultural land use, sewage treatment discharge, and atmospheric
 5    deposition can all result in high loadings of N to the coastal zone. Excessive N inputs contribute to a
 6    range of impacts, including enhanced algal blooms, decreased distribution of seagrasses, and decreased
 7    dissolved oxygen (DO) concentration (Valiela et al., 1992; Nixon, 1995; Borum,  1996; Bricker, 1999).
 8    Because of human population growth and the great popularity of coastal areas, there is substantial
 9    potential for increased N loading to coastal ecosystems from both atmospheric and non-atmospheric
10    sources.


            C.5.1. Interacting Factors with Productivity
11         Results of empirical observations and short-term (3 weeks) marine mesocosm experiments suggest
12    that there can be wide variation in the response of autotroph biomass to nutrient addition (Cloern, 2001;
13    Olsen et al., 2006). Such variation may be attributable to the time scale of the observations, rate of water
14    exchange, grazing pressure, and other environmental factors (Olsen et al., 2006).


            C.5.2. Hydrology Interactions with Phytoplankton  Biomass
15         River discharge has a huge influence on the hydrology and nutrient cycling of estuaries. For
16    example, discharge from the large watershed of the Susquehanna River is important to the seasonal and
17    interannual variability in the hydrology of Chesapeake Bay (Fisher et al., 1988; Malone et al., 1988).
18    When discharge from the Susquehanna River is low, summer phytoplankton biomass in Chesapeake Bay
19    tends to be low compared to spring conditions, and the phytoplankton community is dominated by small
20    and flagellated forms (Marshall and Lacouture, 1986). Under higher river flows, summer phytoplankton
21    biomass in the bay is higher, and has an increased prevalence of diatoms (Paerl et al., 2006).
22         Hydrologic variation interacts with nutrient supply to control phytoplankton seasonal patterns in
23    Chesapeake Bay. High biomass during the spring diatom bloom leads to consequent sedimentation of
24    organic material out of the photic zone during the transition to summer (Malone et al., 1996; Harding
25    et al., 2002). Microbial decomposition of this material then fuels the pattern of summer anoxia in bottom
26    waters  (Paerl et al., 2006). N loading to Chesapeake Bay and its tributaries during spring high runoff
27    periods contributes to periods of P limitation and co-limitation (Boynton et al., 1995). The ecosystem then
28    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
 1          Streams, and to a lesser extent lakes, can serve as indicators of regional environmental change
 2    (Seastedt et al., 2004), partly because they integrate conditions within their watersheds including
 3    atmospheric, edaphic, geologic, and hydrologic conditions. Streams reflect the terrestrial environment
 4    most closely during high flow when much of the stream water enters the channel from the upper soil
 5    where most of the biological activity occurs. The terrestrial signal can be less clear in lakes because they
 6    have the capacity to store water and modify water chemistry through internal processes to a greater degree
 7    than streams (Lawrence et al., in review).
 8          Young and Sanzone (2002) provided a checklist of ecological attributes that should be considered
 9    when evaluating the effects of an environmental stressor on the integrity of ecological systems (Table C-
10    7). The Essential Ecological Attributes (EEAs) listed in the table represent groups of related ecological
11    characteristics (Harwell et al., 1999), including landscape condition, biotic condition, chemical and
12    physical characteristics, ecological processes, hydrology and geomorphology, and natural disturbance
13    regimes. The first three ecological attributes listed in Table C-7 can be classified primarily as "patterns,"
14    whereas the last three are "processes" (cf Bormann and Likens, 1979). They can be affected by a variety
15    of environmental stressors (Figure C-4).
16          Of concern in this annex are relationships between NOX atmospheric deposition, derived from
17    anthropogenic sources, and one or more of the EEAs. The ranges of likely changes in ecosystem patterns
18    and processes associated with changes in deposition are discussed in the subsections that follow.
19          The following  discussion assesses and characterizes the overall condition or integrity of
20    ecosystems within the U.S. that are affected by the deposition of atmospheric N and its role as a nutrient.
21    The six EEAs - landscape condition, biotic condition, chemical/physical characteristics, ecological
22    processes, hydrology/geomorphology, and natural disturbance regimes  (Table C-7) - provide a
23    hierarchical framework for assessing ecosystem status. Characteristics related to structure, composition,
24    or functioning of ecological systems may be determined by the use of endpoints or ecological indicators
25    of condition that are measureable and significant either ecologically or to society (Harwell et al., 1999).
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       Table C-7. 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
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1
2
3
4
5
6
7
                      Hydrolic Alteration
                      Habitat Conversion
                      Habitat Fragmentation
                      Climate Change
                      Invasive Non-native Species
                      Turbidity/Sedimentation
                      Pesticides
                      Disease/Pest Outbreaks
                      Nutrient Pulses
                      Metals
                      Dissolved Oxygen Depletion
                      Ozone (Tropospheric)
                      Nitrogen Oxides
                      Nitrates
                                                    Hydrolic Alteration
                                                    Habitat Conversion
                                                    Habitat Fragmentation
                                                    Climate Change
                                                    Over-Harvesting Vegitation
                                                    Large-Scale Invasive
                                                       Species Introduction
                                                    Large-Scale Disease/Pest
                                                       Outbreaks
                    w
                   Hydrolic Alteration
                   Habitat Conversion
                   Climate Change
                   Turbidity/Sedimentation
                   Pesticides
                   Nutrient Pulses
                   Metals
                   Dissolved Oxygen Depletion
                   Ozone (Tropospheric)
                   Nitrogen Oxides
                   Nitrates
                   Sulfates
                   Salinity
                   Acidic Deoosition
                                                                    Hydrology/
                                                                  Geomorphology
                            Hydrolic Alteration
                            Habitat Conversion
                              Climate Change
                    Over-Harvesting Vegitation
                       Disease/Pest Outbreaks
                           Altered Fire Regime
                         Altered Flood Regime
                                                                                                   o
                                                                                                   
                                                      w
Hydrolic Alteration
Habitat Conversion
Climate Change
Pesticides
Disease/Pest Outbreaks
Nutrient Pulses
Dissolved Oxygen Depletion
Nitrogen Oxides
Nitrates
Sulfates
                                             (A
                                             O
                                             (/>
                                             (A
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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 1          Changes in the biodiversity, composition, and structure of ecosystems relate directly to functional
 2    integrity. Changes in biodiversity are of particular significance in altering ecosystem function. The energy
 3    obtained by plants (producers) from sunlight during photosynthesis and the chemical nutrients taken up
 4    by those plants from the soil and the atmosphere are transferred to other species (consumers) within the
 5    ecosystem through food webs. The movement of chemical nutrients through an ecosystem is cyclic, as the
 6    nutrients are used or stored and eventually returned to the soil by microorganisms and fungi
 7    (decomposers). Energy is transferred among organisms through the food webs and eventually is
 8    dissipated into the environment as heat. The flows of energy and cycling of nutrients provide the
 9    interconnectedness among the elements of the ecosystem and transform the community from a random
10    collection of numerous species into an integrated whole.
11          Human existence and welfare on this planet depend on life-support services provided by the
12    interaction of the EEAs. Both ecosystem structure and function play essential roles in providing goods
13    and services (Table  C-8) (Daily, 1997). Ecosystem processes provide diverse benefits including
14    absorption  and breakdown of pollutants, cycling of nutrients, binding of soil, degradation of organic
15    waste, maintenance  of a balance of gases in the air, regulation of radiation balance and climate, and
16    fixation of solar energy (Westman, 1977; Daily, 1997; World Resources Institute, 2000). These ecological
17    benefits, in turn, provide economic benefits and values to society (Costanza et al., 1997; Pimentel et al.,
18    1997). Goods such as food crops, timber, livestock, fish, and drinking water have market value. The
19    values of ecosystem services such as flood-control, wildlife habitat, cycling of nutrients, and removal of
20    air pollutants are more difficult to measure (Goulder and Kennedy, 1997).  See discussion in Annex F.
21          Biodiversity is an important consideration at all levels of biological  organization, including species,
22    individuals, populations, and ecosystems. Human-induced changes in biotic diversity and alterations in
23    the structure and functioning of ecosystems are the two most dramatic ecological trends of the past
24    century (Vitousek, 1997b; EPA, 2004). The deposition of nutrient N from the atmosphere has the
25    potential to alter ecosystem structure and function by altering nutrient cycling and changing biodiversity.
26    It is important to understand how ecosystems  respond to stress to determine the extent to which
27    anthropogenic stresses, including N deposition, affect ecosystem services and products (Table C-8).
28          Particular concern has developed within the past decade regarding the consequences of decreasing
29    biological diversity  (Hooper and Vitousek, 1997; Chapin et al., 1998; Ayensu et al., 1999; Wall,  1999;
30    Tilman, 2000). Human activities that decrease biodiversity also alter the complexity and stability of
31    ecosystems, and change ecological processes. In response, ecosystem structure, composition and function
32    can be affected (Figure C-5) (Pimm, 1984; Tilman and Downing, 1994; Tilman, 1996; Chapin et al.,
33    1998; Levlin, 1998; Peterson et al.,  1998; Daily and Ehrlich, 1999; Wall, 1999).
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       Table C-8. 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
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Food Supply
and Demand
Water Use and
Nutrient Loss
^
Water Availability

Freshwater
Supply and Demand
 Hydrologic
 CO2 and
 Temperature
 Changes
                N, CH4, N20
                Emissions
        Climate Change
    Land
Transformation
             Precipitation
           and Temperature
                                                                     Erosion,
                                                                     Changes
                                                                     in Water Flow
                                                                     and Temperature
                                     Forest Product
                                   Supply and Demand
                          Loss of Crop
                          Genetic Diversity
                         Change in
                         Transpiration
                         and Albedo
                              Loss and
                              Fragmentation,
                              of Habitat
                    Habitat
                   Change
     Biodiversity Loss
                                                                          Reduced Resilience
                                                                          to Change

                                                                               Source: Modified from Ayensu et al. (1999).

      Figure C-5 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

 1         Scientific understanding of N cycling in forested watersheds is complicated by ecosystem response

 2    to climatic variation, human land use, and various kinds of landscape disturbance, including insect

 3    infestation, wind storm, fire, and disease (Aber and Driscoll, 1997; Goodale, 2000) Mitchell et al., 2006).

 4    N dynamics in watersheds of mixed land use (i.e., agriculture, urban, forest) are even more complicated.

 5    It is clear that disturbances have major impacts on nutrient enrichment from N deposition, and that these

 6    effects can be long-lasting. Nevertheless, the scientific community is only in the early stages of learning

 7    how to quantify these interactions.

 8         Changes in land use can affect nutrient heterogeneity in the mineral soil of forest stands. For

 9    example, Fraterrigo et al. (2005) found that patterns of variance in soil C, N, and Ca2+ concentration

10    increased with the extent of intensive past land use in western North Carolina. Land use might alter the

11    local patchiness of soil nutrients by decoupling interactions among microclimate, topography, vegetation,

12    and soil biota. In particular, mechanical soil mixing and maintenance of agricultural monocultures can
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 1    homogenize soils in cultivated systems (Robertson et al., 1993; Paz-Gonzalez and Taboada, 2000). Such
 2    effects may be important if the land use is changed to forest. Similarly, changes in species composition
 3    can alter the spatial distribution of nutrients in litter inputs (Dijkstra and Smits, 2002; Fraterrigo et al.,
 4    2005).
 5          In the northeastern U.S., concentrations of N in streams of upland forested watersheds tend to be
 6    considerably lower than in streams draining watersheds with other land uses. In a comparison of small
 7    watersheds in eastern New York, concentrations of N were highest and most variable in a stream draining
 8    a watershed where the predominant land use was row crop production. Total dissolved N concentration in
 9    streams in sewered suburban and urban watersheds were somewhat lower and less variable than in
10    streams draining the agricultural watershed. Streams in urban and suburban watersheds may also
11    experience high episodic N loading caused by combined sewer overflows (Driscoll et al., 2003c).


            C.6.3. Timber Harvest and Fire
12          Timber harvest contributes to nutrient removal from the ecosystem via biomass export and
13    acceleration of leaching losses (Bormann et al., 1968; Mann et al., 1988). In particular, logging
14    contributes to loss of N and Ca2+ from the soil (Tritton et al., 1987; Latty, 2004).  The extent of nutrient
15    loss is determined, at  least in part, by the intensity of the logging and whether or not it is accompanied by
16    fire (Latty, 2004). The species composition of the regrowth  vegetation also has important effects on
17    nutrient cycling. Fire  is sometimes followed by establishment of N-fixing vegetation that provides
18    substantial sources of Nr (Johnson, 1995; Johnson et al., 2004). Tree species also vary dramatically in
19    their N cycling properties, especially  in their influence on litter mass and quality  (Finzi et al.,  1998;
20    Ferrari, 1999; Ollinger, 2002). Thus,  over time, the extent of effect of logging and fire on nutrient cycling
21    can increase, depending largely on shifts in tree species composition and the degree to which C and N
22    pools are altered in the mineral soil and the forest floor.
23          Dissolved N exports have been clearly shown to increase substantially after major watershed
24    disturbance, often reaching peak concentrations in streamwater within 1 to 3 years of disturbance, and
25    then returning to background concentrations after about 5 to 10 years (Likens et al.,  1978; Bormann and
26    Likens, 1979; Eshleman et al., 2000). Such transient NO3 leakage has been shown to occur subsequent to
27    both logging (Martin  et al., 1984; Dahlgren and Driscoll, 1994; Yeakley et al., 2003) and insect
28    infestation (Eshleman et al.,  1998, 2004).
29          The extent to which timber harvesting influences leaching of NO3  and base cations from soils to
30    drainage waters depends on changes in primary productivity, nutrient uptake by plants  and
31    microorganisms within the terrestrial  ecosystem, and hydrological pathways for transferring nutrients to
32    drainage water (Hazlett et al., 2007).  Because of the variety of responses and interactions of
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 1    environmental and forest litter and soil conditions, it is difficult to generalize about the influence of
 2    harvesting on N cycling (Grenon et al., 2004; Hazlett et al., 2007).
 3          We do know, however, that land use history constitutes a major influence on N leaching from
 4    forested watersheds that receive moderate to high levels of atmospheric N deposition (Pardo, 1995; Aber
 5    and Driscoll, 1997; Goodale, 2000; Lovett et al., 2000). The severity of effect and length of the recovery
 6    period probably vary according to the nature of the past disturbance. Extensive past logging appears to
 7    have considerable and long-lasting effects on nutrient cycling (Goodale and Aber, 2001; Fisk et al.,
 8    2002). Latty et al. (2004) compared soil nutrient pools and N cycling among three forest stands in the
 9    Adirondack Mountains: old growth, selectively logged, and selectively logged and then burned.  The
10    logging and fire had occurred about 100 years previously. Results suggested that even relatively light
11    logging, plus burning, may influence the extent of subsequent N limitation over time  scales of decades to
12    centuries (Latty, 2004). Models of forest ecosystem response to disturbance incorporate such long-lasting
13    effects of land use  on C and N storage, cycling, and release (Aber et al., 1997).
14          Chen and Driscoll (2004) simulated the response of five forested watersheds in the Adirondack and
15    Catskill regions of New York to changes in atmospheric deposition and land disturbance. Simulation
16    results suggested that forest harvesting caused increased leaching of base cations and NO3  from the
17    watersheds. These  changes also affected model projections of future pH and acid neutralizing capacity
18    (ANC) of lake water. Model results suggested that lakewater pH and ANC were lower in response to
19    forest cutting as compared with undisturbed conditions.
20          Nitrification rates at old growth sites in the White Mountains of New Hampshire (63 ± 4.3 kg
21    N/ha/yr) were  approximately double those at previously burned (34 ± 4.4 kg N/ha/yr) and previously
22    logged (29 ± 4.7 kg N/ha/yr) sites (Goodale, 2001).  Fire and logging disturbances had occurred about 100
23    years previously on these study sites. Nitrification increased as forest floor C:N ratio  decreased, resulting
24    in higher NO3  concentrations in streamwater. These results suggest that forest disturbance can have long-
25    lasting effects  on N cycling and the potential for N saturation.
26          Thus, disturbance can affect N cycling and the response of forest ecosystems to N deposition. In
27    addition, it also appears that vegetative changes stimulated by N deposition may affect the  frequency and
28    severity of disturbance. Excess Nr deposition is thought to  be impacting essential ecological attributes
29    associated with terrestrial ecosystems and how they respond to disturbance. Effects of Nr deposition
30    influence habitat suitability, genetic diversity, community dynamics and composition, nutrient status,
31    energy and nutrient cycling, and frequency and intensity of natural fire disturbance regimes. For example,
32    several lines of evidence suggest that Nr deposition may be contributing to greater fuel loads and thus
33    altering the fire cycle in a variety of ecosystem types (Fenn et al., 2003a).  Invasive grasses, which can be
34    favored by high N  deposition, promote a rapid fire cycle in many locations (D'Antonio and Vitousek,
35    1992). The increased productivity of flammable understory grasses increases the spread of fire and has
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 1    been hypothesized as one mechanism for the recent conversion of coastal sage shrub (CSS) to grassland
 2    in California (Minnich and Dezzani, 1998).
 3          High grass biomass has also been associated with increased fire frequency in the Mojave Desert
 4    (Brooks, 1999; Brooks and Esque, 2002; Brooks et al., 2004). This effect is most pronounced at higher
 5    elevation, probably because the increased precipitation at higher elevation contributes to greater grass
 6    productivity. Increased N supply at lower elevation in arid lands can only increase productivity to the
 7    point at which moisture limitation prevents additional growth. Fire was relatively rare in the Mojave
 8    Desert until the past two decades, but now fire occurs frequently in areas that have experienced invasion
 9    of exotic grasses (Brooks, 1999).


             C.6.4.  Insect Infestation and  Disease
10          Insect infestation and plant disease, via atmospheric N deposition, can alter the effects of nutrient
11    enrichment on forest ecosystems.  Such disturbances alter the pool of Nr in the forest floor with short-term
12    impacts on NO3 and base cation leaching. Positive influences of N deposition on root and seed biomass
13    of an annual plant, common ragweed, were suppressed by herbivory, which increased with higher
14    available plant shoot N (Throop, 2005).
15          Eshleman et al. (2004) applied a regional lithology-based unit N export response function model to
16    simulate NO3 export to streams in the Chesapeake Bay watershed. The model considered the geographic
17    distribution of bedrock classes and the timing and extent of defoliation by gypsy moth (Lymantria dispar)
18    larvae. Modeling results suggested that the regional annual NO3 -N export increased during the year
19    following peak insect defoliation by about 1500%, from an initial rate of 0.1 kg N/ha/yr to nearly 1.5 kg
20    N/ha/yr.
21          Between the mid-1980s and the early 1990s, the southward expanding range of the gypsy moth
22    traversed Shenandoah National Park, VA (Webb, 1999). Some areas of the park were heavily defoliated 2
23    to 3 years in a row. The White Oak Run watershed, for example, was more than 90% defoliated in both
24    1991 and 1992. The gypsy moth population in White Oak Run then collapsed due to pathogen outbreak.
25    This insect infestation of the forest ecosystem resulted in substantial effects on streamwater chemistry.
26    The most notable effects of the defoliation on park streams were dramatic increases in the concentration
27    and export of N and base cations in streamwater. Figure C-6 shows the increase in NO3  export that
28    occurred in White Oak Run. Following defoliation, NO3  export increased to previously  unobserved
29    levels and remained high for over 6 years before returning to predefoliation levels. The very low baseline
30    levels of NO3 export in park streams were consistent with expectations for N-limited, regenerating
31    forests (e.g., Aber, 1989; Stoddard, 1994). Release of NO3 to surface waters following defoliation was
32    likewise consistent with previous  observations of increased N export due to forest disturbance (e.g.,
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 1
 2
 3
 4
 5
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.
                  Change in Annual Streamwater Nitrate Flux
                        Following Watershed Defoliation
          6-
      I
      f -H
                              White Oak Run
                           First Year of
                         Mapped Defoliation
             80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
                                     Year
                                                                   Figure C-6. Effect of watershed
                                                                   defoliation by the gypsy moth
                                                                   caterpillar on N03~ 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 etal. (2003).
 7

 8          The elevated concentrations of NO3 following defoliation did not appear to contribute to baseflow

 9    acidification in White Oak Run. This was due to a concurrent increase in concentrations of base cations in

10    streamwater (Webb et al., 1995). Both NO3 and base cation concentrations also increased during high-

11    runoff conditions, although the increase in base cations did not fully compensate for the episodic increase

12    in NO3~. As a consequence, episodic acidification became more frequent and more extreme (Webb et al.,

13    1995).

14          Large trees in old growth forests may resorb less N from foliage than do younger trees on

15    previously logged or burned sites (cf. Killingbeck, 1996). This process would be expected to contribute to

16    an alleviation of N limitation on plant processes in old-growth forests (Latty, 2004). This  effect might

17    also extend to herbivores, which are often N-limited (Mattson, 1980). Latty et al. (Latty, 2004) attributed

18    the high severity of beech bark disease in old growth forests to such a mechanism. Beech  bark disease is
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 1    caused by an introduced scale insect (Crytococcus fagisuga), which has high N requirements (Wargo,
 2    1988; Houston, 1994). The N-rich foliage in the old growth forest may improve insect fitness,
 3    contributing to a higher rate of infestation in the old growth stands (Latty, 2004).
                                                                                         Source: Sullivan etal. (2003).

            C.6.5. Urbanization
 4          Perhaps the most noteworthy impact of urban land use on processes of nutrient enrichment from N
 5    deposition concerns the transport of Nr to N-limited estuarine and near-coastal waters. In agricultural, and
 6    especially in forested areas, it is generally expected that most atmospherically deposited N is taken up by
 7    terrestrial vegetation. Relatively little of the deposited N is available for transport to downstream
 8    receiving waters. This is not the case for urban land use. Urbanization often involves substantial clearing
 9    of vegetation and compaction of soil (Poff et al., 1997; Burges et al., 1998; Jones et al., 2000; Trombulak
10    and Frissell, 2000; Alberti et al., 2007). Due to the relatively large impervious surface area in the urban
11    landscape (buildings, roads, parking lots, etc.), a higher percentage of precipitation is routed directly to
12    surface waters, with less opportunity for vegetative uptake of deposited N (Arnold and Gibbons,  1996;
13    Montgomery and Buffington, 1998). Therefore, atmospheric N deposition contributes proportionately
14    more NC>3 to surface waters in urban settings than it does with other land uses. The reduction in riparian
15    and wetland coverage and functionality also diminishes the ability of the urban watershed to filter
16    contaminants from runoff, including atmospherically deposited N (Peterjohn and Correll, 1984).  Because
17    many large urban areas are both located close to the coastline and expected to receive relatively high
18    deposition, they can constitute sizeable sources of body contribution to estuarine and marine waters.
            C.6.6. Agriculture
19          Agricultural ecosystems are not sensitive to levels of N deposition typically found in the U.S.
20    Rather, such ecosystems often act as net sources of NH3 emissions rather than as sinks (Griinhage et al.,
21    1992; Krupa, 2003). Atmospheric N deposition can contribute a quantitatively important component of
22    the Nr requirements of pastures and croplands. In such settings, atmospheric N deposition provides an
23    additional chronic source of N fertilizer. This may be viewed as a beneficial outcome. Nevertheless, some
24    of the N that is atmospherically deposited on agricultural land may eventually leach to drainage waters
25    and contribute to eutrophication, especially in estuarine and near-coastal marine environments. Industrial
26    livestock operations also contribute substantial amounts of NHY to the atmosphere, some of which is
27    deposited on coastal waters.
28          Agriculture also experiences indirect effects of NOX emissions through the formation of ground-
29    level O3. Such effects are not considered in this review.

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            C.6.7. Other Disturbances
 1          In some ecosystems, chronic additions of atmospherically derived N may have had irreversible
 2    consequences that involve interactions with invasive, non-native plants. For example, California has
 3    many plant species that occur in shrub, forb, and grasslands that receive high N deposition. There are up
 4    to 200 sensitive plant species in southern California coastal sage scrub (CSS) communities alone (Skinner
 5    and Pavlik, 1994). About 25 plant species are thought to be extinct in California, most of them forbs that
 6    occurred in sites that have experienced conversion to annual grassland (EPA, 2005). As CSS vegetation
 7    continues to convert to grassland dominated by invasive species, loss of additional rare plant species may
 8    occur. Invasive plant species are often identified as a major threat to rare native plant species. However,
 9    the occurrence of invasive species may combine with other stress factors, including N deposition, to
10    promote increased productivity of invasive species at the expense of native species.
11          As sensitive vegetation is lost, wildlife species that depend on these plants can also be adversely
12    affected. There are several threatened or endangered wildlife species listed by the U.S. Fish and Wildlife
13    Service, including the  desert tortoise (Gopherus agassizii) and checkerspot butterfly that are native to
14    plant communities in California thought to be sensitive to atmospheric N input. A native to the San
15    Francisco Bay area, the bay checkerspot butterfly has declined in association with invasion of exotic
16    grasses that replaced the native forbs on which the butterfly depends. In particular, the larval stage of the
17    butterfly is dependent  on Plantago erecta, which is increasingly being outcompeted by exotic grasses
18    (EPA, 2005).
19          Decline in the population of the desert tortoise may be due to a number of co-occurring stresses,
20    including grazing, habitat destruction, drought, disease, and a declining food base. In the desert shrub
21    inter-spaces, sites where native forbs once flourished, invasive grasses now dominate, reducing the
22    nutritional quality of foods available to the tortoise (Nagy et al., 1998; Fenn, 2003a). N deposition
23    contributes to the productivity and density of grasses at the expense of native forbs (Brooks, 2003).


            C.6.8. Multiple  Stress Response
24          Ecosystems are  often subjected to multiple stressors, of which nutrient enrichment from
25    atmospheric deposition of N is only one. Additional stressors are also important, including O3 exposure,
26    climatic variation, natural and human disturbance, the occurrence of invasive non-native plants, native
27    and non-native insect pests, and disease. Atmospheric N deposition interacts with these other stressors to
28    affect ecosystem patterns and processes in ways that we are only beginning to understand.
29          For example, terrestrial ecosystems at many locations are subjected to high N deposition and high
30    exposure to O3. This is especially true in portions of southern California and the Appalachian Mountains.
31    Mixed conifer forests in the San Bernardino and San Gabriel mountains in southern California are

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  1     exposed to high levels of atmospheric O3 and receive atmospheric N deposition in the range of about 5 to

  2     over 30 kg/ha/yr (Takemoto et al., 2001). Spatial variability in N deposition is high due to the patchy

  3     characteristics of these forests and associated canopy effects on dry deposition processes. The forest

  4     ecosystems have reached N-saturation, as evidenced by high NO3  concentrations in stream water.

  5     However, evaluation of N effects on vegetation is complicated by the concurrent effects of O3, which has

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

  7     summarized N/O3 interactions and consequent effects.

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

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

10     growth and N deposition enhancing growth of pine trees (Grulke and Balduman, 1999).

11           Jeffrey and ponderosa pine are the most sensitive western coniferous tree species to injury from  O3

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

13     Mountains, O3 concentrations have been high enough to cause visible foliar injury to these species and

14     reduced needle retention (Bytnerowicz, 2002). Reduced radial growth has also been observed (Peterson

15     et al., 1987, 1991). In the San Bernardino Mountains, trees of these species that exhibit severe foliar

16     injury from O3 do not show growth reductions (Arbaugh et al., 1999), and this has been attributed to the

17     fertilizing effects of high N deposition (Takemoto et al., 2001; Bytnerowicz, 2002). This may be an

18     example of counteracting effects from O3 and N air pollution. It is also possible that N deposition in the

19     western Sierra Nevada Mountains may increase growth of pines, especially on nutritionally poor granitic

20     soils (Takemoto et al., 2001).
       Table C-9. Ecological effects of N deposition described for study sites in the Western U.S.

       P  .  ,  .                                                      Possibility of Broader
       Environmental impact         Location        Level of Uncertainty              "                  Reference
       Effects in Aquatic Systems

       Elevated NO3~ in runoff;    Transverse ranges of        Well-documented      It is unclear how          Williams et al. (1996),
       most severe in southern    southern California; low-     response            widespread this          Fen n and Poth (1999),
       California and in chaparral  elevation catchments in the                     phenomenon is outside the  Fenn et al. (Fenn, 2003)
       catchments in the        Sierra Nevada; high-elevation                    ecosystems listed, because
       southwestern Sierra      catchments in the Colorado                     there is littler information
       Nevada               Front Range                                from low-elevation systems
                                                                   in the Sierra Nevada and
                                                                   elsewhere.

       N enrichment and shifts in  Colorado Front Range; Lake   Documented for two   These effects seem likely in  Baron et al. (2000),
       diatom communities in     Tahoe (California/Nevada     lakes east of the      other N-enriched lakes but  Wolfe et al. (2001),
       alpine lakes            border)                  Continental Divide and  have not been investigated.  Goldman (1988)
                                                 Lake Tahoe

       Reduced lake water clarity  Lake Tahoe              Well-documented      Lake Tahoe is an unusual    Jassby et al. (1994)
       and increased algal       (California/Nevada border);   response; N and P     case because of its        Sickman et al. (2003a)
       growth               high-elevation lakes         deposition believed to  renowned lake clarity;
                           throughout central and       be important factors    extent of occurrence
                           southern Sierra Nevada                        elsewhere in northern Sierra
                                                                   Nevada is unknown.
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Ecological or
Environmental Impact
         Location
                             Level of Uncertainty
                        Possibility of Broader
                             Occurrence
                            (at other sites)
                                                                                         Reference
Increased NO3-
concentrations in high-
elevation lakes
Several regions, mainly
downwind of urban centers
Fairly well established
from lake surveys, but
more data needed for
improved definition of
frequency and severity
Evidence suggests that
urban plumes and
agricultural emissions affect
lake NO3- levels. There is
also evidence of impacts on
low-elevation lakes.
Sickman et al. (2002)
Effects in Terrestrial Systems
Enhanced growth of
invasive species
Lichen community
changes
Deleterious effects on
threatened and
endangered species
Altered fire cycle


Costal sage scrub, southern
California; San Francisco Bay
area
Parts of the Pacific Northwest;
many areas in California;
north and central Colorado
San Francisco Bay area;
southern California
Coastal sage scrub in
southern California


N deposition,
fertilization studies,
and plant community
data supportive, but
moderate uncertainty
remains
Well-established
response; a highly
sensitive air pollution
indicator
Supportive evidence,
but high degree of
uncertainty about the
precise role of N
deposition
Hypothesis based on
observations,
fertilization studies,
and N deposition and
N cycling data; high
level of uncertainty
It is not known if this effect
occurs elsewhere, but it is
expected that nitrophilous
species will be selected for if
N accumulates in soil.
Because of the sensitivity of
many lichen species, it is
likely that this effect occurs
elsewhere.
There is a high likelihood of
effects in some habitats
where N accumulates in
soils
Because it has not been
studied elsewhere, it is
uncertain whether this
effect occurs in other areas.
Weiss (1999), Allen et al. (in
press)
Nash and Sigal (Nash, 1999)
Weiss (1999), Brooks (2003)
Allen et al. (in press)


Altered forest C cycling
and fuel accumulation
                         San Bernardino Mountains
                             Documented response
                      It is uncertain whether this
                      effect occurs in other areas.
                           Grulke and Balduman (1999)
Physiological perturbation
of overstory species
Forest expansion into
grasslands
N emissions as a major
contributor to regional
haze problem
NOx emissions as
precursors for phytotoxic
levels of O3, leading to O3
injury to sensitive plant
species
San Bernardino Mountains      Documented response
Great Plains of western
Canada
National forests and parks
throughout California, the
Pacific Northwest, and some
sites in the Interior West

Southern California; Sierra
Nevada
Supportive evidence
found, but high degree
of uncertainty as to
the role of N
deposition

Well-established
effect; contribution
from Nous pollutants
has been quantified

Well-established effect
This effect has not been
widely studied but is
expected for sensitive plant
species exposed to O3 and
adapted to N  limitation but
growing in N-enriched soils.

It is not know whether this
effect occurs  in other areas.
This is known to occur in
areas far removed from
emissions sources because
of long-range transport.

Significant O3 injury to
vegetation has not been
reported from other sites
downwind of urban centers
but cannot be ruled out as
urban regions expand.
                                                  Grulke et al. (1998),
                                                  Grulke and Balduman (1999),
                                                  Takemoto et al. (2001)
Kbchy and Wilson (2001)
Fenn etal. (2003c),
IMPROVE data (4 March 2003;
http://vista.circa.colostate.edu/
improve)

Miller and McBride (1999),
Carroll et al.  (2003)
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 D.  Critical  Loads
      D.1. Background
 1          Critical loads and critical levels are used to express how much deposition of an atmospheric
 2    pollutant (a "load") or how large a concentration of an airborne pollutant (a "level") can be tolerated by
 3    natural or artificial systems without significant harm or change occurring in those systems (see
 4    Section D.2.1). The critical load and critical level approaches to quantifying the effects of pollutants
 5    attempt to estimate the atmospheric deposition load or concentration that would be likely to cause
 6    environmental harm. The expectation is that environmental harm can be avoided by keeping pollution
 7    levels or loads below these critical values. This approach is commonly used to estimate loads or levels of
 8    pollution required to protect lakes, streams, or forest soils from environmental harm. The basic principles
 9    are, however, transferable to any sensitive receptor. Since the present evaluation deals primarily with the
10    effects of atmospheric deposition of S and N compounds, this chapter focuses on critical loads more than
11    critical levels.
12          Most critical load studies in North America have been undertaken in Canada. The critical load
13    approach has been used in Canada to design emission reduction programs (RMCC, 1990; Jeffries, 1993).
14    Modeling of critical loads for the 1997 Canadian Acid Rain Assessment (Jeffries, 1997) was conducted
15    for six regional clusters of lakes, four in eastern Canada, one in Alberta, but also the Adirondack
16    Mountains in New York. More recently, critical loads have been determined and mapped for waters
17    (Hindar, 2001; Henriksen, 2002; Aherne, 2004; Dupont, 2005; Watmough, 2005) and forest soils (Arp,
18    1996; Moayeri, 2001; Watmough, 2003; Ouimet, 2006), for a number of regions in eastern Canada. There
19    have also been a number of regional critical loads studies (cf Henriksen, 2001; Ouimet, 2006) focused on
20    acid-sensitive lakes on the Canadian pre-Cambrian shield. Much of this work is summarized and
21    presented, along with steady state critical load maps for eastern Canada, in the 2004 Canadian Acid
22    Deposition Science Assessment (Jeffries, 2005).
23          At the regional, cross-border level, critical loads in northeastern North America have been
24    developed by a joint U.S.-Canadian cooperative. The Conference of New England Governors and Eastern
25    Canadian Premiers (NEG/ECP) has undertaken a program with the objective to "estimate sustainable
26    acidic deposition rates and exceedances for upland forests representative of the New England States and
27    the Eastern Canadian Provinces..." (NEG/ECP Forest Mapping Group, 2001). The Forest Mapping
28    Working Group within the NEG/ECP conducts regional assessments of the sensitivity of northeastern
29    North American forests to current and projected S and N emissions levels. The group is charged with
30    identifying specific forested areas most sensitive to continued S and N deposition and estimating

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 1    deposition rates required to maintain forest health and productivity at large spatial scales (cf. Miller,
 2    2006). The NEG/ECP has also provided estimates of critical loads for surface waters in northeastern
 3    North America (Dupont, 2005).
 4          Aside from the NEG/ECP studies, the use of critical loads to assess S and N deposition effects in
 5    the U.S. has not been as geographically extensive as elsewhere in North America or Europe. Most critical
 6    loads studies in the U.S. have focused on smaller sub-regional areas or individual sites. Critical loads
 7    studies for forests in the U.S. have been centered in the northeast and have usually used a catchment-
 8    based approach (Pardo, 1993; Pardo, 1996; cf. Aber, 2003; Driscoll, 2003). Critical load studies for
 9    surface waters have been more extensive along the eastern seaboard. Critical loads have been estimated
10    for lakes in the Northeast (cf. Driscoll, 2001; Pembrook, 2004) and for streams in the Mid-Atlantic States
11    and central Appalachians (cf. Sverdrup et al., 1992; Sullivan et al., 2004). In the western U.S. there have
12    been a few studies of critical loads for acidification of surface waters  (cf. Sullivan et al., 2004). The
13    primary concern in the West, however, has been the critical load of N deposition affecting both terrestrial
14    and aquatic resources through eutrophication and/or through N enrichment and its impact on community
15    structure (cf. Baron, 1994; Baron, 2000); Williams and Tonnessen, 2000; Fenn et al., 2003; Nydick et al.,
16    2003, 2004a; Wolfe et al., 2003; Burns, 2004; Stevens, 2004; Baron, 2006); Bowman et al., 2006).
17          The critical load approach has been used extensively in Europe for organizing information about
18    effects, and for specifying emissions reductions that would be required to protect ecosystems and other
19    sensitive receptors from the harmful effects of atmospheric S N deposition. During the 1970s, it was
20    recognized that transboundary air pollution in Europe had adverse ecological and economic
21    consequences. In response, the countries of the UN Economic Commission for Europe (UNECE)
22    developed the Convention on Long-range Transboundary Air Pollution (LRTAP), the first international
23    legally binding instrument to deal with problems of air pollution on a broad regional basis (see
24    http://www.unece.org/env/lrtap). Signed in 1979, it entered into force in 1983. The LRTAP Convention
25    requires that its Parties cooperate in research into the effects of S compounds and other major air
26    pollutants on the environment, including agriculture, forestry, natural vegetation, aquatic ecosystems, and
27    materials. To this end, the Executive Body for the Convention established a Working Group on Effects
28    (WGE) that is supported by a number of International Cooperative Programmes (ICPs). The ICP for
29    Mapping and Modeling generated maps of critical loads for all of Europe in 1995 (Posch et al. 1995).
30    Those maps are modified on a continuing basis (e.g. Posch et al. 2001). By comparing current or expected
31    future deposition to the critical loads maps, mapped estimates of exceedances have been generated. An
32    exceedance is the  amount of S and N deposition that occurs at some specific time (past, current, or
33    future), above the critical load of deposition that would be required to protect against adverse effects on
34    the environment. The maps of estimated exceedances are used in negotiations to regulate pollutant
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 1    emissions in Europe (for example, the 1999 Gothenberg Protocol via the UNECE Convention on
 2    LRTAP).
 3          Outside of North America and Europe, there is an increasing use of critical loads for assessment
 4    purposes, and to inform policy development. Examples include studies in Siberia (Bashkin et al., 1995),
 5    Thailand (Milindalekha et al., 2001), and South Africa (Van Tienhoven et al., 1995). In China, several
 6    studies have been carried out to  study the sensitivity of surface waters to acidification and the critical
 7    loads of acid deposition (Duan et al., 2000a; Li et al., 2000; Ye et al., 2002; Hao et al., 2001), and to
 8    calculate the critical loads of S and N acidity for soils at both the local and regional scales (Zhao and
 9    Seip, 1991; Xie et al., 1995; Duan et al., 2000b, 2001).


            D.1.1. The Critical  Load Process
10          The process of estimating critical loads is not a purely scientific enterprise. Management or policy
11    input to the process is needed to insure that the appropriate science is included and the appropriate
12    questions are addressed. The critical load process integrates knowledge of a multitude of physical,
13    chemical and biological mechanisms affected by ambient air quality, and presents the current scientific
14    understanding in a format that is most useful for assessing current or future management practices and
15    policy decisions regarding air quality, or the resources affected. The critical loads process provides
16    decision-making insight based on both scientific evidence and policy priorities.
17          Science and policy are closely coupled in the critical loads process. In the development of critical
18    load estimates, it is important to identify those elements that are essentially scientific in nature as opposed
19    to those elements that are driven by management or policy priorities. The scientific elements include tasks
20    such as: relating ambient  air quality to pollutant deposition, quantifying the relationships between
21    pollutant deposition and resource responses, identifying the resources at risk to adverse effects,
22    understanding the temporal and  spatial responses of resources to pollutant deposition, and more. The
23    policy-dependent elements include tasks such as: identifying the environmental resources to be protected,
24    establishing appropriate criteria  for different land use areas (e.g., Class I areas, national parks, wildlife
25    refuges), defining significant harm to protected resources, and more. When all elements are integrated, it
26    is apparent that the critical load process provides a framework for alternate ways of examining and
27    understanding the cascade of effects from ambient air quality to resource effects, described in the
28    preceding four annexes. Changing scientific assumptions  or understanding may result in different critical
29    load estimates for the same resources. Changing policy or management assumptions or priorities may also
30    result in different critical  load estimates.
31          There is, therefore, no single "definitive" critical load for a natural resource.  Critical load estimates
32    are explicitly linked to policy, but their reliability is conditioned on the soundness of the underlying
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 1    science. As elements of the critical load process change, the critical load estimates will change to reflect
 2    both the current state-of-knowledge and policy priorities. Changes in scientific understanding may
 3    include: new dose-response relationships, better resource maps and inventories, larger survey datasets,
 4    continuing time series monitoring, improved numerical models, etc. Changes in the policy elements may
 5    include: new definitions of harm, new mandates for resource protection, focus on new pollutants, or
 6    inclusion of perceived new threats that may exacerbate the pollutant effects (e.g., climate change).
 7          The critical load process is thus an iterative process — as science changes, the content is updated;
 8    as policy needs change, the content is re-directed. Being iterative, the process allows incremental
 9    improvement in understanding resource responses to ambient air quality. Individual elements of the
10    process can be replaced as needed to reflect new science or policy. Continuing to update the process may
11    reduce uncertainty and risk, as new data or techniques allow refinement of existing pieces. The piecewise
12    nature of the process provides adaptability as new policy concerns arise, such as new pollutants or
13    mandates. As the critical load process advances, a "library" of critical load estimates will result.
14    Examining and comparing these accumulated results, and their underlying scientific and policy bases,
15    may produce a "weight of evidence" consensus, even if any single estimate entails substantial uncertainty.
            D.1.2.  Organization of this Annex
16          This Annex chapter is intended as a review of the current state of critical loads science. It is not the
17    intention to address questions of management or policy other than to point out where these activities
18    influence the critical loads process. The material in Section D.2 presents necessary definitions and
19    describes the conceptual framework for a critical load analysis. This framework identifies those elements
20    that are primarily scientific in nature and those elements that require policy input. The framework also
21    describes the steps that are taken in deriving a critical load estimate for a given resource. It is not an
22    objective of this Annex to provide details of all critical loads studies that have been implemented in the
23    U.S. or elsewhere. The conceptual framework, however, provides a generalized summary of the steps
24    most critical loads studies have followed. Section D.3 discusses the time frame of responses for
25    implementation of a critical load. Time frames of resource response are often ignored, or assumed
26    implicitly, in defining a critical load analysis. The time required to implement the policy and technology
27    to achieve a critical load can also affect the responses of the resources at risk. The time frames of
28    response are important for selecting the data and models used to estimate critical loads.  Section D-4
29    summarizes the tools (models and modeling approaches) commonly used in calculating critical loads. The
30    Annex concludes in Section 5 with a summary of the current agreement on critical loads uses in the U.S.
31    that was a product of the Multi-Agency Workshop on Critical Loads held in 2006. The workshop
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 1    produced a series of recommendations for current and future activities related to critical loads analyses in
 2    the U.S.
      D.2. Definitions and  Conceptual Approach
            D.2.1. Critical  Load Definitions
 3          Critical loads and critical levels are used to express how much deposition of an atmospheric
 4    pollutant (a "load") or how large a concentration of an airborne pollutant (a "level") can be tolerated by
 5    natural or artificial systems without significant harm or change occurring in those systems. The generally
 6    accepted definition of a critical load or a critical level of atmospheric pollutants emerged from a pair of
 7    international workshops held in the late 1980s (Nilsson, 1986; Nilsson, 1988). The workshop participants
 8    defined a critical load or a critical level as "a quantitative estimate of an exposure to one or more
 9    pollutants below which significant harmful effects on specified sensitive elements of the environment do
10    not occur according to present knowledge."
11          This evaluation deals primarily with the effects of atmospheric deposition of S and N compounds.
12    This Annex, therefore, will deal exclusively with the concept of critical loads of S and N compounds from
13    atmospheric deposition. Critical levels of pollutant concentration will not be addressed. As discussed in
14    previous annexes, the deposition of both S and N has acidifying effects on receptors (Annex C), and the
15    deposition of oxidized and/or reduced N compounds can produce eutrophication or nutrient-enrichment
16    effects in receptors (Annex D). The following material, therefore, will focus on critical loads of S and N
17    for acidification effects, and on critical loads of N  for nutrient effects.
18          In addition to the generic definition of a critical load/level presented above, the participants in the
19    second international workshop (the Skokloster Workshop) (Nilsson, 1988) developed  a number of specific
20    definitions related to known atmospheric pollutants. Two of those definitions are relevant to this Annex.
21          Recognizing that both S and N compounds contribute to the acidity  of deposition, the workshop
22    participants developed a definition for critical loads of S and N for acidification of an ecosystem: "the
23    highest deposition of acidifying compounds that will not cause chemical changes leading to long-term
24    harmful effects on ecosystem structure and function according to present knowledge." Recognizing that N
25    in both oxidized (e.g. NO, NO2, NO3 ) and reduced (e.g. NH3, MtO forms in deposition may influence
26    the eutrophication and nutrient balances of ecosystems, the workshop participants defined the critical load
27    of N for nutrient effects in an ecosystem as "the highest deposition of N as NHx and/or NOy below which
28    harmful effects in ecosystem structure and function do not occur according to present knowledge."
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 1          All three definitions can be applied to different receptors in a number of different environments
 2    (e.g., terrestrial ecosystems, transitional ecosystems, aquatic ecosystems, groundwater, agricultural crops,
 3    etc.). A sensitive element can constitute a part of, or the whole of, an ecosystem. Harmful effects can
 4    occur to individual organisms, to populations, or to entire communities within an ecosystem. Harmful
 5    effects can also be defined at the level of the ecosystem itself as changes in ecosystem processes,
 6    structure, and/or function.
 7          While the concepts expressed in these definitions of critical loads and levels are easily understood
 8    and intuitively satisfying, the application of the critical load concept requires careful consideration and
 9    definition of a number of terms and procedures. It is apparent that there can be many different critical
10    load values for a given atmospheric pollutant depending on the receptor or sensitive element(s) being
11    considered. There can also be multiple different atmospheric pollutants that can produce the same harmful
12    effects in a given receptor.  Therefore, the critical load of a given pollutant can potentially be dependent on
13    the deposition and/or atmospheric concentration of other pollutant species. Finally, the same atmospheric
14    pollutant can produce a variety of different disturbances in a sensitive ecosystem that might occur at
15    different pollutant loads. For example, N deposition produces both nutrient and acidification effects and
16    the critical load of N for each type of disturbance may be  different.
17          Therefore, in order to derive a quantitative estimate of the critical load of an atmospheric pollutant,
18    a number of factors must be identified and defined Figure D-l. These include disturbance type, receptor,
19    sensitive elements, and definition of what constitutes significant harm. In addition, a numerical
20    relationship between pollutant deposition and the identified receptor response must be formulated,
21    generally based on either an empirical dose-response relationship or a steady state or dynamic numerical
22    model simulation. The next section outlines the steps (decisions) that must be taken to implement this
23    process.
            D.2.2. Critical Load Analysis  Procedures
24          The development of a quantitative critical load estimate requires a number of steps. In this
25    discussion, Figure D-l is used to illustrate the procedure. The Figure is simplified to facilitate general
26    discussion and does not represent the full complexity of the choices that must be made, or the scientific
27    understanding underlying those choices.
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                                                  Table D-l
      Table D-1. An example of the matrix of information that must be considered in the definition and
      calculation of critical loads. Note that multiple alternative biological indicators, critical biological
      responses, chemical indicators, and critical chemical limits
1 ) Disturbance
2) Receptor
3) Biological
indicator
4) Critical
biological
response
5) Chemical
indicator
6) Critical
chemical
limit
7) Atmospheric
pollutant
8) Critical
pollutant load
Acidification
Forest
Sugar
Maple
Failure to
reproduce
Soil % Base
Saturation
10%
SO4, NO3,
NH4
???
Norway
Spruce
Seedling
death
Soil Ca/AI
ratio
1.0
SO4, NO3,
NH4 _
???
Lake
Brook trout
Presence
absence
Lakewater
ANC
0 ueq/L
SO4, NO3,
NH4
???
Fish species
richness
Species
loss
Lakewater
ANC
50 ueq/L
SO4, NO3,
NH4
???
Eutrophication
Grassland
Species
diversity
Species
loss
Soil C/N
ratio
20
NO3, NH4
???
Lake
Primary
productivity
Excess
productivity
Lakewater
NO3
10 ueq/L
NO3, NH4
???
 2          There are eight general steps that must be taken to define the basic critical load question in any
 3    analysis.
 4            1.  Identify the ecosystem disturbance of concern (acidification, eutrophication, etc.). Not all
 5               disturbances will occur in all regions or at all sites, and the degree of disturbance may vary
 6               across landscape areas within a given region or site.
 7            2.  Identify the landscape receptors subjected to the disturbance (forests, surface waters, crops,
 8               etc.) Receptor sensitivity may vary locally and/or regionally, and the hierarchy of receptors
 9               most sensitive to a particular type of disturbance may vary as well.
10            3.  Identify the biological indicators within each receptor that are affected by atmospheric
11               deposition (individual organism, species, population, or community characteristics).
12               Indicators will vary geographically and perhaps locally within a given  receptor type
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 1            4.  Establish the critical biological responses that define "significant harm" to the biological
 2               indicators (presence/absence, loss of condition, reduced productivity, species shifts, etc.).
 3               Significant harm may be defined differently for biological indicators that are already at risk
 4               from other stressors, or for indicators that are perceived as "more valued."
 5            5.  Identify the chemical indicators or variables that produce or are otherwise associated with the
 6               harmful responses of the biological indicators (stream water pH, Al concentration, soil base
 7               saturation, etc.). In some cases, the use of relatively easily measured chemical indicators
 8               (e-g-, surface water pH or ANC) may be used as a surrogate for chemical indicators that are
 9               more difficult to measure (e.g., Al concentration).
10            6.  Determine the critical chemical limits for the chemical indicators at which the harmful
11               responses to the biological indicators occur (e.g., pH < 5, base saturation <  5%, Al
12               concentrations >100 (ig/L, etc.). Critical limits may be thresholds for indicator responses such
13               as presence/absence, or may take on a continuous range of values for continuous indicator
14               responses such as productivity or species richness. Critical limits may vary regionally or
15               locally depending on factors such as temperature, existence of refugia, or compensatory
16               factors (e.g., high calcium concentration mitigates the toxicity of Al to fish  and  plant roots).
17            7.  Identify the atmospheric pollutants that control (affect) the pertinent chemical indicators
18               (deposition  of SO42 , NO3, NH/t, HNO3, etc.). Multiple pollutants can affect the  same
19               chemical variable. The relative importance of each pollutant in producing a given chemical
20               response can vary  spatially and temporally.
21            8.  Determine the critical pollutant loads (kg/ha/yr total deposition of S or NO3~N,  etc.) at which
22               the chemical indicators reach their critical limits. Critical pollutant loads usually include both
23               wet and dry forms of pollutant deposition. The critical pollutant load may vary regionally
24               within a receptor or locally within  a site (as factors such as elevation or soil depth vary) and
25               may vary temporally at the same location (as accumulated deposition alters chemical
26               responses).
27          The definition of the critical load problem for a region or individual site generally requires that we
28    work down the Table from top to bottom (Table D-l) What is the disturbance? What receptors are
29    affected? What indicator organisms are, or were previously present and observable? What chemical
30    indicators are changing and can be measured? What atmospheric pollutant is driving the changes in the
31    chemical indicators?
32          The derivation of a quantitative estimate of a critical load generally requires that  we work from the
33    bottom of the Table back towards the top, as indicated by the arrows in Table D-l. What is the maximum
34    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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 1    indicator response occurs, or does not occur? From this point of view, it can be seen that steps 8 and 6
 2    require the development of dose-response functions for the components of the ecosystem being
 3    considered (arrows in Table D-l). Step 8 describes the response of the chemical indicator as a function of
 4    the pollutant load, and Step 6 describes the responses of the biological indicator as a function of the
 5    chemical variable. As discussed in later sections, these response functions can be derived using empirical
 6    (e.g., statistical) or process-based (e.g., mechanistic) models that are either time-invariant (static or steady
 7    state) or time-variable (dynamic).
 8          Each step in the development of the critical load, as summarized in Table D-l can be classified as
 9    either a predominantly scientific task or as a task benefiting from, or perhaps requiring, collaboration and
10    input from scientists, decision-makers, and other interested parties. For instance, tasks 1, 2, and 3 can be
11    viewed as predominantly scientific tasks that can be completed by asking questions of fact. At task 4,
12    however, questions of what defines "significant harm" entail subjective elements that cannot be
13    determined by scientific techniques alone. In anticipation of the ultimate use of the critical load definition
14    to set policy or establish management strategies, it is appropriate that political, socioeconomic, or perhaps
15    ethical considerations be brought to bear in defining  "significant harm." To define "harm" is to imply a
16    corrective action, the cost of which will have to be borne by someone. Having reached agreement on task
17    4, however, tasks 5, 6, 7, and 8 are again predominantly scientific in nature, requiring determination of the
18    causal links, represented as response functions or models, leading from the loading of the pollutant to the
19    defined "significant harm."
20          This procedure will almost certainly result in calculation of multiple critical load values for a given
21    pollutant and analysis location. The multiple solutions derive from the nested sequence of disturbances,
22    receptors, and biological indicators that must be considered for a given  pollutant. Multiple critical load
23    values may also arise from an inability to agree on a  single definition of "significant harm" at step 4.
24    Finally, there is the inescapable heterogeneity of all natural environments. Consider soils for instance. The
25    high spatial variability of soils almost guarantees that for any reasonably sized soil-based "receptor" that
26    might be defined in a critical load analysis, there will be a continuum of critical load values for any
27    indicator chosen. The range of this continuum of values may be narrow enough to be ignored, but in any
28    critical load analysis there is nevertheless an a priori  expectation of multiple values, or of a range of
29    values.
30          The existence of multiple estimates of critical  loads for a given pollutant and receptor should
31    present no real problem. Examination of the range of critical loads derived may be deemed useful in
32    subsequent discussions of the analysis, and in the decision-making steps that may follow critical load
33    calculation. For instance, the lowest critical load of all those derived may be adopted as "the" critical
34    load, as is often done in Europe. This however, is a policy choice. The scientific task is the derivation of
35    the multiple values using best available information.
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            D.2.3. Target Load Definition
 1          As seen in the previous section, it is expected that a potentially large number of critical load values
 2    may be objectively determined for a given atmospheric pollutant and a given receptor. Like the definition
 3    of "significant harm," the choice of which critical load value to use for management or decision-making
 4    is subjective, and should be driven by socioeconomic, political, and ethical considerations. The target load
 5    concept was developed to address these issues. Target loads are deposition loads of a given pollutant,
 6    based on critical load estimates for the pollutant, which incorporate policy and/or management decisions
 7    about the amount of pollutant deposition, and therefore the amount of resource damage that is deemed
 8    acceptable. Target loads can be set at, above, or below the various critical loads. If the target load is set
 9    above some of the estimated critical loads, one accepts the inevitability that some of the ecosystem
10    components, generally the most sensitive, will be adversely affected. If the target load is set below all of
11    the estimated critical loads, a safety margin has been established to account for uncertainty inherent in the
12    process.
13          Given the spatial heterogeneity of natural systems, target loads might also be used to provide some
14    measure of "cumulative resource protection." As discussed above, there typically exists a range of critical
15    loads for a particular "significant harm" in a particular receptor. Selecting a target load within the range
16    will provide protection for the fraction of the receptor with critical loads above the chosen target load,
17    whereas that fraction with critical loads  below the chosen target load will be expected to suffer some harm
18    at that deposition level. In this way, it is possible to use the target load to define protection for some
19    cumulative proportion of the receptor (i.e., a target load for protection of 95% of the resource from
20    "significant harm").
21          While most of the steps involved in estimating critical loads depend on sound, objective scientific
22    analysis, the selection of target loads is almost entirely a subjective judgment. The selection of target
23    loads must begin with reliable estimates of critical loads to set the constraints, and define the expected
24    consequences of the target load choices. Nevertheless, the final decisions of which indicators are the key
25    indicators, how much cumulative resource should be protected, how much sooner or later resource
26    protection will be implemented, cannot be answered scientifically. Political, socioeconomic, and ethical
27    considerations will form the basis of the final target load selections. Frequently, the legal mandates for
28    various public lands would have a determinant influence on the selection of target loads. For instance,
29    Federal Class I areas may be held to one standard of harm because of mandates to protect "natural
30    condition," whereas Federal mixed-use lands may be held to a different standard of harm, and cropland to
31    yet another standard of harm.
32          It is also important to note that scientific understanding, modeling approaches, and the data used to
33    estimate critical loads are continually improving. Furthermore, the political, economic, and social
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 1    environments surrounding selection of target loads are also constantly shifting. Therefore, the analysis
 2    and estimation of critical and target loads must be an iterative process.
      D.3. Time Frame of Response
 3          The critical load definitions and procedures discussed in the previous section do not explicitly
 4    consider the time frame of ecosystem response. When is "significant harm" expected? How long will it be
 5    before existing harm is reversed? When should critical loads be implemented? How long should a critical
 6    load be maintained? The use of critical and target loads in resource management always has some time
 7    frame of expected response, and some context of management priorities. For instance, it may be that a
 8    target load well below the critical load would hasten the recovery of a receptor with existing harm. Or, it
 9    may be that a receptor that has not yet been damaged can sustain a target load above the critical load for
10    some finite period before incurring "significant harm." Such time frames can be very long (many decades
11    or centuries).
12          The time frame of response between implementation of a critical load and the corresponding
13    changes in biological or chemical indicators is a potentially important factor in establishing critical load
14    analysis procedures and in selecting the final target load. Analyses can be designed to provide estimates
15    of either "steady state critical loads" or "dynamic critical loads" depending on the perceived, or
16    mandated, importance of the time frame of response and the types of models (transfer functions) used.
17          Steady state critical loads analyses provide estimates of the long-term sustainable deposition of a
18    pollutant that will not cause "significant harm" to a receptor. This is the relevant information needed for
19    any receptor to provide protection from damage by the pollutant in perpetuity as the receptor comes into
20    equilibrium with the pollutant critical load (the implicit purpose of steady state analyses). However, no
21    information is given concerning the time to achieve the equilibrium or what may happen to the receptor
22    along the path to equilibrium. Estimated steady state critical loads for  receptors that are currently
23    damaged provide no information concerning when the desired long-term sustainable protection will occur
24    and the existing "significant harm" will be mitigated. There exists the  possibility that receptors with no
25    current damage could suffer "significant harm" while waiting for implementation of the critical load. The
26    possible occurrence, timing, and duration of such "interim periods of harm" are not the subject of steady
27    state analyses.
28          Dynamic critical loads analyses provide estimates of a specifically scheduled deposition load of a
29    pollutant that will not result in "significant harm" to a receptor at a specified time. This is the relevant
30    information needed for any receptor to provide protection from damage by the pollutant within a specified
31    time frame  (the explicit purpose of dynamic analyses). However, care  should be taken in interpreting the
32    results of dynamic analyses to ensure that "significant harm" to the receptor does not occur after the

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 1    specific timetable has been completed. Many receptors can tolerate higher loads of a pollutant for a few
 2    decades (a common length of specified schedules for dynamic analyses) than can be sustained over longer
 3    periods. The use of dynamic critical load estimates in such cases may provide protection from harm
 4    during a time frame of immediate interest, but ultimately fail to provide long-term protection, unless these
 5    issues are considered.
            D.3.1. Steady State Critical Loads
 6          If the time frame of response is not important, for instance, if the target load is to provide long-term
 7    sustainable protection and the immediacy of the responses is not relevant, the use of static or steady state
 8    models (response functions) is justified in the critical load analysis procedure. Using steady state models
 9    to estimate critical loads and compare the estimated critical load to current or future deposition, only two
10    cases can be distinguished: (1) current or future deposition is below the critical load, or (2) current or
11    future deposition exceeds the critical load. In the first case, no problem is apparent, and no target load is
12    deemed necessary, unless increases in deposition are anticipated. In the second case, there is by definition
13    an increased risk of "significant harm" to the receptor and selection of a target load for resource
14    protection is indicated.
15          The lack of explicit consideration of time in a steady state critical load analysis can lead to
16    assumptions that are frequently not warranted. The critical load derived in a steady state analysis is an
17    estimate of the long-term, constant deposition that a receptor can tolerate with no significant harm after it
18    has equilibrated with the critical load deposition. However, biological and geochemical processes that
19    affect a receptor may delay the attainment of equilibrium (steady state  condition) for years, decades, or
20    even centuries. By definition, steady state critical loads do not provide any information on these time
21    scales. As a result, it is often assumed that reducing deposition to, or below the steady state critical load
22    value will immediately eliminate or mitigate "significant harm." That is,  it is assumed that the chemical
23    indicator affected by the atmospheric pollutant immediately attains a non-critical value upon
24    implementation of the critical load, and that there is immediate biological recovery as well. As discussed
25    in the next section, these assumptions may not be valid.
            D.3.2. Dynamic Critical Loads
26          The time frame of receptor response is important if the establishment of target loads is tied to
27    defined schedules of deposition change or receptor responses. The use of time-dependent or dynamic
28    model response functions will be necessary if the critical load analysis considers the response time frame.
29    In the cascade of events that occur from changed deposition of an atmospheric pollutant to development
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 1    of responses of key biological indicators, there are many processes in natural systems that are time and/or
 2    resource dependent and therefore can introduce delays in the response pattern. In the decision-making
 3    process leading to the adoption of target loads, there are likewise considerations of when deposition
 4    changes can be initiated and completed and when biological indicator responses are desired. With
 5    dynamic models, either empirical or process-based, a wide range of estimated critical loads can be
 6    derived for comparison with current or future deposition depending on the temporal constraints imposed
 7    on the critical load analysis. Temporal constraints that can be imposed on a given critical load analysis are
 8    determined by: (1) the receptor responses—the characteristic time scales and inherent lags of the receptor
 9    being analyzed (a function of hydrobiogeochemical processes in the receptor), and (2) the deposition
10    schedules—the years designated for beginning and completing the changes in deposition and for
11    evaluating the  indicator responses (a function of political, socioeconomic, and management constraints).


            D.3.3. Receptor Responses
12          The general conceptual model of the linkages among pollutant deposition and the responses of
13    chemical and biological indicators can be characterized as a series of delays. In the causal chain from
14    deposition of pollutant to damage to key biological indicators there are two major links that can give rise
15    to delays. First, hydrological and biogeochemical processes in catchments can delay the responses of
16    chemical indicators. Second, biological processes and population dynamics can further delay the response
17    of biological indicators. The pattern of chemical and biological indicator responses can be represented
18    conceptually (Figure D-l) (adapted from Jenkins et al., 2003; Posch et al., 2003). Five stages in the
19    conceptual pattern can be distinguished (Figure D-l):
20          •   Stage 1: Pollutant deposition is below the critical load for either the critical chemical limit or
21              the  critical biological response, and there is no "significant harm" to the receptor. As long as
22              deposition stays below the critical load, this is the 'ideal' situation.

23          •   Stage 2: Pollutant deposition rises above the critical load,  but chemical and biological
24              indicators still do not violate their respective criteria because there is a delay. No damage is
25              likely to occur at this stage, despite the exceedance of the  critical load. The time between the
26              first exceedance of the CL and first violation of the biological criterion (first occurrence of
27              "significant harm") is called the Damage Delay Time (DDT = t3-tl).

28          •   Stage 3: Pollutant deposition is above the critical load and both the chemical and biological
29              criteria are violated. Measures to reduce emissions are taken to avoid further harm to the
30              receptor and pollutant deposition begins to decrease.
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 1          •   Stage 4: Pollutant deposition has been reduced to a level below the critical load, but the
 2              chemical and biological criteria are still violated, and thus "recovery" has not yet occurred.
 3              The time between the first non-exceedance of the critical load and the subsequent non-
 4              violation of both criteria can be called the Recovery Delay Time (RDT = t6-t4).

 5          •   Stage 5: This stage is similar to Stage  1. Pollutant deposition has been reduced to a level below
 6              the critical load and neither the chemical nor biological criteria are violated. Only at this stage
 7              can the receptor be considered to have recovered to an undamaged level.

 8          Stages 2 and 4 can be further subdivided into two sub-stages each: chemical damage and recovery
 9    delay times (DDTc = t2-tl and RDTc = t5-t4; dark grey in Figure D-l and (additional) biological damage
10    and recovery delay times (DDTb = t3-t2 and RDTb = t6-t5; light grey). Given opportunities for
11    "confounding effects" (i.e., mechanisms not related to acidic deposition but affecting biological
12    indicators, such as forest pest infestation or climate change) occurring during the "delay periods," it is
13    clear that unambiguous short-term patterns of recovery of biological indicators are unlikely to be
14    observed, even in the presence of rather large declines in pollutant deposition. This has important
15    implications for recovery expectations.


            D.3.4.  Deposition Schedules
16          Dynamic critical loads, by definition, must explicitly account for the receptor time scales and lags
17    described above. Therefore, the process of estimating dynamic critical load values  for a given pollutant
18    must be based on a planned or assumed deposition schedule for changing the pollutant deposition and for
19    assessing the receptor responses. Three different time periods are specified, as illustrated in Figure D-3.
20    [The nomenclature used here for the three years specified in the deposition schedule conforms to that used
21    in European "dynamic target loads analyses" (Posch et al., 2003)].
22
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                 Stage 1
Stage 2       Stage 3     Stage 4
Stage 5
                                                                     Critical Load
                                                                     Critical Limit
                                                                     Critical Response
                              DDT
Figure D-1. 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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                      Historical Deposition Pattern
                                                                    Future Deposition Patterns
                                        Protocol Year
                    Time
                                       Implementation Year
                                Target Year
      Figure D-2. 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.

 1          The first time period is the protocol year when deposition changes moving toward the critical load
 2    are begun. It will be the case that voluntary or mandated changes in deposition will require a number of
 3    years to get underway once a critical load has been calculated or target load has been selected. These
 4    delays in moving toward the critical load will affect the dynamic responses of the chemical and biological
 5    indicators and, therefore, must be included in the dynamic modeling of receptor response.  Before the
 6    protocol year, it must be assumed that pollutant deposition will be continuing along the pattern of recent
 7    or historical deposition change or along the pattern dictated by future deposition scenarios already
 8    planned and assumed to take effect.
 9          The second time period is the implementation year when deposition changes are complete and
10    pollutant deposition has reached the desired critical or target load. It is likely that a number of years will
11    elapse between the time changes in deposition toward the critical load are initiated, and the time when
12    they are completed. During this transition period, pollutant deposition continues at a rate higher, or lower,
13    than the critical load. The effects of these years of deposition inputs above or below the critical load value
14    will affect the dynamic responses of the chemical and biological indicators, and must also  be included in
15    the dynamic modeling of receptor response. It is assumed in dynamic critical loads analyses that the
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 1    pollutant deposition to the receptor remains constant at the critical load for all years after the
 2    implementation year.
 3          The final time period is the target year when the biological indicators are evaluated. Recognizing
 4    that there  are inherent lags in receptor responses following changes in pollutant deposition, it a number of
 5    years will frequently be allowed to elapse after the implementation year before the receptor responses are
 6    assessed. It must also be recognized that receptor responses will continue to change over time. Thus,
 7    selection of the target year will affect attainment of the critical limit.
 8          The deposition schedule for a dynamic critical load analysis can be driven by a number of
 9    considerations, and can be organized from protocol year to target year or vice versa. The selection of the
10    protocol and implementation years is  often a matter of political will and economic possibility. Large-scale
11    pollution abatement programs take time to negotiate. Costs or engineering difficulties may delay the start
12    of the abatement program and affect the length of time it takes to complete the program once it is begun.
13    Once these constraints have been established, it is then possible to select a reasonable target year for
14    evaluation of the receptor responses. Alternately, resource management mandates might require that
15    "significant harm" to receptor indicators be mitigated or eliminated by a certain time. This establishes the
16    target year for the dynamic analysis and the protocol and implementation years must be selected to allow
17    time for any lags in the receptor responses to occur.
18          Clearly, there is tension between the two approaches when developing a deposition schedule. It is
19    possible, for instance,  to defer the protocol and implementation years so far into the future that extensive
20    "significant harm" occurs to the receptor indicators in the intervening years. If that damage is especially
21    severe, the critical load, when the target year is finally reached, may not be achievable. Similarly, if a
22    receptor is currently suffering harm, it is possible to choose a target year for receptor response too close to
23    the present day to allow time for the receptor to recover, even if the pollutant deposition was reduced to
24    zero immediately.
25          Both of these hypothetical scenarios raise  important points about dynamic critical load estimates.
26    Because time is explicitly incorporated, there are certain dynamic critical loads questions that have no
27    answer. Commonly called "you can't  get there from here" problems, these deposition schedules choose
28    protocol, implementation, or target years that are inconsistent with the time scales of receptor response.
29    For example, setting a target year 5 years  in the future for achievement of no "significant harm" in a
30    receptor that is currently badly damaged, and has a history of high pollutant loading, may be asking the
31    impossible. Critical load estimates derived in this case would require having set the pollutant deposition
32    to zero some years in the past. In other words, the state of no significant harm cannot be reached within
33    the specified five years regardless of how deposition is changed within that five-year period ("you can't
34    get there from here"). This problem is moot for steady state critical loads. Steady state critical loads
35    analyses will always provide some sensible estimate (zero or finite) of long-term sustainable pollutant
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 1    deposition for every receptor because time is not a factor. Dynamic critical loads analyses, on the other
 2    hand, may frequently provide non-quantitative results, but these results nonetheless convey useful
 3    information concerning the current status of the  receptor and point out the necessity to continue the
 4    analysis with modified assumptions or expectations in order to develop realistic and achievable target
 5    load values.
            D.3.5. Long-Term Implications
 6          The explicit inclusion of time in critical loads estimation provides useful information for managers
 7    or policymakers when deciding when and how much to alter pollutant emissions and deposition, but the
 8    dynamic approach leads to implicit assumptions that must be recognized. Focus on the near-term aspects
 9    of receptor responses (the years included in the deposition schedule) can be misleading. The implicit
10    assumption is that having attained the desired biological or chemical response in the target year, nothing
11    more will happen, or at least that further changes in the receptor, if they do occur, will not produce
12    "significant harm." Available dynamic model critical load estimates suggest that this is not always true,
13    and the long-term implications of dynamic critical load estimates should be examined carefully.
14          The dynamic critical loads procedure assumes that pollutant deposition remains constant at the
15    critical load from the implementation year until the target year, and the assessment of receptor response.
16    Model simulations can be continued, assuming deposition at the constant critical load value, for a number
17    of years after the specified target year to ensure that lags in the receptor response will not result in
18    "significant harm" appearing at some later date,  even though it was not present in the target year. Some
19    receptors have chemical or biological lags that are many decades long, or longer. Critical loads analyses
20    based on deposition schedules that cover only 20 to 30 years can produce the unwanted result of
21    estimating a dynamic critical load that avoids "significant harm" to the receptor in the target year, only to
22    have the receptor suffer damage some years later.
23          It is important to determine which receptor responses and which deposition schedules might lead to
24    such an unwanted result. There are some general guidelines concerning this potential problem. For any
25    receptor for which there is currently no "significant harm," the estimated critical load provided by the
26    dynamic approach will be one that brings the biological or chemical indicators to the threshold of harm
27    without crossing it (the definition of the critical load). However, this dynamic critical load has then put
28    the biological or chemical indicator on a trajectory away from its currently good status and toward the
29    threshold of harm. In most cases, the trajectory toward harm will continue past the target year and
30    "significant harm" will occur in these receptors some time after the target year.
31          On the other hand, in any receptor for which there  currently is "significant harm," the estimated
32    critical load provided by the dynamic approach will be one that brings the biological and chemical
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 1    indicators to the threshold of harm and crosses it, just to return to a state of no harm. This dynamic critical
 2    load puts the biological or chemical indicator on a trajectory away from harm and towards good status. In
 3    most cases, it is likely that the upward trajectory will continue past the target year and significant further
 4    recovery will occur in these receptors after the target year.
 5          These are, however, merely generalizations. Depending on the geochemical and biological process
 6    affecting a receptor, there exist possibilities that upward trajectories could become downward trajectories
 7    sometime after the target year and vice versa. The most straightforward procedure is to run the model(s)
 8    used in the dynamic critical loads analyses for a sufficiently long time after the target year so that any
 9    reasonable chance of delayed damage to the receptor or delayed recovery is either discovered or
10    discounted.


            D.3.6.  Steady State and Dynamic Critical Loads - Complementary
            Information
11          There is no "correct" choice to be made between steady state and dynamic critical loads analyses.
12    Both provide estimates of pollutant loads that are intended to avoid  "significant harm" to a receptor. Both
13    are valid scientific expressions of the receptor's sensitivity to the pollutant. They differ primarily in the
14    time scales implicit in their use. Steady state analyses provide critical load estimates for long-term
15    sustainable protection, but ignore questions of near-term recovery and avoidance of interim harm.
16    Dynamic analyses provide critical load estimates that can be used to examine short-term or long-term
17    options for recovery of damaged systems and avoidance of interim harm, but may ignore the ultimate
18    long-term sustainability of the estimated deposition, which may evolve over centuries. Clearly, the two
19    approaches provide complementary information.
20          The complementary nature of the two critical loads approaches can be exploited in the selection of
21    a target load estimate for a receptor. Selecting the lower of the two critical load estimates for the receptor
22    (steady state or dynamic) should result in facilitation of recovery, or avoidance of harm, in the short-term,
23    as  well as long-term sustainability once the receptor has reached equilibrium with the  selected target load.
24    Multiple lines of evidence reflecting multiple critical load values can provide important information that
25    collectively provides the foundation for management decision-making.
26          The procedures, data requirements, and computational resources needed for each of the two critical
27    loads approaches may differ significantly depending on the models (response functions) adopted for the
28    analyses. Differences in the approaches may also depend on the disturbance, receptor, or indicator being
29    evaluated. The next two sections discuss the disturbances, receptors, and indicators  relevant to deposition
30    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
 1          The derivation of quantitative estimates of critical loads requires the development of dose-response
 2    functions (models) for the components of the ecosystem being considered. Models are needed to describe
 3    two different classes of dose-response function. Geochemical models describe the changes in the
 4    chemical indicators that occur as functions of changes in the pollutant loads. Biological response models
 5    describe the changes in the biological indicators as functions of changes in the chemical variables.
 6          Models for either class of dose-response function can be developed using two general approaches.
 7    Empirical models are based on direct observations of indicator response to pollutant deposition. They are
 8    usually developed using statistical techniques and generally do not contain a mechanistic pathway linking
 9    pollutant deposition to indicator response. Process-based models are based on conceptual representations
10    of chemical and biological mechanisms, and use mathematical equations to express the inter-relationships
11    among system components. Whereas process-based models frequently also use observations of receptor
12    responses to pollutant deposition for calibration and validation, they are fundamentally different from
13    empirical models in that mechanistic pathways from pollutant deposition to indicator response are
14    explicitly included in the model structures. In general, the geochemical models used to link S and N
15    deposition to chemical indicator response are mostly process-based, whereas biological responses to
16    acidification by S and N are mostly modeled using empirical approaches. Finally, both geochemical
17    models and biological response models, whether developed using either empirical or process-based
18    approaches, can be further classified as static or dynamic depending on whether or not time is included
19    among variables.


            D.4.1.  Empirical Models
20          Empirical models can be constructed relating either chemical or biological indicators to pollutant
21    deposition. The empirical models currently in use  for calculating critical loads employ steady state
22    approaches. This is not a necessary constraint, however, because even with no knowledge of the
23    underlying mechanisms, there exist many statistical techniques for relating the time-series of outputs and
24    inputs of ecosystems. The reason empirical critical loads models are usually based on a steady state
25    approach is primarily because time-series data of long enough duration to parameterize dynamic
26    empirical models are not generally available.  In general, empirical models require less complex datasets,
27    are more straightforward to implement, and are easier to understand than process-based models. For some
28    receptors, the lack of conceptual understanding of the mechanisms of indicator response to pollutant
29    deposition renders the use of process-based models problematic, and the use of empirical models is then
30    the only viable critical load analysis approach.
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            D.4.2. Acidification Effects of Sulfur and N
 1          Empirical models of critical loads for acidity assign critical loads to soils on the basis of soil
 2    mineralogy and chemistry (UNECE, 2004). For example, at the Critical Loads Workshop at Skokloster
 3    (Nilsson, 1988) soil forming materials were divided into five classes on the basis of the dominant
 4    weatherable minerals. A critical load range, rather than a single value, was assigned to each of these
 5    classes according to the amount of acidity that could be neutralized by the base cations produced by
 6    mineral weathering. The classification of soil materials developed at Skokloster used a relatively small
 7    range of primary silicate minerals and carbonates. A larger range of minerals was classified by Sverdrup
 8    and Warfvinge (1988) and Sverdrup et al. (1990) for use in the PROFILE model (Warfvinge and
 9    Sverdrup, 1992).


            0.4.3. Nutrient Effects  of N
10          Empirical models of critical loads for nutrient N have been developed in Europe within LRTAP to
11    set critical loads for atmospheric N deposition (e.g., UNECE, 2004). Empirical critical loads of N for
12    natural and semi-natural terrestrial ecosystems and wetland ecosystems  were first presented in a
13    background document for the 1992 workshop on critical loads  held under the UNECE LRTAP
14    Convention at Lokeberg, Sweden (Bobbink et al., 1992). A number of European expert workshops have
15    taken place in order to reach agreement among specialists regarding the impacts of N  for various
16    ecosystems and related critical loads (Nilsson, 1988; Bobbink, 1992)1996; Hornung, 1995; Achermann,
17    2003). Empirical relationships have also recently been developed in the U.S., particularly for western
18    ecosystems (e.g., (Baron, 1994; Baron, 2000) (Williams, 2000); Fenn, 2003) (Burns, 2004) (Nydick,
19    2004).


            D.4.4. Process-Based Models
20          A number of process-based models are currently in use for calculating critical loads using both
21    steady state and dynamic approaches. Developing a process-based modeling approach that includes all
22    appropriate chemical and biological indicators is a complex task. Some  process models incorporate both
23    geochemical and biological response mechanisms in one program. An alternate approach is to chain
24    individual process-based models, for example taking the output of a geochemical model and passing it as
25    input to a biological-response model. In either approach, the level of process complexity varies a great
26    deal among the various available models. The choice of a particular process-based modeling approach to
27    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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 1    scope of the analysis, the quality and quantity of available data, and the availability of resources (time and
 2    money) for the analysis. The following is a brief overview of some of the process-based biogeochemical
 3    models that are commonly used to calculate critical loads.
            0.4.5. Steady State Models
 4          The Simple Mass Balance (8MB) model is the standard model for calculating critical loads for
 5    terrestrial ecosystems under the LRTAP Convention (Sverdrup et al., 1990; Sverdrup and De Vries, 1994).
 6    The 8MB model is a single-layer model. There also exist multi-layer steady state models for calculating
 7    critical loads in terrestrial ecosystems. Examples are the MACAL model (De Vries 1988) and the widely
 8    used PROFILE model (Warfvinge and Sverdrup,  1992), which has at its core a model for calculating
 9    weathering rates from total mineral analyses.
10          The Steady State Water Chemistry (SSWC) model (Sverdrup et al., 1990; Henriksen et al., 1992;
11    Henriksen and Posch, 2001) calculates critical loads of acidity for surface waters, based on the principle
12    that acid loads should not exceed the balance of non-marine, non-anthropogenic base cation sources and
13    sinks in a catchment, minus a buffer to protect selected biota from being damaged.
14          The First-order Acidity Balance (FAB) model for calculating critical loads for surface waters takes
15    into account sources and sinks within the lake and its terrestrial catchment. The original version of the
16    FAB model was developed and applied to Finland, Norway, and Sweden by Henriksen et al. (1992) and
17    Posch et al. (1997). A modified version was first reported in Hindar et al. (2000, 2001) and is described in
18    more detail by Henriksen and Posch (2001).


            D.4.6. Dynamic Models
19          MAGIC is a lumped-parameter model of intermediate complexity, developed to predict the long-
20    term effects of acidic deposition on surface water chemistry (Cosby, 2001)Cosby et al., 1985a,b, 2001).
21    The model simulates soil solution chemistry and surface water chemistry to predict the monthly and
22    annual average concentrations of the major ions in these waters. MAGIC consists of: (1) a sub-model in
23    which the concentrations of major ions are assumed to be governed by simultaneous reactions involving
24    SC>42- adsorption, cation exchange, dissolution-precipitation- speciation of Al, and dissolution-speciation
25    of inorganic carbon (C) and (2) a mass balance sub-model in which the flux of major ions to and from the
26    soil is assumed to be controlled by atmospheric inputs, chemical weathering, net uptake and loss in
27    biomass, and losses to runoff. At the heart of MAGIC is the size of the pool of exchangeable base cations
28    in the soil. As the fluxes to and from this pool change over time owing to changes in atmospheric
29    deposition, the chemical equilibria between soil and soil solution shift to give changes in surface water
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 1    chemistry. The degree and rate of change of surface water acidity thus depend both on flux factors and the
 2    inherent characteristics of the affected soils. MAGIC is described in more detail in Annex C.
 3          PnET-BGC is an integrated dynamic biogeochemical model that simulates chemical
 4    transformations of vegetation, soil, and drainage water. It was formulated by adding the sub-model BGC
 5    (biogeochemistry) to PnET-CN, a model of C, water, and N balances (Aber and Federer, 1992; Aber and
 6    Driscoll,  1997; Aber et al., 1997), in order to expand the  model to include vegetation and organic matter
 7    interactions of major elements (i.e., Ca2+, Mg2+, K+, Na+, Si, S, P, A13+, Cl~, F~), abiotic soil processes,
 8    solution speciation, and surface water processes (Gbondo-Tugbawa et al., 2001). The model was initially
 9    developed for, and applied to, the northern hardwood forest ecosystem. It was tested extensively at the
10    Hubbard Brook Experimental Forest, New Hampshire, including a detailed sensitivity analysis of
11    parameter values. The model has subsequently been applied to intensively-studied watersheds in the
12    Adirondack and Catskill regions of New York and applied regionally to the Adirondacks (Chen and
13    Driscoll,  2005b) and northern New England (Chen and Driscoll, 2005a,c).  See additional description in
14    Annex C.
15          Simulation Model for Acidification's Regional Trends (SMART2) is a soil acidification and
16    nutrient cycling model and is an extension of the dynamic soil acidification model SMART (Kros et al.,
17    1995). The original model was a relatively simple simulation of the response of soil and soil water quality
18    to atmospheric inputs. Improvements in SMART2 include processes of canopy interactions, litter fall, root
19    decay, mineralization, and root uptake of nutrients. SMART2 has been used primarily in European critical
20    loads studies.
21          The Soil Acidification in Forest Ecosystems model (SAFE) was developed at the University of
22    Lund in Sweden (Warfvinge et al., 1993; Alveteg and Sverdrup, 2002). The main differences between the
23    SAFE and MAGIC models are: (a) weathering of base cations is not calibrated for SAFE, but it is
24    modeled with the PROFILE sub-model, using soil mineralogy as input (Warfvinge and Sverdrup, 1992);
25    b) SAFE is oriented to soil profiles in which water is assumed to move vertically through several soil
26    layers, (c) cation exchange between Al, H, and (divalent) base  cations  is modeled in SAFE with Gapon
27    exchange reactions rather than Gaines-Thomas reactions, and the exchange between the  soil matrix and
28    soil solution is diffusion-limited. The standard version of SAFE does not include S adsorption although a
29    version, in which S adsorption is dependent on SC>42 concentration and pH of soil solution, has recently
30    been developed (Martinson et al., 2003.
31          ForSAFE is a mechanistic model that simulates  N and C cycling and soil chemistry. Climatic
32    drivers within the model include temperature, precipitation, radiation, and deposition. ForSAFE combines
33    three established models (SAFE, PnET-CN, and DECOMP). SAFE simulates soil chemistry (e.g.,
34    chemical weathering, cation exchange, leaching, and solution equilibrium reactions). PnET-CN (Aber
35    et al., 1997) is used to predict forest growth within ForSAFE, through the simulation of C fixation,
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 1    litterfall, and C and nutrient allocation. DECOMP (Walse et al., 1998) is a dynamic, multi-layered
 2    process-oriented decomposition model that incorporates the influences of temperature, moisture, pH, and
 3    Al. Very Simple Dynamic soil acidification model (VSD) only includes a few key processes, such as
 4    cation exchange and N immobilisation, and a mass balance for cations and N (Posch et al., 2003). VSD
 5    does not consider seasonal variations, as the time step in the model is one year. The VSD model is based
 6    on mass balance equations that describe soil input-output fluxes and equations describing the rate-limited
 7    (e.g., uptake and silicate weathering) and equilibrium (e.g., cation exchange) soil processes. Soil solution
 8    chemistry is based solely on the net element input from the atmosphere (i.e., deposition minus net uptake
 9    minus net immobilization) and geochemical interactions in the soil (i.e., CO2 equilibria, weathering of
10    carbonates and silicates, and cation exchange). VSD simulates a single soil layer with a constant density
11    and a fixed depth. The concentration of the soil water leaving the compartment is assumed to be equal to
12    the annual precipitation excess.
      D.5. Use of Critical Loads in the U.S. - Current  Status
13          At the Multi-Agency Critical Loads Workshop for Sulfur and Nitrogen Deposition Effects on
14    Freshwater and Terrestrial Ecosystems, convened by the EPA, the U.S. Forest Service (USFS), the
15    National Park Service (NPS), and the USGS in May 2006, approximately 75 scientists, conservation
16    representatives, and state and federal agency officials gathered to share information, discuss scientific
17    advances, and develop a broad federal strategy for advancing critical loads in the U.S. (EPA, 2006). The
18    conclusions and recommendations of that workshop are presented below. These conclusions and
19    recommendations represent the current understanding of critical loads as scientific  tool and policy
20    instrument in the U.S.
21          The conclusions and recommendations below were reached by the Federal Agencies sponsoring the
22    workshop. It is worth noting that some state agencies have pursued the use of critical loads independently
23    in order to link science and policy in addressing the management of natural resources. For instance, in the
24    State of Colorado, critical loads for N  deposition that were developed for Rocky Mountain National Park
25    (Baron, 2006) are being used to develop goals for N emissions reductions by the State of Colorado, EPA,
26    and NPS. (See "Nitrogen Deposition Reduction Plan" at http://www.cdphe.state.co.us/ap/rmnp.html)
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            D.5.1. Current Recommendations on Critical Loads Uses  in the
            U.S.
 1          The participants in the Multi-Agency Critical Loads Workshop developed a set of findings and
 2    recommendations to help advance critical loads usage in the U.S. The "areas of agreement" published in
 3    the workshop report (EPA, 2006) included the following:
 4          "A critical load is defined as: a quantitative estimate of the exposure to one or more pollutants
 5             below which significant harmful effects on specific sensitive elements of the environment do
 6             not occur according to present knowledge (Nilsson, 1988).

 7          •  Despite reductions in S and N emissions in the U.S., deposition rates still exceed preindustrial
 8             levels and acidification and eutrophication effects remain widespread.

 9          •  Critical loads can be used to better understand impacts of atmospheric deposition, assess the
10             effectiveness of emissions programs, and guide natural resource management.

11          •  The development of critical loads is a process that is subject to continued development and
12             improvement as knowledge advances.

13          •  Adequate information exists to move forward with the development and limited application of
14             critical loads in some regions and ecosystems in the U.S.

15          "An intensive research and monitoring agenda should be pursued to support the development
16             and refinement of critical loads in the U.S.

17          •  Critical loads should be based on a matrix of biological and chemical indicators for aquatic and
18             terrestrial ecosystems that account for acidification, N saturation, and eutrophication effects
19             and are relevant to the geographic area or ecosystem of concern.

20          •  Adequate information exists to establish harmful effect thresholds for some indicators based on
21             specific protection and recovery objectives defined by policymakers and managers.
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 1          •   Dynamic models provide the most accurate site-specific information and account for time-
 2             dependent processes, but are generally too data intensive to be applied across large geographic
 3             areas at present. Simple mass balance models can be applied to current conditions in large
 4             geographic areas, but in some instances do not adequately highlight some sensitive areas
 5             because they tend to average conditions across the landscape. Hybrid approaches that link
 6             observational datasets with dynamic and steady state models represent a useful approach for
 7             regionalizing site-specific information.


            D.5.2.  Questions and Limitations Regarding Critical Loads  Uses
            in the  U.S.
 8         The participants in the Multi-Agency Critical Loads Workshop also developed a set of "Questions
 9    Needing Further Discussion" (EPA, 2006):
10          •   What are the appropriate applications of critical load estimates to policy and management
11             issues given current knowledge? For applications where buy-in to an incremental process does
12             not exist, greater investment in critical loads methods may be needed before this application
13             can be pursued.

14          •   How strong is the relationship between specific indicators, thresholds, and biological
15             responses?

16          •   What are the suitable interpretations and uses of existing databases for the development of
17             national  simple mass balance critical load models?


            D.5.3.  Critical  Loads Research and  Monitoring Needs
18         Finally, the participants in the Multi-Agency Critical Loads Workshop presented a list of "Critical
19    Loads Research and Monitoring Needs" (EPA, 2006), which are summarized below.


            D.5.4.  Emissions and Deposition
20          •   Update N and S emissions inventories on a state-by-state basis back to the 1900s to correspond
21             with methods used in current emissions inventories.

22          •   Develop NH3 emissions inventory.
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 1          •   Improve dry deposition estimates for S and N.

 2          •   Improve total S and N deposition estimates.

 3          •   Measure gaseous NH3 concentrations.

 4          •   Add NH3 deposition measurements to current networks.

 5          •   Improve estimates of total deposition in complex terrain.

 6          •   Develop N and S deposition maps for North America.


            D.5.5.  Soils
 7          •   Improve spatial coverage and representativeness of soil chemistry databases, particularly in
 8              sensitive terrain.

 9          •   Increase soil monitoring.

10          •   Improve estimates of mineral weathering rates.

11          •   Develop soil archiving and well characterized reference samples to promote cross-laboratory
12              comparisons.

13          •   Expand research on the nature and size of soil nutrient pools.

14          •   Conduct research on threshold values of soil quality for biologic responses.

15          •   Determine N supply rates in different soil types.

16          •   Investigate N soil accumulation rates in arid lands and implications for critical loads.


            D.5.6.  Surface Waters
17          •   Incorporate TIME and LTM  surface water monitoring programs into a larger network with
18              better geographic coverage (e.g., the West and Southeast).

19          •   Improve spatial coverage and representativeness of surface water chemistry databases,
20              particularly in sensitive and complex terrain.


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 1          •   Integrate fixed-site monitoring with regional probability monitoring design.

 2          •   Continue to monitor major drivers of acidity.

 3          •   Build critical loads considerations (e.g., validation, improvement, regionalization) into
 4              monitoring from the start by combining chemistry, hydrology, deposition and biology, and
 5              integrating site-specific models and measurements into regional contexts.

 6          •   Expand research to understand what is driving dissolved organic carbon (DOC) changes in the
 7              East.

 8          •   Analyze the impact of groundwater transport on recovery times.
            D.5.7.  Biological Effects
 9          •   Develop better understanding of the link between chemical indicators and biological response
10              (e-g-, quantify the minimum N level at which plankton communities shift).

11          •   Conduct additional research on the sequential impacts of N and relationship between N
12              deposition and ecosystem impacts.

13          •   Integrate critical load estimates with biodiversity and climate change interactions.

14          •   Undertake more research on biological change and "harmful effects" to help establish
15              appropriate critical loads thresholds (e.g., in arid lands, what level of productivity of exotic
16              invasive species will cause the reduction versus the extinction of native species?).

17          •   Collect sediment cores from lakes that vary in rates of N deposition to track changes in diatom
18              assemblages.


            D.5.8.  Critical Loads Models
19          •   Improve representation of N dynamics in models.

20          •   Expand models to include NH3.

21          •   Improve explicit consideration of changing base cations and DOC.

22          •   Conduct ground-truthing of forest sensitivity and other models.

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1
2
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.
      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.
      Ecosystem Type
                                             Biological Effect Indicators
                   Empirical Critical
                        Load
                     (kg N/ha/yr)
      Grasslands and tall forb habitats (E)
      Sub-Atlantic semi-dry calcareous grassland

      Non-Mediterranean 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;         15-25
                       decreased diversity
                       Increase in nitrophilous graminoids, decline of typical species                     10-20

                       Decrease in lichens, increase in biomass, accelerated succession                  10-20
                       Increased tall grasses, decreased diversity                                    20-30
                       Increase in nitrophilous graminoids, changes in diversity                         10-20
                       Increase in tall graminoids, decreased diversity, decrease in bryophytes            10-25
                       Increase in nitrophilous graminoids, changes in diversity                         10-15
                       Effects on bryophytes and lichens                                           5-10
                       Decreased heather dominance, transition heather to grass, decline in lichens and     10-20
                       mosses
                       Transition heather to grass, decline in lichens                                 10-20
                       Decline in lichens, mosses, and evergreen shrubs                              5-15

                       Increased biomass, increased N leaching                                     10-20
                       Increase in tall grasses, decreased prostrate plants, increased N leaching           10-20
                       Increase in plant production, increased N leaching, accelerated succession          10-20
                       Increase in biomasss and tall graminoids                                     10-25

                       Changed species composition, N saturation  of Spagnum                         5-10
                       Increased sedges and vascular plant, negative effects on mosses                  10-20
                       Increase in tall graminoids, decreased diversity, decrease of characteristic mosses    15-35
                       Increase in vascular plants, decrease in bryophytes                             15-25

                       Reduced sporocarp production, reduced below ground species composition          10-20
                       Changed species composition, increased nitrophilous species; increased             10-15
                       susceptibility to parasites (insects, fungi, virus)
                       Increase in algae; decrease in lichens	10-15
      Source: Adapted from Achermann and Bobbink (Achermann, 2003).
      August 2008
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11          2577-2582.
12    Waddell, E.; Greenwood, R. (2006) Pacific Northwest nitrogen and sulfur deposition critical
13          loads workshop; September; North Cascades Environmental Learning Center. Workshop
14          summary report. Co-sponsored by: Northwest Clean Air Agency; National Park Service;
15          U.S. Department of Agriculture, Forest Service; U.S. Geological Survey. Available:
16          http://nadp.sws.uiuc.edu/clad/PNWCL2006WS.pdf [4 December, 2007].
17    Walse, C.; Gerg, B.; Sverdrup, H. (1998) Review and synthesis of experimental data of organic
18          matter decomposition with respect to the effect of temperature, moisture and acidity.
19          Environ. Rev.  6: 25-40.
20    Warfvinge, P.; Sverdrup, H.  (1992) Calculating critical loads of acid deposition with PROFILE -
21          - a steady-state soil chemistry model. Water  Air Soil Pollut. 63: 119-143.
22    Warfvinge, P.; Falkengren-Grerup, U.; Sverdrup, H.; Andersen, B. (1993) Modelling long-term
23          cation supply in acidified forest stands. Environ. Pollut. 80: 209-221.
24    Watmough, S. A.; Dillon, P. J. (2003) Calcium losses from a forested catchment in south-central
25          Ontario, Canada. Environ. Sci. Technol. 37:  3085-3089.
26    Watmough, S. A.; Aherne, J.; Dillon, P. J. (2005) Effect of declining lake base cation
27          concentration on freshwater critical load calculation. Environ. Sci. Technol. 39:  3255-
28          3260.
29    Williams, M.; Tonnessen, K. (1999) Critical loads for inorganic nitrogen deposition in the
30          Colorado Front Range, USA. Ecol. Appl. 10: 1648-1665.
31    Wilmot, T. R.; Ellsworth, D. S.; Tyree, M. T. (1995) Relationships among crown condition,
32          growth, and stand nutrition in seven northern Vermont sugarbushes. Can. J. For. Res.
33          386-397.
34    Wolfe, A. P.; Baron, J. S.; Cornett, R. J. (2001) Anthropogenic nitrogen deposition induces rapid
35          ecological changes in alpine lakes of the Colorado Front Range (USA). J. Paleolimnol.
36          25: 1-7.
37    Wolfe, A. P.; Van Gorpe, A. C.; Baron, J. S. (2003)  Recent ecological and biogeochemical
38          changes in alpine lakes of Rocky Mountain National Park (Colorado, USA): a response to
39          anthropogenic nitrogen deposition. Geobiology 1(2): 153-168.
40    Woodward, D. F.; Farag, A.  M.; Mueller, M. E.; Little, E. E.; Vertucci, F. A. (1989) Sensitivity
41          of endemic Snake River cutthroat trout to acidity and elevated aluminum. Trans. Am.
42          Fish. Soc. 118: 630-641.
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1    Xie, S.; Hao, J.; Zhou, Z.; Qi, L.; Yin, H. (1995) Assessment of critical loads in Liuzhou, China
2          using static and dynamic models. Water Air Soil Pollut. 85: 2401-2406.
3    Ye, X. M.; Hao, J. M.; Duan, L.; Zhou, Z. P. (2002) Acidification sensitivity and critical loads of
4          acid deposition for surface waters in China. Sci. Total Environ. 289: 189-203.
5    Zhao, D. W.; Seip, H. M.  (1991) Assessing effects of acid deposition in southwestern China
6          using the MAGIC  model. Water Air Soil Pollut. 60: 83-97.
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       Annex E.  Effects of  NOY,  NHX, and SOX on

                        Structures  and  Materials


      E.1. Introduction
 1         The purpose of this chapter is to summarize the research published since the most recent AQCDs
 2    on materials and structures damage caused by NOy, and NHX, SOx Materials and structures exposed to
 3    the environment are subject to damage from exposure to sunlight, moisture, salt, windblown dust, and
 4    cycles of temperature and humidity, whether or not air pollutants are present. However, NOY, NHX, and
 5    SOX may cause such damage to be greater or occur more rapidly than with natural environmental factors
 6    alone. Damage to materials and structures may be physical, potentially affecting the durability or
 7    maintenance needs of a material or structure, or may be purely aesthetic, affecting only the outward
 8    appearance of the material or structure. In the case of historical buildings, monuments, or artifacts,
 9    aesthetic damage may be a relevant concern.
10         Note that very extensive work related to materials damage from acidic deposition related to S and
11    N was conducted in the 1980s as part of the National Acid Precipitation Assessment Program (NAPAP,
12    1991) and so are not discussed here. In compiling information for this chapter on NOy/NHx/SOx effects,
13    the information presented in the 1993 AQCD for Oxides of Nitrogen (EPA, 1993) and the 2004 PM
14    AQCD (EPA, 2004) was  updated by literature searches reaching back to approximately 1992. This update
15    was based on peer-reviewed literature, with a focus on studies that were conducted in the U.S., that
16    evaluated effects at realistic ambient air pollutant levels, and that treated NOy/NHx/SOx as components
17    of a complex mixture of air pollutants. These latter two factors result in an emphasis  on studies done with
18    exposures to ambient atmospheric pollution, rather than exposures at high levels, e.g., in test chambers.
19    The studies cited in this chapter were selected from those found in a broad literature search based on
20    criteria that they (1)  address damage caused by exposure to atmospheric contaminants, (2) focus on S and
21    N containing species, (3) provide a clear link between pollutant concentrations and damage, and (4) give
22    complete information on  methods and data analysis used.
23         Broadly speaking,  the pace of research on NOy, NHx, and SOx materials effects has slowed
24    considerably since the publication of the previous AQCDs. In particular, although the literature searches
25    conducted for this update emphasized studies conducted in the U.S., the great majority of the relevant
26    publications found originated in Europe or Asia. The relative scarcity of recent U.S. studies on structural
27    and materials damage from NOy/NHx/SOx may be a natural fall-off in research in this area, following the
28    extensive efforts that were summarized in the previous AQCDs and in the NAPAP report. Certainly the


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 1    greater number and age of aesthetically valuable buildings and archeological sites in Europe and Asia,
 2    relative to the U.S., may be a driving force for current research in those geographic areas. In this chapter,
 3    each discussion of the effects of NOy/NHx/SOx on a material type begins with a brief summary of the
 4    state of knowledge as represented in the previous AQCDs, and then continues with a description of recent
 5    research on that type of material.
      E.2.  Environmental Exposures of Materials
            E.2.1. Mechanisms of Materials Damage
 6         As noted in the introduction to this chapter, materials damage may occur by natural physical
 7    processes without the involvement of NOy/NHx/SOx air pollutants. When those pollutants are involved,
 8    the destructive processes may be chemical, physical, or even biological. Chemical processes include
 9    direct reactions with gaseous pollutants such as NO2, SO2, or nitric acid (HNO3), reaction with
10    electrolytes (proton (tT), ammonium (NH/), nitrate (NO3 ), SO42 , etc.) in water on material surfaces,
11    and reactions with chemicals in deposited particulate matter. An example of a physical process is the
12    deterioration of stone that occurs when gypsum (CaSO/^E^O) forms from reaction of SC>2 with the
13    calcium carbonate (CaCOs) in the stone. The gypsum thus formed occupies a larger volume than the
14    original stone, causing the surface to deteriorate. Biological degradation can occur when deposited
15    pollutants are oxidized to acids by fungi or bacteria.
16         A key factor affecting damage to certain materials, primarily metals and stone, is the frequency and
17    duration of wetting of the surface. Liquid water on materials surfaces can dissolve deposited pollutants,
18    producing reactive electrolyte solutions, and can serve  as a reaction medium in which S and N oxides are
19    converted to more damaging acids. Pollutants deposited on surfaces may contain or form hygroscopic
20    salts, which enhance the formation of liquid water and thereby increase materials damage. As Dubowski
21    et al. (2004) (2004) have shown, the deposition of HNOs onto surfaces can increase the extent of wetting
22    of surfaces, and promote the damaging effects of both HNOs and other pollutants.


            E.2.2. Deposition Processes
23         Air pollutants come into contact with surfaces through both dry and wet depositional processes.
24    Dry deposition occurs in the absence of precipitation and is governed by factors such as atmospheric
25    turbulence, the chemical and physical properties of the pollutant (e.g., water solubility and reactivity for
26    gases; size, density, and shape for particles), and surface properties (e.g., reactivity, roughness, moisture
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 1    level, and pH). The deposition rate of a pollutant is proportional to the atmospheric concentration of that
 2    pollutant. Dry deposition of gases depends primarily on the water solubility of the gas, the moisture level
 3    on the surface, and the pH of the electrolyte formed on the surface of a material. Nitric acid and NH3 are
 4    deposited very efficiently to most surfaces regardless of the surface properties of the material. Particle
 5    size plays an important role in determining the rate of deposition of particles to a surface. For very small
 6    particles, Brownian diffusion is the dominant deposition mechanism. For larger particles, inertial
 7    impaction and gravitational settling are important deposition processes. Particles between 0.05 and 2 (im,
 8    which include most atmospheric particles containing NO3 , SO42 , and NH4+, may have long atmospheric
 9    lifetimes in the absence of moisture.
10          Wet deposition occurs when gas or particle species come into contact with moisture (as rain, fog,
11    snow, or ice). Atmospheric species can be dissolved into moisture and then deposited as the moisture falls
12    to the ground. Solubility and the chemical reactions of the dissolved species determine the degree of wet
13    deposition.  For acid gases, high dissolution is observed due to the dissociation of the dissolved species in
14    water. Wet deposition of pollutants occurs at a faster rate than dry deposition, but is only an important
15    mechanism when moisture is present.


            E.2.3.  Chemical Interactions of N  and S Oxide Species
16          N and S oxide species are subject to many atmospheric reactions in both the gaseous and
17    particulate phase. Emissions of S and N oxides are primarily in the form of gas phase SC>2 and NOx  In
18    the atmosphere, these species can be  oxidized by reaction with other atmospheric species to gas and
19    particle phase product species. On the surface of materials, the oxides  are generally oxidized to their acid
20    forms (nitrous acid (FINC^), FINO3, sulfurous acid (H2SO3), and sulfuric acid (F^SO/O), which then
21    dissociate to form nitrite, nitrate, sulfite, and sulfate ions. These acids are the primary species responsible
22    for damage to materials by S and N pollutants. NH3, the primary gaseous basic compound in the
23    atmosphere, can partly or completely neutralize these acids in particulate matter or in the aqueous phase,
24    forming NFf4+ ions.
25          On the surfaces of materials, N and S species can react to form a variety of degradation products.
26    On metals and stone, the possible degradation products include nitrite, nitrate, sulfite, and sulfate species
27    as well as minerals that incorporate nitrate or sulfate into a more  complex composition. These degradation
28    products may be more or less reactive to further degradation than the original material. Degradation
29    products that are more reactive, or those that are soluble in water, do not have long lifetimes on a material
30    surface. They undergo further chemical reactions and are transformed to other species, or they are washed
31    off the surface by precipitation. Products which are less reactive and less soluble in water than the original
32    material may form a protective layer on the surface of the material which inhibits or prevents further
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 1    damage from atmospheric pollutants. Products that are more reactive or water-soluble than the original
 2    material are readily removed, exposing the surface to more damage. The protectiveness of the products
 3    formed depends on the complex mixture of species present and the physical/chemical properties of the
 4    material.
 5          Synergistic effects, which influence the rate of degradation of materials, are possible in
 6    atmospheres containing a complex mixture of pollutants. NOx may enhance the oxidation of sulfite to
 7    sulfate and lead to faster rates of corrosion. The deposition velocity of SO2 and NOX may be influenced
 8    by the presence of HNO3 deposited to the surface due to the increased degree of surface wetting.


            E.2.4. Materials Damage Experimental Techniques
 9          The NOy/NHx/SOx air pollutants are comprised of numerous distinct chemical species, which may
10    exist in the gaseous and/or particulate phases in the atmosphere, as well as in dissolved form in
11    atmospheric precipitation and in condensed water on surfaces. In order to test the  damaging effects of
12    NOY/NHX/SOX species on man-made materials, it is often necessary to simplify the system by testing
13    under controlled laboratory conditions, typically with a very limited set of pollutants in a test chamber.
14    Such tests generally use pollutant concentrations that are greatly elevated relative to ambient atmospheric
15    levels, and may also use exaggerated temperature, humidity, or wetting, to accelerate the development of
16    materials damage so that it can be detected. Chamber tests may not accurately mimic the mass transfer of
17    pollutants in the atmosphere, and efforts in such tests to isolate the effects of one pollutant from the
18    complex mixture present in the atmosphere are unrealistic. As a result, chamber tests may provide
19    valuable information on potential effects and mechanisms involving ambient air pollutants, but cannot
20    accurately predict the corrosion rates or effects of such pollutants in real situations.
21          Exposing materials of interest to the ambient atmosphere for extended time periods can provide a
22    realistic look at the effects of air pollutants on materials. However, such ambient exposure tests are
23    limited by the occurrence of natural (i.e., non-air pollutant) materials damage, and by the complexity of
24    the NOy/NHx/SOx system. While it is relatively easy to determine which materials suffer more or less
25    damage during equivalent exposures to ambient air pollution, it is extremely difficult to determine which
26    air pollutants are responsible for the observed damage. This is due to the co-occurrence of all air
27    pollutants simultaneously, complexities in accurately measuring the suite of NOY/NHX/SOX species, and
28    interconversions among species (e.g. SOX and SO42 , NOX and HNO3) related to contact with materials or
29    with moisture. The amount of time that surfaces are wet is a key factor in the extent of materials damage,
30    and this factor may be difficult to determine in ambient exposures, because the presence of air pollutants
31    themselves may enhance surface wetness on the microscale beyond that expected based on
32    meteorological conditions (Dubowsky, 2004). Ambient exposure tests lead to retrospective analyses, in
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 1    which meteorological and air pollutant data, surface analyses, and measurements of chemical and
 2    physical properties are evaluated statistically to estimate the impacts of air pollutants on the exposed
 3    materials.
      E.3.  Effects on Dyes and Textiles
            E.3.1. Fading of Dyes
 4         The fading of dyes by N oxides has long been recognized, and dye manufacturers have worked to
 5    produce products less susceptible to this effect, through both improved dye chemicals and the use of
 6    inhibitors in dye formulations to minimize fading. Fading has been observed with both red and blue dyes,
 7    both on natural fibers (e.g., cotton, silk, wool) and on synthetics (e.g., nylon, rayon, polyester). The fading
 8    effect of NO2 is generally reported to be greater than that of NO on various dyes with various fabrics. In
 9    exposures of dyed fabrics to ambient air, test samples must be shielded from sunlight, to avoid the
10    substantial fading of dyes that results from sunlight exposure. Under such conditions, NO2 and ozone (O3)
11    are often found to be about equally important in the fading of dyed fabrics.
12         and DeVries, 1993).
            E.3.2. Degradation of Textile Fibers
13         N oxides can degrade a variety of synthetic fibers, with the greatest effects seen with nylon. With
14    NO2, the damage to nylon occurs due to breaking of the polymer chain (i.e., chain-scissioning). Similar
15    weakening of nylon has been observed in tests with elevated concentrations of HNO3. A synergistic effect
16    was observed between mechanical stress and NOX in the degradation of oriented nylon-6 fibers (Smith
17    and DeVries, 1993).
      E.4.  Effects on Plastics and  Elastomers
18         The group of materials called plastics includes a wide variety of polymeric materials such as
19    polyethylene, polypropylene, polystyrene, polyurethanes, acrylic polymers, phenolics, and fluorocarbon
20    polymers, among others. Plastic materials may include other components such as hardeners or
21    plasticizers, and fillers that may impart properties such as physical strength. Elastomers are polymers that
22    can stretch to at least their twice their normal dimensions and then return to their original dimensions
23    when the stress is removed. Examples of elastomers include various rubber formulations and neoprene.

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 1    Plastics and elastomers can be damaged by NO2, SO2, and 03, as well as by UV radiation in sunlight, and
 2    some studies have been designed to separate the effects of these factors.
 3          Chamber studies at relatively high pollutant concentrations with sunlight or UV light have
 4    generally shown greater damage than from the pollutants alone. NO2 is damaging to a variety of polymers
 5    and elastomers, causing either chain-scissioning or cross-linking (formation of additional bonds between
 6    polymer chains) depending on the polymer. Polypropylene is reported to be damaged more severely by
 7    SO2 than by NO2. Elastomers are damaged more severely than plastics. In tests where light and NO2 have
 8    been present simultaneously, much of the damage observed in chamber tests has been attributed to O3,
 9    produced by the interactions of the pollutants and UV light, rather than to NO2 alone. In studies in which
10    the same pollutant concentrations are present both with and without light, the greater damage observed in
11    samples exposed to the light is often attributed to the light itself, when in fact chemical processes initiated
12    by light (such as the formation of 03) undoubtedly also play a part.
13          Cellulose nitrate can break down through hydrolytic, thermal, and photochemical reactions.
14    Addition of plasticizer to cellulose nitrate slows the degradation substantially. NO2 is of particular interest
15    with regard to cellulose nitrate because it is not only capable of causing damage, but is also produced as a
16    result of damage to the material (Shashoua, 2006). NO2 is formed when N-O bonds connecting cellulose
17    rings are broken. The NO2 formed will then further degrade cellulose nitrate, thus the degradation is an
18    autocatalytic process.
      E.5. Effects on Metals
19          Metals are considered to be the materials most subject to damage from the NOY/NHX/SOX air
20    pollutants, and have been the subject of a great deal of research. The nature and concentration of the
21    pollutant, its rate of deposition, and especially the duration of wetting of the surface are key factors in the
22    corrosion of metals. Numerous studies have indicated corrosion rates of metal surfaces on the order of 1
23    to several micrometers per year ((im/yr) under real or simulated atmospheric conditions.
24          Table E-l summarizes the materials tested, exposure conditions, and findings of recent studies
25    related to the effects of NOy/NHx/SOx air pollutants on metals. The studies listed in Table E-l are
26    discussed where applicable in the following sections.

            E.5.1. Role of NOY, NHX, and SOX in  the Corrosion Process
27          In the atmosphere the NOy/NHx/SOx pollutants occur together, along with other pollutants such as
28    03 or chloride salts. While wetting of metals surfaces is the single greatest factor promoting corrosion, an
29    important observation is the enhanced damage that occurs due to interactions among this mixture of

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 1    pollutants. It must be noted that in many studies the various NOy/NHx/SOx species have not been
 2    adequately separated or quantified, and this may be the cause of conflicting observations from some
 3    studies. However, some generalizations can be made. Sulfur and chloride pollutants are generally more
 4    important at causing metals corrosion than N pollutants, however NOx (or NOy) and 862 together have
 5    been shown to be more damaging than 862 alone. The combination of NC>2 and 862 has been shown to
 6    result in a synergistic effect where the total damage from the mixture is greater than the additive damage
 7    from the two pollutants separately (Svensson and Johansson, 1993a). This effect may be due to enhanced
 8    wetting of the surfaces caused by NOY pollutants, resulting in corrosion at lower relative humidities than
 9    would otherwise be the case. This enhancement has been attributed to the formation of hygroscopic
10    nitrate salts, but may also be caused directly by the deposition of gaseous HNO3 onto the surface
11    (Dubowsky, 2004). The corrosion effect of HNOs  on zinc, copper, and steel is larger than that of SC>2
12    alone or a mixture of SC>2 and NC>2 (Sarnie et al., 2007).
13         Although deposition of NOy/NHx/SOx species in particulate matter can soil metal surfaces, such
14    deposition does not directly result in substantial metals damage. However, under wet conditions these
15    soluble species form an electrolytic solution that can cause corrosion. Corrosion of steel and zinc has been
16    found to depend on the surface electrolyte irrespective of the presence of particles (Askey et al., 1993).
17         Temperature has been found to have a complex effect on metals corrosion. Lower temperatures
18    tend to increase surface wetness, but decrease the diffusivity of gaseous pollutants, and may reduce the
19    rates of some reactions that convert 862 and NOx to sulfuric and nitric acids. Thus, the effect of
20    temperature changes on long-term corrosion rates  can be hard to predict.
            E.5.2. Effect on Economically Important Metals
21          Steel is the most common and economically important structural metal, and is often used in
22    galvanized form (i.e., with a protective coating of zinc). SC>2 is generally reported to be more corrosive
23    than NOx, however for well-protected steel the effects of ambient air pollutants are usually a small
24    increment on top of the natural weathering process. The N pollutants can have an enhancing effect on the
25    corrosion caused by the S pollutants. This is attributed to the increased wetting that can result from the
26    presence of hygroscopic NOs salts. Relative humidity has been shown to be very important in the
27    corrosion of steels by SO2 with much slower corrosion rates observed when the relative humidity is below
28    70% (Dehri et al.,  1994). The presence of SO2 has been shown to reduce the corrosion pitting of iron
29    induced by sodium chloride (NaCl) but there may be an overall synergistic effect among SO2, NO2, and
30    NaCl (Weissenrieder et al.,  2004). Steel corrosion rates have been shown to decrease over time (Almeida,
31    2000) (Damian and Fako, 2000) approaching steady state rates after approximately 4000 days (Damian
32    and Fako, 2000).
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 1          Zinc corrosion has been shown to be inversely dependent on temperature (Svensson and Johansson,
 2    1996). Corrosion products formed on zinc in polluted environments are less water soluble, and therefore
 3    more protective against further corrosion, than corrosion products formed in clean environments (Vilche
 4    et al., 1995). The combination of 862 and NC>2 showed synergistic (i.e., greater than simply additive)
 5    corrosive effects on zinc (Svensson and Johansson, 1993a). 862 slowed the NaCFinduced corrosion of
 6    zinc while NC>2 accelerated that corrosion (Svensson and Johansson, 1993b).
 7          Aluminum is naturally protected from corrosion by a formation of a durable surface film, but some
 8    effects of the NOY/NHX/SOX pollutants have been observed. Minimal damage is caused to Al by NOX.
 9    The mixture of SO2 and NOX is variously said to be either more or less corrosive to Al than SO2 alone.
10    The deposition rate of SO2 to Al was shown to increase in the presence of O3, but no effect on SO2
11    deposition rate was found for NC>2 (Blucher et al., 2005). Interaction between SC>2 and NaCl results in an
12    increased corrosion rate but decreased pitting of Al compared to NaCl alone (Blucher et al., 2005). Oesch
13    and Faller (1997) found that SC>2 is more corrosive to Al than NC>2 and that there is no difference in Al
14    corrosion rate when exposed to NO or clean air.
15          Mixtures of NOX and SC>2 are more corrosive to copper than either pollutant alone. When hydrogen
16    sulfide (H2S) and 03 were also evaluated for damage to copper, they also were found to be more
17    damaging to copper than NOX. The corrosion rate of copper exposed to SC>2 or NC>2 has been shown to
18    slow overtime (Oesch,  1997) (Oesch and Faller, 1997; Leuenberger-Minger et al., 2002). The corrosion
19    rate of copper in the presence of NO2 is greater than in clean air. In the first 24 h of exposure to NO2, an
20    acidic electrolyte is formed on the surface that dissolves copper oxides and results in an increased
21    corrosion rate (Dante and Kelly,  1993). Synergistic effects have been seen between SO2 and O3 (strong)
22    and NO2 (weak) (Aastrup et al., 2000). High corrosion rates were observed for field exposures of copper
23    at sites with a combination of SO2 and O3. After four years of exposure, 90% of the corrosion products
24    formed on copper remained on the surface (Leuenberger-Minger et al., 2002). The copper hydroxy
25    sulfates brochantite and antlerite are stable copper corrosion products formed in the presence  of SO2, O3,
26    and NO2 (Strandberg, 1998).
27          Nickel is also damaged more severely by SO2 or chloride salts than by NOX. Nickel samples
28    deployed at urban, industrial, and rural sites showed that corrosion rates increase with SO2
29    concentrations. Soluble hydrated nickel sulfates were the main corrosion  products and are easily removed
30    from the surface by rainfall events, thereby exposing the underlying surface (Jouen et al., 2004).
31          Kim et al. (2004) conducted a study of the effects of ambient SO2 and NO2 on steel, bronze,
32    copper, and marble at sites in China, Korea, and Japan. Both sheltered and unsheltered samples were
33    exposed with the corrosion rates of the unsheltered samples higher in all cases. The corrosion rate of steel
34    was the highest, followed by marble, bronze, and copper. Higher corrosion rates (especially for
35    unsheltered samples) were found to be correlated with high SO2 concentrations.
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            E.5.3. Effects on Electronics
 1          The increasingly wide penetration of electronic devices into daily life offers greater opportunities
 2    for environmental damage to sensitive components. The hardware of communication systems may be
 3    exposed to pollutants in outdoor air, and the ubiquitous cell phones may be exposed in both indoor and
 4    outdoor environments. Sulfur and N oxides have been shown to corrode the metallic contacts in electronic
 5    equipment, which are often made of copper or brass coated with a precious metal such as gold, palladium,
 6    or nickel. Such materials are corroded more by NO2 than by SO2, but a mixture of these two pollutants is
 7    more corrosive than either alone. The combination of SO2 and H2S is also less damaging than either NO2
 8    alone or a combination of NO2 and these pollutants. NO2 is also moderately corrosive to solder in
 9    electronic components.
      E.6. Effects on Paints
10          Painted surfaces are extremely common as a means of preventing damage to other materials, and
11    may be categorized as architectural coatings (e.g., house paint), product coatings (e.g., automobile
12    finishes), and special-purpose coatings (e.g., bridge paint). Environmental damage to painted surfaces is
13    expected, and periodic repainting is normal, but any factor that causes more rapid degradation or
14    discoloration of paints will require more frequent repainting and thus result in higher costs. Paint
15    formulations may differ widely for different applications, so the extent of air pollution damage in  a given
16    application cannot necessarily be predicted from published information. Previous work at elevated
17    pollutant concentrations has shown that oil-based house paint is readily damaged by SO2 and moisture,
18    and is more subject to damage by SO2 than by NO2. Sample weights increased with increasing NO2, but it
19    is not clear if this indicates direct reaction of NO2 or an enhancement of the effects of SO2 and moisture
20    by NO2. The effect of SO2 may be due to reaction with CaCOs and zinc oxide (ZnO) present in the paint.
21    Tests with various paints showed that NOx becomes incorporated into the paint surface upon long
22    exposure, apparently by reaction with polymers that make up the cured paint. In other tests HNOs was
23    found to produce substantially more damage to paints containing both low and high levels of carbonate
24    (CO3-) than did an equal mixing ratio of NO2.
25          Grosjean et al. (1983) studied the fading of colorants on cellulose paper. Twelve colorants were
26    exposed to various atmospheres including purified air, NO2, SO2, and a mixture of oxidants (03, NO2, and
27    peroxyacetyl nitrate (PAN)). Not all colorants were tested in each atmosphere. The mixture of oxidants
28    resulted in the largest color change for each tested colorant. Only one colorant was exposed to SO2, NO2,
29    and the mixture. For that colorant, the color change induced by the mixture of oxidants was
30    approximately three times the color change with NO2 alone; the color change with SO2 was

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 1    approximately 70% of the color change with NC>2. An increase in relative humidity results in increased
 2    fading of colorants (Grosjean et al., 1993, 1994). Of 35 colorants exposed to a mixture of 03, NC>2, and
 3    PAN, nine exhibited substantial color changes and three exhibited moderate color changes (Grosjean
 4    etal., 1993).
 5         Paint samples were exposed to UV light, NOx, 862, and a combination of the treatments by
 6    Colombini et al. (2002). The exposure conditions were chosen to produce accelerated aging of paint
 7    samples. Non-pigmented paints were chosen to isolate degradation of the paint binder from  synergistic
 8    effects with pigments. Exposure to the combination of treatments resulted in increased cross-linking in
 9    the paint binder as well as formation of organic acids. An examination of paint samples taken from
10    naturally aged paintings confirmed the presence of organic acids as degradation products.
      E.7.  Effects on Stone and  Concrete
11         The effects of NOy/NHx/SOx air pollutants on stone and concrete are undoubtedly the most widely
12    studied because of their impact on historic buildings, monuments, and archeological treasures. Table E-2
13    summarizes studies of the effects of NOy/NHx/SOx on stone, concrete, and mortars. Calcareous stone
14    (i.e., that consisting of CaCOs, such as marble, limestone, and cement) is most susceptible to damage.
15    Mortar used in stone construction is often more porous than calcareous stone, and therefore more subject
16    to damage. The damage to such materials is attributed primarily to the effect of 862 in forming gypsum
17    (CaSO4-2H2O):

                            CaCO3 + SO2 + 2H2O -^ CaSO4-2H2O + CO2
18         The gypsum thus formed occupies a larger volume than the original carbonate, so the stone surface
19    becomes pitted and damaged. Gypsum is also more soluble than the carbonate, so it can be removed by
20    precipitation, exposing the surface to further reaction and damage. As a result, dry deposition of SC>2 to
21    the stone surface between rain events is important, as it causes continued damage. The reaction of 862
22    with calcareous stone is more energetically favorable than the reaction of N oxides, and thus 862 is the
23    primary cause of damage to stone, however the combination of 862 and NOx is more damaging than 862
24    alone. This effect may be due to enhanced wetting of the stone, to oxidation of 862 by the N oxides, or to
25    formation of calcium nitrate (Ca(NO3)2), which is much more soluble than CaCO3 and  is easily washed
26    off the stone surface by precipitation. Removal of the nitrate salts in this way may result in
27    underestimation of the role of N oxides in stone damage when surface layers are analyzed for chemical
28    composition.
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 1          Concrete is more susceptible to damage from N oxides than are the calcareous stones, because
 2    concrete contains calcium hydroxide (Ca(OH)2), which can react to form calcium nitrate (Ca(NO3)2). This
 3    product is soluble and can be washed out of the concrete, weakening the material.
 4          Deposition of particulate matter onto stone primarily results in soiling of the stone, due to the
 5    elemental carbon and organic compound content of the deposited particles. The orientation of the surfaces
 6    and size of the particles affect deposition: vertical surfaces are more affected by deposition of fine
 7    particles, whereas horizontal surfaces are more affected by large particles. The fine particles carry the
 8    bulk of the carbon and organic material. Metal oxides present in deposited particles may enhance the
 9    reaction of SO2 to form gypsum.
10          Gypsum crusts form more readily in rain-sheltered environments than on rain-washed stone
11    surfaces (Zappia et al., 1998). The presence of fog water has been shown to increase the rate of gypsum
12    formation on surfaces sheltered from rain washing (Del Monte and Rossi, 1997). Gypsum crust formation
13    has been shown to proceed at a faster rate when the stone surface is sprinkled with fly-ash particles. In
14    addition, fly-ash (or other carbonaceous particles) can become entrained in the gypsum matrix and affixed
15    to the stone surface. Normally, gypsum crusts are gray in color, but when carbon containing particles are
16    entrained, they become black (Ausset et al., 1999). While gypsum crusts are composed primarily of
17    sulfates, they have been found to contain nitrate compounds as well (Marinoni et al.,  2003; Martinez-
18    Arkarazo et al., 2007). The inclusion of nitrates in gypsum crusts suggests that N oxides, as well as 862,
19    play a role in the degradation of stone exposed to the atmosphere. The presence of particulate matter, in
20    addition to 862, has been shown to increase gypsum formation by 20% (Boke et al.,  1999). Ambient 862
21    concentrations alone are not adequate to predict the degree of damage to stone samples (Torfs and van
22    Grieken, 1996).
23          Dolomite has been shown to be less sensitive to sulfation than calcite (Lan, 2005). Corrosion of
24    marble due to 8 species has been found to be of the same order of magnitude as that caused by N species.
25    Damage is caused not by the gas phase oxides (SO2, NO2, NO) but by acid (H2SO4, HNO3) and salt
26    (SO42 , NC>3 )  species present in the electrolyte which forms on the marble surface (Sikiotis and Kirkitsos,
27    1995). The rate of marble surface recession by rain washing is faster than the rate of gypsum crust
28    formation due to dry deposition of S and N containing pollutants. Marble is damaged by rain washing
29    through two mechanisms, dissolution of the gypsum crust and dissolution of the underlying marble. The
30    gypsum crust is more soluble in water than marble and is rapidly dissolved in rain. The naturally
31    occurring acidity in rainwater from the dissolution of carbon dioxide (CC^) is an important mechanism by
32    which stone  samples are degraded, but additional acidity from the dissolution of SC>2 and NC>2 in
33    rainwater does not greatly increase the solubility of marble in rainwater (Yerrapragada, 1996).
34          While gypsum is the primary degradation product found on stone, mortar, and  concrete samples,
35    other damage products do occur. Sulfite species have been found on mortars as intermediate damage
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 1    products. On mortars, a secondary damage mechanism exists in which gypsum reacts with calcium Al
 2    hydrates present in the mortar to produce ettringite (Ca6Al2(SO)4(OH)i2-26H2O). Ettringite is an
 3    insoluble sulfate that may cause damage by expansion and lead to cracking of mortars (Sabbioni,
 4    2002)(Sabbioni et al., 2001, 2002).
      E.8.  Effects of NOX on  Paper and Archival Materials
 5         The cellulose fibers that make up paper are reactive with NO2 and other NOy species, and storage
 6    condition standards have been set regarding acceptable levels of NOx for archives, libraries, and
 7    museums. Exposure of archival materials to NOy species in such facilities can arise from normal outdoor
 8    or indoor sources, but also from generation of such NOy species from the materials themselves.
 9    Specifically, stored materials that include cellulose nitrate, e.g., in the form of photographic film,
10    adhesives, or recording media, can slowly decompose to release NOx and product species such as HNO3.
11    These emissions can degrade archival materials, and even be a safety hazard if allowed to accumulate. In
12    terms of outdoor air pollutants, it is likely that HNO3 is a key reactant in the degradation of paper
13    archives. The rapid deposition velocity of HNO3 and the numerous surfaces in archival facilities provide
14    opportunity for attack by HNO3, and probably result in the effects of HNO3 being underestimated,
15    relative to those of NOx, based on indoor air measurements. Artists' pigments can also be damaged by
16    extended exposure to ambient atmospheric NO2.
17         The effects of SO2 and O3 on paper were studied by Johansson and Lennholm (2000). The
18    deposition rate of SO2 to fresh paper was found to decrease rapidly with time and approached steady state
19    after ten hours. The deposition rate of SO2 to fresh paper in the presence of O3 was found to be elevated
20    compared to SO2 alone.  The deposition rates to aged paper were much lower and there was no effect on
21    the SO2 deposition rate observed in the presence of O3. The decrease in deposition rate with time is
22    thought to be due to protonation of all available carboxylate ions to carboxylic acid.
23         Pigments in works of art can be degraded or discolored by atmospheric pollutants. H2S has been
24    shown to react with both copper and lead pigments, but only lead white has been seen to darken over time
25    (Smith and Clark, 2002). A synergistic effect has been detected between NO2 and both benzene and
26    toluene resulting in an increased rate of attack on pigment oxides (Agelakopoulou et al., 2007).
27    Deposition of S to the surface of paintings, either as SO2 or ammonium sulfate ((NH^SO/^ particles, can
28    damage, varnish, or cause discoloring of paint (Gysels et al., 2004).  Paint models subjected to accelerated
29    aging in SO2 (10 ppm) and NOx (10 PPm) as well as UV radiation for 15 days exhibited a variety of
30    damage markers. Both nitrate and sulfate damage mechanisms were observed with sulfation sometimes
31    masking other processes (Arbizzani et al., 2004).
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      E.9.  Costs  of  Materials Damage from  NOY, NHX, and SOX
 1         Materials exposed to the ambient atmosphere are degraded and damaged through a number of
 2    mechanisms. Damage associated with air pollutants result in effects such as decreased usable lifetime,
 3    increased maintenance frequency, and loss of aesthetic appeal. It is difficult to separate the costs
 4    associated with air pollutants from costs associated with other damage mechanisms.  Some estimates of
 5    cost have been based on empirically derived dose-response functions for specific materials. Other
 6    estimates have been developed using inspection of actual materials damage and maintenance guidelines
 7    for the materials (Cowell and Apsimon, 1996). Estimation of costs over large geographic areas is subject
 8    to considerable uncertainty due to unknown distribution of materials at risk and spatial variations in
 9    pollutant concentrations. A cost estimate for material cost savings from SO2 emission reductions in
10    Europe was performed by Cowell and Apsimon (1996). In this study, the cost savings of theoretical future
11    emission reductions across Europe was modeled using cost data extrapolated from a study conducted in
12    three Norwegian cities.  Total theoretical SO2 reductions of 15,904 kilotons per year resulted in modeled
13    annual cost savings of $9,504 million total for Europe (Cowell and Apsimon, 1996; Apsimon and Cowell,
14    1996).
      E.10.  Summary
15         Many types of materials, including metals, electronics, plastics, paints, stone, and paper may be
16    damaged by atmospheric NOy/NHx/SOx species. Damage occurs due to dry and/or wet deposition of the
17    pollutants onto the surface of a material and subsequent formation of an electrolytic solution in water
18    present on the surface. At low relative humidity, when little water is present on surfaces, damage rates
19    have been observed to be much lower, and in some cases, no damage has been observed. Both SO2 and
20    NO2 have been implicated in damage processes for different materials. In general, damage to materials by
21    SO2 is greater than by NO2. Little work has been conducted to investigate the effects of NO on material
22    damage. What work has been conducted shows no damage, or very minor damage for NO containing
23    environments compared to clean air. Synergistic effects between SO2 and NO2 lead to increased damage
24    rates for the gases in combination. Other species such as 03, NaCl, organics, or particulate matter have
25    also been shown to have synergistic effects with SO2 and NO2. The corrosive effects of nitric acid have
26    been found to be stronger than effects of other NOy/NHx/SOx species. Costs associated with damage to
27    materials by atmospheric pollutants are difficult to estimate because of the many sources of uncertainty in
28    the estimation process. For heavily polluted environments, the cost savings due to decreased rates  of
29    material degradation could offset a significant portion of the costs to reduce emissions. In general, for
30    polluted environments, reductions in SO2 or HNO3 concentrations will reduce damage rates more than

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1      reductions in NC>2, NO, or NH3. In areas with low SC>2 concentrations, reductions in NC>2, 03, or

2      particulate matter concentrations may reduce damage rates.
       Table E-1. Studies on corrosive effects of NOY/NH3/SOX effects on metals.
       Materials
                               Exposure Conditions
                                                                        Findings
                                                                                                                               Reference
       Zinc
       Mild Steel
       Copper
       Zinc
       Galvanized
       Iron
       Zinc
       Aluminum
       Zinc
       Zinc
       Copper
       Zinc
       Aluminum
       Copper
       Copper
Samples were exposed to fly-ash in clean air and in
air with SO2 and/or HCI (presentation rates of 27 x
1CT6 and 4.7 x 1CT6 mg/cm2s, 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 NaCI 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/m2day to non-detectable levels across the
sites. Time of wetness was also measured at each
site.

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.
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).
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.
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.
Corrosion was found to depend on the surface electrolyte         Askey et al.
irrespective of the presence of particles. Inert particles were       (1993)
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.

The corrosion rate of copper in the presence of NO2 was much     Dante and Kelly
greater than in clean air. The surface electrolyte was found to     (1993)
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.

SO2 slowed the corrosion of zinc with moderate to high surface    Svensson and
concentrations of NaCI due to the formation of sodium zinc        Johansson
hydroxychloride sulfate.  NO2 (which, alone, is unreactive toward   (1993b)
zinc) accelerated the corrosion of zinc in the presence of small
amounts of NaCI.

Corrosion rates in SO2 were found  to be largely dependent on     Dehri et al.
relative humidity. No such  humidity dependence was observed     (1994)
for corrosion induced by NH3. Corrosion  rates in  both gases
decreased sharply with time  (approaching steady state values
after 30 h).

Corrosion products that developed in rural environments were     Vilche et al.
found to be easily removed from the surface and thus result in     (1995)
poor protectiveness. Corrosion products formed in more
aggressive environments were found to  be more protective
against continuing corrosion.

SO2 induced corrosion was found to be  inversely dependent on    Svensson and
temperature. The maximum  corrosion rate (at 107 ppb SO2) of    Johansson
11 mg/cm2d was observed at 4 C. The  corrosion rate at 30 C      (1996)
was 6.8 mg/cm2d.

NO was found to  have no effect on the  corrosion of copper, zinc,   Oesch and
or aluminum. Copper in the presence of SO2 (10 ppm) and NO2    Faller (1997)
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.

Tenorite reacted rapidly with SO2 to form brochantite and other    Strandberg
sulfate containing products. Cuprite reacted slowly with SO2       (1998)
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.

Copper sulfite and cuprous oxide formed on copper surfaces       Aastrup et al.
exposed to SO2. With O3 present, an increased rate of mass gain   (2000)
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.
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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 urban-industrial
            and rural  atmospheres for 20 yrs. Avg SO2
            concentrations were 90 and <10 ug/m3 (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 ug/m3) (33, 8, and 2 ppb),
            NO2 (50.1, 24.2, and 8.7 ug/m3) (26, 13, and
            5 ppb), SO2 (22.3, 29.0, and 12.2 ug/m3) (8, 11,
            and 5 ppb), and O3 (25.8, 47.1, and 60.1 ug/m3)
            (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
            63  (each 200 ppb). The same exposure conditions
            were used for iron samples with NaCI deposited on
            the surface.
Samples exposed at sites with moderate SO2 and chloride         Almeida et al.
deposition rates formed compact, rounded corrosion structures.    (2000)
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    Damian and
values of 0.1 and 0.08 mm/yr for the  urban and rural             Fako (2000)
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  Leuenberger-
with the highest combination of SO2 and O3. For zinc, the highest  Minger et al.
corrosion losses were observed at the site with the highest SO2    (2002)
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 ug/cm2y were determined    Jouen et al.
for urban, industrial, and rural environments, respectively. Mass    (2004)
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.
            Aluminum samples were exposed to SO2 (96 ppb)
            either alone or in the presence of other pollutants
            (NaCI, NO2, or O3).
            Samples were exposed to HNO (50-180 ppb) in a
            laboratory exposure chamber. Tests were conducted
            at 65% and 85% relative humidity.
No corrosion products were detected on samples exposed to       Weissenrieder
humidified air alone. The addition of SO2 alone was not enough    et al. (2004)
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 NaCI induced corrosion of iron, but
the combination of SO2 and NO2 was found to accelerate NaCI
induced corrosion.

SO2 alone resulted in the loss of metallic luster. 50% of the       Blucher et al.
surface had developed corrosion products after 672 h. Samples    (2005)
exposed to  NaCI alone showed significantly larger mass gain than
samples exposed to only  SO2. The combination of SO2 and NaCI
resulted in the largest mass gain (fastest corrosion rate). While
the rate of mass gain was highest with a combination of SO2 and
NaCI, the pitting observed in the presence of NaCI 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 HNO3 on carbon steel were larger than    Sarnie et al.
on zinc or copper. The corrosion effect of HNO3 was found to be   (2007)
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.
Table E-2. Studies on corrosive
                                                                          on stone.
Materials
                          Exposure Conditions
                                                                                     Comments
                                                                                                                          Reference
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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
4174 ug/m3 (21 to 1603 ppb). Field exposures were
conducted in several stages in Greece. Avg
concentrations for field exposures were 1.41 ug/m3
(0.5 ppb) HNO3, 2.39 ug/m3 (3 ppb) NH3, 4.84 ug/m3
NO3-, 14.61 ug/m3 SO/', and 5.01 ug/m3 NH4+.

Samples of differing thickness were exposed in the
field and runoff water collected for 5 mos. The avg
SO2 concentration was 60 ug/m3 (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
mean pollutant concentrations were 17.2 ug/m3
(7 ppb) SO2, 265 ug/m3 (139 ppb as NO2) NOX, and
131 ug/m3 suspended  particles.

Samples were exposed to urban environments in both
sheltered and unsheltered configurations. Pollutant
concentrations were not reported.
Samples were exposed to 340 ug/m3 (125 ppb) SO2
and 98 ug/m3 (50 ppb) NO2 in the laboratory. Samples
were either exposed naked or sprinkled with fly-ash
particles. Field exposure was  conducted for 1 yr with
samples sheltered from rainwater. Avg SO2
concentration during the  field exposure was 107
ug/m3 (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
levels have declined from 350 ug/m3 (132 ppb) in
1970 to ~10 ug/m3 (4 ppb) in 2002. NOX
concentrations have remained relatively constant
around 100 ug/m3 (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   Sikiotis and
of corrosion by sulfates and nitrates were found to be of the     Kirkitsos
same order of magnitude. Corrosion was found to be caused by  (1995)
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  Torfs and van
sulfate content in runoff water from the ambient SO2            Grieken (1996)
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 urn for rain sheltered samples     Yerrapragada
after 1 yr of exposure. SO2 was found to be the dominant factor  et al. (1996)
in crust formation. Surface  recession due to rain washing  was
14.5 um/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      Del Monte and
following exposure to fog water in  a polluted environment.       Rossi (1997)
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     Zappia et al.
intense on mortars than on stones due to higher porosity.       (1998)
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         Ausset et al.
greater intensity for samples sprinkled with fly-ash than for      (1999)
naked samples. The growth of gypsum crystals fixed the fly-ash
to the surface of the limestone. Fly-ash was found to be an
important factor in crust formation by facilitating gypsum
crystal formation. Fly-ash particles  also darken gypsum crusts
from gray to black.

Reaction between calcium carbonate and SO2 was found to take  Boke et al.
place in a liquid film on the calcium carbonate surface. The      (1999)
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  Sabbioni et al.
was found as an intermediate damage product in the sulfation    (2001)
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   Sabbioni et al.
the mortars sampled. A secondary  damage mechanism was      (2002)
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)    Marinoni et al.
results in formation of dendritic crusts on concrete. Nitrates      (2003)
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  Lan et al.
containing dolomite was less sensitive to SO2 than calcite        (2005)
marble. For relative humidity  greater than 72%, humidity was
an important factor in determining  the sulfation rate.
Limestone   Samples were collected from 3 facades of a historical
Sandstone   buildin9 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       Martinez-
                                                  nitrate compounds. Samples with black crusts on the surface     Arkarazo
                                                  were found to have predominantly gypsum and soot, but        et al.(2007)
                                                  nitrate compounds were identified in the crusts as well.
                                                  Sandstone samples were much more damaged than limestone
                                                  samples due to their higher porosity.
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                          ANNEX  E  - References
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1   Yerrapragada, S. S.; Chirra, S. R.; Jaynes, J. H.; Bandyopadhyay, J. K.; Gauri, K. L. (1996)
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5          materials in urban atmosphere. Sci. Total Environ. 224: 235-244.
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        Annex  F.  Valuation of the  Environmental
               Effects of N and  S  (non-materials)
      F.1.  Introduction
 1        The monetary valuation of ecological effects associated with NOx and SOx emissions starts with
 2    natural  science endpoints. These may be things that people value directly, such as loss of a particular
 3    species, or some remote effect on a resource that is not clearly understood by the general public or not
 4    valued by the public for itself, such as forest soils. Of course, damage to forest soils will affect the
 5    terrestrial ecosystem in ways that may be valuable to humans, such as tree growth, habitat, and even the
 6    aesthetics of the forest. This Annex is a review of the literature that estimates such values for various
 7    ecosystem endpoints or that provide values for effects that can be reasonably inferred from what is
 8    provided.
 9        The purpose of this Annex is to provide an assessment of the economics literature on the effects of
10    NOx and SOx emissions on terrestrial, transitional, and aquatic ecosystems.


          F.1.1. Valuation in the Context of NOX and SOX
11        Figure F-l provides a schematic representation of how economic valuation is derived from changes
12    to NOX and SOX secondary standards. Starting at the upper left-hand side, the NOX and SOX standards
13    are set and emissions reductions occur to change the ambient concentrations of NOx and SOx. Reading
14    down from "Change in Ambient Concentrations," these reductions will lead to changes in a variety of
15    ecological endpoints (as identified in the ISA) in terrestrial, transitional, and aquatic ecosystems. The box
16    below, "Change in Economic Endpoints," refers to physical endpoints that people care about, in which
17    changes can be valued (at least in principle) in monetary terms. Many times, these are referred to as
18    ecosystem services. In a few cases, such as agricultural crop  growth and yield, ecological and economic
19    endpoints are nearly the same. Finally, at the bottom of this diagram is a box labeled "Valuation
20    Methods," which notes alternative approaches for placing monetary values on these economic endpoints.
21    As endpoints are discussed in detail in the ISA, this Annex focuses solely on valuation.
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           NOX/SOX Standards
Change in Ambient Concentration
     (NOX, SOX, and Ozone*)
                                                            Deposition Rate
                                             Change in Ecological Endpoints
                            Terrestrial Ecosystem
                            • forest growth and
                             appearance
                            • crop growth
                            • soil chemistry
                            • species composition
                             and richness
                            • population abundance
     Transitional Ecosystem
     • water chemistry
      and quality
     • species composition
      and richness
     • population abundance
Aquatic Ecosystem
• water chemistry
 and quality
• species composition
 and richness
• population abundance
                                              Change in Economic Endpoints
                            Terrestrial Ecosystem
                            • crop yields
                            • timber yields
                            • recreational activities
                            • aesthetics
                            • other non-consumptive
                              uses
     Transitional Ecosystem
     • recreational activities
     • non-consumptive uses
Aquatic Ecosystem
• recreational activities
• commercial fishery
 yields
• non-consumptive uses
                                                  Valuation Methods
                                                  • Stated Preference
                                                  • Revealed Preference
                                                  • Avoided Cost
                                                  • Replacement Cost
                                                  • Benefit Transfer
     Figure F-1. Illustration chart of the assessment.


     F. 1.1.1.  Ecosystem Services

1           Broadly defined, ecosystem services are the benefits that people obtain from ecosystems
2    (Millennium Ecosystem Assessment, 2003). In the Millennium Ecosystem Assessment (MA), ecosystem
3    services are classified into provisioning, regulating, supporting, and cultural services. Provisioning
4    services denote the products people obtain from ecosystems; regulating services are associated with the
5    ecosystem functions that regulate climate, nutrient cycle, water filtration, and so forth;  supporting services
6    are ecosystem functions, such as primary productivity and production of O2, that support the provision of
7    ecosystem services; and cultural services are the non-material benefits ecosystems provide to people
8    through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences.
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 1          Ecosystems are productive systems in which various biological and physical factors, as well as
 2    their interactions, serve various functions in the production of ecosystem services. However, economic
 3    valuation of the environment has focused mostly on the contributions of individual goods and services to
 4    human well-being. Alternately, ecosystem services valuation is based on the various benefits generated by
 5    the ecosystem (Polasky et al., 2005). In this case, benefits include both marketed and non-marketed
 6    services, and their valuation considers the environment as a natural capital asset that generates returns on
 7    investment in ecosystem protection and management.
 8          For example, wetlands constitute a form of natural capital. They serve as flood barriers, soaking up
 9    excess water and slowing and preventing floodwaters from spreading uncontrollably. Wetlands help
10    replenish groundwater and improve both ground and surface  water quality by slowing down the flow of
11    water, and absorbing and filtering out sediments and contaminants. They also provide spawning habitat
12    for fish, supporting the regeneration of fisheries. In addition, wetlands provide habitat for many wildlife
13    species and support commercial and sport fishing, as well as  hunting and other forms of recreation.
14          Though different functions and processes of ecosystems, such as water filtration, may be
15    economically important,  they need to be viewed as inputs of or mechanisms for the production of
16    economically valuable services, such as drinking water, timber, or recreational benefits. The end products,
17    not the elements of the production process, ultimately generate economic well-being. Along these lines,
18    Boyd and Banzhaf (2007) advocate defining ecosystem services as "components of nature, directly
19    enjoyed, consumed, or used to yield human well-being." In other words, ecosystem services are the end
20    products of nature to which ecosystems contribute as intermediate inputs or production technologies.
21    Though this distinction may at first seem unimportant, it is crucial for the accurate valuation of ecosystem
22    services. Regarding the incorporation of ecosystem services into the measurements of national income
23    and the value of goods and services produced in an economy, such as gross domestic product (GDP)
24    accounts, Boyd and Banzhaf (2007) note that:
25          If intermediate and final goods are not distinguished, the value of intermediate goods is double-
26    counted because the value of intermediate goods is embodied in the value of final goods. For example,
27    clean drinking water, which is consumed directly by a household, is dependent on a range of intermediate
28    ecological goods, but these intermediate goods should not be counted in an ecosystem service welfare
29    account. Also important is that ecosystem services are attributed only the incremental value they
30    contribute to the production of valuable end products. Using  the above example, the value of ecosystem
31    services associated with drinking water denotes the marginal contribution of ecosystems in the production
32    of drinking water, not the full value of the final product.
33          Given the complexity and variety of ecosystems and their services, their valuation poses several
34    challenges. According to the National Academy of Sciences' Committee on the Valuation of Ecosystem
35    Services, the importance of ecosystem functions and services is often taken for granted and overlooked in
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 1    environmental decisionmaking. Moreover, the key challenge in the valuation of ecosystem services lies in
 2    the difficult integration of economic valuation and ecological production theory. This is no
 3    straightforward task, because many ecosystem goods and services are not quantifiable using available
 4    methods, and the application of economic valuation methods may be subject to judgment, uncertainty, and
 5    bias (Heal, 2005).
 6          A study by Costanza et al. (1997), seeking to determine the value of global ecosystem services,
 7    exemplifies the problems and pitfalls in the valuation of ecosystem services. Deriving and summing value
 8    estimates from the existing literature for a wide range of ecosystem attributes and services, this study
 9    suggested that the total value of global ecosystem services likely ranges from $16 to $54 trillion annually,
10    or roughly one to three times global GDP. The study has been  influential and widely quoted and used,
11    especially among scientists and environmentalists. Economists consider it fundamentally problematic
12    both conceptually and methodologically, preferring to focus on the value of changes to ecosystem
13    services, which is relevant for policy, or what is termed the marginal value of ecosystem services. More
14    profoundly, the entire concept of the value of global ecosystem services is problematic; without the global
15    ecosystem we would all perish. The estimate of the value of global ecosystem services by Costanza et al.
16    (1997) has therefore been characterized as a "serious underestimate of infinity" (cf Toman, 1998; Smith,
17    2007).

      F.1.1.2. Use of the Valuation  Literature to Define Adversity
18          A secondary standard, as defined in Section 109(b)(2) of the Clean Air Act, must "specify a level of
19    air quality the attainment and maintenance of which, in the judgment of the Administrator, based on such
20    criteria, is required to protect the public welfare from any known  or anticipated adverse effects associated
21    with the presence of [the] pollutant in the ambient air."  One way to quantify adverse effects is through
22    monetary valuation.
23          Adversity is difficult to quantify and measure, and there are several challenges to using a monetary
24    valuation approach. A major effect that is geographically extensive might be considered to be more
25    adverse than a more severe effect limited to one geographic location. Another problem is aggregation.
26    Any change in pollution may have multiple effects (i.e. effects on many types of ecosystem services)
27    leading to difficulty in aggregating in a consistent way.
28          Monetary values on any service or resource degradation reflect human preferences about what is a
29    severe effect. Larger unit values correlate with more severe effects, other things equal. Also, more
30    extensive effects, will  contribute to larger welfare loss (or gain). In addition, since monetary units can be
31    added, the aggregation issue can be addressed by "simply" summing the welfare losses (or gains).
32    Although this is not strictly true (e.g., values for improvements in water quality and fish populations may
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  1     not be additive), in principle the differences in how people conceptualize ecosystem improvements can be
  2     captured in the way resource improvements are valued in monetary terms.
  3           Clearly, there are many practical problems associated with using monetary value as a way of
  4     defining adversity. First, many resources and services have not been valued and efforts to credibly transfer
  5     the results of valuation studies to other areas and resources have been minimal. Second, studies
  6     addressing multiple effects are particularly difficult to transfer and few in number. Finally, even with the
  7     first two problems addressed, a judgment would still need to be made on whether the air quality standard
  8     was the contributing factor for eliminating adverse (highly monetarily valued) effects.

       F.1.1.3. Methods for Selecting Literature for this Assessment
  9           Assessing the economics literature on the effects of NOX and SOX emissions on terrestrial,
10     transitional, and aquatic  ecosystems requires identifying and reviewing relevant studies addressing these
11     effects. Multiple methods were used for this Annex: searching existing databases of this valuation
12     literature;  conducting systematic searches of the economics literature;  reviewing a large number of key
13     articles, reports, authors, and journals; and identifying studies based on the expertise and familiarity with
14     the relevant literature of lead researchers.
15           Two existing databases on environmental valuation studies - the Environmental Valuation
16     Reference Inventory (EVRI) and the Beneficial Use Values Database (BUVD) - were particularly useful
17     for this assessment. The  EVRI database, which includes nearly 1,900 valuation articles/studies on
18     environmental and human health effects, was screened according to criteria regarding the potential
19     relevancy  of geographical location (U.S.), types of environmental goods and services valued (ecological
20     functions, extractive uses, non-extractive uses, passive uses); and environmental stressor. This resulted in
21     over 200 articles/studies of interest. BUVD is a relatively  small database (131 articles/studies), so it was
22     imported into the literature review database in its entirety, with unrelated articles/studies later excluded on
23     an individual basis.
24           A large number of additional journals and literature databases were identified that publish and
25     cover research potentially relevant to this assessment. The selected peer-reviewed journals1 and library
26     databases2 were then reviewed using search engines and a range of key words developed to find studies
27     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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 1    tables of contents of those journals that could not be searched electronically were reviewed in hard copy
 2    and relevant articles were added to the literature review database. These searches were augmented by
 3    reviews of the bibliographies of the following EPA reports: EPA Report to Congress: The Benefits and
 4    Costs of the Clean Air Act 1990-2010 (November 1999); Air Quality Criteria for Ozone and Related
 5    Photochemical Oxidants (February 2006); Air Quality Criteria for Paniculate Matter (October 2004).
 6          The results from these searches were checked for duplicates and clearly irrelevant studies, after
 7    which over 500 potentially relevant articles/studies were identified for initial assessment. Relevancy of
 8    each study for this assessment was determined according to the following key criteria.
          •   Does the study address  an ecological endpoint sensitive to reductions in NOx and SOx
              emissions?
 9          •   Does the study value quality changes in the ecological endpoint, which is actually or
10              potentially attributed to reductions in NOx and SOx emissions?

          •   Is the study peer-reviewed and preferably, published in an academic journal?
11          •   Very few, if any studies fully satisfy all above criteria. For this reason, studies that at least
12              partially  satisfy these criteria were deemed potentially relevant for this project. Finally,
13              reviews and meta-analyses were  included in the assessment whenever they were available and
14              dealt with potentially relevant ecological endpoints.

15          In the initial assessment, each record's potential relevancy to the assessment was rated on a scale of
16    1 to 4, with 1 indicating that the record appears directly relevant to this assessment (the study addresses
17    quality change of an ecological  endpoint, which is actually or potentially attributable to NOX/SOX).
18    Records rated 2  only partially satisfied the "relevancy criteria," but were considered important to  be
19    referenced in this report. Records rated 3 were to be reviewed more closely to determine their usefulness,
20    and those  rated 4 were found not relevant for the purposes of this assessment. Because our goal in the
21    initial assessment was to avoid missing potentially relevant studies, we classified borderline cases to the
22    lower number category.
23          All studies rated 1 through 3 were next reviewed using several attributes, including ecological
24    endpoints, valuation techniques, geographical area, use vs. non-use value category, and other details  of
25    interest. Of those studies, about half addressed aquatic ecosystems (Figure F-2). The reviewed studies
26    addressed many different ecological endpoints, such as sport fishing, commercial fisheries, aquatic
27    recreation (e.g.,  swimming and boating), general water quality, ecosystems services provided by aquatic
28    ecosystems, and coral reefs (Figure F-3). Nearly one third of the studies addressed terrestrial ecosystems
29    (e.g., forestry/commercial timber, outdoor recreation, and agriculture); the rest dealt with transitional
30    ecosystems (e.g., ecosystems services provided by wetlands and wetlands recreation).
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                           Aquatic
                         ecosystems
                                                            Terrestrial
                                                            ecosystems
                                                        Transitional
                                                        ecosystems
     Figure F-2. Reviewed studies by ecosystem addressed.


     F.2.  Conceptual Framework

           F.2.1. Taxonomy of Values for Environmental Goods and Services
 1         The economic value derived by society from these ecological goods and services can be
 2   categorized as either "use" or "non-use" values. Use values comprise values for those goods and services
 3   that can be used either directly or indirectly. Non-use values denote the characteristics of the ecological
 4   goods and services that are not used at all but still hold economic value. The schematic in Figure F-4
 5   illustrates these divisions of values for environmental goods and services.
 6         Direct use values are held for those goods and services which can be directly consumed or utilized
 7   by individuals or society. Some direct use goods, such as fish caught or timber cut are sold in markets and
 8   thus valued using market data. Other direct uses, such as recreational use of ecosystems for fishing,
 9   hunting, and sightseeing, are usually not bought or sold through a market and are therefore more difficult
10   to value. Similarly, changes in their value are more difficult to quantify.  Thus, direct use can be
11   subdivided into directly used market and non-market goods or services.
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     Figure F-3. Reviewed studies by ecological endpoint.
                               Good or Service
          Non-Use Value
                                                                             Indirect Use
            Market Use
Non-Market Use
  Non-Market Use
                Source: EPA (2002).
     Figure F-4. Taxonomy of values for environmental goods and services.
1         Examples of direct, market uses of ecological goods and services include commercially sold food
2    sources (fish, crops, other animals); building materials (wood, stone); fuel sources (wood, coal, oil);
3    drinking water (groundwater, surface water); chemicals; and minerals (EPA, 2002). Examples of direct,
4    non-market uses of ecological goods and services include recreational fishing and hunting; beach use
5    (sunbathing, swimming, and walking); boating; hiking; camping; wildlife watching; and sightseeing
6    (EPA, 2002).
7         Indirect use values capture those ecological services that are not used directly but still provide
8    benefits of economic value to society. These services include flood control, storm water treatment, ground
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 1    water recharge, climate control, pollution mitigation, wave buffering, soil generation, nutrient cycling,
 2    habitat value, and biodiversity (EPA, 2002).
 3          Non-use values denote the characteristics of the ecological goods and services that are not used at
 4    all but still hold economic value. These non-use values include what are known as existence and bequest
 5    values-the value of simply knowing that certain ecosystems exist and of ensuring that they continue to
 6    exist for future generations, respectively. These services are also not traded in a market and quantifying
 7    their value is a challenge.
 8          Importantly, empirical methods for addressing non-use value generally estimate the total value of a
 9    resource. Distinguishing between the use and non-use values often is not possible, and the valuation
10    results including non-use values should therefore generally be considered total valuation rather than non-
11    use valuation studies. Examples of non-use values of ecological goods and services include existence
12    value, cultural/historical value, intrinsic value,  bequest value, and altruistic value (EPA, 2002).
13          In this Annex, the focus is on the potential incremental benefit that might be realized from reducing
14    levels of SOx or NOx by a relatively small amount, rather than the total value of the affected ecosystems.
15    Therefore, the term "total value" in this Annex generally denotes total marginal benefits that contain both
16    use and non-use values, not the total value of the entire resource. This notation is consistent with the
17    principles of economic valuation of the environment, which generally focus on predicting damages or
18    benefits from marginal changes in environmental conditions.


            F.2.2. Welfare Economics
19          Any environmental goods or services that somehow contribute to human well-being are
20    economically valuable. Their economic value reflects the capacity of the environment to satisfy different
21    human needs, which is related to the direct consumption of different goods and services derived from the
22    environment. It also includes different indirect, passive, and non-use values for environmental goods and
23    services, such as recreational benefits, enjoyment of natural landscape, purity of air and water, and
24    provision of habitat for species other than humans.
25          Economic valuation is rooted in the basic principle of consumer sovereignty.  Rather than judging
26    whether an individual's choices are right or wrong, each person is considered able to make rational
27    choices that advance his or her well-being, given the possibilities available. The principle of consumer
28    sovereignty extends also to the valuation of environmental goods and services. Even in the absence of
29    markets for environmental benefits, each individual is considered able to assess the  importance of
30    changes in environmental quality on personal well-being or, as economists commonly refer to it, utility.
31          Consumer (CS) and producer surpluses (PS) are the basic monetary measures of well-being (or
32    welfare) in economics. They denote the  "excess utility" consumers and producers enjoy when consuming
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 1    or producing specific goods or services after paying for them. Producer surplus is measured by profit, the
 2    value of production after accounting for all costs. Consumer surplus, which is a more subtle concept, can
 3    be thought of as the difference between the price and the maximum value that an individual holds for a
 4    good or service. However, it generally is not an exact measure of changes in welfare because CS does not
 5    fix the baseline level of utility, thereby ignoring the income effects of a changing baseline
 6          CS has two exact (Hicksian) measures: willingness-to-pay (WTP) and willingness-to-accept
 7    (WTA). In the  context of environmental valuation, WTP denotes an individual's maximum willingness to
 8    pay for an environmental good or service. WTA, on the other hand, stands for the minimum compensation
 9    an individual is willing to accept to forgo an environmental good or service. Though WTP and WTA both
10    are exact measures of CS, they generally are not equal, meaning that the CS in general does not have a
11    unique measure. WTP and WTA for usual marketed goods and services are similar and within narrow
12    bounds of income effects (Willig, 1976). Similar findings can be expected with environmental goods and
13    services only when they have close substitutes (Shogren, 1994; Hanemann, 1991).
14          Since many environmental goods and services cannot be easily substituted, WTP and WTA are
15    expected to differ, sometimes substantially. In fact, the WTP-WTA divergence can range from zero to
16    infinity, depending on the substitutability of an environmental good and other market or non-market
17    goods (Hanley et al.,  1997). Using WTA to measure welfare changes might often be justified on the
18    grounds of economic theory and property rights, but many studies choose to estimate WTP for its
19    practicality. In addition, WTP generally is lower than WTA and therefore is conservative, providing
20    another justification for using WTP for valuing the environment. Finally, both WTP and WTA can be
21    difficult to measure, and valuation studies therefore sometimes estimate the ordinary CS. It approximates
22    WTP and WTA and is between these two exact measures of welfare. In the special case of no income
23    effects, all different measures of consumer welfare coincide.
24          Various  methods have been developed to determine the value of different ecological goods and
25    services by estimating the change in social welfare or WTP for changes in the quantity or quality of a
26    given environmental resource (see Table F-l). Some valuation techniques obtain WTP from observing
27    people's actions (revealed preferences or RP) while others rely on people's responses to hypothetical
28    situations (stated preferences or SP). Yet another set of valuation methods relies on other studies, either by
29    transferring their estimates into another context (benefits transfers) or by conducting statistical meta-
30    analyses of earlier studies to examine their systematic findings.  See Section F.2.3  for details about these
31    approaches.
32          As noted by Kramer et al. (2003), several forest protection valuation studies also considered the
33    sensitivity of contingent valuation estimates to various preference elicitation methods, including
34    dichotomous choice, payment card, and open-ended techniques. Haefele et al. (1992) used payment card
3 5    and dichotomous choice techniques  in a contingent valuation survey measuring the WTP of Southern
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 1    Appalachian residents for protecting high-elevation spruce-fir trees from exotic insects and air pollution.
 2    Estimates for mean WTP were $20.86 per year using a payment card method, and $99.57 per year using a
 3    discrete choice method. Sample sizes for each method were relatively small (232 and 236 respondents,
 4    respectively). Another limitation concerns the wide span between the two valuation statistics, which
 5    despite a large difference in the mean estimates showed no overlap of the 95% confidence intervals.
 6    Similar studies have been performed by Loomis et al. (1996) and Kramer and Mercer (1997).
            F.2.3.  Benefit Estimation Approaches
 7          Table F-l introduces different valuation techniques and indicated which type of value (direct use,
 8    indirect use, total value) each can be used to estimate. Studies addressing non-use values are referred to in
 9    this Annex as total valuation studies. This is because non-use valuation methods in fact generally estimate
10    the total value of a resource, including both its use and non-use components.
11          TraVGl  COSt (including site choice models) and hedonic pricing methods are perhaps the most
12    regularly applied revealed preference (RP) methods for valuing the environment, whereas contingent
13    valuation and choice experiments are the most popular stated preference (SP) methods. One of the key
14    differences between the RP and SP methods is that RP methods can only fully address direct and indirect
15    use values, whereas SP methods are required for the estimation of total values of environmental resources,
16    including their non-use value component.
17          Travel  COSt Studies predict use values  for ecological resources, such as natural parks, by
18    examining individuals' travel expenditures to  utilize that resource (most often at a park or some other
19    recreational site), including the opportunity cost of work time missed while traveling to and utilizing the
20    resource. Travel cost studies commonly use a random utility framework, which infers individuals' WTP
21    for an ecological resource (again usually a recreational site) by observing their choice from among one or
22    more alternatives. While the travel cost method uses changes in the quality of one resource to  ascertain its
23    value, the random utility model uses individuals' choices among various options of various qualities at
24    various prices to do the same.
25          Hedonic price Studies predict the value of ecological resources by examining their effect on
26    property values. Assuming that all the benefits from living in a specific location and house are capitalized
27    into the market value of the property, hedonic models estimate the independent effects of different
28    housing characteristics on housing prices. Controlling for all observable housing and location
29    characteristics, hedonic pricing models examine environmental values for, for example, proximity to
30    forests or particular watersheds by estimating the implicit incremental  price people are willing to  pay for
31    that proximity. Hedonic pricing relies on assumptions such as efficiently functioning housing market and
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 1    perfect information and mobility by individuals. However, because WTP is not necessarily tied to
 2    ecosystem changes, this Annex does not consider property values as a method of valuation.
 3          Other market or RP-based approaches to valuing changes in ecological goods and services include
 4    the alternative/replacement cost method; avoidance expenditures/averting behavior method; referendum
 5    method; user fee method; market price and market simulation method; and a host of different variations of
 6    these and other valuation approaches. See, for example, Freeman (2003) for the theory and applications of
 7    different methods for valuing environmental quality.
 8          The RP methods have the advantage of gleaning their value estimates from individuals' real world
 9    actions. However, because they do not include the non-use value of ecological resources, none of them
10    capture total value. This problem has given rise to the development of a variety of non-market valuation
11    methods that use surveys to elicit preferences for public goods. Because these methods are generally
12    based on eliciting "stated" rather than "revealed" preferences, they are broadly categorized as  SP
13    methods.
14          The Contingent valuation method is the most common SP method. It involves developing and
15    administering surveys, in which respondents are presented with a scenario or  a program with specified
16    environmental outcomes and costs. Each respondent is asked to indicate approval or disapproval of the
17    proposed environmental  scenario and its monetary cost. Researchers vary the proposed costs across
18    different survey respondents and use their choices to estimate how much people on average are willing to
19    pay for different scenarios to improve the environment. Because some of the respondents may use a
20    public good also for direct enjoyment, say viewing an endangered bird, the surveys actually capture total
21    value for the improvements, rather than just their non-use value.
22          ChoiCG experiment methods (including contingent ranking, contingent choice, and conjoint
23    analysis) separate an environmental good into its constituent attributes, recombine those attributes into
24    different bundles, and elicit respondents' preferences for those bundles. Often a monetary value can be
25    assigned to those attributes and thus the process allows researchers to determine WTP for the bundle and
26    each attribute. Conjoint analysis usually is performed by choosing the most preferred attribute bundle
27    from a group (choice  experiment, contingent choice) or via ranking a series of attribute bundles
28    (contingent ranking).  Using conjoint analysis, researchers may be  able to simultaneously value various
29    relevant goods or services that an environmental resource provides. For example, improving a public
30    body of water provides improved recreational opportunities, drinking water, and support of aquatic
31    ecosystems.
32          Valuing the environment as a factor Of production monetizes the incremental benefits from using
33    the environment as an input to production. In other words, this method treats the environment  as a
34    production input comparable to other raw materials and infrastructure, such as land, capital, labor, and so
35    forth. This method is  appropriate for valuing environmental effects that have direct value as a factor of

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 1    production. Examples of such cases include the effects of water quality on the productivity of commercial
 2    fisheries, or the impact of soil characteristics on agricultural productivity. However, this method is limited
 3    to market goods and services and can only address their role as part of the production process. This
 4    method does not address non-market environmental goods and services or non-use values.
 5          Benefits transfer approaches translate the entire estimated demand function from one application
 6    to another. Sometimes the function is adjusted to meet the specific criteria of the target site, and then new
 7    WTP value estimates are generated for the environmental good/service at the new site using the demand
 8    function. Using the transferred demand function, both changes in the level of use and the unit value
 9    benefits for the new site can be estimated (EPA, 2002). There are four general types of benefits transfer
10    technique: mean unit value transfer/ adjusted unit value transfer, benefit/demand function or model
11    transfer, meta-function transfer, and structural benefits transfer. The first three methods dominate the
12    literature (Smith et al., 2006).
13          Mean unit value transfer/adjusted unit value transfer entails taking the value of a specific
14    environmental good or service (such as recreational hunting), sometimes from a single study of the same
15    good and sometimes estimated by averaging a range of value estimates from various primary studies, and
16    transferring that value to the same good or service at a new site.
17          Benefit/demand function or model transfer is the translation of the entire estimated demand
18    function from one site to another. Sometimes the function is adjusted to meet the  specific criteria of the
19    target site, and then new WTP value estimates are generated for the environmental good/service at the
20    new site using the demand function. Using the transferred demand function, both changes in the level of
21    use and the unit value benefits for the new site can be estimated (EPA, 2002). Meta-function transfer
22    involves the use of meta-regressions to combine the results of numerous valuation studies and allows
23    researchers to account for influencing factors, thus enabling them to create value  estimates for new policy
24    sites. Structural benefits  transfer, also known as preference calibration, requires selection of a preference
25    model which can describe individual choices over a set of market and associated non-market goods to
26    maximize utility when faced with budget constraints (Rosenberger and Loomis, 2001; Smith et  al., 2006).
27          Meta-Study review provides a useful way of summarizing the  literature on the valuation of
28    ecological endpoints from reductions in NOx and SOx, though there  are few studies exactly on this topic.
29    But there are studies summarizing monetary valuation efforts for particular  sets of endpoints, such as the
30    economic valuation of fresh water ecosystem services. Where such studies are available, they are
31    summarized in the appropriate section of this Annex.
32          Even more valuable are meta-analyses, which perform statistical analyses of the results of original
33    studies. Such studies explain variation in monetary value estimated for various endpoints using  features of
34    the original studies' methodologies as well as the characteristics of the site being studied and other factors.
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 1    Smith and Pattanayak (2002) defined meta-analysis as the practice of using a collection of formal and
 2    informal statistical methods to synthesize the results found in a well-defined class of empirical studies.
 3          In general, the three uses of meta-analysis are: (1) synthesize or "take stock" of the literature on a
 4    particular valuation topic; 2) test hypotheses with respect to the effects of explanatory variables on the
 5    value construct of interest; and (3) use the estimated meta-analysis model to predict estimates of the value
 6    construct across time and space (Bergstrom and Taylor, 2006). Bergstrom and Taylor (2006) provided a
 7    review of the techniques and theory behind the use of meta-analysis for benefits transfer. They noted that
 8    to conduct a successful meta-analysis for benefits transfer, it is important to be as comprehensive as
 9    possible in terms of the studies to be included. Excluding a study would be equivalent to applying a zero
10    weight to the information in that study. The authors list some additional criteria to be  considered when
11    identifying studies for inclusion in a meta-analysis, including controlling for the valuation method and
12    estimated welfare measure, as well as addressing the temporal and spatial scales of the valued commodity.
13          Extensive  evaluation of the relative merits of, and issues with, different environmental valuation
14    methods is not within the scope of this Annex. Vast amounts of research have been conducted to develop
15    and evaluate alternative environmental valuation methods. For example, the Handbook of Environmental
16    Economics recently dedicated an entire 1,100-page volume to addressing methods for valuing
17    environmental changes (Maler and Vincent, 2005).
18          The validity of environmental valuation methods is sometimes questioned, in particular that of SP
19    methods. Because survey-based valuation methods are based on what people say rather than what they do,
20    there is a tendency to question the credibility of the results. For this reason, nonmarket valuation
21    researchers, following the lead of the National Oceanic and Atmospheric Administration (NOAA) Expert
22    Panel that reviewed the highly publicized studies valuing damages from the EXXXXXXXXXXon Valdez
23    oil spill, often build into their surveys a series of validity tests, such as testing for the  sensitivity of WTP
24    to the scope of resource being valued (Federal Register, 1993). Additionally, SP surveys are vulnerable to
25    a variety of issues dealing with the design and administration of surveys, as well as analyzing their data
26    (e.g., Carson and Hanemann, 2005).
27          Though examining actual choices lends credence to RP methods, they are not free of problems. For
28    example, hedonic pricing studies of housing markets rest on the assumptions that the housing market is at
29    equilibrium and that housing choices accurately reflect the attributes of interest, such as air pollution or
30    environmental amenities associated with the residential location. Hedonic studies and other RP studies,
31    such as recreation trip demand analysis, are also susceptible to potential biases. These include the
32    omission of important variables, which may thwart the efforts to accurately value environmental quality.
33          A number of studies have compared SP analyses with RP analyses, such as hedonic property value
34    studies. Generally, these comparisons have suggested that when similar environmental values are
35    examined, RP methods  generally yield  somewhat higher value estimates than SP methods. For example,
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 1    Carson et al. (1996) reviewed over 80 studies which included comparisons of SP and RP methods, and
 2    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
 3          The impacts of NOx and SOx can occur over many different terrestrial ecosystems and use-value
 4    efforts must look at each of these ecosystems individually. One serious threat to agriculture from NOx
 5    emissions comes from ambient ozone (O3), which is a byproduct of atmospheric reactions between
 6    Volatile Organic Compounds (VOCs) and NOx. In commercial forests, air pollution effects have not been
 7    addressed in economics beyond evaluating the potential effects of O3. Unlike in agriculture, the scientific
 8    understanding of O3 effects on trees is more limited and related mostly to visual injury to leaves in young
 9    saplings, an indicator that is difficult to link to tree growth in mature  forests. However, the valuation of O3
10    has already been evaluated in detail in the Air Quality Criteria Document (AQCD)yfor Ozone (EPA,
11    2006), and will not be covered in this Annex or the ISA (see also Tables F-2 and F-3).
12          Some studies have attempted to specifically measure use values associated with forest quality,
13    without regard to the factor that is altering forest quality. For example, an early study by Leuschner and
14    Young (1978a) considered the effects of crown density changes due to insect infestation around  19
15    lakeside campgrounds in east Texas. A travel-cost model estimated the CS losses from a 10% reduction in
16    crown density to fall between 0.69% and  6.5%, depending on the number of substitute recreation sites
17    available. More studies that look at how forest quality affects various uses are listed in Table F-4. Also,
18    Table F-5 lists value estimates from a meta-analysis but does not reflect the considerable variation of
19    value estimates across underlying studies.
20          Figure F-5 depicts the  linkages between aesthetic welfare benefits and air pollution. One such
21    linkage that is shown is the visual quality of forests. Changes in woodland appearances are monitored
22    using scientific indicators of ecosystem change, such as crown condition, mortality, foliar damage,
23    vegetation structure, and plant diversity (McLaughlin and Percy, 1999). However, in comparison to
24    valuation of changes in commercial timber, measuring the economic  value of aesthetic changes in forests
25    and natural ecosystems can be more directly based on scientific information regarding the effects of
26    pollution on forest health. Although the scientific linkages between air pollution and visual forest quality
27    include large degrees of uncertainty, historic examples of air pollutant effects on forest aesthetics facilitate
28    their empirical valuation. Figure F-6 shows the regions and species that have been identified as
29    historically affected by air pollutants.
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                                        Emissions Data
                                       Emissions Scenario
                                            Model
                                                 Amounts emitted
                                      Fate and Transport
                                            Model
                                                 Deposition predictions
                                         Visual Injuries
                                        Response Model
                                                 Change in visual injuries
                                      Forest Aesthetics
                                           Model
                                                 Change in forest aesthetics
                                        Economic Model
                               Value of the impact on the ecosystem
Figure F-5. Linkages from emissions to forest aesthetics.
                                                                                        Source: Exhibit 1 in lEc (1999a).
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            U.S. Major Forest Types Affected by Air Pollution-Induced Visual Injuries
                                                                              White Mountains (NH)
                                                                            Green Mountains (VT)
                                                                            Adirondacks(NY)
       San Bernadino
       Mountains (CA)
                                                                                                 Maine
                                      Mt. Rogers (VA)
                                      Mt. Mitchell (NC)
                                     Great Smoty
                                     Mountains
          | Eastern Spruce/Fir Forest

           I Eastern Hardwood Forest
        j  I Sierra and Los Angeles Basin Ecosystem

            Note: Only areas affected by non-point source pollution are shown. Scientific certainty varies with location. Direct
            ozone-induced injuries also occur in several other locations not indicated, (e. g., Southern Forests, Berraug et al., 1995.)
            Sources: NAPAP (1991); White and Cogbill (1992).
                                                                                           Source: Figure 2 in EC (1
      Figure F-6. Geographic distribution of forested areas historically affected by air pollution.
 1          When the aesthetic quality of forests is important to people, visible injuries to trees can affect
 2    consumer welfare. Loss of or damage to foliage, changes in tree density, changes in species composition,
 3    and changes in vegetation structure can all affect the enjoyment that people derive from forests, and are
 4    therefore appropriate for economic assessment. Estimation of the aesthetic value of forest condition uses
 5    RP or SP methods and often focuses on long-term outcome stressors, rather than current intermediate
 6    situations. Nevertheless, there are few scientific studies that relate changes in pollution concentrations to
 7    these endpoints, and that make a direct linkage between science and economics. A memorandum by
 8    Industrial Economics, Inc. (IEc) (1999b) produced for Benefit-Cost Analysis of the Clean Air Act 1999,
 9    provided a comprehensive review of early forest aesthetics valuation literature. A recent literature review
10    by Kramer et al. (2003) also summarized several key studies described in this report. Table F-5 lists and
11    describes studies which have estimated monetary value for the types of non-consumptive use and non-use
12    values for forests and natural ecosystems which may be affected by N and sulfur emissions.
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            F.3.2.  Total Values
 1          Several studies were reviewed by Kramer et al. (2003), and measured the total WTP of the general
 2    public for forest aesthetics and attempted to separate contributions of use values and non-use values. Most
 3    of these studies indicated that in the aggregate, non-use values (including option, bequest, and existence
 4    value) were significantly greater than use values (recreation).
 5          Walsh et al. (1990) employed a contingent valuation study using an iterative bidding technique to
 6    estimate WTP of Colorado residents for protecting ponderosa pine (Pinus ponderosa) forests from the
 7    mountain pine beetle (Dendroctonus ponderosae). Presented with pictures of mid-level forest quality
 8    representing current conditions (100 to 125 live trees measuring more than 6-inches in diameter per acre),
 9    respondents were asked their WTP to prevent the lowest forest quality (0 to 50 trees per acre) and to attain
10    the highest forest quality (125 to 175 trees per acre). Overall mean WTP for changes in forest quality was
11    estimated at $47 per household.  In addition, respondents decomposed total value into four categories of
12    value (recreational use, option, existence, and bequest). Results showed that use values accounted for
13    27.4% of total value, and non-use values accounted for 72.6% of total value. The study was limited by
14    small sample size (200 respondents) and possible bias from framing the scenario as "one of the most
15    important issues facing Colorado residents."
16          A study by Haefele et al. (1992) that measured WTP to protect Southern Appalachian spruce-fir
17    forests, used a decomposition approach to determine dominate influences on total values. The authors
18    found that non-use values (bequest and existence values) overshadowed use values as reasons to protect
19    forests. Holmes and Kramer (1996) relied on results from Haefele et al. (1992) to compare WTP between
20    recreational forest users and members of the general public. Mean household estimates for forest-users
21    ($36.22) were substantially larger than estimates for nonusers ($10.37).
22          Kramer et al. (2003) used a dichotomous-choice, contingent-valuation format to determine WTP of
23    Southern Appalachian residents  for protecting spruce-fir forests from insects and air pollution. The study
24    measured incremental levels of forest protection using two scenarios: the first increment occurred along
25    road and trail corridors, a scenario that would appeal to people valuing the ecosystem primarily for
26    recreational use; the second level was for the entire ecosystem, a scenario that would appeal to people
27    valuing  the continued existence  of the entire threatened ecosystem. Using randomly assigned values
28    between $2 and $500 dollars, respondents were asked if they would pay a certain tax amount for each
29    scenario. Results suggested that preferences for forest ecosystem protection created a well-behaved
30    demand curve, with incremental WTP increasing at a decreasing rate.
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            F.3.3.  National-Scale Valuation
 1          An EPA-contracted valuation study by lEc (1999b) developed a benefits-transfer model using
 2    results from Holmes and Kramer (1996), Peterson et al. (1987, and Walsh et al. (1990) to estimate
 3    national benefits from the 1990 Clean Air Act Amendments (CAAA). Calculations applied WTP values
 4    from these previous studies across all households in the affected states. (See Table F-6 for more
 5    information on the underlying original studies, and Table F-7 for benefits transfer results.) Estimates for
 6    the total value of avoiding air pollution-induced damage to forest aesthetics in the U.S. during the period
 7    1990-2010 were found to range from $3 to $17 billion. Due to limitations in the original studies and the
 8    simplicity of the benefits-transfer model, the authors warned that the estimates should only be used to
 9    consider the general magnitude of benefits to forest aesthetics (in the range of billions of dollars) rather
10    than as precise values.
            F.3.4. Valuation of Degrees of Injury
11          A few studies—Hollenhorst et al. (1993, Ruddell et al. (1989, Hammitt et al. (1994, Buhyoff et al.
12    (1982) —measured general preferences for forest aesthetics without estimating changes in welfare.
13    Hollenhorst et al. (1993) considered the effect of tree mortality on perceived forest aesthetics. On a 1 to
14    10 scale for visual and recreational appeal, 400 respondents ranked pictures of 25 sites with tree mortality
15    ranging from 6% to 98% from gypsy moth (Lymantria dispaf) damage. The results produced a hill-
16    shaped function, whereby site appeal increased with mortality at levels as high as 40% but then declined.
17    The authors speculated that respondents had a general distaste for tree mortality but valued light
18    penetration to the forest understory, which allows for the growth of wild flowers and lower-level
19    vegetation.
20          Other studies confirmed this visual preference for light penetration, including Ruddell et al. (1989)
21    and Hammitt et al. (1994, who observed positive responses to forest edges and open middle grounds
22    without light-obstructing tree canopies. Buhyoff et al. (1982) tested how awareness of environmental
23    damages affects perception of aesthetic losses. The study compared aesthetic rankings of photographs
24    between a control group and a group that is informed about the cause of forest damage prior to the
25    ranking session. Results show a heightened level of sensitivity to forest damage by the "informed" group
26    of subjects.
27          Several other studies investigated the notion of aesthetic thresholds, or discontinuous jumps in
28    aesthetic preferences across small changes in visual injuries. Of particular interest to  early studies were
29    thresholds at the lower limit of visual perceptibility.
30          Contingent valuation studies by Vaux et al. (1984) and Flowers et al. (1985) assessed aesthetic
31    preferences at recreational areas with fire damage and found that small differences in site appearance can

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 1    produce large changes in recreation preferences. This finding is generally consistent with the results from
 2    two early studies by Buhyoff et al. (1979) and Buhyoff and Wellman (1980, which asked respondents to
 3    rate various levels of insect damage. The authors found that preferences seemed to be most affected by
 4    the presence or absence of insect damage, as opposed to the degree of damage.
 5          Crocker (1985)  surveyed  100 recreationists for their WTP for recreational experiences at forested
 6    sites with slight injury, moderate injury, or severe injury. Mean WTP estimates for the environment with
 7    slight injury were three times higher than WTP for environments with moderate and severe injury,
 8    suggesting that people are willing to pay a premium for recreational access below the lower limit of
 9    perceptibility. As a group, respondents did not indicate a clear preference  ordering between the moderate
10    and severe injury environments.
11          Holmes et al. (2006) use a hedonic method to predict the impact of forest damage due to the
12    hemlock woolly adelgid (Adelges tsugae), an invasive insect, on the value of residential properties.
13    Examining 3,379 residential property  sales in New Jersey between 1992 and 2002, the study analyzed
14    how the appearance/health of the forest on the home's parcel and within 0.1, 0.5,  and  1 km buffers around
15    it affects the housing market. Controlling for other relevant variables, the  study estimated that hemlock
16    health status had a statistically significant effect on property values. The estimation results suggest, for
17    example, that a 1-point increase (e.g., from 10% to 11%) in the percentage of healthy hemlocks (less than
18    25% foliar damage) of all forests on the home's parcel was associated with a 0.66% sales price increase.
19    Similar changes in the hemlock forests and their health status in the home's near proximity are predicted
20    to be associated with yet larger increments in the sales price.
21          At least one study seems to provide evidence against the concept of thresholds at the lower limit of
22    perceptibility. A referendum-type contingent valuation survey by Jenkins  et al. (2002) used two sample
23    populations to value a forest protection program. The initial forest condition was  described as "pristine"
24    for one group and already "somewhat damaged"  for another group. The degree of forest damage incurred
25    in the absence of a forest protection program was the  same for both sample groups.  Regressions for the
26    entire sample population showed no statistically significant difference in WTP for forest protection
27    between the two groups.
28          Further analysis of Jenkins et al. (2002) suggested that aesthetic preferences and thresholds differ
29    between recreational groups. Comparisons among recreational groups revealed that consumptive forest
30    users (hunters and anglers) held values that were sensitive to change in forest condition, while non-
31    consumptive forest users (hikers and campers) held values that were insensitive to the same amount of
32    change. Overall, however, non-consumptive forest users expressed higher values  than consumptive forest
33    users. Aesthetic thresholds also seem apparent for recovering forests. Paquet and  Belanger (1997) found
34    that the aesthetic effects of clear cutting were largely removed once re-growth reached a height of 4 m.
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 1          Finally, there is substantial literature related to the amenity value of urban/suburban forests and
 2    open space. McConnell and Walls (2005) recently reviewed this literature, evaluating more than 60
 3    published articles that have attempted to estimate the value of different types of open space. Two lines of
 4    research emerge in this literature: studies that estimate the hedonic value of open space proximity to
 5    residential properties, and studies that use SP methods to value preservation of open  space. Unfortunately,
 6    neither line of research generally comprises studies that provide information on the WTP for quality
 7    changes to open space that might be caused by changes  in NOX and SOX emissions.  In most cases, the
 8    proximity or preservation of open space or urban/suburban forest is valued without reference to the
 9    quality or even the type of open space or forest. Although this literature demonstrates the value of the
10    availability and preservation of open space, its relevancy for the purposes of this assessment is at best
11    limited.


            F.3.5.  Limitations and  Uncertainties
12          Shortcomings in the air pollution terrestrial valuation literature have been well documented in
13    recent reviews (Adams and Horst, 2003; EPA, 2006). In existing economics literature, the effects of O3 on
14    terrestrial endpoints overshadow assessments of other effects of NOX and SOX pollution, such as acid
15    deposition and N fertilization. The latter effects have been well documented in the scientific literature, but
16    the lack of valuation studies related to them limits full assessments of terrestrial damages from NOX and
17    SOX pollution. Incomplete scientific understanding of the effects of air pollutants on ecosystems and
18    economic endpoints extends to many valuation studies.  Even in the valuation of 03 effects, which have
19    been relatively thoroughly studied, improvements in noneconomic input data could play an important role
20    in evaluating the magnitude of vegetation damages  from 03.
21          Economic models should incorporate more specific temporal (dynamic) and spatial data regarding
22    terrestrial effects of air pollution to reflect more realistic situations. For example, the 03 effects valuation
23    literature analyzes air pollution effects across a period of time using only two or three scenarios that
24    represent large, static changes. In the real world, however, these changes would occur gradually and
25    incrementally. Future studies should try to consider more dynamic models that can describe effects of
26    marginal air quality changes. From a spatial perspective, previous studies often assumed that producer
27    responses are similar across large geographic areas. However, regional factors may be important,
28    suggesting a possible need for finer-scale agricultural and forestry data that would allow models to
29    consider micro-level physical and economic factors (Adams and Horst, 2003).
30          With regards to the effects of air pollution on agricultural crops, most economics studies date from
31    the 1980s and many focus on 03. Therefore, there is a general need for updated valuation studies that
32    consider agricultural damages from air pollution, especially with regards to the roles played by NOX and
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 1    SOx  Specific issues include the need to develop new exposure-response functions for sensitive crops and
 2    the effects of air pollution on crop yields under actual farm conditions.
 3          Assessments of welfare losses in commercial forestry from air pollution are mainly limited by
 4    scientific uncertainty regarding the extent of ecological damage. Restrained by the large size and slow
 5    growth of trees, scientific research has primarily considered  63 effects on seedlings and has little
 6    transferability to mature trees.
 7          Existing literature on valuation of changes in forest aesthetics has focused primarily on historical
 8    cases of acute air pollution or insect infestation and may be overly simplistic. Although these valuations
 9    are useful, no existing assessments have examined the effects of reduced air pollution on forest aesthetics.
10    Understanding the effects of marginal changes in air pollution would require the collection  of long-term
11    high-quality data about forest health, as well as improved casual linkages between air pollutants and
12    visible injuries to trees. Scientific advancements at this scale would require a sophisticated monitoring
13    network operating over several decades. Until the impacts of pollution on the foundational services of
14    terrestrial ecosystems are better understood, valuation assessments may be premature. Additionally, most
15    studies also fail to  distinguish between marginal values of forest health and average values of forest
16    health, an oversight that may create bias in final estimates.
17          A major limitation of both non-economic and economic forest health assessments concerns the
18    limited extent of documented ecosystem-level changes. Most forest health  surveys focus on average forest
19    conditions that allow for modeling of near-term trends in economic value, but fail to detect  fundamental
20    changes in ecosystem processes that sustain natural capital in the long run (McLaughlin, 1999).
21    Quantifications of impacts on species effectively conform to existing valuation methods; however, these
22    effects may be overshadowed by long- term or irreversible reductions in ecosystem  structure and function
23    (EPA, 1999). Assessing ecosystem-level changes presents  new challenges to both science and economics
24    as they include a large degree of uncertainty in the scale and nature of effects. In particular, economic
25    assessments of ecosystem impacts may require  regional- to national-scale modeling of numerous
26    ecosystem functions rather than analyses of specific service flows that directly contribute to human
27    welfare (EPA, 1999). In spite of these challenges, the relationship between air pollution and forest health
28    has enormous implications for policy development and should be addressed in future research.
29          More advanced methods to address uncertainty and irreversibility are essential to future modeling
30    of complex ecosystem-level impacts. Several alternative methods to address ecological uncertainties are
31    currently being developed and should play a central role in future models (EPA,  1999).
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      F.4.  Valuation of Transitional  Ecosystems
 1          The economic analysis of the benefits on wetlands (transitional ecosystems) from reduced
 2    emissions are limited in this Annex to three recent and comprehensive meta-analyses. Brander et al.
 3    (2006) provided a comprehensive review and meta-analysis of the wetland valuation literature. This study
 4    improved upon previous similar studies (Brouwer, 1999) (Brouwer et al., 1999; Woodward and Wui,
 5    2001) by including tropical wetlands estimates from other valuation methodologies, other wetland
 6    services, and estimates from more countries.
 7          It is well known that wetlands serve a variety of potentially valuable ecological functions,
 8    including flood and flow control, storm buffering, sediment retention, groundwater discharge, and habitat
 9    for plant and animal species. Wetlands also contribute to climate stabilization, carbon sequestration, and
10    the overall quality of the natural environment. Brander et al. (2006) (Table F-8) described these  functions
11    and their associated goods and services, as well as their values as determined by common valuation
12    methods.


            F.4.1. Use and Non-Use Values
13          The Brander et al. (2006) meta-analysis included  191 different wetland valuation studies from 25
14    different countries. Eighty of those studies, half of which were from North America, provided useful
15    empirical data for the meta-analysis. A total of 215 individual observations were gleaned from those
16    studies for use in meta-regressions. The study reviewed the literature, discussed valuation techniques,
17    calculated average wetland values using the results from the combined data set, and then performed a
18    meta-regression of the data to determine which explanatory variables had the greatest effect on wetland
19    value. Non-market use values of wetlands, as well as non-use/existence values, were also included in the
20    analysis. Because not all of the studies generated WTP value estimates, wetland size and population
21    statistics were used to convert estimates from the diverse valuation methodologies to 1995 dollar values
22    per hectare per year, following the example of Woodward and Wui (2001), which is described below.
23          The results of the Brander et al. (2006) meta-analysis showed an average value for wetlands of
24    $2800/ha/yr (1995 dollars), and median value of $150/ha/yr. Individual values were calculated by wetland
25    type, the wetland service provided, contingent, and valuation method used. The meta-regression also
26    revealed that studies using the contingent valuation method consistently returned the highest wetland
27    values. It is noted that this may have been due to the type of wetland values that this method was applied
28    to, rather than something intrinsic to the methodology.
29          To look at the value differences from a reduction specifically in N emissions, there is a focus on
30    Brander et al.'s (2006) analysis of wetland value changes that resulted from a small change in water
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 1    quality or some other ecosystem service. These results are valuable because they may be related to
 2    changes in acid deposition as a result of changes in the concentrations of NOx or SOx  In the meta-
 3    regression results of Brander et al. (2006), the only such ecosystem service which was shown to have a
 4    statistically significant effect on wetland value was hunting. Hunting had an unexpected negative
 5    influence on wetland value. Although not statistically significant, water quality, habitat and nursery
 6    service (specifically support for commercial fisheries and hunting), fishing, and biodiversity all had
 7    positive effects on wetland value. The accuracy of the value transfer exercise in Brander et al. (2006)
 8    seems to be in line with other similar efforts, with an average transfer error of 74%. Overall, the value
 9    transfer function from Brander et al. (2006) produced higher-value transfer error for less valuable
10    wetlands (and systematically over-predicted their value) and lower value transfer error for more valuable
11    wetlands (and slightly under-predicted their value).
12          Woodward and Wui (2001) found 39 studies that contained sufficient data for inter-study
13    comparison, including peer-reviewed and gray literature. Similar to the approach of Brander et al.  (2006),
14    Woodward and Wui (2001) assumed that a wetland's value is a function  of its ecological characteristics
15    and socio-economic environment, and that there exists a true public WTP at a given moment for a
16    particular wetland. With these assumptions, they attributed variability in wetland value to two principal
17    sources: differing characteristics of the wetland and error in the estimation of the true value (Woodward
18    and Wui, 2001).
19          Brouwer et al. (1999) analyzed the WTP of 30 CV studies addressing ecosystem functions of
20    wetlands, providing a total of 103 estimates. The  studies were mostly conducted in the U.S., but were not
21    in any way comprehensive spatially. They  ranged from  1981 to 1997 and covered a wide range of
22    commodity definitions, including preserving wetlands threatened by flooding, maintaining or improving
23    current catch levels and fish populations, saving the bald eagle (Haliaeetus leucocephalus),
24    improving/maintaining habitat, water quality ladder changes, preventing shellfish bed closures, increasing
25    the number of protected rivers, and explicitly preserving a measure of water quality. No attempt was made
26    to standardize the degree of change being valued. Instead, the authors attempted to address differences in
27    the studies through the use of dummy variables and some degree of sub-setting.
28          Based on all of the included studies, Brouwer et al. (1999) estimated that the average WTP for the
29    addressed ecosystem functions was about $90 per household per year. These studies covered types of
30    wetlands (primarily fresh water systems), ecosystem functions (water quality and biodiversity, and to a
31    lesser extent water quantity and flood control), and wetlands size. Cutting across types of systems,
32    location, and function enhanced, the WTP  for salt and fresh water improvements was about the same.
33    Within fresh water wetland systems, riverine wetland improvements appeared to be more highly valued
34    than palustrine wetland improvements. Across ecosystem function, flood control was more highly valued
3 5    than biodiversity, water quality and water generation, in that order. Improvements to larger wetlands
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 1    appeared to be somewhat more highly valued than to improvements to smaller wetlands. Improvements in
 2    use values averaged slightly more than $100 per household per year, whereas improvements in non-use
 3    values only showed values about half this size. Spatially, values were higher in California, followed by
 4    Georgia and Louisiana.
 5         The review of outdoor recreation valuation studies by Rosenberger and Loomis (2001) included 13
 6    studies and 59 value estimates associated with waterfowl hunting, a popular activity in transitional
 7    ecosystems. On average, these studies estimated that the value of waterfowl hunting is about $32 per
 8    person per day (1996 dollars). However, different value estimates ranged from about $2 to over $160 per
 9    person per day. In their meta-analysis, Rosenberger and Loomis (2001) predicted a $40 value per person
10    for a day for waterfowl hunting (Table XXXXXXXXXX). These results demonstrate potential benefits
11    from waterfowl hunting, but do not address changes in the quality of transitional ecosystems that may
12    possibly be associated with reduced NOx/SOx emissions.


            F.4.2. Limitations and Uncertainties
13         Meta-analyses of wetland value (and the underlying original studies) are not directly useful because
14    they do not measure changes in ecosystem services that would follow from some tightening or loosening
15    of the standard for NOX or SOX. Nevertheless, they have indirect use by pointing out the importance of
16    various methodologies, the overall values commonly assigned to wetland  improvement, and types of
17    ecosystem services that might be affected by air pollutants. For example, both Brander et al. (2006) and
18    Woodward and Wui (2001) found that CV studies generally yielded greater value per acre than travel cost
19    studies. This is not surprising, as the former would generally include non-use values. Furthermore,
20    Woodward and Wui's (2001) meta-analysis showed that the availability of bird hunting, bird watching,
21    and amenity services affect wetland value. For example, bird watching (which could be affected by bird
22    populations which, in turn, could be affected by nutrient enrichment) contributes more value to an
23    ecosystem than any of the other ecosystem services. This is mainly due to the popularity of bird watching
24    and the large numbers of people who engage in this recreational activity.
      F.5.  Valuation of Aquatic Ecosystems
25          As discussed in the ISA and the preceding annexes, acidification and eutrophication are the two
26    main effects of NOx and SOx on aquatic ecosystems. In economic valuation of the effects of NOx and
27    SOx deposition on aquatic ecosystems, these effects are reflected partly in the WTP for recreational
28    fishing (with effects on the catch rate and fishing quality in a particular aquatic ecosystem) and partly on
29    aesthetic and non-use (and total) values. Valuation methods used to assess the value of aquatic ecosystems

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 1    include contingent valuation (CV) (open-ended, discrete choice, etc.), choice experiments, hedonic-
 2    pricing valuation, travel cost (TC) (hedonic travel cost and traditional travel cost), cost-effectiveness
 3    analysis, avoided-damage cost, replacement cost, market analysis, and so forth. The CV method has
 4    gained popularity for total (use plus non-use) value estimation, whereas the travel cost method plays an
 5    important role in deriving use values. Currently, there is also a tendency to use stated and RP techniques
 6    together for the valuation of recreational activities. Supplemental to the below discussions about valuation
 7    of acidification and eutrophication on aquatic ecosystems, Table F-9 describes different aquatic valuation
 8    studies according to the ecological effect examined (acidification, eutrophication) and lists their relevant
 9    details.
10          An extensive literature review by Wilson and Carpenter (1999) provides an excellent starting point
11    to the valuation literature on aquatic ecosystems. This paper examined 30 studies published between 1971
12    and 1997 that estimated the value of nonmarket ecosystem services provided by fresh water bodies in the
13    U.S. Sixteen were SP studies,  with the rest split between travel cost and hedonic pricing approaches. The
14    travel cost studies covered very specific water bodies and stressors, such as a beatable to swimmable
15    change in water quality for  13 recreation sites along the Monongahela River in Pennsylvania (Smith and
16    Desvousges, 1986), and a change in water quality measured by the Uttormark's Lake Condition Index for
17    recreational use of Pike Lake in Wisconsin (Bouwes and Schneider, 1979). Beyond spatial and stressor
18    differences, the studies differ in their units for expressing value. The Monongahela River study used
19    benefits per household, with the largest value expressed for a change from beatable to swimmable ($51).
20    The Bouwes and Schneider (1979) study concluded that the total (aggregate) annual mean CS was almost
21    $86,000.
22          There are several additional meta-analyses that describe methods of valuation for aquatic
23    ecosystems, but not specifically relating to effects caused by NOX or SOX. Van Houtven  et al. (2007)
24    analyzed WTP for fresh water quality improvements, covering 131 valuation estimates from 18 SP studies
25    that used or could be transformed to use the water quality ladder (beatable, fishable, swimmable)
26    modified into a 10-point scale. The analysis started with 90 studies but found that most were not
27    sufficiently comparable for  statistical analyses. The studies ranged from 1977 to 2003 and covered rivers,
28    lakes and estuaries.
29          A meta-analysis by Johnston et al. (2005) examined the value offish catch based on a total of 48
30    studies published in the U.S. and Canada between 1977 and 2001. The study statistically examined WTP
31    for fish relative to variations in resource, context, angler, and policy attributes, as well as methodological
32    attributes of the studies themselves. The attributes examined included species targeted, geographic region,
33    water body type, catch rate, angler demographics, and fishing  method. Among 391 WTP observations,
34    122 were estimated using CV  methods, 59 using TC methods and the remaining from discrete choice
35    models.
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 1          Johnston et al. (2005) found that SP studies have generally resulted in lower WTP estimates than
 2    RP studies, which is consistent with earlier multiple study reviews and meta-analyses. Dichotomous
 3    choice CV also has produced lower WTP estimates than choice experiment or conjoint surveys, compared
 4    to the default of open-ended surveys (including payment cards and iterative bidding). However, these
 5    findings were not robust across all regression model specifications. The study supported previous findings
 6    (Poe et al., 2000) that WTP is systematically associated with variations in resource, context, and angler
 7    attributes. The study also concluded that WTP per fish varies systematically across study methods, but the
 8    variation due to methodology accounted for a relatively small proportion of total WTP variation. Again,
 9    these results demonstrate the value of recreational fishing, but do not address changes in water quality
10    that may be associated with reduced NOX/SOX emissions.


             F.5.1.  Acidification
11          The Adirondack Park in New York is the best documented of all areas affected by acidic deposition
12    in the U.S. Due to air pollution that largely originated from power plants and other sources to the west
13    and southwest of the  park, many watersheds  in the park have experienced slow acidification of water and
14    soil since the late 18th century. This park has been the  subject of numerous valuation studies in recent
15    decades.
16          Morey and Shaw (1990) applied the travel cost model and estimated recreational  fishing values
17    associated with water quality change resulting from acidic deposition in the Adirondack Park area. The
18    results showed that the aggregate expected conditional compensating variation for a 50% increase in catch
19    rate of two trout species at four sites was $3,863 (1977 dollars;  $13,175 in 2007 dollars), based on 607
20    survey respondents with an estimated standard deviation  of $89 ($304 in 2007 dollars).
21          A travel cost model was also applied by Mullen and Menz (1985) to link acidic deposition and
22    recreational fishing in the Adirondack Park. A net average economic value per angler day was calculated
23    to be $20 (1976 dollars; $73 in 2007 dollars) for lakes, ponds, and streams in the Adirondacks, with an
24    estimated total loss in net economic value of $1.07 million (1976 dollars; $3.89 million in 2007 dollars)
25    per year for a 5% reduction in fishable area. These estimates excluded streams because limited knowledge
26    existed to assess the effect of pH fluctuations on aquatic life in streams. Shaw (1989) questioned the
27    reliability of these results, arguing that the demand equation was not compatible with economic theory
28    and that aggregating separate CS measures from different equations was inappropriate.
29          Englin et al. (1991) assessed the economic impact on recreational fishing in four upper northeastern
30    states resulting from acidic deposition control. Biological effects of acidification were linked with
31    hedonic travel cost and random utility models through the acid stress index (ASI) and fish catch per unit
32    effort (CPUE). The analysis was based on multiple data sources and yielded positive social welfare for
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 1    2030 under a scenario of reducing by 50% the ambient deposition in 1989. This level of decline in acidic
 2    deposition was expected to occur on the basis of analyses conducted by the National Acid Precipitation
 3    Assessment Program (NAPAP).
 4          Cameron and Englin (1997) measured welfare under an assumption that people are not certain
 5    about participation in fishing. Based on WTP results from a survey and a random utility model, surpluses
 6    were estimated for preventing a 20% loss in the availability of high-elevation lakes for fishing in
 7    northeastern states.
 8          Randall and Kriesel (1990) employed a SP method to value a National Pollution Control Program,
 9    which led to improved air and water quality by 25% within 5 years. A nationwide survey conducted in
10    multiple modes found an estimated annual willingness to pay of $694 ($1,098 in 2007 dollars) per
11    household. The researchers concluded that the estimated value was lower than their multimarket hedonic
12    estimates.
13          Banzhaf et al. (2006) conducted a CV survey, which was designed to estimate the total value (use
14    plus non-use values) of reducing acidification by liming the lakes and forests in the Adirondack Park to a
15    degree matching recent SOx and NOx reductions under emissions trading programs. The mean WTP for
16    improving 600 lakes "of concern," and small increases in populations of two bird species and one tree
17    species was $48 (2003 dollars; $54 in 2007 dollars) per year per New York household. The mean WTP for
18    improving 900 lakes "of concern," four bird species and three tree species was $54 (2003 dollars; $61 in
19    2007 dollars) per year per New York household.
20          Sequential implementation of several regulations, including in particular the CAA, has
21    substantially reduced acidic deposition, especially in the eastern part of the U.S. Burtraw et al. (1997)
22    conducted a benefit-cost analysis for the  NAPAP program. Besides improving public health and visibility,
23    emissions controls contributed to decreased lake acidification which was projected to have economic
24    benefit associated with improved recreational fishing in the Adirondack Park. The ASI and CPUE
25    approaches were used in the Tracking and Analysis Framework (TAP) model to capture the response of
26    three recreationally fished species to water quality  improvement in 33 statistically selected Adirondack
27    lakes. The per capita recreational fishing benefits in 2010 were estimated to be $0.62 (1990 dollars; $0.98
28    in 2007 dollars) annually per angler fishing in the Adirondack area.


            F.5.2.  Eutrophication
29          As is the  case for acidification, the endpoints most studied in the valuation literature for nutrient
30    enrichment concern fishing and non-use  (or total) values. Change in aquatic recreational behavior is
31    another endpoint for looking at valuation of such ecosystems, although it is difficult to link those changes
32    due to ambient NOx or SOx concentrations.
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      F.5.2.1. Recreation
 1          Bockstael et al. (1989a) measured the benefits of improvements in water quality of the Chesapeake
 2    Bay using travel cost and SP methods. A telephone survey with 959 respondents revealed an aggregate
 3    WTP for water quality improvement from current level to a level acceptable for swimming and/or other
 4    water activity of about $91 million (1987 dollars; $166 million in 2007 dollars) for households in
 5    Maryland. Based on three other surveys, a travel cost model yielded aggregate use values of about $34.7,
 6    $4.7, and $1.4 million ($63.1, $8.6, and $2.6 million in 2007 dollars) for a 20% water quality
 7    improvement for beach use, boating, and sport fishing, respectively.
 8          Morgan and Owens (2001) simulated water quality change under a baseline (without additional
 9    pollution control) and a scenario with pollution control in the Chesapeake Bay watershed and then
10    calculated aggregate benefits for beach use, boating, and  striped bass sport fishing by transferring the
11    benefits from Bockstael et al. (1989a). Lower bound aggregate benefits for beach use, boating, and sport
12    fishing were $288.8, $6.7, and $288.8  million (1996 dollars; $380.4, $8.8, and $380.4 million  in 2007
13    dollars), respectively for a 60% improvement  in Chesapeake Bay water quality.
14          Lipton (2004) mailed out an open-ended contingent valuation survey to 2,510 randomly selected
15    Maryland boaters regarding willingness to pay for one unit improvement in the water quality of the
16    Chesapeake Bay. The boaters ranked their perception of water quality on a scale of one to five. The
17    median WTP for a one step improvement in water quality was $17.50 ($19.66 in 2007 dollars) per year
18    and the mean was $63 ($71 in 2007 dollars), with 38% of respondents expressing a zero WTP. It was
19    found that the boaters who keep their boats in the water would pay more than those who keep them on
20    trailers. Individuals who ranked ambient water quality lower and had more concern for the  health effects
21    from water quality degradation would likely pay more for water quality improvement. The  aggregated
22    WTP for the Chesapeake Bay boaters in  Maryland was approximately $7.3 million ($8.2 million in 2007
23    dollars) per year with a $146 million ($164 million in 2007  dollars) present value (a 5% discount rate).
24          Smith et al. (1991) estimated the recreational fishing  value of Albemarle-Pamlico Estuary in North
25    Carolina using a hedonic travel cost model. The analysis was based on an intercept survey with 1,012
26    interviews at 35 boat sites and 44 bank sites in 1981  and  1982. The estimated benefit derived from the
27    application of a conventional demand model to the boat site sample ranged from $1.49 to $2.58 (1982
28    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
29    $2.38 in 2007 dollars) for an increase in  catch rate of one fish per hour per person.
30          Four years later, Kaoru (1995) and Kaoru et al. (1995) used the information collected from the
31    same survey to estimate the recreation value from improvements in Albemarle and Pamlico Sounds using
32    a household production model and a random utility model. They linked the effluent loading and quality of
33    sport fishing with the fisherman's decision on site selection. Instead offish availability being estimated as
34    the average number offish caught per person per hour for each entry point, Kaoru et al. (1995) used total

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 1    catch per trip per person as their key measure of fishing success. They included not only the estimated N
 2    loadings but also the effect on biochemical O2 demand as an influence on fishing quality. The results
 3    suggested that depending on aggregation methods, CS ranged between $7.05 and $36.19 ($9.56 and
 4    $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
 5    2007 dollars) for a 36% decrease in N loadings at all sites.
 6          Whitehead et al. (2000) used a joint stated and RP model to estimate recreation value of
 7    improvements to Albemarle and Pamlico  Sounds in terms of CS per trip and per season. A telephone
 8    survey was conducted in 1995 to learn how North Carolina residents would value improvements of water
 9    quality resulting in a 60% increase in fish catch and a 25% increase in open shellfish beds. The analysis
10    was based on 765 completed responses. The CS per trip was estimated at $64 (1995 dollars; $87 in 2007
11    dollars) for current quality and $85 ($115 in 2007 dollars) for improved quality and the CS per season
12    was $ 121 ($ 164 in 2007 dollars) for current and $ 15 5 ($210 in 2007 dollars) for improved quality.
13          The Catawba River in North and South Carolina is used for electric power generation, recreation,
14    drinking water, and wastewater assimilation. With population growth and land use change, the health and
15    vitality of the river system are declining as the river flows downstream. Through a mail-phone survey of
16    1,085 residents randomly selected from 16 counties of Catawba River Basin, Kramer and Eisen-Hecht
17    (2002) presented a management plan designed to maintain the water quality at the current level over time.
18    The water quality in the entire watershed was classified as good, fair or poor on maps to illustrate the
19    distribution of the tributaries with different water quality. The mean WTP was estimated to be $139  ($160
20    in 2007 dollars) for protecting current water quality from deterioration.
21          A site choice model applied jointly with a trip frequency model was used by Needelman and Kealy
22    (1995) to assess the relative benefits of eliminating eutrophication, fecal bacteria, and oil and grease in
23    New Hampshire lakes. The site choices analysis was based on 53 individuals and 1,021 trips, while the
24    trip frequency model was based on a total of 519 individuals including the responses for the site choices
25    model. The mean seasonal benefits were valued at $1.40 ($1.90 in 2007 dollars) for eliminating
26    eutrophication from all sources ($1.33; $1.80 in 2007 dollars) for eliminating nonpoint source pollution
27    alone and $0.09 ($0.12 in 2007 dollars) for eliminating point source pollution alone) with an aggregate
28    seasonal benefit of $1.16 million ($1.57 million in 2007 dollars), with $1.11 million for nonpoint source
29    and $0.08 million for point source pollution. However, the economic benefit estimates were exclusively
30    for swimming and day trips. The measures of water quality were not from the year of the survey (1989)
31    but from  a range of years from  1976 to 1991.
32          The Tar-Pamlico River has experienced declining fish catches, disease in fish and shellfish beds,
33    algae blooms, and aquatic grass losses. More than half of the pollution impairing one third of the river
34    was estimated to come from agricultural nonpoint sources. Whitehead and Groothuis (1992) proposed a
3 5    management program in which farmers used best management practices (BMPs) to significantly improve
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 1    the water quality of the Tar-Pamlico River such that recreational anglers would be able to catch twice as
 2    many fish per trip. A mail survey was sent to 179 households in four counties in Tar-Pamlico River basin
 3    (with a 61% response rate). The mean WTP for doubling fish catch was estimated at $25 ($38 in 2007
 4    dollars), which was bounded from above by a $35 use value ($53 in 2007 dollars) and from below by a
 5    $21 ($32 in 2007 dollars) non-use value. The benefits of water quality improvement in the study area
 6    were aggregated at $1.62 million (1991 dollars; $2.46 million in 2007 dollars) each year. The researchers
 7    believed that non-use value could account for as high as 84% of the estimate. However, the study suffered
 8    from small sample size and relatively high non-response rate (11%) to the WTP questionnaire.
 9           Some portions  of the coastal coral reefs in the Florida Keys are projected to disappear within 10 to
10    25 years. In 2000, the U.S. government announced a long-term plan to save coral reefs proposing that
11    20% of all coral reefs in American-controlled waters would become ecological preserves by 2010. Park
12    et al. (2002) investigated the WTP to preserve current water quality and health of the coral reefs in the
13    Florida Keys. Based on 460 responses to a CV survey and 4,035 respondents to a travel cost survey (460
14    responses used in the  analysis), the annual use value was estimated at $481 per person per snorkeling trip
15    ($553 in 2007 dollars). The mean predicted WTP per trip from a Tobit model was $735 ($844 in 2007
16    dollars). Over 85% of the predicted WTP value was within plus or minus $50 ($57 in 2007 dollars) of the
17    total trip expenses from the contingent valuation scenario.

      F.5.2.2. Commercial Fisheries
18          As one of six background studies for the National Science and Technology Council (NSTC), Diaz
19    and Solow (1992) used a time series estimation approach to examine  the effects from hypoxia1 in the Gulf
20    of Mexico. The study failed to confirm the relationship between the annual occurrence of hypoxia and
21    commercial fishery health, based on catch rate per unit effort of three major species from the 1960s to
22    1990s. Although the benefit assessment did not detect effects attributable to hypoxia, this does not
23    necessarily mean that the economic effects would not occur. NSTC (2000) identified alternatives for
24    reducing the adverse effects of hypoxia and examined the costs associated with reduction of N and P
25    inputs. A net cost estimated by the U.S. Mathematical Programming Model for Agriculture was about
26    $0.8 per kilogram ($0.36 per pound; $0.96 per kilogram/$0.36 per pound in 2007 dollars) for a 20% edge-
27    of-field N loss reduction on agricultural lands, whereas restoring 5 million acres of wetland would have  a
28    net cost of $1.00 per kilogram ($0.45 per pound) ($1.2 per kilogram/$0.54 per pound in 2007 dollars) of
29    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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 1          The Neuse River in North Carolina is important to the commercial blue crab (Callinectes sapidus)
 2    fishery in the eastern U.S. It accounted for about a quarter of the blue crab harvest from 1994 to 2002.
 3    Smith and Crowder (2005) simulated the progress of eutrophication in the Neuse River using a series of
 4    ordinary differential equations, which linked changes in the quantity of nutrients, algal growth, spatial
 5    population distribution of blue crab and its prey species with fishing efforts. Results suggested that a 30%
 6    reduction in N loading in the Neuse River watershed over a 50-year period would result in about a $2.56
 7    million ($2.71  million in 2007 dollars) discounted present value generated in fishery rent (the  difference
 8    between fishing revenues and costs including fixed and opportunity cost).

      F.5.2.3. Water Clarity
 9          In addition to effects on fish populations, eutrophication reduces water clarity due to excessive
10    growth of algae. Boyle et al. (1999) studied the social welfare related to water clarity of four lakes in
11    Maine employing a two-stage hedonic demand model. Based on data from 1990 to 1995 on property sales
12    for 25 lakes, tax records, mail survey,  and water clarity data from the Maine Department of
13    Environmental Protection (DEP), the CS for water clarity improvement from the average ambient level
14    (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
15    2007 dollars) for differently specified  demand systems (Cobb-Douglas, semilog, and linear demand
16    models, respectively). Social welfare loss for visibility deterioration from an average level to 2.41 m
17    ranged from $25,388 to $46,750 ($31,497 to $57,989 in 2007 dollars). One of the interesting findings in
18    the study was that the slope of the Cobb-Douglas demand model increased to 3.0 m, a threshold used by
19    the Maine Department of Environmental Protection to indicate improved water quality and public
20    preferences for it.
21          Poor et al. (2006) evaluated water quality in a small watershed of Maryland using a hedonic
22    property valuation model. The watershed is located in a peninsula surrounded by the Potomac and
23    Patuxent Rivers and the Chesapeake Bay. Due to nonpoint source pollution runoff, water quality has
24    deteriorated, which might have negative impacts on residential housing prices, especially for those close
25    to the river. To estimate the possible extent of the impact, total  suspended solids (TSS) and dissolved
26    inorganic nitrogen (DIN) were averaged by year and included in the model as indicators of ambient water
27    quality. The estimates showed that a marginal increase in TSS reduced average housing prices by $1,086
28    ($1,113 in 2007 dollars), and a marginal increase in DIN decreased housing  prices by $17,642 ($18,087 in
29    2007 dollars).
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            F.5.3. Avoided Costs
 1          Based on a model projection, lEc (1999c) estimated the benefits of decreased N deposition to the
 2    estuaries in the eastern U.S. using the avoided cost method. According to their results, the annual avoided
 3    cost in 2010 would range from $26 to $102 million if 12.8 million pounds of N loading was reduced
 4    annually in Long Island Sound. An annual reduction of 58 million pounds of N loading into Chesapeake
 5    Bay would avoid an annual cost of $349 to $1,278 million. Uncertainty associated with model
 6    assumptions and the inappropriate use of avoided cost estimates to value benefits or damages are two
 7    major sources of potential error in generating such estimates. In addition, the avoided costs method
 8    generally does not measure values conceptually accurately, and these values also are not linked to benefits
 9    from reduced pollution.


            F.5.4. Limitations and  Uncertainties
10          Several general limitations apply  to the valuation of water quality changes. First, the definition of
11    water quality is too ambiguous to quantify comparisons across studies. Second, the degree of water
12    quality improvement is not often clear. A common obstacle in any environmental economic valuation is
13    the availability of data. Problems stemming from lack of data extend from biological data on the
14    populations of target species to limitations of the available economic data on the value of commercial and
15    recreational fisheries to small samples of survey respondents (Smith and Crowder, 2005).
16          The geographical focus of valuation studies is another limitation. The studies reviewed here largely
17    focused  on the eastern part of the U.S., represented by the Adirondacks, Chesapeake Bay, and Albemarle
18    and Pamlico  Sounds. Several studies investigated the total value of aquatic ecosystems and the majority
19    of the studies calculated recreational values of water quality improvement. Only a few studies addressed
20    the economic value of commercial fisheries. There is considerable uncertainty in the estimates of benefits
21    as they vary significantly even for studies in the same area, with similar changes in the commodity, and
22    use of the same valuation methodology.
23          Almost every study reviewed here mentioned the problem of uncertainties about the natural science
24    (Diaz and Solow, 1992; NSTC, 2000). For example, the processes involved in the development of
25    hypoxia are not fully known. There is also no clear connection between a decrease in pollution level and
26    an increase in catch rate (Bockstael et al., 1989b). Additionally, uncertainty arises from the selection of
27    parameter values (Smith and Crowder, 2005) and models (Burtraw et al., 1997). Due to the uncertainty
28    about water quality improvement in tributaries of the Chesapeake Bay, the estimates provided by Morgan
29    and Owens (2001) excluded benefits from water recovery in the tributaries.
30          Study coverage also affects benefit estimates. For example, Morey and Shaw (1990) only included
31    those fishermen who could make day trips to one or more of the study sites,  fishermen with complete

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 1    records, and fishermen whose distance to the farthest site was less than 200 miles. Morey and Shaw
 2    (1990) evaluated four sites and two kinds of trout in the Adirondacks while Englin et al. (1991) valued
 3    only one trout species and lakes in four states.
 4          Many studies relied on information from surveys, which may surfer from various biases. For
 5    example, several studies reviewed here used intercept surveys, which are not necessarily representative of
 6    the target population (Bockstael et al., 1989b; Smith et al., 1991). Double counting of values may also
 7    introduce errors into value estimation. For example, people who are boating may also go fishing
 8    (Bockstael et al., 1989b).
      F.6. Summary
 9          Previous ecosystem valuations presented in AQCDs were very limited because they considered
10    only studies that could directly attribute monetary values to changes in emissions. The assessment of the
11    literature in this Annex has a different approach: the voluminous literature that values various ecosystem
12    services affected by emissions is included, whether or not the actual linkages all the way from emissions
13    to those effects have been quantified. This approach nevertheless necessitates that the natural science
14    underpinnings be examined in the context of preferences, that is, descriptions of damages (or benefits)
15    from NOx and SOx emissions (or reductions in those emissions) from the natural sciences that translate
16    into things that the public cares about. These include, for example, whether the water is beatable or
17    swimmable, the marketable yield associated with changes in a forest or a crop, and effects on aesthetics of
18    wetlands.
19          The physical endpoints and their corresponding valuation studies are divided into different
20    ecological and value endpoints applicable to terrestrial, transitional, and aquatic ecosystems. There is
21    significant valuation literature on the effects of 03 on crops (and to a far lesser degree on forest yield), but
22    this topic is beyond of the scope of this assessment. Beyond the 03 effects, there is little quantification of
23    the science describing the effects of pollution related to NOx and SOx on ecosystems. Valuation studies
24    are themselves classified into meta-analyses and original studies, the latter into market studies (e.g.,
25    commercial fishing), RP studies (e.g., those related to recreation behavior, property values, etc.), and SP
26    studies (those that ask people survey questions about their WTP for hypothetical ecosystem
27    improvements).
28          For valuation of terrestrial effects, survey methods are  most common.  Supplemented by travel cost
29    approaches, this literature leads to a variety of estimates of WTP for improved forest quality that could
30    prove useful for estimating benefits of N and S reductions (provided a number of linkages can be made),
31    even though some of the endpoints valued are related to insect damage. One meta-analysis  is available
32    that summarizes this valuation literature.

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 1           For wetland valuation, three meta-analyses are available that summarized the valuation literature.
 2     This literature is problematic because it focuses on values per wetland acre rather than WTP for changes
 3     in services provided by a wetland affected by deposition of N and S pollutants. However, some of the
 4     available studies provide WTP for discreet ecosystem services, which is helpful in matching these values
 5     to services affected by reductions in N and S deposition. Nevertheless, because there is a very poor
 6     understanding of the scientific basis for linking NOx and SOx emissions to ecosystem health endpoints,
 7     the ability to make the necessary linkages to estimate benefits of pollution reductions is very limited.
 8           The aquatic service valuation literature is the most voluminous of the three categories reviewed. It
 9     contains many recreation value studies, a number of property value studies, some total (use  plus non-use)
10     value studies, some studies on commercial fishery damages and several good meta-analyses of primary
11     valuation studies, which provide estimates of the WTP of households for improvements in the aquatic
12     ecosystem related to N and S changes. Again, however, there is little  focus on NOx or SOx  as the cause of
13     alterations to the aquatic ecosystem.
14           Overall, there is a robust literature valuing a variety of ecosystem services that could  be related to
15     reductions in N and S emissions. Therein addition, issues affecting the credibility of any individual study
16     and even the studies grouped by technique, such studies can only be used for general valuation purposes.
17     The most important limitation is establishment of the linkages between the physical,  chemical, and
18     biological effects of air pollutants on natural ecosystems and changes in exposure to NOx and
       Table F-1. Commonly adopted environmental valuation methods based on revealed or state
       preferences.
                                                    Description
       Revealed Preference  Observing individual choices and behavior to predict their preferences for environmental
       Methods          goods and services.
       Avoided expenditure  Predicting the cost of mitigating the effects of reduced environmental quality.                X       X
       method
       Derived demand     Estimate the value of environmental goods and services by deriving the demand functions of     X       X
       functions          households or firms for them (e.g. water use)
       Hedonic valuation    Estimating an implicit price for the environmental quality attributes of marketable goods, such    X       X
       method           as housing.
       Market analysis      Used for valuing market goods using data on prices and quantities of outputs and inputs. May    X       X
                       use prices of close substitutes, methods of deriving shadow prices, or simulation of changes
                       in market conditions.
       Referenda         Examining voting results related to environmental resources to predict values for them.         X       X
       Replacement cost    Measures expenditures incurred in replacing or restoring environmental good or service lost      X       X
                       (e.g. water filtration). Provides accurate valuation only under strict assumptions usually not
                       met.
       Travel cost method   Values the environmental attributes of recreational sites by examining visitation frequency       X       X
                       and cost differential incurred in reaching site with different attributes.
       User fee          Examine user fees paid to gain access to an ecological resource such as a park to estimate      X       X
                       the lower bound of society's value for that resource.
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Methods
Stated Preferences
Approach
Choice experiments,
conjoint analysis
Description
Directly surveying individuals to predict their preferences for environmental goods and
services.
Elicits individuals' choices from several alternatives associated with different environmental
and cost outcomes. Elicits data on WTP indirectly (by eliciting cost-environmental outcome
tradeoffs, similar to contingent ranking).
Direct
Use
X
Indirect
Use
X
Total
Value
X
Contingent valuation   Elicits data on individuals' willingness to pay for environmental goods and services
                    (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
                    cost outcomes. Elicits data on WTP  indirectly (by eliciting cost-environmental outcome
                    tradeoffs, similar to choice experiments, conjoint analysis).
Table F-2. Economic effects of ozone and other pollutants on agriculture,  as reported in the 1996
ozone criteria document.
Model Features Results (Annual 1980 U.S. Dollar)
Study

Adams
etal.
(1986a)b



Kopp
etal.
(1985)b
Adams
etal.
(1986b)
Adams
and
Crocker
(1989)



a-ninn Pollutant and
Reglon Concentration

U.S. Ozone, 25% reduction
from 1980 level for
each statec



U.S. Ozone, universal
reduction from 53 ppb
to 40 ppba
U.S. Acid deposition, 50%
reduction in wet acidic
deposition
U.S. Ozone, seasonal
standard of 50 ppb
with 95%
complianced; Includes
adjustments for 1985
Farm Bill

Price Output Input Quality Crops
Changes Substitutions Substitutions Changes

Yes Yes Yes No Corn,
soybeans,
cotton,
wheat,
sorghum,
barley
Yes Yes Yes No Corn,
soybeans,
wheat, cotton
Yes Yes Yes No Soybeans


Yes Yes Yes No Corn,
soybeans,
cotton,
wheat,
sorghum,
rice, hay,
barley
Consumer
Benefits

1160x106





Not
reported

172x106


905x106






Producer
Benefits

550x106





Not
reported

-30x106


769x106






Total
Benefits
(Costs)
1700x106





Not
reported

142x106


1674x106






" Seven-h growing season geometric mean. Given a long-normal distribution of air pollution events, a 7-h seasonal ozone level of 40 ppb is
approximately equal to an hourly standard °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 twelve-h growing season geometric mean. Analysis includes both fixed rollbacks (e.g., 25% and seasonal standards (with variable
compliance rates).
Source: Adams and Horst (2003)
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Table F-3. Economic effects of ozone on marketable benefits from forests.
Reference
                 Pollutant/Coverage
   Response and Air Quality Data
                                                                               Economic Model
                       Annual Damages or Benefits
                                of Control
Callaway      All pollutants. Forest products
et al.         (hardwood and softwood) in
(1986)a       the eastern U.S.
Crocker       Acid deposition.
(1985)        Forest products and forest
             ecosystem service flows for
             eastern U.S.


Crocker and   Acid deposition. Forest
Forster       products and forest
(1986)        ecosystem services for
             eastern Canada.

Maynes and   Air pollutants, including acid
Adams        precipitation. Losses
(1992)        estimated for eastern U.S.
             softwoods.
Bentley and   Ozone. All hardwoods and
Horst (1998)  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.

Dose-response 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
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).
$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
Econometric model of
U.S. timber sector
(TAMM).
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.
'We used the updated version of Crocker et al. from 1986, while Adams and Horst cited a prior version, from 1985.
bWe drew different numbers from the Crocker and Forster study than those reported by Adams and Horst.
Source: Adams and Horst (2003), exceptions: Table 2 in Bentley and Horst (1998).
Table F-4. Forecasted average values for select activities, per day per person in 1996.

Activity                      Northeast         Southeast          Intermountain          Pacific Coast      Alaska       USA
Swimming
General recreation*
Fishing
Waterfowl hunting
Big game hunting
14
30
37
40
45
9
25
32
35
40
19
34
41
44
50
9
25
32
34
40
14
30
37
40
45
14
30
37
39
45
*The activity category includes camping, picnicking, sightseeing, hiking, small game hunting, wildlife viewing, and other general recreation. In the
meta-analysis, values for these activity categories do not differ statistically significantly. Source: from the meta-analysis benefit function by
Rosenberger and Loomis (2001), Table 6.
Table F-5. Typical impacts of specific pollutants on the visual quality of forests.
Pollutant
Ozone
Geographic
Extent
Area or
regional
effects
Injury
Type
Direct
injuries
Major Types of Visual 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.
Notes

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Pollutant
 Geographic    injury
   Extent       Type
                                                   Major Types of Visual Injuries
                                                                                                                 Notes
Acidic       Area or
Deposition   regional
            effects
Sulfur
Dioxide
Hydrogen
Fluoride
Point source
pollution
Point source
pollution
Indirect   Increased susceptibility to visual injuries may result from other
Injuries   adverse environmental factors, such as insect attacks. For
          example, increased needle/leaf abscission, elevated mortality
          rates, and/or changes in species composition.

Indirect   Increased susceptibility to visual injuries may result from other
Injuries   adverse environmental factors, such as climatic factors. For
          example, increased needle/leaf abscission, elevated mortality
          rates, and/or changes in species composition.


Direct     Foliar injuries including leaf/needle discoloration and necrosis.
Injuries   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.

Direct     Foliar injuries including leaf/needle discoloration and necrosis.
Injuries   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 (lEc) (1999a)
Table F-6. Economic valuation studies related to forest aesthetics.
Study
Crocker
(1985)
Haefele
etal. (1992)
Holmes and
Kramer
(1996)a
Holmes
et al. (2006)
Method
CV (open ended)
CV (payment
card,
dichotomous
choice)
CV (payment
card,
dichotomous
choice)
Hedonic pricing
Study Area
Southern
California
Southern
Appalachian
Mountains
Southern
Appalachian
Mountains
New Jersey
Description
Evaluating visits to recreational
sites with slight, moderate, and
severe O3 induced damages to
ponderosa and Jeffery pine
stands.
Protect high-elevation spruce
firs from insect and air
pollution
Protect high-elevation spruce
firs from insect and air
pollution. Analysis compared
forest users and non-users.
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.
Economic
Endpoint
Household
WTP
(per trip)
Household
WTP (per
yr)
Household
WTP (per
yr)
Housing
price
Economic Value
Slight damage: $2.09
Moderate damage: $0.66
Severe damage: $0.74
PC: $21
DC: $100
Forest users: $36
Non-users: $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
lEc
(1999b)b


Jakus and
Smith
(1991)
Jenkins
(2002)
             Benefits transfer  National
CV (dichotomous  Maryland and
choice)           Pennsylvania
CV (open ended)   Southern
                  Appalachian
                  Mountains
                                 Protect trees from various       Welfare
                                 different types of damage (see   Loss (1990-
                                 description of source studies)    2010)
                  Protect private homeowner
                  property and surrounding
                  public lands from gypsy moth
                  damages (reduce tree
                  defoliation by 25%)

                  Protect high-elevation spruce
                  firs from insects and air
                  pollution along roads and
                  throughout ecosystem
Household
WTP
(for entire
program)

Household
WTP (per
yr)
                                                             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
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)
Method
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)
Study Area
Southern
Appalachian
Mountains
National (USA)
Oregon
Texas
New
Hampshire
Los Angeles
area
Missouri
Colorado
Rockies
Colorado
Description
Protect high-elevation 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.
Economic
Endpoint
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)
Economic Value
Road side only: $18
Entire ecosystem: $28
Non-use: 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
Non-use: 73% of total value
Use: 27% of total value



level of substitute
of reduction in tree


in tree density

"Holmes and Kramer study applies same results found in Haefele et al. (1991)
bBenefits transfer was based on results from Holmes and Kramer (1996), Peterson et al. (1987), Walsh et al. (1990).
Table F-7. 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)!
  Total Value of
Change for Region
 (Current Dollars)
  Total Value of
Change for Region
  (1990 Dollars)!
Peterson et al.    Ozone damage to San
(Peterson, 1987)  Bernardino and Angeles
               National Forests
                   $6.31 - $32.70ii
                    $7.26 - $37.62
                    $27 - $140 million      $31 - $161million
Walsh et al.
(1990)
Holmes and
Kramer (1996)
Visual damage to
Colorado's Front Range
Visual damage to spruce-
fir forests in southern
Appalachia
$47
$10.81 nonusers
$36.22 users
$61.68
$10.37 nonusers
$34.76 users
$55.7 million
NA
$73.09 million
NA
Source: Exhibit 4 Industrial Economics (lEc) (1999a).
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Table F-8. Estimated value of avoiding forest damage in the U.S.
Affected System States Included HoiShSd
Sierra Nevada and Los Angeles CA $7.26 - $37.62
Basin
Eastern Spruce Fir and Selected ME, VT, NH, MA, NY, PA, WV, $7.26 - $37.62
Eastern Hardwood TN, KY, NC, VA
Households! ^v^""**
10.4 million $75.5 million -
$391.2 million
23.2 million $168 million -
$872.8 million
Cumulative Value
(1990-2010)!!
$1.02
billion
$2.27
billion
billion - $5.27

billion - $11.75

Source: Exhibit 5 Industrial Economics (lEc) (1999a)
Table F-9. Ecological wetland
applicable valuation methods
Ecological Function
Flood and flow control
Storm buffering
Sediment retention
Groundwater recharge/discharge
Maintenance/Nutrient retention
Habitat and nursery for plant and animal
species
Biological diversity
Micro-climate stabilization
Carbon sequestration
Ecological Function
Natural environment
functions, economic goods and
Economic Goods and Services
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
services, types of value, and
Value Commonly Used Valuation Methods
Type (s)a
Indirect use Replacement cost
Market prices
Opportunity cost
Indirect use Replacement cost
Production function
Indirect use Replacement cost
Production function
Indirect use Production function
NFI Replacement cost
Indirect use CVM
Direct use Replacement cost
Direct use Market prices, NFI
Direct use TCM, CVM
Direct use Market prices
Direct use Market prices
Non-use CVM
Indirect use Production function
Indirect use Replacement cost
Value Type Commonly Used Valuation
Direct use HP, CVM
Direct use CVM, TCM
Non-use CVM









Methods (s)a

a Acronyms refer to the contingent valuation method (CVM), hedonic pricing (HP), net factor income (NFI), and the travel cost method (TCM).
Source: Table 1, Brander et al. (2006).
Source: with modifications adapted from Barbier (1991); Barbier et al. (1997); Brouwer et al. (1999); and Woodward and Wui (2001).
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Table F-10. Economic valuation studies related to acidification and eutrophication in  aquatic
ecosystems.
Reference
Study Area
Method
Ecological
Endpoint
Valuation
Endpoint
Economic Value
Note
Acidification

Banzhaf      Injured lakes
etal. (2006)  in
             Adirondacks,
             NY
              CV
                               Fish, bird
                               species, and
                               tree species
              WTP (total
              value)
Bockstael
etal.
(1989a)
Chesapeake
Bay, MA and
DC
                           CV and TC
                                            Fish
              Aggregate
              benefit/
              consumer
              surplus
              (recreational
              value)
Cameron
and Englin
(1997)
             Northeast U.S.  CV and RUM
                               Trout fishing
                               (uncertain
                               about use)
              Consumer
              surplus
              (recreational
              value, existence
              value)
Englin etal.
(1991)
ME, NH, NY
(excluding
New York
City) and VT
                           HTC and RUM
Catch per      Consumer
unit effort      surplus
(number of    (recreational
fish caught in   value)
an h)
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 OP-I
model and $436
for OP-D.

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
yr-to-yr 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
Kaoruetal. Albemarle and CV and RUM Fish
(1995) Pamlico
Sounds, NC







Morey and Four fishing CV and TC Brook and
Shaw sites in Lake Trout
(1990) Adirondacks,
NY




Morgan and Chesapeake Water quality Water quality
Owens Bay model and
(2001) Benefit transfer










Mullen and Adirondack, TC Fish
Menz (1985) NY








Randall and Nationwide, CV Water/air
Kriesel U.S. quality
(1990)


Smith et al. Albemarle and HTC and demand Fish
(1991) Pamlico function
Sounds, NC







Valuation
Endpoint
Consumer
surplus
(recreational
value)






Consumer
surplus
(conditional
compensating
variations)
(recreational
value)


Aggregate
benefit
(recreational
value)









Net economic
value per angler
day/ Consumer
surplus
(recreational
value)





WTP (total
value)



Benefit/
consumer
surplus
(recreational
value)





Economic Value
1. 35-site model: $36.19 for 5%
increase of total catch at all sites
(full-wage)
2. 35-site model: $6.52 for 36%
decrease in N loadings at all
sites (full-wage)





1. $475.87 for 5% increase in
catch rates for trout (1977
dollars)
2. $2162.04 for 25% increase in
catch rates for trout (1977
dollars)
3. $3862.94 for 50% increase in
catch rates for trout (1977
dollars)
1. Lower bound total benefit for
beach use was $288. 8m from a
60% improvement in
Chesapeake Bay water quality.
2. Lower bound total benefit for
trailered boating was $6. 7m
from a 60% improvement in
Chesapeake Bay water quality.
3. Lower bound total benefit for
striped bass sport fishing was
$288. 8m from a 60%
improvement in Chesapeake Bay
water quality.
1. Net economic value per angler
day was avgd at $19.90 for
entire waterbody including lakes,
ponds, and streams.
2. Total angler value was
estimated $31.3 million for entire
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.
1. The estimated annual
willingness to pay was $694.42
per household.


1. Conventional demand model:
$2.58 for boat, $1.11 for bank
fishing
2. Simple inverse demand
model: $0.79 for boat, $0.62 for
bank fishing
3. Detailed inverse demand
model: $0.88 for boat, $0.79 for
bank fishing

Note
1. Estimated N loadings
and biochemical O2
demand, were used in
household production
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.
1. Economic values were
aggregate CCV for 607
survey respondents.
2. Catch rate, as an
indicator of acid
deposition, was included
as a variable in the
model.

1. The study also
provided aggregate
benefits at avg and high
level for three
recreational uses.
2. Benefit transfer was
based on the results in
Bockstael et al. (1988).





1. The values were in
1976 dollars.
2. Total value was
aggregated by number of
trips.






1. The study valued a
National Pollution Control
Program, which improved
air and water quality by
25 percent in 5 yrs.
1. Benefit was calculated
for an one-fish increase
in catch rate per h per
person
2. Catch rate was
included in the model.
3. The paper also
estimated benefit based
on opportunity cost at
1/3 wage.
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Reference Study Area Method Ecological
Endpoint
Whitehead Albemarle and CV and TCM Fish catches
et al. (2000) Pamlico and shellfish
Sounds, NC beds











Eutrophication
Boyle, et al. Four lakes, HTC (two stages, Water clarity
(1999) ME hedonic demand (visibility)
model







Valuation
Endpoint
Consumer
surplus per
season/ per trip
(recreational
value)










Consumer
surplus
(recreational
value)






Economic Value
1. The consumer surplus per trip
is $64 for current quality and
$85 for 60% increase in fish
catch and 25% more open
shellfish beds.
2. The consumer surplus per
season is $121 for current
quality and $155 for 60%
increase in fish catch and 25%
more open shellfish beds.






1. The consumer surplus (+) for
an avg visibility improvement
from 3.78m to 5.15m was
$3,765 and $3,677 for semi-log
and Cobb-Douglas model.
2. The welfare losses (-) for an
avg visibility decrease from
3.78m to 2.41m was $25,388
and $46,750 for semi-log and
Cobb-Douglas model.
Note
1. The consumer surplus
per trip difference
between two baseline
and scenario was not
significant at the 0.10
level while the consumer
surplus per season
difference between two
situations was.
2. The studies also
estimated annual
aggregate consumer
surplus per season for 41
counties within the
watershed.

1. The welfare was also
measured in linear
demand model.
However, the own price
of water clarity is not
significant.




Diaz and
Solow
(1992)
             Gulf of Mexico  Time series
Kramer and   Catawba
Eisen-Hecht   River, NC and
(2002)       SC
Lipton
(2004)
Needelman
and Kealy
(1995)
Chesapeake
Bay, MA
Lakes, NH
                            CV
                            CV
                                Brown         Mean catch rate
                                shrimp, white  per unit effort
                                shrimp, and
                                Menhaden
                                Water quality  WTP (total
                                              value)
                 Water quality  WTP (use value)
Site choice
model and a trip
frequency model
Water quality
Benefit
(recreational
value)
vNSTC
(2000)
Park et al.
(2002)
Gulf of
Mexico,
Mississippi-
Atchafalay
River Basin
Florida Keys,
FL
Cost effective,    Fish, shrimp,  Social cost for
simulation model  and marine    reduction/
(US Mathematic   ecosystem     restoration
Programming
Model in
Agriculture)
                            CV
                 Water quality  WTP per trip
                 and health of  expenses
                 coral  reefs    (recreational
                               value)
                                                 1. The study failed to quantify
                                                 economic effects of hypoxia
                                                 based on past data.
                                1. A mean willingness to pay
                                was revealed at $139 for
                                maintaining current water quality
                                overtime.

                                1. The median WTP for a one
                                scale improvement in water
                                quality was $17.50 per boater
                                per yr and the mean  was $63,
                                with 38% expressing a zero
                                WTP.
                                                 1. Hypoxia was
                                                 measured in terms of
                                                 area (zone) and index in
                                                 the model to calculate
                                                 correlation.

                                                 1. Phone-mail-phone and
                                                 mail-phone survey
                                                 formats applied.
1. The mean seasonal benefits
estimates were $1.4 for
eliminating eutrophication from
all sources ($1.33 for nonpoint
source and $0.09 for point
source) and the aggregate
seasonal benefits were
estimated at $1,163,000 for
eliminating eutrophication from
all sources ($1,105,000  for
nonpoint source and $75,000 for
point source).

1 For 20% edge-of-field N-loss
reduction, the net cost was
estimated at $0.8/kg
($0.36/pound), while restoring 5
million acres of wetland  would
have net cost of $1.00/kg
($0.45/pound).
1. The aggregate WTP
for the Chesapeake Bay
boaters in Maryland was
about $7.3 million per yr,
total improvement gets a
$146 million  present
value.

1. The study also
reported  mean and
aggregate seasonal
benefits for eliminating
bacteria,  oil and grease,
eutrophication problems,
and all pollution from all
sources.
2. The benefit estimates
address exclusively
swimming and day trips.

1. The benefit to reduce
N loadings to the Gulf
was difficult to calculate
because economic
analysis failed to show
direct effects  on Gulf
fisheries,  which was a
background study
conducted by Diaz and
Solow (1992).
                                1. The annual avg use value was
                                $481.15 per person per
                                snorkeling trip.
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Reference    Study Area
                               Method
                                 Ecological
                                 Endpoint
                                  Valuation
                                  Endpoint
                                                                                  Economic Value
                                                                                                                   Note
Poor et al.
(2006)
Smith and
Crowder
(2005)
Whitehead
and
Groothuis
(1992)
St. Mary's
River
watershed,
MA
Neuse River,
NC
Tar-Pamlico
River, NC
                           HPVM
                 Ambient
                 water quality
Bio-economic
model
                           CV
                                            Blue crab
                                            Catch rate
Avg housing
price within the
watershed
(commercial
value)
Fish rent
(commercial
value)
                               WTP (total value,
                               use, and non-use
                               value)
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.
Various Effects
Johnston
etal.
(Johnston,
2005)
U.S. and
Canada
Meta-analysis     Catch rate
WTP             1. WTP per fish over the sample
(recreational      ranged from $.048 to $612.79,
use)             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 Tar-
                                                Pamlico 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 Tar-Pamlico
                                                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 rosette-
       forming 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
       02, 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 A13+ 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., N2ON20 or
       N2N2), normally accomplished by denitrifying bacteria.
Dry deposition
       The movement of gases and particles from the atmosphere to surfaces in the absence of precipitation (e.;
       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 02 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 02 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 (H2OH20), carbon dioxide (C02), nitrous oxide (N2ON20), methane (CH4), and ozone (0303) 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 02.
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.
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Nitrogen mineralization
       The conversion of organic nitrogen into plant-available inorganic forms (e.g. NH3 or NH4+NH4+) by
       microorganisms.
Nitrogen-retention capacity
       The length of time that an ecosystem can retain nitrogen in (?) organisms (e.g., plant or microbe) and soil-
       organic 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 = ~-log10[H+], 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.
Semi-arid 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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