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) ------- 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 ------- DISCLAIMER This document is a second external review draft being released for review purposes only and does not constitute U.S. Environmental Protection Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. August 2008 ii DRAFT-DO NOT QUOTE OR CITE ------- Table of Contents List of Tables List of Figures Acronyms and Abbreviations Annex A. Ecosystem Monitoring and Models A.1.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 August 2008 iii DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 iv DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 v DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 vi DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 vii DRAFT-DO NOT QUOTE OR CITE ------- List of Tables Table A-3. Parameter Estimates, Probability Levels, and Regression Results for the Chesapeake Bay Total Nitrogen Sparrow Model A-47 Table A-4. Effect of spatial referencing on measures of regression model performance for predicting total N flux using the sparrow model. A-48 Table A-5. Summary of N retention rates used in recent WATERSN studies. A-52 Table A-6. Some examples of models that could contribute to development of a better understanding of the ecological efforts of atmospheric S and N deposition, but that are not explicitly addressed in this Annex. A-56 Table B-1. N-saturated forests in North America, including estimated N inputs and outputs. B-136 Table B-2. Summary of measured ANC, pH, and Al concentrations compared with reference values in the six high-interest areas. B-137 Table B-3. Sources of data and sample sizes for datasets analyzed by Stoddard et al. (2003), along with estimates of the condition of surface waters in each region in the 1980s. B-138 Table B-4. Estimates of change in number and proportion of acidic surface waters in acid-sensitive regions of the North and East, based on applying current rates of change in Gran ANC to past estimates of population characteristics from probability surveys. B-138 Table B-5. Regional trend results for long-term monitoring sites for the period 1990 through 2000. B-139 Table B-6. Slopes of trends in Gran ANC in acidic, low ANC and moderate ANC lakes and streams for the period 1990-2000. B-139 Table B-7. Changes in key chemical characteristics during periods of record in aquatic systems in Maine. B-140 Table B-8. Projected changes (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 August 2008 viii DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 ix DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 x DRAFT-DO NOT QUOTE OR CITE ------- List of Figures Figure A-1. Location of acid-sensitive regions of the northern and eastern U.S. A-5 Figure A-2. Location LTM sites used in the 2003 Surface Water report. A-7 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 August 2008 xi DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 xii DRAFT-DO NOT QUOTE OR CITE ------- Acronyms and Abbreviations ACCENT Atmospheric Composition Change: the European Network of excellence AIRMoN Atmospheric Integrated Research Monitoring Network AIRS Atmospheric Infrared Sounder (instrument) Al aluminum 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 August 2008 XIII DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XIV DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 xv DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XVI DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XVII DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XVIII DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XIX DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 xx DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XXI DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XXII DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XXIII DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 XXIV DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-1 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-2 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-3 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-4 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-5 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-6 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-7 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-8 DRAFT-DO NOT QUOTE OR CITE ------- 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, August 2008 A-9 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-10 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 A-11 DRAFT-DO NOT QUOTE OR CITE ------- • 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 August 2008 A-12 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-13 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-14 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-15 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-16 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-17 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-18 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-19 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 A-20 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-21 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-22 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-23 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-24 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-25 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-26 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-27 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-28 DRAFT-DO NOT QUOTE OR CITE ------- 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, August 2008 A-29 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-30 DRAFT-DO NOT QUOTE OR CITE ------- 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: August 2008 A-31 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-32 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-33 DRAFT-DO NOT QUOTE OR CITE ------- 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, August 2008 A-34 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-35 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-36 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-37 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-38 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-39 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-40 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-41 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-42 DRAFT-DO NOT QUOTE OR CITE ------- 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, August 2008 A-43 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-44 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-45 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-46 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-47 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-48 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-49 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-50 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-51 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 A-52 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-53 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 A-54 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-55 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-56 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-57 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 A-58 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 A-59 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-60 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-61 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 A-62 DRAFT-DO NOT QUOTE OR CITE ------- ANN EX A-References 1 Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N deposition on N 2 cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11: 639- 3 648. 4 Aber, J. D.; Federer, C. A. (1992) A generalized, lumped-parameter model of photosynthesis, 5 evapotranspiration and net primary production in temperate and boreal forest ecosystems. 6 Oecologia 92: 463-474. 7 Aber, J. D.; Ollinger, S.; Driscoll, C. (1997) Modeling nitrogen saturation in forest ecosystems in 8 response to land use and atmospheric deposition. Ecol. Model. 101: 61-78. 9 Alexander, R. B.; Smith, R. A.; Schwarz, G. E. (2000) Effect of stream channel size on the 10 delivery of nitrogen to the Gulf of Mexico. Nature 403: 758-761. 11 Alexander, R. B.; Smith, R. A.; Schwarz, G. E.; Preston, S. D.; Brakebill, J. W.; Srinivasan, R.; 12 Pacheco, P. A. (2001) Atmospheric nitrogen flux from the watersheds of major estuaries 13 of the United States: An application of the SPARROW watershed model. In: Valigura, 14 R.; Alexander, R.; Castro, M.; Meyers, T.; Paerl, H.; Stacey, P.; Turner, R. E., eds. 15 Nitrogen loading in coastal water bodies: an atmospheric perspective. Washington, DC: 16 American Geophysical Union; pp. 119-170. 17 Alveteg, M.; Sverdrup, H. (2002) Manual for regional assessments using the SAFE model (draft 18 version 8 April 2002). Lund, Sweden: Lund University, Department of Chemical 19 Engineering II. 20 Appelo, C. A. J.; Postma, D. (1993) Geochemistry, groundwater, and pollution. Rotterdam, The 21 Netherlands: Balkema. 22 Beier, C.; Hultberg, H.; Moldan, F.; Wright, R. F. (1995) MAGIC applied to roof experiments 23 (Risdalsheia, N; Gardsjon, S.; Klosterhede, D. K.) to evaluate the rate of reversibility of 24 acidification following experimentally reduced acid deposition. Water Air Soil Pollut. 25 85: 1745-1751. 26 Bhattacharya, A.; Mudgal, R.; Taneja, A. (2004) Acid deposition and critical load analysis in 27 Agra, India. J. Hazard. Mat. B 106: 157-160. 28 Bormann, F. H.; Likens, G. E. (1967) Nutrient cycling. Science 155: 424-429. 29 Bormann, F. H.; Likens, G. E. (1979) Pattern and process in a forested ecosystem. New York, 30 NY: Springer-Verlag. 31 Boyer, E. W.; Alexander, R. B.; Parton, W. J.; Li, C.; Butterbach-Bahl, K.; Donner, S. D.; 32 Skaggs, R. W. (2006) Modeling denitrification in terrestrial and aquatic ecosystems at 33 regional scales. Ecol. Appl. 16: 2123-2142. 34 Brand, G. J.; Nelson, M. D.; Wendt, D. G.; Nimerfro, K. K. (2000) The hexagon/panel system 35 for selecting FIA plots under an annual inventory. In: McRoberts, R. E.; Reams, G. A.; 36 Van Deusen, P. C., eds. Proceedings of the first annual forest inventory and analysis 37 symposium. St. Paul, MN: Department of Agriculture, Forest Service, North Central 38 Research Station Gen. Tech. Rep. NC-213 U.S.; pp. 8-13. August 2008 A-63 DRAFT-DO NOT QUOTE OR CITE ------- 1 Castro, M. S.; Driscoll, C. T. (2002) Atmospheric nitrogen deposition to estuaries in the Mid- 2 Atlantic and Northeastern United States. Environ. Sci. Tech. 36: 3242-3249. 3 Castro, M. S.; Driscoll, C. T.; Jordan, T. E.; Reay, W. G.; Boynton, W. R.; Seitzinger, S. P.; 4 Styles, R. V.; Cable, J. E. (2001) Contribution of atmospheric depostition to the total 5 nitrogen loads to thirty-four estuaries on the Atlantic and Gulf Coasts of the United 6 States. In: Valigura, R. A.; Alexander, R. B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; 7 Stacey, P. E.; Turner, R. E., eds. Nitrogen loading in coastal water bodies: an atmospheric 8 perspective. Washington, DC: American Geophysical Union; pp. 77-106. 9 Castro, M. S.; Driscoll, C. T.; Jordan, T. E.; Reay, W. G.; Boynton, W. R. (2003) Sources of 10 nitrogen to estuaries in the United States. Estuaries 26: 803-814. 11 Chen, L.; Driscoll, C. T. (2004) An evaluation of processes regulating spatial and temporal 12 patterns in lake sulfate in the Adirondack region of New York. Glob. Biogeochem. 13 Cycles 18(GB3024): 10-10. 14 Chen, L.; Driscoll, C. T. (2005a) A two-layered model to simulate the seasonal variations in 15 surface water chemistry draining a northern forest watershed. Water Resour. Res. 16 41(W09425): 10.1029/2004WR003625. 17 Chen, L.; Driscoll, C. T. (2005b) Regional assessment of the response of acid-base status of lake- 18 watersheds in the Adirondack region of New York to changes in atmospheric deposition 19 using PnET-BGC. Environ. Sci. Tech. 39: 787-794. 20 Chen, L.; Driscoll, C. T. (2005c) Regional application of an integrated biogeochemical model to 21 northern New England and Maine. Ecol. Appl. 15: 1783-1797. 22 Chen, C. W.; Gherini, S. A.; Dean, J. D.; Hudson, R. J. M.; Goldstein, R. A. (1984) 23 Development and calibration of the Integrated lake-Water shed Acidification Model. In: 24 Schnoor, J. L., ed. Modeling of Total Acid Precipitation Impacts. Stoneham, MA: 25 Butterwoirth Publishers; pp. 175-203. 26 Clair, T. A.; Dennis, I. F.; Amiro, P. G.; Cosby, B. J. (2004) Past and future chemistry changes in 27 acidified Nova Scotian Atlantic salmon (Salmo salar) rivers: a dynamic modeling 28 approach. Can. J. Fish. Aquat. Sci. 61: 1965-1975. 29 Clow, D. W.; Mast, M. A. (1999) Long-term trends in stream water and precipitation chemistry 30 at five headwater basins in the northeastern United States. Water Resour. Res. 35: 541- 31 554. 32 Cosby, B. J.; Hornberger, G. M.; Galloway, J. N.; Wright, R. F. (1985a) Modeling the effects of 33 acid deposition: assessment of a lumped parameter model of soil water and streamwater 34 chemistry. Water Resour. Res. 21: 51-63. 35 Cosby, B. J.; Hornberger, G. M.; Galloway, J. N.; Wright, R. F. (1985b) Time scales of 36 catchment acidification: a quantitative model for estimating freshwater acidification. 37 Environ. Sci. Tech. 19: 1144-1149. 38 Cosby, B. J.; Wright, R. F.; Hornberger, G. M.; Galloway, J. N. (1985c) Modelling the effects of 39 acid deposition: estimation of long-term water quality responses in a small forested 40 catchment. Water Resour. Res. 21: 1591-1601. August 2008 A-64 DRAFT-DO NOT QUOTE OR CITE ------- 1 Cosby, B. I; Jenkins, A.; Ferrier, R. C.; Miller, J. D.; Walker, T. A. B. (1990) Modelling stream 2 acidification in afforested catchments: longterm reconstructions at two sites in central 3 Scotland. J. Hydrol. 120: 143-162. 4 Cosby, B. J.; Ryan, P. F.; Webb, J. R.; Hornberger, G. M.; Galloway, J. N.; Charles, D. F. (1991) 5 Mountains of western Virginia. In: Charles, D. F., ed. Acidic deposition and aquatic 6 ecosystems: regional case studies. New York, NY: Springer-Verlag; pp. 297-318. 7 Cosby, B. J.; Wright, R. F.; Gjessing, E. (1995) An acidification model (MAGIC) with organic 8 acids evaluated using whole-catchment manipulations in Norway. J. Hydrol. 170: 101- 9 122. 10 Cosby, B. J.; Norton, S. A.; Kahl, J. S. (1996) Using a paired catchment manipulation 11 experiment to evaluate a catchment-scale biogeochemical model. Sci. Total Environ. 183: 12 49-66. 13 Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Emmett, B. A.; Wright, R. F.; Tietema, A. (1997) 14 Modelling the ecosystem effects of nitrogen deposition: model of ecosystem retention 15 and loss of inorganic nitrogen (MERLIN). Hydrol. Earth Syst. Sci. 1: 137-158. 16 Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Wright, R. F. (2001) Modeling the effects of acid 17 deposition: refinements, adjustments and inclusion of nitrogen dynamics in the MAGIC 18 model. Hydrol. Earth Syst. Sci. 5: 499-517. 19 Davidson, E. A.; Keller, M.; Erickson, H. E.; Verchot, L. V.; Veldkamp, E. (2000) Testing a 20 conceptual model of soil emissions of nitrous and nitric oxides. BioScience 50: 667-680. 21 De Vries, W.; Posch, M.; Kamari, J. (1989) Simulation of the long-term soil response to acid 22 deposition in various buffer ranges. Water Air Soil Pollut. 48: 349-390. 23 De Vries, W.; Reinds, G. J.; Posch, M.; Kamari, J. (1994) Simulation of soil response to acidic 24 deposition scenarios in Europe. Water Air Soil Pollut. 78: 215-246. 25 Del Grosso, S. J.; Parton, W. J.; Mosier, A. R.; Hartman, M. D.; Brenner, J.; Ojima, D. S.; 26 Schimel, D. S. (2001) Simulated interaction of carbon dynamics and nitrogen trace gas 27 fluxes using the DAYCENT model. In: Shaffer, M. J.; Ma, L.; Hansen, S., eds. Modeling 28 Carbon and Nitrogen Dynamics for Soil Management. Boca Raton, FL: Lewis 29 Publishers; pp. 303-332. 30 DeWalle, D. R.; Swistock, B. R. (1994) Differences in oxygen-18 content of throughfall and 31 rainfall in hardwood and coniferous forests. Hydrol. Process. 8: 75-82. 32 Driscoll, C. T.; Van Dreason, R. (1993) Seasonal and long-term temporal patterns in the 33 chemistry of Adirondack lakes. Water Air Soil Pollut. 67: 319-344. 34 Driscoll, C. T.; Lehtinen, M. D.; Sullivan, T. J. (1994) Modeling the acid-base chemistry of 35 organic solutes in Adirondack, New York, lakes. Water Resour. Res. 30: 297-306. 36 Driscoll, C. T.; Postek, K. M.; Kretser, W.; Raynal, D. J. (1995) Long-term trends in the 37 chemistry of precipitation and lake water in the Adirondack region of New York, USA. 38 Water Air Soil Pollut. 85: 583-588. 39 Driscoll, C. T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C. L.; 40 Groffman, P.; Hopkinson, C.; Lambert, K.; Lawrence, G.; Ollinger, S. (2003) Nitrogen 41 pollution in the northeastern United States: sources, effects, and management options. 42 BioScience 53: 357-374. August 2008 A-65 DRAFT-DO NOT QUOTE OR CITE ------- 1 Driscoll, C. T.; Lambert, K. F.; Chen, L. (2007) Acidic deposition: sources and effects. In: 2 Visgilio, G. R.; Whitelaw, D. M., eds. Acid in the Environment. New York, NY: 3 Springer; pp. 332. 4 Ferrier, R. C.; Helliwell, R. C.; Cosby, B. I; Jenkins, A.; Wright, R. F. (2001) Recovery from 5 acidification of lochs in Galloway, south-west Scotland, UK: 1979-1998. Hydrol. Earth 6 Syst. Sci. 5:421-431. 7 Fisher, D. C.; Oppenheimer, M. (1991) Atmospheric nitrogen deposition and the Chesapeake 8 Bay Estuary. Ambio 20: 102-108. 9 Ford, J.; Stoddard, J. L.; Powers, C. F. (1993) Perspectives in environmental monitoring: an 10 introduction to the U.S. EPA Long-Term Monitonring (LTM) project Water Air Soil 11 Pollut. 67: 247-255. 12 Gbondo-Tugbawa, S. S.; Driscoll, C. T. (2002) Evaluation of the effects of future controls on 13 sulfur dioxide and nitrogen oxide emissions on the acid-base status of a northern forest 14 ecosystem. Atmos. Environ. 36: 1631-1643. 15 Gbondo-Tugbawa, S. S.; Driscoll, C. T.; Aber, J. D.; Likens, G. E. (2001) Evaluation of an 16 integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem. 17 Water Resour. Res. 37: 1057-1070. 18 Gherini, S. A.; Mok, L.; Hudson, R. J.; Davis, G. F.; Chen, C. W.; Goldstein, R. A. (1985) The 19 ILWAS model: formulation and application. Water Air Soil Pollut. 26: 425-459. 20 Goldstein, R. A.; Gherini, S. A.; Chen, C. W.; L. Mok; Hudson, R. J. M. (1984) Integrated 21 acidification study (ILWAS): a mechanistic ecosystem analysis. Phil. Trans. R. Soc. 22 Lond. Ser. B, 305: 409-425. 23 Goodale, C.; Lajtha, K.; Nadelhoffer, K. J.; Boyer, E. W.; Jaworski, N. A. (2002) Forest nitrogen 24 sinks in large eastern U.S. watersheds: estimates from forest inventory and an ecosystem 25 model. Biogeochemistry 57/58: 239-266. 26 Gran, G. (1952) Determination of the equivalence point in potentiometric titrations. Int. Congr. 27 Anal. Chem. 77:661-671. 28 Grossman, G. D.; Ratajczak, R. E., Jr.; Crawford, M. K.; Freeman, M. C. (1998) Assemblage of 29 organization in stream fishes: effects of environmental variation and interspecific 30 interactions. Ecol. Monogr. 68: 395-420. 31 Hartman, M. D.; Baron, J. S.; Ojima, D. S. (2007) Application of a coupled ecosystem-chemical 32 equilibrium model, DayCent-Chem, to stream and soil chemistry in a Rocky Mountain 33 watershed. Ecol. Model. 200: 493-510. 34 Hendrickson, O. Q. (1990) Asymbiotic nitrogen fixation and soil metabolism in three Ontario 35 forests. Soil Biol. Biochem 22: 967-971. 36 Herlihy, A. T.; Larsen, D. P.; Paulsen, S. G.; Urquhart, N. S.; Rosenbaum, B. J. (2000) 37 Designing a spatially balanced, randomized site selection process for regional stream 38 surveys: the EMAP mid-Atlantic pilot study. Environ. Monitor. Assess. 63: 95-113. 39 Hornbeck, J. W.; Bailey, S. W.; Buso, D. C.; Shanley, J. B. (1997) Streamwater chemistry and 40 nutrient budgets for forested watersheds in New England" variability and management 41 implications. For. Ecol. Manage. 93: 73-89. August 2008 A-66 DRAFT-DO NOT QUOTE OR CITE ------- 1 Hornberger, G. M.; Cosby, B. J.; Wright, R. F. (1989) Historical reconstructions and future 2 forecasts of regional surface water acidification in southernmost Norway. Water Resour. 3 Res. 25: 2009-2018. 4 Howarth, R. W.; Billen, G.; Swaney, D.; Townsend, A.; Jaworski, N.; Lajtha, K.; Downing, J. 5 A.; Elmgren, R.; Caraco, N.; Jordan, T.; Berendse, F.; Freney, J.; Kudeyarov, V.; 6 Murdoch, P. S.; Zhao-Liang, Z. (1996) Regional nitrogen budgets and riverine N & P 7 fluxes for the drainages to the North Atlantic Ocean: natural and human influences. 8 Biogeochemistry 35: 75-139. 9 Jacobs, T. C.; Gilliam, J. W. (1985) Riparian losses of nitrate from agricultural drainage waters. 10 J. Environ. Qual. 14: 472-478. 11 Jaworski, N. A.; Howarth, R. S.; Hetling, L. J. (1997) Atmospheric deposition of nitrogen oxides 12 onto the landscape contributes to coastal eutrophication in the northeast United States. 13 Environ. Sci. Tech. 31: 1995-2004. 14 Jenkins, A.; Cosby, B. J.; Ferrier, R. C.; Walker, T. A. B.; Miller, J. D. (1990) Modelling stream 15 acidification in afforested catchments: an assessment of the relative effects of acid 16 deposition and afforestation. J. Hydrol. 120: 163-181. 17 Johnson, D. W.; Lindberg, S. E. (1992) Atmospheric deposition and forest nutrient cycling: a 18 synthesis of the integrated forest ecological series. New York, NY: Springer. 19 Johnson, D. W.; Swank, W. T.; Vose, J. M. (1993) Simulated effects of atmospheric sulfur 20 deposition on nutrient cycling in a mixed deciduous forest. Biogeochemistry 23: 169-196. 21 Johnson, D. W.; Binkley, D.; Conklin, P. (1995) Simulated effects of atmospheric deposition, 22 harvesting, and species change on nutrient cycling in a loblolly pine forest. For. Ecol. 23 Manage. 76: 29-45. 24 Johnson, D. W.; Susfalk, R. B.; Brewer, P. F. (1996) Simulated responses of red spruce forest 25 soils to reduced sulfur ad nitrogen deposition. J. Environ. Qual. 25: 1300-1309. 26 Jordan, T. E.; Weller, D. E. (1996) Human contributions to terrestrial nitrogen flux. BioScience 27 46: 655-664. 28 Jordan, T. E.; Correll, D. L.; Weller, D. E. (1993) Nutrient interception by a riparian forest 29 receiving inputs from adjacent cropland. J. Environ. Qual. 22: 467-473. 30 Kahl, J. S.; Norton, S. A.; Cronan, C. S.; Fernandez, I. J.; Bacon, L. C.; Haines, T. A. (1991) 31 Maine. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case 32 studies. New York, NY: Springer-Verlag; pp. 203-235. 33 Kahl, J. S.; Fernandez, I. J.; Nadelhoffer, K. J.; Driscoll, C. T.; Aber, J. D. (1993) Experimental 34 inducement of nitrogen saturation at the watershed scale. Environ. Sci. Tech. 27: 565- 35 567. 36 Kelly, R. H.; Parton, W. J.; Hartman, M. D.; Stretch, L. K.; Ojima, D. S.; Schimel, D. S. (2000) 37 Intra- and interannual variability of ecosystem processes in shortgrass steppe. J. Geophys. 38 Res. 105(D15): 20093-20100. 39 Kendall, C.; Silva, S. R.; Chang, C. C.; Campbell, D. H.; Burns, D. A.; Shanley, J. B. (1995) Use 40 of oxygen and nitrogen isotopes to trace sources of nitrate during snowmelt in forested 41 catchments. In: Tonnessen, K. A.; Williams, M. W.; Tranter, M., eds. Biogeochemistry of August 2008 A-67 DRAFT-DO NOT QUOTE OR CITE ------- 1 Seasonally Snow-Covered Catchments. International Association of Hydrologic 2 Sciences; pp. 339-347. 3 Kjonaas, O. J.; Wright, R. F. (1998) Nitrogen leaching from N limited forest ecosystems: the 4 MERLIN model applied to Gardsjon, Sweden. Hydrol. Earth Syst. Sci. 2: 415-429. 5 Knoepp, J. D.; Swank, W. T. (1994) Long-term soil chemistry changes in aggrading forest 6 ecosystems. Soil Sci. Soc. Am. J. 58: 325-331. 7 Kurz, D.; Alveteg, M.; Sverdrup, H. (1998) Integrated assessment of soil chemical status. 2. 8 Application of a regionalized model to 622 forested sites in Switzerland. Water Air Soil 9 Pollut. 105: 11-20. 10 Larsen, D. P.; Urquhart, N. S. (1993) A framework for assessing the sensitivity of the EMAP 11 design. In: Larsen, D. P.; Christie, S. J., eds. EMAP-Surface Waters 1991 Pilot Report. 12 Corvallis, OR: U.S. Environmental Protection Agency; pp. 4.1-4.37. 13 Larsen, D. P.; Thornton, K. W.; Urquhart, N. S.; Paulsen, S. G. (1994) The role of sample 14 surveys for monitoring the condition of the nation's lakes. Environ. Monitor. Assess. 32: 15 101-134. 16 Lawrence, G. B.; Momen, B.; Roy, K. M. (2004) Use of stream chemistry for monitoring acidic 17 deposition effects in the Adirondack Region of New York. J. Environ. Qual. 33: 1002- 18 1009. 19 Lawrence, G. B.; Sutherland, J. W.; Boylen, C. W.; Nierzwicki-Bauer, S. A.; Momen, B.; 20 Baldigo, B. P.; Simonin, H. A. (2007) Acid rain effects on aluminum mobilization 21 clarified by inclusion of strong organic acids. Environ. Sci. Technol. 41: 93-98. 22 Likens, G. E. (1985) An ecosystem approach to aquatic ecology: Mirror Lake and its 23 environment. New York, NY: Springer. 24 Likens, G. E. (1992) The ecosystem approach: its use and abuse. Oldendorf/Luhe, Germany: 25 Ecology Institute. 26 Likens, G. E.; Bormann, F. H. (1995) Biogeochemistry of a forested ecosystem. 2nd ed. New 27 York, NY: Springer-Verlag. 28 Likens, G. E.; Bormann, F. H.; Pierce, R. S.; Eaton, J. S.; Johnson, N. M. (1977) 29 Biogeochemistry of a Forested Ecosystem. New York, NY: Springer Verlag. 30 Likens, G. E.; Bormann, F. H.; Pierce, R. S.; Reiners, W. A. (1978) Recovery of a deforested 31 ecosystem. Science 199: 492-496. 32 Likens, G. E.; Driscoll, C. T.; Buso, D. C. (1996) Long-term effects of acid rain: response and 33 recovery of a forest ecosystem. Science (Washington, DC) 272: 244-246. 34 Likens, G. E.; Driscoll, C. T.; Buso, D. C.; Mitchell, M. J.; Lovett, G. M.; Bailey, S. W.; 35 Siccama, T. G.; Reiners, W. A.; Alewell, C. (2002) The biogeochemistry of sulfur at 36 Hubbard Brook. Biogeochemistry 60: 235-316. 37 Liu, S.; Munson, R.; Johnson, D.; Gherini, S.; Summers, K.; Hudson, R.; Wilkinson, K.; Pitelka, 38 L. (1991) Application of a nutrient cycling model (NuCM) to a northern mixed hardwood 39 and a southern coniferous forest. Tree Physiol. 9: 173-184. August 2008 A-68 DRAFT-DO NOT QUOTE OR CITE ------- 1 Lovett, G. M.; Burns, D. A.; Driscoll, C. T.; Jenkins, J. C.; Mitchell, M. J.; Rustad, L.; Shanley, 2 J. B.; Likens, G. E.; Haeuber, R. (2007) Who needs environmental monitoring? Front. 3 Ecol. Environ. 5: 253-260. 4 Lowrance, R. R.; Todd, R. L.; Asmussen, L. E. (1983) Waterborne nutrient budgets for the 5 riparian zone of an agricultural watershed. Agric. Ecosyst. Environ. 10: 371-384. 6 Maag, M.; Malinovsky, M.; Nielson, S. M. (1997) Kinetics and temperature dependence of 7 potential denitrification in riparian soils. J. Environ. Sci. 26: 215-223. 8 Martinson, L.; Alveteg, M.; Warfvinge, P. (2003) Parameterization and evaluation of sulfate 9 adsorption in a dynamic soil chemistry model. Environ. Pollut. 124: 119-125. 10 McNichol, D. K. (2002) Relation of lake acidification and recovery to fish, common loon and 11 common merganser occurrence in Algoma Lakes. Water Air Soil Pollut. Focus 2: 151- 12 168. 13 McNichol, D. K.; Mallory, M. L.; Wedeles, C. H. R. (1995) Assessing biological recovery of 14 acid-sensitive lakes in Ontario, Canada. Water Air Soil Pollut. 85: 457-462. 15 McRoberts, R. E.; McWilliams, W. H.; Reams, G. A.; Schmidt, T. L.; Jenkins, J. C.; O'Neill, K. 16 P.; Miles, P. D.; Brand, G. J. (2004) Assessing sustainability using data from the Forest 17 Inventory and Analysis program of the United States Forest Service. J. Sustain. For. 18: 18 23-46. 19 Meyer, J. L.; Swank, W. T. (1996) Ecosystem management challenges ecologists. Ecol. Appl. 6: 20 738-740. 21 Meyers, T.; Sickles, J.; Dennis, R.; Russell, K.; Galloway, J.; Church, T., eds. (2000) 22 Atmospheric nitrogen deposition to coastal estuaries and their watersheds, p. 53-76. In: 23 Valigura, R. M.; Alexander, R. B.; Castro, M. S.; Greening, H.; Meyers, T.; Paerl, H.; 24 Turner, R. E. (eds.) An assessment of nitrogen loads to US estuaries with an atmospheric 25 perspective. Washington, D.C.: American Geophysical Union. (Coastal and Estuarine 26 Studies). 27 Moldan, F.; Wright, R. F.; Ferrier, R. C.; Andersson, B. I; Hultberg, H. (1998) Simulating the 28 Gardsjon covered catchment experiment with the MAGIC model. In: Hultberg, H.; 29 Skeffington, R. A., eds. Experimental reversal of acid rain effects. The Gardsjon Roof 30 Project. Chichester, UK: Wiley and Sons; pp. p.351-362. 31 Morel, F. M. M.; Hering, J. G. (1993) Principles and Applications of Aquatic Chemistry. New 32 York, NY: Wiley-Interscience. 33 Mueller, D. K.; Ruddy, B. C.; Battaglin, W. A. (1997) Logistic model of nitrate in streams of the 34 upper-midwestern United States. J. Environ. Qual. 26: 1223-1230. 35 Muir, P. S.; McCune, B. (1988) Lichens, tree growth, and foliar symptoms of air pollution: are 36 the stories consistent? J. Environ. Qual. 17: 361-370. 37 Murdoch, P. S.; Stoddard, J. L. (1993) Chemical characteristics and temporal trends in eight 38 streams of the Catskill Mountains. Water Air Soil Pollut. 67: 367-395. 39 Norton, S. A.; Kahl, J. S.; Fernandez, I. J.; Rustad, L. E.; Scofield, J. P.; Haines, T. A. (1994) 40 Response of the West Bear Brook watershed, Maine, USA, to the addition o 41 3-year results. For. Ecol. Manage. 68: 61-73. August 2008 A-69 DRAFT-DO NOT QUOTE OR CITE ------- 1 Norton, S.; Kahl, J.; Fernandez, I. (1999a) Altered soil-soil water interactions inferred from 2 stream water chemistry at an artificially acidified watershed at Bear Brook Watershed, 3 Maine USA. Environ. Monitor. Assess. 55: 97-111. 4 Norton, S.; Kahl, J.; Fernandez, L; Haines, T.; Rustad, L.; Nodvin, S.; Schofield, J.; Strickland, 5 T.; Erickson, J.; Wignington, P., Jr.; Lee, J. (1999b) The Bear Brook Watershed, Maine 6 (BBWM), USA. Environ. Monitor. Assess. 55: 7-51. 7 Oreskes, N.; Shrader-Frechette, K.; Belitz, K. (1994) Verification, validation, and confirmation 8 of numerical models in the earth sciences. Science 263: 641-646. 9 Parkhurst, D. L.; Appelo, C. A. J. (1999) User's guide to PHREEQC (version 2)—A computer 10 program for speciation, batch-reaction, one-dimensional transport, and inverse 11 geochemical calculations. Denver, CO: U.S. Geological Survey Water-Resources 12 Investigations Report. 13 Parton, W. J.; Schimel, D. S.; Ojima, D. S.; Cole, C. V. (1994) A general model for soil organic 14 matter dynamics: sensitivity to litter chemistry, texture and management. In: Bryant, R. 15 B.; Arnold, R. W., eds. Quantitative Modeling of Soil Forming Processes. Madison, WI: 16 Soil Sci. Soc. Am.; pp. 147-167. 17 Parton, W. J.; Hartman, M.; Ojima, D.; Schimel, D. (1998) DAYCENT and its land surface 18 submodel: description and testing. Global Planet Change 19: 35-48. 19 Paulsen, S. G.; Larsen, D. P.; Kaufmann, P. R.; Whittier, T. R.; Baker, J. R.; Peck, D.; McGue, 20 J.; Hughes, R. M.; McMullen, D.; Stevens, D.; Stoddard, J. L.; Lazorchak, J.; Kinney, 21 W.; Selle, A. R.; Hjort, R. (1991) EMAP - surface waters monitoring and research 22 strategy, fiscal year. Corvallis, OR: U.S. Environmental Protection Agency, Office of 23 Research and Development. 24 Peterjohn, W. T.; Correll, D. L. (1984) Nutrient dynamics in an agricultural watershed: 25 observation on the role of a riparian forest. Ecology 65: 1466-1475. 26 Posch, M.; Reinds, G. J. (2003) VSD - User manual of the very simple dynamic soil acidification 27 model. Bilthoven, The Netherlands: Coordination Center for Effects, RIVM. 28 Posch, M.; Reinds, G. J.; De Vries, W. (1993) SMART - a simulation model for acidification's 29 regional trends: model description and user manual. Helsinki, Finland: Mimeograph 30 Series of the National Board of Waters and the Environment, no. 477. 31 Posch, M.; Hettelingh, J. P.; Slootweg, J. (2003) Manual for dynamic modelling of soil response 32 to atmospheric deposition. Bilthoven, The Netherlands: Coordination Center for Effects, 33 RIVM. 34 Preston, S. D.; Brakebill, J. W. (1999) Application of spatially reference regression modeling for 35 the evaluation of total nitrogen loading in the Chesapeake Bay Watershed. U.S. 36 Geological Survey. 37 Reuss, J. O. (1980) Simulation of soil nutrient losses due to rainfall acidity. Ecol. Model. 11: 15- 38 38. 39 Rodriguez, L.; Macias, F. (2006) Eutrophication trends in forest soils in Galicia (NW Spain) 40 caused by the atmospheric deposition of nitrogen compounds. Chemosphere 63: 1598- 41 1609. August 2008 A-70 DRAFT-DO NOT QUOTE OR CITE ------- 1 RTI International. (2001) Evaluation of Waquoit Bay models to estimate the water quality and 2 ecological improvements resulting from atmospheric nitrogen reduction regulatory 3 scenarios. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of 4 Air Quality Planning and Standards, contract no. 68-D-99-024. 5 Schlesinger, W. L.; Hartley, A. E. (1992) A global budget for atmospheric NH3. 6 Biogeochemistry 15: 191-211. 7 Schofield, K. A.; Pringle, C. M.; Meyer, J. L.; Sutherland, A. B. (2001) The importance of 8 crayfish in the breakdown of rhododendron leaf litter. Freshwater Biol. 46: 1-14. 9 Scott, M. C.; Helfman, G. S. (2001) Native invasions, homogenization, and the mismeasure of 10 integrity offish assemblages. Fisheries 26: 6-15. 11 Shanley, J. B.; Chalmers, A. (1999) The effect of frozen soil on snowmelt runoff at Sleepers 12 River, Vermont. Hydrol. Process. 13: 1843-1857. 13 Smith, R. A.; Schwarz, G. E.; Alexander, R. B. (1997) Regional interpretation of water-quality 14 monitoring data. Water Resour. Res. 33: 2781-2798. 15 Stanford, G.; Dzienia, S.; Vander Pol, R. A. (1975) Effect of temperature on denitrification rate 16 in soils. Soil Sci. Soc. Am. Proc. 39: 867-870. 17 Stevenson, F. J., ed. (1982) Origin and distribution of nitrogen in soil. Nitrogen in agricultural 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 24 trends. Water Resour. Res. 32: 2529-2538. 25 Stoddard, J. L.; Driscoll, C. T.; Kahl, J. S.; Kellogg, J. H. (1998a) A regional analysis of lake 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.; 32 Kellogg, J. H.; Murdoch, P. S.; Webb, J. R.; Webster, K. E. (2003) Response of surface 33 water chemistry to the Clean Air Act Amendments of 1990. Research Triangle Park, NC: 34 U.S. Environmental Protection Agency, Office of Research and Development, National 35 Health and Environmental Effects Research Laboratory. EPA 620/R-03/001. 36 Sullivan, T. J. (2000) Aquatic effects of acidic deposition. Boca Raton, FL.: Lewis Publishers. 37 Sullivan, T. J.; Cosby, B. J. (1998) Modeling the concentration of aluminium in surface waters. 38 Water Air Soil Pollut. 105: 643-659. 39 Sullivan, T. J.; Johnson, D. W.; Munson, R. K.; Joslin, J. D. (2002) Assessment of the effects of 40 acidic deposition on forest resources in the Southern Appalachian Mountains. Asheville, 41 NC: Southern Appalachian Mountains Initiative. August 2008 A-71 DRAFT-DO NOT QUOTE OR CITE ------- 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, 3 J. S. (2003) Assessment of air quality and related values in Shenandoah National Park.: 4 U.S. Department of the Interior, National Park Service, Northeast Region. 5 NPS/NERCHAL/NRTR-03/090. 6 Sullivan, T. J.; Cosby, B. J.; Herlihy, A. T.; Webb, J. R.; Bulger, A. J.; Snyder, K. U.; Brewer, 7 P.; Gilbert, E. H.; Moore, D. L. (2004) Regional model projections of future effects of 8 sulfur and nitrogen deposition on streams in the southern Appalachian Mountains. Water 9 Resour. Res. 40(W02101): 10.1029/2003WR001998. 10 Sullivan, T. J.; Driscoll, C. T.; Cosby, B. J.; Fernandez, I. J.; Herlihy, A. T.; Zhai, J.; Stemberger, 11 R.; Snyder, K. U.; Sutherland, J. W.; Nierzwicki-Bauer, S. A.; Boylen, C. W.; 12 McDonnell, T. C.; Nowicki, N. A. (2006) Assessment of the extent to which intensively- 13 studied lakes are representative of the Adirondack Mountain Region, final report. 14 Prepared for: Albany, NY: New York State Energy Research and Development 15 Authority. Corvallis, OR: E&S Environmental Chemistry, Inc.; report no. 06-17. 16 Swank, W. T.; Vose, J. M. (1997) Long-term nitrogen dynamics of Coweeta forested watersheds 17 in the southeastern United States of America. Glob. Biogeochem. Cycles 11: 657-671. 18 Urquhart, N. S.; Paulsen, S. G.; Larsen, D. P. (1998) Monitoring for regional and policy-relevant 19 trends over time. Ecol. Appl. 8: 246-257. 20 U.S. Congress. (1990) Clean Air Act amendments of 1990, PL 101-549, November 15. 21 Washington, DC: U.S. Government Printing Office. 22 Van Griensven, A.; Bauwens, W. (2001) Integral water quality modelling of catchments. Water 23 Sci. Technol. 43:321-328. 24 Wade, A. J.; Neal, C.; Whitehead, P. G.; Flynn, N. J. (2005) Modelling nitrogen fluxes from the 25 land to the coastal zone in European systems: a perspective from the INC A project. J. 26 Hydrol. (Amsterdam) 304: 413-429. 27 Warfvinge, P.; Sverdrup, H. (1992) Calculating critical loads of acid deposition with PROFILE - 28 a steady-state soil chemistry model. Water Air Soil Pollut. 63: 119-143. 29 Warfvinge, P.; Falkengren-Greup, U.; Sverdrup, H.; Andersen, B. (1993) Modelling long-term 30 cation supply in acidified forest stands. Environ. Pollut. 80: 209-221. 31 Webb, J. R.; Cosby, B. J.; Galloway, J. N.; Hornberger, G. M. (1989) Acidification of native 32 brook trout streams in Virginia. Water Resour. Res. 25: 1367-1377. 33 Webb, J. R.; Cosby, B. J.; Deviney, F. A.; Galloway, J. N.; Maben, S. W.; Bulger, A. J. (2004) 34 Are brook trout streams in western Virginia and Shenandoah National Park recovering 35 from acidification? Environ. Sci. Technol. 38: 4091-4096. 36 Webster, K. E.; Brezonik, P. L.; Holdhusen, B. J. (1993) Temporal trends in low alkalinity lakes 37 of the upper Midwest (1983-1989). Water Air Soil Pollut. 67: 397-414. 38 Whitall, D.; Bricker, S. (2006) Assessment of eutrophication in estuaries: pressure-state-response 39 and source apportionment. USDA Forest Service Proceedings RMRS-P-42CD. Silver 40 Spring, MD: National Oceanic and Atmospheric Administration, National Centers for 41 Coastal Ocean Science, Center for Coastal Monitoring and Assessment. August 2008 A-72 DRAFT-DO NOT QUOTE OR CITE ------- 1 Whitall, D.; Castro, M.; Driscoll, C. (2004) Evaluation of management strategies for reducing 2 nitrogen loadings to four US estuaries. Sci. Total Environ. 333: 25-36. 3 White, G. N.; Zelazny, L. W. (1986) Charge properties of soil colloids. In: Sparks, D. L., ed. Soil 4 Physical Chemistry. Boca Raton, FL: CRC Press; pp. 39-81. 5 Whitehead, P. G.; Reynolds, B.; Hornberger, G. M.; Neal, C.; Cosby, B. I; Paricos, P. (1988) 6 Modeling long term stream acidification trends in upland Wales at Plynlimon. Hydrol. 7 Process. 2: 357-368. 8 Whitehead, P. G.; Barlow, I; Haworth, E. Y.; Adamson, J. K. (1997) Acidification in three Lake 9 District tarns: historical long term trends and modelled future behaviour under changing 10 sulphate and nitrate deposition. Hydrol.Earth System Sci. 1: 197-204. 11 Whittier, T. R.; Paulsen, S. B.; Larsen, D. P.; Peterson, S. A.; Herlihy, A. T.; Kaufmann, P. R. 12 (2002) Indicators of ecological stress and their extent in the population of northeastern 13 lakes: a regional-scale assessment. BioScience 52: 235-247. 14 Woodmansee, R. G. (1978) Additions and losses of nitrogen in grassland ecosystems. 15 BioScience 28: 448-453. 16 Wright, R. F.; Cosby, B. J.; Platen, M. B.; Reuss, J. O. (1990) Evaluation of an acidification 17 model with data from manipulated catchments in Norway. Nature 343: 53-55. 18 Wright, R. F.; Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Bulger, A. J.; Harriman, R. (1994) 19 Changes in the acidification of lochs in Galloway, southwestern Scotland, 1979-1988: the 20 MAGIC model used to evaluate the role of afforestation, calculate critical loads, and 21 predict fish status. J. Hydrol. 161: 257-285. 22 Wright, R. F.; Emmett, B. A.; Jenkins, A. (1998) Acid deposition, land-use change and global 23 change: MAGIC7 model applied to Risdalsheia, Norway (RAIN and CLEVIEX projects) 24 and Aber, UK (NITREX project). Hydrol. Earth Syst. Sci. 2: 385- August 2008 A-73 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-1 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-2 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-3 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-4 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-5 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-6 DRAFT-DO NOT QUOTE OR CITE ------- 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, August 2008 B-7 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-8 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-9 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-10 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-11 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-12 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-13 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-14 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-15 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-16 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-17 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-18 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-19 DRAFT-DO NOT QUOTE OR CITE ------- 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 B-20 DRAFT-DO NOT QUOTE OR CITE ------- 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 ------- 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 B-22 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-23 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-24 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-25 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-26 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-27 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-28 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-29 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-30 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-31 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-32 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-33 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-34 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-35 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-36 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-37 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-38 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-39 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-40 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-41 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-42 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-43 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-44 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-45 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-46 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-47 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-48 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-49 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-50 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-51 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-52 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-53 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-54 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-55 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-56 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-57 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-58 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-59 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-60 DRAFT-DO NOT QUOTE OR CITE ------- Z 25 0 A2 50 B1 -90 -60 -30 0 30 60 -90 -60 -30 0 30 B3 -90 -60 -30 0 30 SO ID I "5 "c I C 0) 1 g TJ $ L> -o - n II Q; o UUUQ D -90 -60 -30 30 60 -90-60 -30 0 30 60 -90 -60 -30 0 30 60 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 August 2008 B-61 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-62 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-63 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-64 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-65 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-66 DRAFT-DO NOT QUOTE OR CITE ------- 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: August 2008 B-67 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-68 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-69 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-70 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-71 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-72 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-73 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-74 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-75 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-76 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-77 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-78 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-79 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-80 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-81 DRAFT-DO NOT QUOTE OR CITE ------- +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. August 2008 B-82 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-83 DRAFT-DO NOT QUOTE OR CITE ------- 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 A t k t .- / ,'• /• W si \ in 1 I \^\^f\^ V* 'i-^ T T ^ » »Ą 5 0 25 50 75 100 125 150 175 200 225 250 V '(" , \ f A T I 1 ^ I 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 August 2008 B-84 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-85 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-86 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-87 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-88 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-89 DRAFT-DO NOT QUOTE OR CITE ------- 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; August 2008 B-90 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-91 DRAFT-DO NOT QUOTE OR CITE ------- 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, August 2008 B-92 DRAFT-DO NOT QUOTE OR CITE ------- 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; August 2008 B-93 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-94 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-95 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-96 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-97 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-98 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-99 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-100 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-101 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-102 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-103 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-104 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-105 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-106 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-107 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-108 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-109 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-110 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-111 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-112 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-113 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-114 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-115 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-116 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-117 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-118 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-119 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-120 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-121 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-122 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-123 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-124 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-125 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-126 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-127 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-128 DRAFT-DO NOT QUOTE OR CITE ------- 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, August 2008 B-129 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-130 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-131 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 B-132 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-133 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-134 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-135 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-136 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-137 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-138 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-139 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-140 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-141 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-142 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-143 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-144 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-145 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-146 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-147 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-148 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-149 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-150 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-151 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 B-152 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-153 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-154 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-155 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-156 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 B-157 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-158 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 B-159 DRAFT-DO NOT QUOTE OR CITE ------- ANNEX B - References 1 Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N deposition of N 2 cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11(4): 3 639-648. 4 Aber, J. D.; Nadelhoffer, K. J.; Steudler, P.; Melillo, J. M. (1989) Nitrogen saturation in northern 5 forest ecosystems: excess nitrogen from fossil fuel combustion may stress the biosphere. 6 BioScience 39: 378-386. 7 Aber, J. D.; Mellilo, J. M.; Nadelhoffer, K. J.; Pastor, J.; Boone, R. D. (1991) Factors controlling 8 nitrogen cycling and nitrogen saturation in northern temperate forest ecosystems. Ecol. 9 Appl. 1:303-315. 10 Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.; McNulty, S.; 11 Currie, W.; Rustad, L.; Fernandez, I. (1998) Nitrogen saturation in temperate forest 12 ecosystems. BioScience 48: 921-934. 13 Aber, J. D.; Ollinger, S. V.; Driscoll, C. T.; Likens, G. E.; Holmes, R. T.; Freuder, R. J.; 14 Goodale, C. L. (2002) Inorganic nitrogen losses from a forested ecosystem in response to 15 physical, chemical, biotic, and climatic perturbations. Ecosystems 5: 648-658. 16 Aber, J. D.; Goodale, C. L.; Ollinger, S. V.; Smith, M.-L.; Magill, A. H.; Martin, M. E.; Hall, R. 17 A.; Stoddard, J. L. (2003) Is nitrogen deposition altering the nitrogen status of 18 northeastern forests? Bioscience 53: 375-389. 19 Adams, M. B. (1999) Acidic deposition and sustainable forest management in the central 20 Appalachians, USA. For. Ecol. Manage. 122: 17-28. 21 Adams, M. B.; Angradi, T. R.; Kochenderfer, J. N. (1997) Steam water and soil solution 22 responses to 5 years of nitrogen and sulfur additions at the Fernow Experimental Forest, 23 West Virginia. Forest Ecol. Manage. 95: 79-91. 24 Adams, M. B.; Burger, J. A.; Jenkins, A. B.; Zelazny, L. (2000) Impact of harvesting and 25 atmospheric pollution on nutrient depletion of eastern U.S. hardwood forests. For. Ecol. 26 Manage. 138:301-319. 27 Alewell, C.; Gehre, M. (1999) Patterns of stable S isotopes in a forested catchment as indicators 28 for biological S turnover. Biogeochemistry 47: 319-333. 29 Allen, E. B.; Sirulnik, A. G.; Egerton-Warburton, L.; Kee, S. N.; Bytnerowicz, A.; Padgett, P. E.; 30 Temple, P. J.; Fenn, M. E.; Poth, M. A.; Meixner, T. (2005) Air pollution and vegetation 31 change in southern California coastal sage scrub: a comparison with chaparral and 32 coniferous forest. In: Kus, B. E.; Beyers, J. L., eds. Planning for biodiversity: bringing 33 research and management together. Albany, CA: U.S. Department of Agriculture, Forest 34 Service, Pacific Southwest Research Station; general technical report PSW-GTR-195; pp. 35 79-95. 36 Aimer, B.; Dickson, W.; Ekstrom, C.; Hornstrom, E.; Miller, U. (1974) Effects of acidification 37 on Swedish lakes. Ambio 3: 30-36. August 2008 B-160 DRAFT-DO NOT QUOTE OR CITE ------- 1 Altshuller, A. P.; Linthurst, R. A., eds. (1984) The acidic deposition phenomenon and its effects: 2 critical assessment review papers. Washington, DC: U.S. Environmental Protection 3 Agency; report no. EPA-600/8-83-016-BF. 4 Alvo, R.; Hussell, D. J. T.; Berrill, M. (1988) The breeding success of common loons (Gavia 5 immer) in relation to alkalinity and other lake characteristics in Ontario. Can. J. Zool. 66: 6 746-752. 7 Arrington, D. V.; Lindquist, R. C.; Beck, B. F.; Wilson, W. L. (1987) Thickly mantled karst of 8 the Interlachen, Florida area. In: Balkema, A. A., ed. Karst hydrology: engineering and 9 environmental applications; pp. 31-39. 10 Asp, H.; Berggren, D. (1990) Phosphate and calcium uptake in beech (Fagus sylvatica) in the 11 presence of aluminium and natural fulvic acids. Physiol. Plant. 80: 307-314. 12 Audet, C.; Wood, C. M. (1988) Do rainbow trout (Salmo gairdneri) acclimate to low pH? Can. J. 13 Fish. Aquat. Sci. 45: 1399-1405. 14 Bailey, S. W.; Hornbeck, J. W.; Driscoll, C. T.; Gaudette, H. E. (1996) Calcium inputs and 15 transport in a base-poor forest ecosystem as interpreted by Sr isotopes. Water Resour. 16 Res. 32: 707-719. 17 Bailey, S. W.; Horsley, S. B.; Long, R. P.; Hallet, R. A., eds. (1999) Influence of geologic and 18 pedologic factors on health of sugar maple on the Allegheny Plateau. U.S. Department of 19 Agriculture, Forest Service; general technical report NE-261; pp. 63-65. 20 Bailey, S. W.; Buso, D. C.; Likens, G. E. (2003) Implications of sodium mass balance for 21 interpreting the calcium cycle of a forested ecosystem. Ecology 84: 471-484. 22 Bailey, S. W.; Horsley, S. B.; Long, R. P.; Hallett, R. A. (2004) Influence of edaphic factors on 23 sugar maple nutrition and health on the Allegheny Plateau. Soil Sci. Soc. Am. J. 68: 243- 24 252. 25 Bailey, S. W.; Horsley, S. B.; Long, R. P. (2005) Thirty years of change in forest soils of the 26 Allegheny Plateau, Pennsylvania. Soil Sci. Soc. Am. J. 69: 681-690. 27 Baker, L. A.; Brezonik, P. L. (1988) Dynamic model of in-lake alkalinity generation. Water 28 Resour. Res. 24: 65-74. 29 Baker, L. A.; Christensen, S. W. (1991) Effects of acidification on biological communities. In: 30 Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case studies. New 31 York, NY: Springer-Verlag; pp. 83-106. 32 Baker, J. P.; Schofield, C. L. (1980) Aluminum toxicity to fish as related to acid precipitation 33 and Adirondack surface water quality. In: Drabl0s, D.; Tollan, A., eds. Ecological impact 34 of acid precipitation: proceedings of an international conference; March; Sandefjord, 35 Norway. Oslo-As, Norway: SNSF project; pp. 292-295. 36 Baker, J. P.; Schofield, C. L. (1982) Aluminum toxicity to fish in acidic waters. Water Air Soil 37 Pollut. 18: 289-309. 38 Baker, J. P.; Schofield, C. L. (1985) Acidification impacts on fish populations: a review. In: 39 Adams, D. D.; Page, W. P., eds. Acid deposition: environmental, economic, and policy 40 issues. New York, NY: Plenum Press; pp. 183-221. August 2008 B-161 DRAFT-DO NOT QUOTE OR CITE ------- 1 Baker, L. A.; Pollman, C. D.; Eilers, J. M. (1988) Alkalinity regulation in softwater Florida 2 lakes. Water Resour. Res. 24: 1069-1082. 3 Baker, J. P.; Bernard, D. P.; Christensen, S. W.; Sale, M. J. (1990a) Biological effects of changes 4 in surface water acid-base chemistry. Washington, DC: National Acid Precipitation 5 Assessment Program. State of Science/Technology Report 13. 6 Baker, J. P.; Gherini, S. A.; Christensen, S. W.; Driscoll, C. T.; Gallagher, J.; Munson, R. K.; 7 Newton, R. M.; Reckhow, K. H.; Schofield, C. L. (1990b) Adirondack lakes survey: an 8 interpretive analysis offish communities and water chemistry, 1984-1987. Ray Brook, 9 NY: Adirondack Lakes Survey Corporation. 10 Baker, L. S.; Kaufmann, P. R.; Herlihy, A. T.; Eilers, J. M. (1991a) Current status of surface 11 water acid-base chemistry. In: Irving, P. M., ed. Acidic deposition: state of science and 12 technology, v. II, aquatic processes and effects. Washington, DC: National Acid 13 Precipitation Assessment Program; NAPAP State of Science/Technology report 9; pp. 9- 14 5 - 9-367, 9-A1, 9-B1, 9-C1, 9-D1, and 9-CP1. 15 Baker, L. A.; Herlihy, A. T.; Kaufmann, P. R.; Eilers, J. M. (1991b) Acidic lakes and streams in 16 the United States: the role of acidic deposition. Science (Washington, DC, U.S.) 252: 17 1151-1154. 18 Baker, T. R.; Allen, H. L.; Schoeneberger, M. M.; Kress, L. W. (1994) Nutritional response of 19 loblolly pine exposed to ozone and simulated acid rain. Can. J. For. Res. 24: 453-461. 20 Baker, J. P.; Van Sickle, J.; Gagen, C. J.; DeWalle, D. R.; Sharpe, W. E.; Carline, R. F.; Baldigo, 21 B. P.; Murdoch, P. S.; Bath, D. W.; Kretser, W. A.; Simonin, H. A.; Wigington, P. J., Jr. 22 (1996) Episodic acidification of small streams in the northeastern United States: effects 23 on fish populations. Ecol. Appl. 6: 423-437. 24 Baldigo, B. P.; Murdoch, P. S. (1997) Effect of stream acidification and inorganic aluminum on 25 mortality of brook trout (Salvelinus fontinalis) in the Catskill Mountains, New York. 26 Can. J. Fish. Aquat. Sci. 54: 603-615. 27 Baldigo, B. P.; Murdoch, P. S.; Burns, D. A. (2005) Stream acidification and mortality of brook 28 trout (Salvelinus fontinalis) in response to timber harvest in Catskill Mountain 29 watersheds, New York, USA. Can. J. Fish. Aquat. Sci. 62: 1168-1183. 30 Baldigo, B. P.; Lawrence, G. B.; Simonin, H. A. (2007) Persistent mortality of brook trout in 31 episodically acidified streams of the southwestern Adirondack Mountains, New York. 32 Trans. Am. Fish. Soc. 136: 121-134. 33 Barinaga, M. (1990) Where have all the froggies gone? Science (Washington, DC, U.S.) 247: 34 1033-1034. 35 Barmuta, L. A.; Cooper, S. D.; Hamilton, S. K.; Kratz, K. W.; Melack, J. M. (1990) Responses 36 of zooplankton and zoobenthos to experimental acidification in a high-elevation lake 37 (Sierra Nevada, California, U.S.A.). Freshwater Biol. 23: 571-586. 38 Baron, J.; Norton, S. A.; Beeson, D. R.; Hermann, R. (1986) Sediment diatom and metal 39 stratigraphy from Rocky Mountain lakes with special reference to atmospheric 40 deposition. Can. J. Fish. Aquat. Sci. 43: 1350-1362. August 2008 B-162 DRAFT-DO NOT QUOTE OR CITE ------- 1 Baron, J. S.; Ojima, D. S.; Holland, E. A.; Parton, W. J. (1994) Analysis of nitrogen saturation 2 potential in Rocky Mountain tundra and forest: implications for aquatic systems. 3 Biogeochemistry 27: 61-82. 4 Baron, J. S.; Rueth, H. M.; Wolfe, A. M.; Nydick, K. R.; Allstott, E. J.; Minear, J. T.; Moraska, 5 B. (2000) Ecosystem responses to nitrogen deposition in the Colorado Front Range. 6 Ecosystems 3: 352-368. 7 Battoe, L. E.; Lowe, E. F. (1992) Acidification of Lake Annie, Highlands Co., FL. Water Air 8 Soil Pollut. 65:69-80. 9 Beamish, R. J. (1976) Acidification of lakes in Canada by acid precipitation and the resulting 10 effects on fishes. Water Air Soil Pollut. 6: 501-514. 11 Beamish, R. J.; Harvey, H. H. (1972) Acidification of the La Cloche mountain lakes, Ontario, 12 and resulting fish mortalities. J. Fish. Res. Board Can. 29: 1131-1143. 13 Beggs, G. L.; Gunn, J. M. (1986) Response of lake trout (Salvelinus namaycush) and brook trout 14 (S. fontinalis) to surface water acidification in Ontario. Water Air Soil Pollut. 30: 711- 15 717. 16 Bell, J. N. B. (1985) SO2 effects on the productivity of grass species. In: Winner, W. E.; 17 Mooney, H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation: physiology, 18 ecology, and policy issues. Stanford, CA: Stanford University Press; pp. 250-263. 19 Belnap, J.; Sigal, L.; Moir, W.; Eversman, S. (1993) Identification of sensitive species, in lichens 20 as bioindicators of air quality. In: Huckaby, L. S., ed. Lichens as bioindicators of air 21 quality. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky 22 Mountain Forest and Range Experimental Station; pp. 67-88. 23 Berggren, D.; Mulder, J. (1995) The role of organic matter in controlling aluminum solubility in 24 acidic mineral soil horizons. Geochim. Cosmochim. Acta 59: 4167-4180. 25 Bergman, H. L.; Mattice, J. S.; Brown, D. J. A. (1988) Lake acidification and fisheries project: 26 adult brook trout (Salvelinus fontinalis). Can. J. Fish. Aquat. Sci. 45: 1561-1562. 27 Billett, M. F.; Parker-Jervis, F.; Fitzpatrick, E. A.; Cresser, M. S. (1990) Forest soil chemical 28 changes between 1949/50. J. Soil Sci. 41: 133-145. 29 Billings, W. D. (1978) Plants and the ecosystem. 3rd ed. Belmont, CA: Wadsworth Publishing 30 Company, Inc.; pp. 1-62, 83-108. 31 Birks, H. J. B.; Berge, F.; Boyle, J. F.; Gumming, B. F. (1990) A palaeoecological test of the 32 land-use hypothesis for recent lake acidification in south-west Norway using hill-top 33 lakes. J. Paleolimnol. 4: 69-65. 34 Blair, R. B. (1990) Water quality and the summer distribution of common loons in Wisconsin. 35 Passenger Pigeon 52: 119-126. 36 Blake, L.; Goulding, K. W. T.; Mott, C. J. B.; Johnston, A. E. (1999) Changes in soil chemistry 37 accompanying acidification over more than 100 years under woodland and grass at 38 Rothamsted Experimental Station, UK. Eur. J. Soil Sci. 50: 401-412. 39 Blancher, P. J.; McNicol, D. K. (1988) Breeding biology of tree swallows in relation to wetland 40 acidity. Can. J. Zool. 66: 842-849. August 2008 B-163 DRAFT-DO NOT QUOTE OR CITE ------- 1 Blancher, P. J.; McNicol, D. K. (1991) Tree swallow diet in relation to wetland activity. Can. J. 2 Zool. 69: 2629-2637. 3 Blum, J. D.; Johnson, C. E.; Siccama, T. G.; Eagar, C.; Fahey, T. J.; Likens, G. E.; Klaue, A.; 4 Nezat, C. A.; Driscoll, C. T. (2002) Mycorrhizal weathering of apatite as an important 5 calcium source in base-poor forest ecosystems. Nature (London, U.K.) 417: 729-731. 6 Bobbink, R.; Hornung, M.; Roelofs, J. G. M. (1998) The effects of air-borne nitrogen pollutants 7 on species diversity in natural and semi-natural European vegetation. J. Ecol. 86: 717- 8 738. 9 Bondietti, E. A.; McLaughlin, S. B. (1992) Evidence of historical influences of acidic deposition 10 on wood and soil chemistry. In: Johnson, D. W.; Lindberg, S. E., eds. Atmospheric 11 deposition and forest nutrient cycling: a synthesis of the integrated forest study. New 12 York, NY: Springer-Verlag: pp. 358-377. (Billings, W. D.; Golley, F.; Lange, O. L.; 13 Olson, J. S.; Remmert, H., eds. Ecological studies analysis and synthesis: v. 91). 14 Booth, C. E.; McDonald, D. G.; Simons, B. P.; Wood, C. M. (1988) Effects of aluminum and 15 low pH on net ion fluxes and ion balance in the brook trout (Salvelinus fontinalis). Can. 16 J. Fish. Aquat. Sci. 45: 1563-1574. 17 Bowman, W. D.; Gartner, J. R.; Holland, K.; Wiedermann, M. (2006) Nitrogen critical loads for 18 alpine vegetation and terrestrial ecosystem response: are we there yet? Ecol. Appl. 16: 19 1183-1193. 20 Bradford, D. F.; Swanson, C.; Gordon, M. S. (1992) Effects of low pH and aluminum on two 21 declining species of amphibians in the Sierra Nevada, California. J. Herpetol. 26: 369- 22 377. 23 Bradford, D. F.; Tabatabai, F.; Graber, D. M. (1993) Isolation of remaining populations of the 24 native frog, Rana muscosa, by introduced fishes in Sequoia and Kings Canyon National 25 Parks, California. Conserv. Biol. 7: 882-888. 26 Brenner, M.; Binford, M. W. (1988) Relationships between concentrations of sedimentary 27 variables and trophic state in Florida lakes. Can. J. Fish. Aquat. Sci. 45: 294-300. 28 Brezonik, P. L.; Eaton, J. G.; Frost, T. M.; Garrison, P. J.; Kratz, T. K.; Mach, C. E.; 29 McCormick, J. H.; Perry, J. A.; Rose, W. A.; Sampson, C. J.; Shelley, B. C. L.; Swenson, 30 W. A.; Webster, K. E. (1993) Experimental acidification of Little Rock Lake, Wisconsin: 31 chemical and biological changes over the pH range 6.1 to 4.7. Can. J. Fish. Aquat. Sci. 32 50: 1101-1121. 33 Bricker, O. P.; Rice, K. C. (1989) Acidic deposition to streams: a geology-based method predicts 34 their sensitivity. Environ. Sci. Technol. 23: 379-385. 35 Brooks, P. D.; Williams, M. W.; Schmidt, S. K. (1996) Microbial activity under alpine 36 snowpacks, Niwot Ridge, Colorado. Biogeochemistry 32: 93-113. 37 Brown, D. J. A. (1982) The effect of pH and calcium on fish and fisheries. Water Air Soil Pollut. 38 18:343-351. 39 Brown, D. J. A. (1983) Effect of calcium and aluminum concentrations on the survival of brown 40 trout (Salmo trutta) at low pH. Bull. Environ. Contam. Toxicol. 30: 582-587. August 2008 B-164 DRAFT-DO NOT QUOTE OR CITE ------- 1 Buckler, D. R.; Mehrle, P. M.; Cleveland, L.; Dwyer, F. J. (1987) Influence of pH on the toxicity 2 of aluminum and other inorganic contaminants to east coast striped bass. Water Air Soil 3 Pollut. 35: 97-106. 4 Bugas, P. E., Jr.; Mohn, L. O.; Kauffman, J. W. (1999) Impacts of acid deposition on fish 5 populations in St. Marys River, Augusta County, Virginia. Banisteria 13: 191-200. 6 Bukaveckas, P.; Shaw, W. (1998) Phytoplankton responses to nutrient and grazer manipulations 7 among northeastern lakes of varying pH. Can. J. Fish. Aquat. Sci. 55: 958-966. 8 Bulger, A. J. (1986) Coincident peaks in serum osmolality and heat-tolerance rhythms in 9 seawater-acclimated killifish (Fundulus heteroclitus). Physiol. Zool. 59: 169-174. 10 Bulger, A. J.; Lien, L.; Cosby, B. J.; Henriksen, A. (1993) Trout status and chemistry from the 11 Norwegian thousand lake survey: statistical analysis. Can. J. Fish. Aquat. Sci. 50: 575- 12 585. 13 Bulger, A. J.; Dolloff, C. A.; Cosby, B. J.; Eshleman, K. N.; Webb, J. R.; Galloway, J. N. (1995) 14 The Shenandoah National Park: fish in sensitive habitats (SNP:FISH) project. An 15 integrated assessment offish community responses to stream acidification. Water Air 16 Soil Pollut. 85: 309-314. 17 Bulger, A. J.; Cosby, B. J.; Dolloff, C. A.; Eshleman, K. N.; Webb, J. R.; Galloway, J. N. (1999) 18 SNP:FISH, Shenandoah National Park: fish in sensitive habitats. Project final report. 19 Volume I: project description and summary of results; Volume II: stream water chemistry 20 and discharge, and synoptic water quality surveys. Volume III: basin-wide habitat and 21 population inventories, and behavioral responses to acid in a laboratory stream. Volume 22 IV: stream bioassays, aluminum toxicity, species richness and stream chemistry, and 23 models of susceptibility to acidification. Charlottesville, VA: University of Virginia, 24 Department of Environmental Sciences. Project Completion Report to the National Park 25 Service. Cooperative Agreement CA-4000-2-1007, Supplemental Agreement #2. 26 Bulger, A. J.; Cosby, B. J.; Webb, J. R. (2000) Current, reconstructed past, and projected future 27 status of brook trout (Salvelinus fontinalis) streams in Virginia. Can. J. Fish. Aquat. Sci. 28 57: 1515-1523. 29 Burton, T. M.; Stanford, R. M.; Allan, J. W. (1985) Acidification effects on stream biota and 30 organic matter processing. Can. J. Fish. Aquat. Sci. 42: 669-675. 31 Bytnerowicz, A. (2002) Physiological/ecological interactions between ozone and nitrogen 32 deposition in forest ecosystems. Phyton 42: 13-28. 33 Bytnerowicz, A.; Fenn, M. E. (1996) Nitrogen deposition in California forests: a review. 34 Environ. Pollut. 92: 127-146. 35 Caine, N. (1989) Hydrograph separation in a small alpine basin based on inorganic solute 36 concentrations. J. Hydrol. 1: 89-101. 37 Cairns, J., Jr.; Pratt, J. R. (1993) A history of biological monitoring using benthic 38 macroinvertebrates. In: Rosenberg, D. M.; Resh, V. H., eds. Freshwater biomonitoring 39 and benthic macroinvertebrates. New York, NY: Chapman and Hall; pp. 10-28. 40 Campbell, D. H.; Clow, D. W.; Ingersoll, G P.; Mast, M. A.; Spahr, N. E.; Turk, J. T. (1995) 41 Processes controlling the chemistry of two snowmelt-dominated streams in the Rocky 42 Mountains. Water Resour. Res. 31(11): 2811-2821. August 2008 B-165 DRAFT-DO NOT QUOTE OR CITE ------- 1 Campbell, D. H.; Kendall, C.; Chang, C. C. Y.; Silva, S. R.; Tonnessen, K. A. (2002) Pathways 2 for nitrate release from an alpine watershed: determination using 515N and 51 O. Water 3 Resour. Res. 31:2811-2821. 4 Campbell, D. H.; Muths, E.; Turk, J. T.; Corn, P. S. (2004) Sensitivity to acidification of 5 subalpine ponds and lakes in north-western Colorado. Hydrol. Processes 18: 2817-2834. 6 Cape, J. N. (1993) Direct damage to vegetation caused by acid rain and polluted cloud: definition 7 of critical levels for forest trees. Environ. Pollut. 82: 167-180. 8 Cappellato, R.; Peters, N. E.; Meyers, T. P. (1998) Above-ground sulfur cycling in adjacent 9 coniferous and deciduous forests and watershed sulfur retention in the Georgia Piedmont, 10 USA. Water Air Soil Pollut. 103: 151-171. 11 Castro, M. S.; Morgan, I. R. P. (2000) Input-output budgets of major ions for a forested 12 watershed in western Maryland. Water Air Soil Pollut. 119(1-4): 121-137. 13 Chapin, F. S., Ill; Ruess, R. W. (2001) The roots of the matter. Nature (London) 411: 749, 751- 14 752. 15 Charles, D. F., ed. (1991) Acidic deposition and aquatic eosystems: regional case studies. New 16 York, NY: Springer-Verlag. 17 Charles, D. F.; Norton, S. A. (1986) Paleolimnological evidence for trends in atmospheric 18 deposition of acids and metals. In: Acid deposition: long-term trends. Washington, DC: 19 National Academy Press, Committee on monitoring and assessment of trends in acid 20 deposition; pp. 335-435. 21 Charles, D. F.; Batarbee, R. W.; Renberg, I; Dam, H. V.; Smol, J. P.; Norton, S. A.; Linberg, S. 22 E.; Pages, A. L. (1989) Paleoecological analysis of lake acidification trends in North 23 America and Europe using diatoms and chrysophytes. In: Aquatic processes and lake 24 acidification. New York, NY: Springer-Verlag; pp. 207-276. 25 Charles, D. F.; Binford, M. W.; Furlong, E. T.; Kites, R. A.; Mitchell, M. J.; Norton, S. A.; 26 Oldfield, F.; Paterson, M. J.; Smol, J. P.; Uutala, A. J.; White, J. R.; Whitehead, D. F.; 27 Wise, R. J. (1990) Paleoecological investigation of recent lake acidification in the 28 Adirondack Mountains, N.Y. J. Paleolimnol. 3: 195-241. 29 Chen, L.; Driscoll, C. T. (2005) Regional assessment of the response of the acid-base status of 30 lake-watersheds in the Adirondack region of New York to changes in atmospheric 31 deposition using PnET-BGC. Environ. Sci. Technol. 39: 787-794. 32 Chen, C. W.; Gherini, S. A. P. N. E.; Murdoch, P. S.; Newton, R. M.; Goldstein, R. A. (1984) 33 Hydrologic analyses of acidic and alkaline lakes. Water Resour. Res. 20: 1875-1882. 34 Chorover, J.; Visousek, P. M.; Everson, D. A.; Esperanza, A. M.; Turner, D. (1994) Solution 35 chemistry profiles of mixed-conifer forests before and after fire. Biogeochem. 26: 115- 36 144. 37 Christensen, S. W.; Beauchamp, J. J.; Shaakir-Ali, J. A.; Coe, J.; Baker, J. P.; Smith, E. P.; 38 Gallagher, J. (1990) Patterns offish distribution in relation to lake/watershed 39 characteristics: regression analyses and diagnostics. In: Interpretative analysis of the 40 Adirondack Lakes Survey. Final report to the Adirondack Lakes Survey Corporation; pp. 41 A-l-A-62. August 2008 B-166 DRAFT-DO NOT QUOTE OR CITE ------- 1 Church, M. R.; Shaffer, P. W.; Thornton, K. W.; Cassell, D. L.; Liff, C. I; Johnson, M. G.; 2 Lammers, D. A.; Lee, J. J.; Holdren, G. R.; Kern, J. S.; Liegel, L. H.; Pierson, S. M.; 3 Stevens, D. L.; Rochelle, B. P.; Turner, R. S. (1992) Direct/delayed response project: 4 future effects of long-term sulfur deposition on stream chemistry in the mid-Appalachian 5 region of the eastern United States. Corvallis, OR: U.S. Environmental Protection 6 Agency; report no. EPA/600/R-92/186. 7 Clow, D. W.; Mast, M. A. (1999) Long-term trends in stream water and precipitation chemistry 8 at five headwater basins in the northeastern United States. Water Resour. Res. 35: 541- 9 554. 10 Cook, R. B.; Jager, H. I. (1991) Upper midwest: the effects of acidic deposition on lakes. In: 11 Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case studies. New 12 York, NY: Springer-Verlag, Inc. 13 Cook, R. B.; Kreis, R. G., Jr.; Kingston, J. C.; Camburn, K. E.; Norton, S. A.; Mitchell, M. J.; 14 Fry, B.; Shane, L. C. K. (1990) An acidic lake in northern Michigan. J. Paleolimnol. 3: 15 13-34. 16 Cook, R. B.; Elwood, J. W.; Turner, R. R.; Bogle, M. A.; Mulholland, P. J.; Palumbo, A. V. 17 (1994) Acid-base chemistry of high-elevation streams in the Great Smoky Mountains. 18 Water Air Soil Pollut. 72: 331-356. 19 Corn, P. S.; Stolzenburg, W.; Bury, R. B. (1989) Acid precipitation studies in Colorado and 20 Wyoming: interim report of surveys of mountain amphibians and water chemistry. U.S. 21 Fish and Wildlife Service; biological report 80 (40.26). 22 Cosby, B. J.; Hornberger, G. M.; Galloway, J. N.; Wright, R. F. (1985) Modeling the effects of 23 acid deposition: assessment of a lumped parameter model of soil water and streamwater 24 chemistry. Water Resour. Res. 21: 51-63. 25 Cosby, B. J.; Wright, R. F.; Gjessing, E. (1995) An acidification model (MAGIC) with organic 26 acids evaluated using whole-catchment manipulations in Norway. J. Hydrol. 170: 101- 27 122. 28 Cosby, B. J.; Norton, S. A.; Kahl, J. S. (1996) Using a paired-catchment manipulation 29 experiment to evaluate a catchment-scale biogeochemical model. Sci. Total Environ. 183: 30 49-66. 31 Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Wright, R. F. (2001) Modelling the effects of acid 32 deposition: refinements, adjustments and inclusion of nitrogen dynamics in the MAGIC 33 model. Hydrol. Earth Syst. Sci. 5: 499-517. 34 Courchesne, F.; Cote, B.; Fyles, J. W.; Hendershot, W. H.; Biron, P. M.; Roy, A. G.; Tunnel, 35 M.-C. (2005) Recent changes in soil chemistry in a forested ecosystem of southern 36 Quebec, Canada. Soil Sci. Soc. Am. J. 69: 1298-1313. 37 Cowardin, L. M.; Golet, F. C.; LaRoe, E. T. (1979) Classification of wetland and deepwater 38 habitats of the United States. U.S. Department of the Interior: Fish and Wildlife Service; 39 report no. FWS/OBS-79/31. 40 Cowling, E.; Dochinger, L. S. (1980) Effects of acidic precipitation on health and productivity of 41 forests. U.S. Department of Agriculture, Forest Service; technical report no. PSW-43; pp. 42 165-173. August 2008 B-167 DRAFT-DO NOT QUOTE OR CITE ------- 1 Craig, B. W.; Friedland, A. J. (1991) Spatial patterns in forest composition and standing dead red 2 spruce in montane forests of the Adirondacks and northern Appalachians. Environ. 3 Monit. Assess. 18: 129-140. 4 Cronan, C. S.; Aiken, G. R. (1985) Chemistry and transport of soluble humic substances in 5 forested watersheds of the Adirondack Park, New York. Geochim. Cosmochim. Acta 49: 6 1697-1705. 7 Cronan, C. S.; Goldstein, R. A. (1989) ALBIOS: a comparison of aluminum biogeochemistry in 8 forested watersheds exposed to acidic deposition. In: Acidic precipitation, vol. 1: case 9 studies. New York, NY: Springer-Verlag; pp. 113-135. (Advances in environmental 10 science). 11 Cronan, C. S.; Grigal, D. F. (1995) Use of calcium/aluminum ratios as indicators of stress in 12 forest ecosystems. J. Environ. Qual. 24: 209-226. 13 Cronan, C. S.; Schofield, C. L. (1979) Aluminum leaching response to acid precipitation: effects 14 on high elevation watersheds in the Northeast. Science (Washington, DC, U.S.) 204: 304- 15 306. 16 Cronan, C. S.; Reiners, W. A.; Reynolds, R. C. J.; Lang, G. E. (1978) Forest floor leaching: 17 contributions from mineral, organic, and carbonic acids in New Hampshire subalpine 18 forests. Science (Washington, DC, U.S.) 200: 309-311. 19 Cronan, C. S.; Walker, W. J.; Bloom, P. R. (1986) Predicting aqueous aluminum concentrations 20 in natural waters. Nature (London, U.K.) 324: 140-143. 21 Cronan, C. S.; April, R.; Bartlett, R. J.; Bloom, P. R.; Driscoll, C. T.; Gherini, S. A.; Henderson, 22 G. S.; Joslin, J. D.; Kelly, J. M.; Newton, R. M.; Parnell, R. A.; Patterson, H. H.; Raynal, 23 D. J.; Schaedle, M.; Schofield, C. L.; Sucoff, E. I; Tepper, H. B.; Thornton, F. C. (1989) 24 Aluminum toxicity in forests exposed to acidic deposition: the ALBIOS results. Water 25 Air Soil Pollut. 48: 181-192. 26 Cronan, C. S.; Driscoll, C. T.; Newton, R. M.; Kelly, J. M.; Schofield, C. L.; Bartlett, R. J.; 27 April, R. (1990) A comparative analysis of aluminum biogeochemistry in a northeastern 28 and a southeastern forested watershed. Water Resour. Res. 26: 1413-1430. 29 Cumming, J. R.; Weinstein, L. H. (1990) Aluminum-mycorrhizal interactions in the physiology 30 of pitch pine seedlings. Plant Soil 125: 7-18. 31 Cumming, B. F.; Smol, J. P.; Kingston, J. C.; Charles, D. F.; Birks, H. J. B.; Camburn, K. E.; 32 Dixit, S. S.; Uutala, A. J.; Selle, A. R. (1992) How much acidification has occurred in 33 Adirondack region lakes (New York, USA) since preindustrial times? Can. J. Fish. 34 Aquat. Sci. 49: 128-141. 35 Cumming, B. F.; Davey, K. A.; Smol, J. P.; Birks, H. J. B. (1994) When did acid-sensitive 36 Adirondack lakes (New York, USA) begin to acidify and are they still acidifying? Can. J. 37 Fish. Aquat. Sci. 51: 1550-1568. 38 Dahlgren, R. A. (1994) Soil acidification and nitrogen saturation from weathering of ammonium- 39 bearing rock. Nature (London) 368: 838-841. 40 Dahlgren, R. A.; Walker, W. J. (1993) Aluminum release rates from selected Spodosol Bs 41 horizons: effect of pH and solid-phase aluminum pools. Geochim. Cosmochim. Acta 57: 42 57-66. August 2008 B-168 DRAFT-DO NOT QUOTE OR CITE ------- 1 Dail, D. B.; Davidson, E. A.; Chorover, J. (2001) Rapid abiotic transformation of nitrate in an 2 acid forest soil. Biogeochemistry 54: 131-146. 3 Dangles, O.; Malmqvist, B.; Laudon, H. (2004) Naturally acid freshwater ecosystems are diverse 4 and functional: evidence from boreal streams. Oikos 104: 149-155. 5 David, M. B.; Driscoll, C. T. (1984) Aluminum speciation and equilibria in soil solutions of a 6 Haplorthod in the Adirondack Mountains ( New York, USA). Geoderma 33: 297-318. 7 David, M. B.; Lawrence, G. B. (1996) Soil and soil solution chemistry under red spruce stands 8 across the northeastern United States. Soil Sci. 161: 314-328. 9 David, M. B.; Mitchell, M. J.; Scott, T. J. (1987) Importance of biological processes in the sulfur 10 budget of a northern hardwood ecosystem. Biol. Pert. Soils 5(3): 258-264. 11 David, M.; Vance, G.; Kahl, J. (1999) Chemistry of dissolved organic carbon at Bear Brook 12 Watershed, Maine: stream water response to (NEL^SC^ additions. Environ. Monit. 13 Assess. 55: 149-163. 14 Davidson, E. A.; Chorover, J.; Dail, D. B. (2003) A mechanism of abiotic immobilization of 15 nitrate in forest ecosystems: the ferrous wheel hypothesis. Global Change Biol. 9: 228- 16 236. 17 Davies, L.; Bates, J. W.; Bell, J. N. B.; James, P. W.; Purvis, O. W. (2007) Diversity and 18 sensitivity of epiphytes to oxides of nitrogen in London. Environ. Pollut. 146: 299-310. 19 Davis, R. B.; Anderson, D. S.; Berge, F. (1985a) Loss of organic matter, a fundamental process 20 in lake acidification: paleolimnological evidence. Nature (London, U.K.) 316: 436-438. 21 Davis, R. B.; Anderson, D. S.; Berge, F. (1985b) Paleolimnological evidence that lake 22 acidification is accompanied by loss of organic matter. Nature (London, U.K.) 316: 436- 23 438. 24 Davis, R. B.; Anderson, D. S.; Norton, S. A.; Whiting, M. C. (1994) Acidity of twelve northern 25 New England (USA) lakes in recent centuries. J. Paleolimnol. 12: 103-154. 26 De Wit, H. A.; Mulder, J.; Nygaard, P. H.; Aamlid, D. (2001) Testing the aluminium toxicity 27 hypothesis: A field manipulation experiment in mature spruce forest in Norway. Water 28 Air Soil Pollut. 130: 995-1000. 29 DeConinck, F. (1980) Major mechanisms in formation of spodic horizons. Geoderma 24: 101- 30 128. 31 DeHayes, D. H.; Schaberg, P. G.; Hawley, G. J.; Strimbeck, G. R. (1999) Acid rain impacts on 32 calcium nutrition and forest health. BioScience 49: 789-800. 33 Deevey, E. S.; Binford, M. W.; Brenner, M.; Whitmore, T. J. (1986) Sedimentary records of 34 accelerated nutrient loading in Florida lakes. Hydrobiologia 143: 49-53. 35 Denning; A.S, B.; J. Mast, M. A.; Arthur, M. (1991) Hydrologic pathways and chemical 36 composition of runoff during snowmelt in Loch Vale watershed, Rocky Mountain 37 National Park, Colorado, USA. Water Air Soil Pollut. 59: 107-123. 38 Dennis, T. E.; Bulger, A. J. (1995) Condition factor and whole-body sodium concentrations in a 39 freshwater fish: evidence for acidification stress and possible ionoregulatory over- 40 compensation. Water Air Soil Pollut. 85: 377-382. August 2008 B-169 DRAFT-DO NOT QUOTE OR CITE ------- 1 Dennis, T. E.; Bulger, A. J. (1999) The susceptibility of blacknose dace (Rhinichthys atratulus) 2 to acidification in Shenandoah National Park. In: Bulger, A. J.; Cosby, B. J.; Dolloff, C. 3 A.; Eshleman, K. N.; Galloway, J. N.; Webb., J. R., eds. Shenandoah National Park: fish 4 in sensitive habitats. Project final report, Volume IV: stream bioassays, aluminum 5 toxicity, species richness and stream chemistry, and models of susceptibility to 6 acidification. Chapter 6B. Project completion report to the National Park Service. 7 Cooperative agreement CA-4000-2-1007. 8 Dennis, T. D.; MacAvoy, S. E.; Steg, M. B.; Bulger, A. J. (1995) The association of water 9 chemistry variables and fish condition in streams of Shenandoah National Park (USA). 10 Water Air Soil Pollut. 85: 365-370. 11 DesGranges, J.-L.; Darveau, M. (1985) Effect of lake acidity and morphometry on the 12 distribution of aquatic birds in southern Quebec. Holarctic Ecol. 8: 181-190. 13 Deviney, F. A. J.; Rice, K. C.; Hornberger, G. M. (2006) Time series and recurrence interval 14 models to predict the vulnerability of streams to episodic acidification in Shenandoah 15 National Park, Virgnia. Water Resour. Res. 42(W09405): 10.1029/2005WR004740. 16 DeWald, L. E.; Sucoff, E. I; Ohno, T.; Buschena, C. A. (1990) Response of northern red oak 17 (Quercus rubra L.) seedlings to soil solution aluminum. Can. J. For. Res. 20: 331-336. 18 DeWalle, D. R.; Swistock, B. R. (1994) Causes of episodic acidification in five Pennsylvania 19 streams on the northern Appalachian Plateau. Water Resour. Res. 30: 1955-1963. 20 DeWalle, D. R.; Dinicola, R. S.; Sharpe, W. E. (1987) Predicting baseflow alkalinity as an index 21 to episodic stream acidification and fish presence. Water Resour. Bull. 23: 29-35. 22 Dickson, W. (1978) Some effects of the acidification of Swedish lakes. Verh. Int. Ver. Theor. 23 Angew. Limnol. 20: 851-856. 24 Dillon, P. J.; Reid, R. A.; Girard, R. (1986) Changes in the chemistry of lakes near Sudbury, 25 Ontario, following reduction of SCh emissions. Water Air Soil Pollut. 31: 59-65. 26 Dise, N. B.; Wright, R. F. (1995) Nitrogen leaching from European forests in relation to nitrogen 27 deposition. For. Ecol. Manage. 71: 153-161. 28 Dise, N. B.; Matzner, E.; Gundersen, P. (1998) Synthesis of nitrogen pools and fluxes from 29 European forest ecosystems. Water Air Soil Pollut. 105: 143-154. 30 Dixit, S. S.; Keller, W.; Dixit, A.; Smol, J. P. (2001) Diatom-inferred dissolved organic carbon 31 reconstructions provide assessments of past UV-B penetration in Canadian Shield lakes. 32 Can. J. Fish. Aquat. Sci. 58: 543-550. 33 Dobson, J. E.; Rush, R. M.; Peplies, R. W. (1990) Forest blowdown and lake acidification. Ann. 34 Assoc. Amer. Geogr. 80: 343-361. 35 Drever, J. L; Hurcomb, D. R. (1986) Neutralization of atmospheric acidity by chemical 36 weathering in an alpine drainage basin in the North Cascade Mountains. Geology 14: 37 221-224. 38 Driscoll, C. T.; Bisogni, J. J. (1984) Weak acid/base systems in dilute acidified lakes and streams 39 of the Adirondack region of New York State. In: Schnoor, J. L., ed. Modeling of total 40 acid precipitation. Boston, MA: Butterworth Publishers; pp. 53-72. August 2008 B-170 DRAFT-DO NOT QUOTE OR CITE ------- 1 Driscoll, C. T.; Newton, R. M. (1985) Chemical characteristics of Adirondack lakes. Environ. 2 Sci. Technol. 19: 1018-1024. 3 Driscoll, C. T.; Van Dreason, R. (1993) Seasonal and long-term temporal patterns in the 4 chemistry of Adirondack lakes. Water Air Soil Pollut. 67: 319-344. 5 Driscoll, C. T., Jr.; Baker, J. P.; Bisogni, J. I, Jr.; Schofield, C. L. (1980) Effect of aluminum 6 speciation on fish in dilute acidified waters. Nature (London) 284: 161-164. 7 Driscoll, C. T.; Van Breemen, N.; Mulder, J. (1985) Aluminum chemistry in a forested spodosol. 8 Soil Sci. Soc. Am. J. 49: 437-444. 9 Driscoll, C. T.; Wyskowski, B. J.; Consentini, C. C.; Smith, M. E. (1987) Processes regulating 10 temporal and longitudinal variations in the chemistry of a low-order woodland stream in 11 the Adirondack region of New York. Biogeochemistry 3: 225-241. 12 Driscoll, C. T.; Johnson, N. M.; Likens, G. E.; Feller, M. C. (1988) Effects of acidic deposition 13 on the chemistry of headwater streams: a comparison between Hubbard Brook, New 14 Hampshire, and Jamieson Creek, British Columbia. Water Resour. Res. 24: 195-200. 15 Driscoll, C. T.; Likens, G. E.; Hedin, L. O.; Eaton, J. S.; Bormann, F. H. (1989) Changes in the 16 chemistry of surface waters. Environ. Sci. Tech. 23: 137-143. 17 Driscoll, C. T.; Newton, R. M.; Gubala, C. P.; Baker, J. P.; Christensen, S. W. (1991) 18 Adirondack mountains. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems: 19 regional case studies. New York, NY: Springer-Verlag; pp. 133-202. 20 Driscoll, C. T.; Lehtinen, M. D.; Sullivan, T. J. (1994) Modeling the acid-base chemistry of 21 organic solutes in Adirondack, New York, lakes. Water Resour. Res. 30: 297-306. 22 Driscoll, C. T.; Postek, K. M.; Kretser, W.; Raynal, D. J. (1995) Long-term trends in the 23 chemistry of precipitation and lake water in the Adirondack region of New York, USA. 24 Water Air Soil Pollut. 85: 583-588. 25 Driscoll, C. T.; Lawrence, G. B.; Bulger, A. J.; Butler, T. J.; Cronan, C. S.; Eagar, C.; Lambert, 26 K. F.; Likens, G. E.; Stoddard, J. L.; Weather, K. C. (2001a) Acid rain revisited: 27 advances in scientific understanding since the passage of the 1970 and 1990 Clean Air 28 Act Amendments. Hanover, NH: Hubbard Brook Research Foundation. Science links 29 publication vol. 1, no. 1. 30 Driscoll, C. T.; Lawrence, G. B.; Bulger, A. J.; Butler, T. J.; Cronan, C. S.; Eagar, C.; Lambert, 31 K. F.; Likens, G. E.; Stoddard, J. L.; Weathers, K. C. (2001b) Acidic deposition in the 32 northeastern United States: sources and inputs, ecosystem effects, and management 33 strategies. BioScience 51: 180-198. 34 Driscoll, C.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C.; Groffman, P.; 35 Hopkinson, C.; Lambert, K.; Lawrence, G.; Ollinger, S. (2003a) Nitrogen pollution: 36 sources and consequences in the U.S. Northeast. Environment 45: 8-22. 37 Driscoll, C. T.; Driscoll, K. M.; Roy, K. M.; Mitchell, M. J. (2003b) Chemical response of lakes 38 in the Adirondack region of New York to declines in acidic deposition. Environ. Sci. 39 Technol. 37: 2036-2042. 40 Driscoll, C. T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C. L.; 41 Groffman, P.; Hopkinson, C.; lambert, K.; Lawrence, G.; Ollinger, S. (2003c) Nitrogen August 2008 B-171 DRAFT-DO NOT QUOTE OR CITE ------- 1 pollution in the northeastern United States: sources, effects, and management options. 2 BioScience 53: 357-374. 3 Driscoll, C. T.; Driscoll, K. M.; Roy, K. M.; Dukett, J. (2007) Changes in the chemistry of lakes 4 in the Adirondack Region of New York following declines in acidic deposition. Appl. 5 Geochem. 22: 1181-1188. 6 Drohan, J. R.; Sharpe, W. E. (1997) Long-term changes in forest soil acidity in Pennsylvania, 7 U.S.A. Water Air Soil Pollut. 95: 299-311. 8 Drohan, P. J.; Stout, S. L.; Petersen, G. W. (2002) Sugar maple (Acer saccharum Marsh.) decline 9 during 1979-1989 in northern Pennsylvania. For. Ecol. Manage. 170: 1-17. 10 Eagar, C.; Adams, M. B., eds. (1992) Ecology and decline of red spruce in the eastern United 11 States. New York, NY: Springer-Verlag. (Ecological studies: v. 96). 12 Eagar, C.; Van Miegroet, H.; McLaughline, S. B.; Nicholas, N. S. (1996) Evaluation of effects of 13 acidic deposition to terrestrial ecosystems in class I areas of the Southern Appalachians. 14 Southern Appalachian Mountains Initiative; technical report. 15 Edwards, J. H.; Horton, B. D. (1977) Aluminum-induced calcium deficiency in peach seedlings. 16 J. Am. Soc. Hortic. Sci. 102: 459-461. 17 Edwards, P. J.; Helvey, J. D. (1991) Long-term ionic increases from a central Appalachian 18 forested watershed. J. Environ. Qual. 20: 250-255. 19 Edwards, D.; Brown, J. A.; Whitehead, C. (1987) Endocrine and other physiological indicators 20 of acid stress in the brown trout. Ann. Soc. R. Zool. Belg. 117(suppl. 1): 331-342. 21 Egerton-Warburton, L. M.; Allen, E. B. (2000) Shifts in arbuscular mycorrhizal communities 22 along an anthropogenic nitrogen deposition gradient. Ecol. Appl. 10: 484-496. 23 Eilers, J. M.; Glass, G. E.; Webster, K. E.; Rogalla, J. A. (1983) Hydrologic control of lake 24 susceptibility to acidification. Can. J. Fish. Aquat. Sci. 40: 1896-1904. 25 Eilers, J. M.; Brakke, D. F.; Landers, D. H. (1988) Chemical and physical characteristics of lakes 26 in the upper midwest, United States. Environ. Sci. Technol. 22: 164-172. 27 Elwood, J. W.; Sale, M. J.; Kaufmann, P. R.; Cada, G. F. (1991) The Southern Blue Ridge 28 Province. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case 29 studies. New York, NY: Springer-Verlag; pp. 319-364. 30 Emmett, B. A.; Boxman, D.; Bredemeier, M.; Gunderson, P.; Kj0aas, O. J.; Moldan, F.; 31 Schleppi, P.; Tietema, A.; Wright, R. F. (1998) Predicting the effects of atmospheric 32 nitrogen deposition in conifer stands: evidence from the NITREX ecosystem-scale 33 experiments. Ecosystems 1: 352-360. 34 Eriksson, E. (1981) Aluminium in groundwater, possible solution equilibria. Nord. Hydrol. 12: 35 43-50. 36 Eriksson, M. O. G. (1983) The role offish in the selection of lakes by non-piscivorous ducks: 37 mallard, teal and goldeneye. Wildfowl 34: 27-32. 38 Eriksson, M. O. G. (1986) Fish delivery, production of young, and nest density of Osprey 39 (Pandion haliaetus) in southwest Sweden. Can. J. Zool. 64: 1961-1965. 40 Eshleman, K. N. (1988) Predicting regional episodic acidification of surface waters using 41 empirical models. Water Resour. Res. 24: 1118-1126. August 2008 B-172 DRAFT-DO NOT QUOTE OR CITE ------- 1 Eshleman, K. N.; Hyer, K. E. (2000) Discharge and water chemistry at the three intensive sites. 2 In: Bulger, A. J.; Cosby, B. J.; Dolloff, C. A.; Eshleman, K. N.; Webb, J. R.; Galloway, J. 3 N., eds. Shenandoah National Park: fish in sensitive habitats. Project Final Report - Vol. 4 II. Stream water chemistry and discharge, and synoptic water quality surveys; pp. 51-92. 5 Eshleman, K. N.; Davies, T. D.; Tranter, M.; Wigington, P. J., Jr. (1995) A two-component 6 mixing model for predicting regional episodic acidification of surface waters during 7 spring snowmelt periods. Water Resour. Res. 31: 1011-1021. 8 Eshleman, K. N.; Morgan II, R. P.; Webb, J. R.; Deviney, F. A.; Galloway, J. N. (1998) 9 Temporal patterns of nitrogen leakage from mid-Appalachian forested watersheds: role of 10 insect defoliation. Water Resour. Res. 34(8): 2005-2116. 11 Evans, C. D.; Monteith, D. T. (2001) Chemical trends at lakes and streams in the UK Acid 12 Waters Monitoring Network, 1988-2000: evidence for recent recovery at a national scale. 13 Hydrol. Earth Syst. Sci. 5: 351-366. 14 Everhart, W. H.; Youngs, W. D. (1981) Principles of fishery science. Ithaca, NY: Cornell 15 University Press. 16 Evers, D. C.; Han, Y.; Driscoll, C. T.; Kamman, N. C.; Goodale, M. W.; Lambert, K. F.; Holsen, 17 T. M.; Chen, C. Y.; Clair, T. A.; Butler, T. (2007) Biological mercury hotspots in the 18 northeastern United States and southeastern Canada. BioScience 57: 29-43. 19 Exley, C.; Phillips, M. J. (1988) Acid rain: implications for the farming of salmonids. In: Muir, J. 20 F.; Roberts, R. J., eds. Recent advances in aquaculture. London, United Kingdom: Croom 21 Helm; pp. 225-342. 22 Falkengren-Grerup, U.; Erikson, H. (1990) Changes in soil, vegetation, and forest yield between 23 1947 and 1988 in beech and oak sites of southern Sweden. For. Ecol. Manage. 38: 37-53. 24 Farag, A. M.; Woodward, D. F.; Little, E. E.; Steadman, B.; Vertucci, F. A. (1993) The effects of 25 low pH and elevated aluminum on Yellowstone cutthroat trout (Oncorhynchus clarki 26 bouvieri). Environ. Toxicol. Chem. 12: 719-731. 27 Farmer, G. J.; Beamish, F. W. H. (1969) Oxygen consumption of Tilapia nilotica in relation to 28 swimming speed and salinity. J. Fish. Res. Board Can. 26: 2807-2821. 29 Farmer, A. M.; Bates, J. W.; Bell, J. N. B. (1992) Ecophysiological effects of acid rain on 30 bryophytes and lichens. In: Bates, J. W.; Farmer, A. M., eds. Bryophytes and lichens in a 31 changing environment. Oxford: Claredon Press. 32 Federer, C. A.; Hornbeck, J. W.; Tritton, L. M.; Martin, C. W.; Pierce, R. S. (1989) Long-term 33 depletion of calcium and other nutrients in eastern US forests. Environ. Manage. (N. Y.) 34 13: 593-601. 35 Fenn, M. E.; Poth, M. A. (1998) Indicators of nitrogen status in California forests. U.S.D.A. 36 Forest Service Gen. Tech. Rep. PSW-GTR-166. 37 Fenn, M. E.; Poth, M. A. (1999) Temporal and spatial trends in streamwater nitrate 38 concentrations in the San Bernardino Mountains, southern California. J. Environ. Qual. 39 28: 822-836. 40 Fenn, M. E.; Poth, M. A.; Johnson, D. W. (1996) Evidence for nitrogen saturation in the San 41 Bernardino Mountains in southern California. For. Ecol. Manage. 82: 211-230. August 2008 B-173 DRAFT-DO NOT QUOTE OR CITE ------- 1 Fenn, M. E.; Poth, M. A.; Aber, J. D.; Baron, J. S.; Bormann, B. T.; Johnson, D. W.; Lemly, A. 2 D.; McNulty, S. G.; Ryan, D. F.; Stottlemyer, R. (1998) Nitrogen excess in North 3 American ecosystems: predisposing factors, ecosystem responses, and management 4 strategies. Ecol. Appl. 8: 706-733. 5 Fenn, M. E.; Baron, J. S.; Allen, E. B.; Rueth, H. M.; Nydick, K. R.; Geiser, L.; Bowman, W. D.; 6 Sickman, J. O.; Meixner, T.; Johnson, D. W.; Neitlich, P. (2003) Ecological effects of 7 nitrogen deposition in the western United States. BioScience 53: 404-420. 8 Fenn, M. E.; Geiser, L.; Bachman, R.; Blubaugh, T. J.; Bytnerowicz, A. (2007) Atmospheric 9 deposition inputs and effects on lichen chemistry and indicator species in the Columbia 10 River Gorge, USA. Environ. Pollut. 146: 77-91. 11 Fernandez, I. J.; Rustad, L. E.; Norton, S. A.; Kahl, J. S.; Cosby, B. J. (2003) Experimental 12 acidification causes soil base-cation depletion at the Bear Brook Watershed in Maine. 13 Soil Sci. Soc. Am. J. 67: 1909-1919. 14 Findlay, D. L. (2003) Response of phytoplankton communities to acidification and recovery in 15 Killarney Park and the Experimental Lakes Area, Ontario. Ambio 32: 190-195. 16 Findlay, D. L.; Kasian, S. E. M. (1996) The effect of incremental pH recovery on the Lake 223 17 phytoplankton community. Can. J. Fish. Aquat. Sci. 53: 856-864. 18 Findlay, D. L.; Hecky, R. E.; Kasian, S. E. M.; Stainton, M. P.; Hendzel, L. L.; Schindler, E. U. 19 (1999) Effects on phytoplankton of nutrients added in conjunction with acidification. 20 Freshwater Biol. 41: 131-145. 21 Finley, M. T.; Stendell, R. C. (1978) Survival and reproductive success of black ducks fed 22 methyl mercury. Environ. Pollut. 16: 51-64. 23 Finley, M. T.; Stickel, W. H.; Christensen, R. E. (1979) Mercury residues in tissues of dead and 24 surviving birds fed methylmercury. Bull. Environ. Contam. Toxicol. 21: 105-110. 25 Fisher, D. C.; Oppenheimer, M. (1991) Atmospheric nitrogen deposition and the Chesapeake 26 Bay estuary. Ambio 20: 102-108. 27 Fitzhugh, R. D.; Lovett, G. M.; Venterea, R. T. (2003) Biotic and abiotic immobilization of 28 ammonium, nitrite, and nitrate in soils developed under different tree species in the 29 Catskill Mountains, New York, USA. Global Change Biol. 9: 1591-1601. 30 Fjellheim, A.; Raddum, G. G.; Sagen, T. (1985) Effect of aluminum at low pH on the mortality 31 of elvers. J. Exp. Zool. 262(3): 247-254. **CHECK REFERENCE LIST FOR TITLE** 32 Flower, R. J.; Battarbee, R. W. (1983) Diatom evidence for recent acidification of two Scottish 33 Lochs. Nature (London, U.K.) 305: 130-133. 34 Flum, T.; Nodvin, S. C. (1995) Factors affecting streamwater chemistry in the Great Smoky 35 Mountains, USA. Water Air Soil Pollut. 85: 1707-1712. 36 Ford, J.; Stoddard, J. L.; Powers, C. F. (1993) Perspectives in environmental monitoring: an 37 introduction to the U.S. EPA Long-Term Monitonring (LTM) project. Water Air Soil 38 Pollut. 67: 247-255. 39 Foster, N. W.; Nicolson, J. A.; Hazlett, P. W. (1989) Temporal variation in nitrate and nutrient 40 cations in drainage waters from a deciduous forest. J. Environ. Qual. 18: 238-244. August 2008 B-174 DRAFT-DO NOT QUOTE OR CITE ------- 1 Freda, J.; McDonald, D. G. (1988) Physiologic correlates of interspecific variation in acid 2 tolerance in fish. J. Exp. Biol. 136: 243-258. 3 Fremstad, E.; Paal, J.; Mols, T. (2005) Impacts of increased nitrogen supply on Norwegian 4 lichen-rich alpine communities: a 10-year experiment. J. Ecol. 93: 471-481. 5 Frenette, J. J.; Richard, Y.; Moreau, G. (1986) Fish responses to acidity in Quebec lakes: a 6 review. Water Air Soil Pollut. 30: 461-475. 7 Frey, S. D.; Knorr, M.; Parrent, J. L.; Simpson, R. T. (2004) Chronic nitrogen enrichment affects 8 the structure and function of the soil microbial community in temperate hardwood and 9 pine forests. For. Ecol. Manage. 196: 159-171. 10 Frost, T. M.; Fischer, J. M.; Klug, J. L.; Arnott, S. E.; Montz, P. K. (2006) Trajectories of 11 zooplankton recovery in the Little Rock Lake whole-lake acidification experiment. Ecol. 12 Appl. 16: 353-367. 13 Fuller, R. D.; Driscoll, C. T.; Lawrence, G. B.; Nodvin, S. C. (1987) Processes regulating 14 sulphate flux after whole-tree harvesting. Nature (London) 325: 707-710. 15 Gagen, C. J.; Sharpe, W. E.; Carline, R. F. (1993) Mortality of brook trout, mottled sculpins, and 16 slimy sculpins during acidic episodes. Trans. Am. Fish. Soc. 122: 616-628. 17 Gallagher, J.; Baker, J. L. (1990) Current status offish communities in Adirondack lakes. In: 18 Adirondack Lakes survey: an interpretative analysis offish communities and water 19 chemistry, 1984-87. Ray Brook, NY: Adirondack Lake Survey Corporation; pp. 3-11-3- 20 48. 21 Galloway, J. N. (1996) Anthropogenic mobilization of sulphur and nitrogen: immediate and 22 delayed consequences. Annu. Rev. Energy Environ. 21: 261-292. 23 Garcia, E.; Carignan, R. (2000) Mercury concentrations in northern pike (Esox lucius) from 24 boreal lakes with logged, burned, or undisturbed catchments. Can. J. Fish. Aquat. Sci. 25 57(suppl. 2): 129-135. 26 Garner, J. H. B. (1994) Nitrogen oxides, plant metabolism, and forest ecosystem response. In: 27 Alscher, R. G.; Wellburn, A. R., eds. Plant responses to the gaseous environment: 28 molecular, metabolic and physiological aspects, [3rd international symposium on air 29 pollutants and plant metabolism]; June 1992; Blacksburg, VA. London, United Kingdom: 30 Chapman & Hall; pp. 301-314. 31 Gbondo-Tugbawa, S. S.; Driscoll, C. T.; Mitchell, M. J.; Aber, J. D.; Likens, G. E. (2002) A 32 model to simulate the response of a northern hardwood forest ecosystem to changes in S 33 deposition. Ecol. Appl. 12: 8-23. 34 Gensemer, R. W.; Playle, R. C. (1999) The bioavailability and toxicity of aluminum in aquatic 35 environments. Crit. Rev. Environ. Sci. Tech. 29: 315-450. 36 Gerritsen, J.; Carlson, W. E.; Dycus, D. L.; Faulkner, C.; Gibson, G.; Harcum, J.; Markowitz, S. 37 A. (1998) Lake and reservoir bioassessment and biocriteria. Washington, DC: U.S. 38 Environmental Protection Agency; report no. EPA 841-B-98-007. 39 Gilliam, F. S.; Adams, M. B.; Yurish, B. M. (1996) Ecosystem nutrient responses to chronic 40 nitrogen inputs at Fernow Experimental Forest, West Virginia. Can. J. For. Res. 26: 196- 41 205. August 2008 B-175 DRAFT-DO NOT QUOTE OR CITE ------- 1 Glooschenko, V.; Blancher, P.; Herskowitz, J.; Fulthorpe, R.; Rang, S. (1986) Association of 2 wetland acidity with reproductive parameters and insect prey of the Eastern Kingbird 3 (Tyrannus tyrannus) near Sudbury, Ontario. Water Air Soil. Pollut. 30: 553-567. 4 Gochfeld, M. (1980) Tissue distribution of mercury in normal and abnormal young common 5 terns. Mar. Pollut. Bull. 11: 362-366. 6 Godbold, D. L.; Fritz, E.; Hiittermann, A. (1988) Aluminum toxicity and forest decline. Proc. 7 Natl. Acad. Sci. U. S. A. 85: 3888-3892. 8 Goede, R. W.; Barton, B. A. (1990) Organismic indices and an autopsy-based assessment as 9 indicators of health and condition offish. Am. Fish. Soc. Symp. 8: 80-93. 10 Goodale, C. L.; Aber, J. D. (2001) The long-term effects of land-use history on nitrogen cycling 11 in northern hardwood forests. Ecol. Appl. 11: 253-267. 12 Goodale, C. L.; Aber, J. D.; Vitousek, P. M. (2003) An unexpected nitrate decline in New 13 Hampshire streams. Ecosystems 6: 75-86. 14 Goransson, A.; Eldhuset, T. D. (1987) Effects of aluminum on growth and nutrient uptake of 15 Betula pendula seedlings. Physiol. Plant. 69: 193-199. 16 Gordon, C.; Wynn, J. M.; Woodin, S. J. (2001) Impacts of increased nitrogen supply on high 17 Arctic heath: the importance of bryophytes and phosphorus availability. New Phytol. 18 149:461-471. 19 Gorham, E.; Janssens, J. A.; Wheeler, G. A.; Glaser, P. H. (1987) Natural and anthropogenic 20 acidification of peatlands: an overview. In: Hutchinson, T. C.; Meema, K., eds. Effects of 21 atmospheric pollutants on forests, wetlands, and agricultural ecosystems. New York, NY: 22 Springer-Verlag; pp. 493-512. 23 Goriup, P. D. (1989) Acidic air pollution and birds in Europe. Oryx 23: 82-86. 24 Graetz, D. A.; Pollman, C. D.; Roof, B.; Will, E. (1985) Effects of acidic treatments on soil 25 chemistry and microbiology. In: Florida acid deposition study. Phase IV report. 26 Gainesville, FL: Environmental Science and Engineering, Inc. Vol. I. ESENo. 83-152- 27 0106/0207/0307; pp. 4-5 - 4-123. 28 Grant, E. H. C.; Jung, R. E.; Rice, K. C. (2005) Stream salamander species richness and 29 abundance in relation to environmental factors in Shenandoah National Park, Virginia. 30 Am. Midi. Nat. 153: 348-356. 31 Griffiths, R. W.; Keller, W. (1992) Benthic macroinvertebrate changes in lakes near Sudbury, 32 Ontario following a reduction in acid emissions. Can. J. Fish. Aquat. Sci. 49(suppl. 1): 33 63-75. 34 Gubala, C. P.; Driscoll, C. T.; Newton, R. M.; Schofield, C. F. (1991) The chemistry of a near- 35 shore lake region during spring snowmelt. Environ. Sci. Technol. 25: 2024-2030. 36 Guderian, R.; Tingey, D. T.; Rabe, R. (1985) Effects of photochemical oxidants on plants. In: 37 Guderian, R., ed. Air pollution by photochemical oxidants: formation, transport, control, 38 and effects on plants. Berlin, Federal Republic of Germany: Springer-Verlag; pp. 127- 39 333. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. 40 Ecological studies: analysis and synthesis, v. 52). August 2008 B-176 DRAFT-DO NOT QUOTE OR CITE ------- 1 Guerold, F.; Boudot, J. P.; Jacqumin, G.; Vein, D.; Merlet, D.; Rouiller, J. (2000) 2 Macroinvertebrate community loss as a result of headwater stream acidification in the 3 Vosges Mountains (N-E France). Biodiversity Conserv. 9: 767-783. 4 Gundersen, P.; Rasmussen, L. (1995) Nitrogen mobility in a nitrogen limited forest at 5 Klosterhede, Denmark, examined by NfLjNOs addition. For. Ecol. Manage. 71: 75-88. 6 Gunn, J. M. (1989) Survival of lake charr (Salvelinus namaycush) embryos under pules exposure 7 to acidic runoff water. In: Nriagu, J. O.; Lakshminarayana, J. S. S., eds. Aquatic 8 toxicology and water quality management. New York, NY: John Wiley & Sons; pp. 23- 9 45. (Advances in environmental science and technology: v. 22). 10 Gunn, J. M.; Keller, W. (1984a) In situ manipulation of water chemistry using crushed limestone 11 and observed effects on fish. Fisheries 9: 19-24. 12 Gunn, J. M.; Keller, W. (1984b) Spawning site water chemistry and lake trout (Salvelinus 13 namaycush) sac fry survival during spring snowmelt. Can. J. Fish. Aquat. Sci. 41: 319- 14 329. 15 Gunn, J. M.; Keller, W. (1990) Biological recovery of an acid lake after reductions in industrial 16 emissions of sulphur. Nature (London, U.K.) 355: 431-433. 17 Gunn, J. M.; Mills, K. H. (1998) The potential for restoration of acid-damaged lake trout lakes. 18 Restor. Ecol. 6: 390-397. 19 Gunn, J. M.; Noakes, D. L. G. (1987) Latent effects of pulse exposure to aluminum and low pH 20 on size, ionic composition and feeding efficiency of lake trout (Salvelinus fontinalis) 21 alevins. Can. J. Fish. Aquat. Sci. 44: 1418-1424. 22 23 24 Gunn, J. M.; McMurtry, M. J.; Casselman, J. M.; Keller, W.; Powell, M. J. (1988) Changes in 25 the fish community of a limed lake near sudbury, Ontario: effects of chemical 26 neutralization or reduced atmospheric deposition of acids? Water Air Soil Pollut. 41: 27 113-136. 28 Gunn, J.; Keller, W.; Negusanti, J.; Potvin, R.; Beckett, P.; Winterhalder, K. (1995) Ecosystem 29 recovery after emission reductions: Sudbury, Canada. Water Air Soil Pollut. 85: 1783- 30 1788. 31 Haines, T. A.; Baker, J. P. (1986) Evidence offish population responses to acidification in the 32 eastern United States. Water Air Soil Pollut. 31: 605-629. 33 Hall, L. W. (1987) Acidification effects on larval striped bass, Morone saxatilis, in Chesapeake 34 Bay tributaries: a review. Water Air Soil Pollut. 35: 87-96. 35 Hall, R. J.; Ide, F. P. (1987) Evidence of acidification effects on stream insect communities in 36 central Ontario between 1937 and 1985. Can. J. Fish. Aquat. Sci. 44: 1652-1657. 37 Hall, R. J.; Likens, G. E.; Fiance, S. B.; Hendrey, G. R. (1980) Experimental acidification of a 38 stream in the Hubbard Brook Experimental Forest, New Hampshire. Ecology 61: 976- 39 989. 40 Hallett, R. A.; Bailey, S. W.; Horsley, S. B.; Long, R. P. (2006) Influence of nutrition and stress 41 on sugar maple at a regional scale. Can. J. For. Res. 36: 2235-2246. August 2008 B-177 DRAFT-DO NOT QUOTE OR CITE ------- 1 Hamburg, S. P.; Yanai, R. D.; Arthur, M. A.; Blum, J. D.; Siccama, T. G. (2003) Biotic control 2 of calcium cycling in northern hardwood forests: acid rain and aging forests. Ecosystems 3 6: 399-406. 4 Harriman, R.; Morrison, B. R. S. (1982) Ecology of streams draining forested and non-forested 5 catchments in an area of central Scotland subject to acid precipitation. Hydrobiologia 88: 6 251-263. 7 Hartman, M. D.; Baron, J. S.; Ojima, D. S. (2005) Calculating pre-measurement atmospheric 8 deposition, stream chemistry, and soil chemistry for an alpine watershed in Rocky 9 Mountain National Park, Colorado [abstract]. In: Abstracts of the Ecological Society of 10 America 90th annual meeting; Montreal, Canada; Aug 7-12. 11 Hartman, M. D.; Baron, J. S.; Ojima, D. S.; Parton, W. J. (2005) Modeling the timeline for 12 surface water acidification from excess nitrogen deposition for Rocky Mountain National 13 Park [abstract]. In: Abstracts of the George Wright Society biennial conference; 14 Philadelphia, PA. 15 Hartman, M. D.; Baron, J. S.; Ojima, D. S. (2007) Application of a coupled ecosystem-chemical 16 equilibrium model, DayCent-Chem, to stream and soil chemistry in an alpine watershed. 17 Ecol.Modell. 200:493-510. 18 Havas, M. (1985) Aluminum bioaccumulation and toxicity to Daphnia magna in soft water at 19 low pH. Can. J. Fish. Aquat. Sci. 42: 1741-1748. 20 Havas, M. (1986) Effects of acid deposition on aquatic ecosystems. In: Stern, A., ed. Air 21 pollution. New York, NY: Academic Press; pp. 351-389. 22 Havas, M.; Hutchinson, T. C.; Likens, G. E. (1984) Red herrings in acid rain research. Environ. 23 Sci. Technol. 18: 176A-186A. 24 Havas, M.; Woodfme, D. G.; Lutz, P.; Yung, K.; Maclsaac, H. J.; Hutchinson, T. C. (1995) 25 Biological recovery of two previously acidified, metal-contaminated lakes near Sudbury, 26 Ontario, Canada. Water Air Soil Pollut. 85: 791-796. 27 Havens, K. E.; Carlson, R. E. (1998) Functional complementarity in plankton communities along 28 a gradient of acid stress. Environ. Pollut. 101: 427-436. 29 Hawley, G. J.; Schaberg, P. G.; Eagar, C.; Borer, C. H. (2006) Calcium addition at the Hubbard 30 Brook Experimental Forest reduced winter injury to red spruce in a high-injury year. Can. 31 J. For. Res. 36: 2544-2549. 32 Haya, K.; Waiwood, B. A. (1981) Acid pH and chorionase activity of Atlantic salmon (Salmo 33 salar) eggs. Bull. Environ. Contam. Toxicol. 27: 7-12. 34 Hedin, L. O.; Likens, G. E.; Postek, K. M.; Driscoll, C. T. (1990) A field experiment to test 35 whether organic acids buffer acid deposition. Nature (London, U.K.) 345: 798-800. 36 Hedin, L. O.; Granat, L.; Likens, G. E.; Buishand, T. A.; Galloway, J. N.; Butler, T. J.; Rodhe, 37 H. (1994) Steep declines in atmospheric base cations in regions of Europe and North 38 America. Nature (London) 367: 351-354. 39 Hedin, L. O.; Armesto, J. J.; Johnson, A. H. (1995) Patterns of nutrient loss from unpolluted, old- 40 growth temperate forests: evaluation of biogeochemical theory. Ecology 76: 493-509. August 2008 B-178 DRAFT-DO NOT QUOTE OR CITE ------- 1 Heil, G. W.; Bruggink, M. (1987) Competition for nutrients between Calluna vulgaris (L.) Hull 2 and Molinia caerulea (L.) Moench. Oecologia 73: 105-108. 3 Heinz, G. (1974) Effects of low dietary levels of methyl mercury on mallard reproduction. Bull. 4 Environ. Contam. Toxicol. 11: 386-392. 5 Heinz, G. H. (1979) Methylmercury: reproductive and behavioral effects on three generations of 6 mallard ducks. J. Wildl. Manage. 43: 394-401. 7 Hemond, H. F. (1990) Wetlands as the source of dissolved organic carbon to surface waters. In: 8 Perdue, E. M.; Gjessing, E. T., eds. Organic acids in aquatic ecosystems. New York, NY: 9 John Wiley & Sons; pp. 301-313. 10 Hemond, H. F. (1994) Role of organic acids in acidification of fresh waters. In: Steinberg, C. E. 11 W.; Wright, R. F., eds. Acidification of freshwater ecosystems: implications for the 12 future: report of the Dahlem workshop on acidification of freshwater ecosystems; Berlin, 13 Germany; September-October 1992. New York, NY: Wiley; pp. 103-116. 14 Hendry, C. D.; Brezonik, P. L. (1984) Chemical composition of softwater Florida lakes and their 15 sensitivity to acid precipitation. Water Resour. Bull. 20: 75-86. 16 Henriksen, A. (1980) Acidification of freshwaters - a large scale titration. In: Drablos, D.; 17 Tollan, A., eds. Ecological impact of acid precipitation: proceedings of an international 18 conference; March; Sandefjord, Norway. Oslo, Norway: The Norwegian Interdisciplinary 19 Research Programme; pp. 68-74. (SNSF Project). 20 Henriksen, A. (1984) Changes in base cation concentrations due to freshwater acidification. 21 Verh. - Int. Ver. Theor. Angew. Limnol. 22: 692-698. 22 Herlihy, A. T.; Kaufman, P. R.; Mitch, M. E. (1991) Stream chemistry in the eastern United 23 States. 2. Current sources of acidity in acidic and low acid-neutralizing capacity streams. 24 Water Resour. Res. 27: 629-642. 25 Herlihy, A. T.; Kaufmann, P. R.; Church, M. R.; Wigington, P. J., Jr.; Webb, J. R.; Sale, M. J. 26 (1993) The effects of acidic deposition on streams in the Appalachian Mountain and 27 Pidemont Region of the mid-Atlantic United States. Water Resour. Res. 29: 2687-2703. 28 Hoffman, D. J.; Moore, J. M. (1979) Teratogenic effects of external egg applications of methyl 29 mercury in the mallard, Anas platyrynchos. Teratology 20: 453-461. 30 Holmes, R. S.; Whiting, M. L.; Stoddard, J. L. (1989) Changes in diatom-inferred pH and acid 31 neutralizing capacity in a dilute, high elevation, Sierra Nevada lake since A.D. 1825. 32 Freshwater Biol. 21:295-310. 33 Holt, C.; Yan, N. D. (2003) Recovery of crustacean zooplankton communities from acidification 34 in Killarney Park, Ontario, 1971-2000: pH 6 as a recovery goal. R. Swed. Acad. Sci. 32: 35 203-207. 36 Holtze, K. E.; Hutchinson, N. J. (1989) Lethality of low pH and Al to early life stages of six fish 37 species inhabiting precambrian shield waters in Ontario. Can. J. Fish. Aquat. Sci. 46: 38 1188-1202. 39 Horsley, S. B.; Long, R. P.; Bailey, S. W.; Hallet, R. A.; Hall, T. J. (1999) Factors contributing 40 to sugar maple decline along topographic gradients on the glaciated and unglaciated 41 Allegheny Plateau. In: Horsely, S. B.; Long, R. P., eds. Sugar maple ecology and health: August 2008 B-179 DRAFT-DO NOT QUOTE OR CITE ------- 1 proceedings of an international symposium; June 1998; Warren, PA. Radnor, PA: U.S. 2 Department of Agriculture, Forest Service; pp. 60-62. (General technical report NE-261). 3 Horsley, S. B.; Long, R. P.; Bailey, S. W.; Hallett, R. A.; Hall, T. J. (2000) Factors associated 4 with the decline disease of sugar maple on the Allegheny Plateau. Can. J. Forest. Res. 30: 5 1365-1378. 6 Hosker, R. P., Jr.; Lindberg, S. E. (1982) Review: atmospheric deposition and plant assimilation 7 of gases and particles. Atmos. Environ. 16: 889-910. 8 Houle, D.; Carignan, R.; Ouimet, R. (2001) Soil organic sulfur dynamics in a coniferous forest. 9 Biogeochemistry 53(1): 105-124. 10 Houlton, B. Z.; Driscoll, C. T.; Fahey, T. J.; Likens, G. E.; Groffman, P. M.; Bernhardt, E. S.; 11 Buso, D. C. (2003) Nitrogen dynamics in ice storm-damaged forest ecosystems: 12 implications for nitrogen limitation theory. Ecosystems 6: 431-443. 13 Hrabik, T. R.; Watras, C. J. (2002) Recent declines in mercury concentration in a freshwater 14 fishery: isolating the effects of de-acidification and decreased atmospheric mercury 15 deposition in Little Rock Lake. Sci. Total Environ. 297: 229-237. 16 Hulsman, P. F.; Powles, P. M.; Gunn, J. M. (1983) Mortality of walleye eggs and rainbow trout 17 yolk-sac larvae in low-pH waters of the LaCloche Mountain area, Ontario. Trans. Am. 18 Fish. Soc. 112: 680-683. 19 Hultberg, H.; Andersson, I. (1982) Liming of acidified lakes: induced long-term changes. Water 20 Air Soil Pollut. 18:311-331. 21 Hunter, M. D.; Schultz, J. C. (1995) Fertilization mitigates chemical induction and herbivore 22 responses within damaged oak trees. Ecology 76: 1226-1232. 23 Hunter, M. L.; Jones, J. J.; Gibbs, K. E.; Moring, J. R. (1986) Duckoing responses to lake 24 acidification: do black ducks and fish compete? Oikos 47: 26-32. 25 Huntington, T. G. (2000) The potential for calcium depletion in forest ecosystems of 26 southeastern United States: review and analysis. Global Biogeochem. Cycles 14: 623- 27 638. 28 Husar, R. B.; Sullivan, T. J. (1991) Historical trends in atmospheric sulfur deposition and 29 methods for assessing long-term trends in surface water chemistry. In: Charles, D. F., ed. 30 Acidic deposition and aquatic ecosystems: regional case studies. New York, NY: 31 Springer-Verlag; pp. 65-82. 32 Hutchinson, T. C.; Bozic, L.; Munoz-Vega, G. (1986) Responses of five species of conifer 33 seedlings to aluminum stress. Water Air Soil Pollut. 31: 283-294. 34 Hutchinson, N. J.; Holtze, K. E.; Munro, J. R.; Pawson, T. W. (1989) Modifying effects of life 35 stage, ionic strength and post-exposure mortality n lethality of H+ and Al to lake trout and 36 brook trout. Aquat. Toxicol. 15: 1-26. 37 Hutchinson, J.; Maynard, D.; Geiser, L. (1996) Air quality and lichens - a literature review 38 emphasizing the Pacific Northwest, USA. U.S. Department of Agriculture, Forest 39 Service, Pacific Northwest Region Air Resource management Program. 40 Hyer, K. E.; Webb, J. R.; Eshleman, K. N. (1995) Episodic acidification of three streams in 41 Shenandoah National Park, Virginia, USA. Water Air Soil Pollut. 85: 523-528. August 2008 B-180 DRAFT-DO NOT QUOTE OR CITE ------- 1 Ilvesniemi, H. (1992) The combined effect of mineral nutrition and soluble aluminum on Pinus 2 sylvestris and Picea abies seedlings. For. Ecol. Manage. 51: 227-238. 3 Ingersoll, C. G. (1986) The effects of pH, aluminum, and calcium on survival and growth of 4 brook trout (Salvelinus fontinalis) early life stages [dissertation]. Laramie, WY: 5 University of Wyoming, Department of Zoology and Physiology. Available from: 6 ProQuest, Ann Arbor, MI; publication no. AAD86-23090. 7 Ingersoll, C. G.; Gulley, D. D.; Mount, D. R.; Mueller, M. E.; Fernandez, J. D.; Hockett, J. R.; 8 Bergman, H. L. (1990) Aluminum and acid toxicity to two strains of brook trout 9 (Salvelinus fontinalis). Can. J. Fish. Aquat. Sci. 47: 1641-1648. 10 Ingersoll, C. G; Mount, D. R.; Gulley, D. D.; La Point, T. W.; Bergman, H. L. (1990) Effects of 11 pH, aluminum, and calcium on survival and growth of eggs and fry of brook trout 12 (Salvelinus fontinalis). Can. J. Fish. Aquat. Sci. 47: 1580-1592. 13 Ito, M.; Mitchell, M. J.; Driscoll, C. T.; Roy, K. M. (2005) Factors affecting acid neutralizing 14 capacity in the Adirondack region of New York: a solute mass balance approach. 15 Environ. Sci. Technol. 39: 4076-4081. 16 Jackson, D. A.; Harvey, H. H. (1995) Gradual reduction and extinction offish populations in 17 acid lakes. Water Air Soil Pollut. 85: 389-394. 18 Jagoe, C. H.; Haines, T. A.; Kircheis, F. W. (1984) Effects of reduced pH on three life stages of 19 Sunapee char Salvelinus alpinus. Bull. Environ. Contam. Toxicol. 33: 430-438. 20 Jeffries, D. S.; Clair, T. A.; Couture, S.; Dillon, P. J.; Dupont, J.; Keller, W.; McNicol, D. K.; 21 Turner, M. A.; Vet, R.; Weeber, R. (2003) Assessing the recovery of lakes in 22 southeastern Canada from the effects of acidic deposition. R. Swedish Acad. Sci. 32: 23 176-182. 24 Jeremiason, J. D.; Engstrom, D. R.; Swain, E. B.; Nater, E. Q.; Johnson, B. M.; Almendinger, J. 25 E.; Monson, B. A.; Kolka, R. K. (2006) Sulfate addition increases methylmercury 26 production in an experimental wetland. Environ. Sci. Technol. 40: 3800-3806. 27 Johannessen, M.; Henriksen, A. (1978) Chemistry of snow meltwater: changes in concentration 28 during melting. Water Resour. Res. 14: 615-619. 29 Johansson, N.; Kihlstrom, J. E. (1975) Pikes (Esox lucius L.) shown to be affected by low pH 30 values during first weeks after hatching. Environ. Res. 9: 12-17. 31 Johansson, N.; Milbrink, G. (1976) Some effects of acidified water on the early development of 32 roach (Rutilus rutilus L.) and perch (Perca fluviatilis L.). Water Resour. Bull. 12: 39-48. 33 Johansson, N.; Runn, P.; Milbrink, G. (1977) Early development of three salmonid species in 34 acidified water. Zoon 5: 127-132. 35 Johnson, G. V. (1991) General model for predicting crop response to fertilizer. Agron. J. 83: 36 367-373. 37 Johnson, D. W.; Cole, D. W. (1980) Mobility in soils: relevance to nutrient transport from forest 38 ecosystems. Environ. Int. 3: 79-90. 39 Johnson, D. W.; Fernandez, I. J. (1992) Soil-mediated effects of atmospheric deposition on 40 eastern U.S. spruce-fir forests. In: Eagar, C.; Adams, M. B., eds. Ecology and decline of August 2008 B-181 DRAFT-DO NOT QUOTE OR CITE ------- 1 red spruce in the eastern United States. New York, NY: Springer-Verlag; pp. 235-270. 2 (Ecology studies: v. 96). 3 Johnson, D. W.; Lindberg, S. E., eds. (1992a) Atmospheric deposition and forest nutrient 4 cycling: a synthesis of the integrated forest study. New York, NY: Springer-Verlag, Inc. 5 (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. Ecological 6 studies: analysis and synthesis: v. 91). 7 Johnson, D. W.; Lindberg, S. E. (1992b) Nitrogen chemistry, deposition, and cycling in forests. 8 In: Johnson, D. W.; Lindberg, S. E., eds. Atmospheric deposition and forest nutrient 9 cycling: a synthesis of the integrated forest study. New York, NY: Springer-Verlag, Inc.; 10 pp. 150-213. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. 11 Ecological studies: analysis and synthesis: v. 91). 12 Johnson, D. W.; Mitchell, M. J. (1998) Responses of forest ecosystems to changing sulfur inputs. 13 In: Maynard, D. G., ed. Sulfur in the environment. New York, NY: Marcel Dekker, Inc; 14 pp. 219-262. (Books in soils, plants, and the environment v. 67). 15 Johnson, N. M.; Driscoll, C. T.; Eaton, J. S.; Likens, G. E.; McDowell, W. H. (1981) 'Acid rain,' 16 dissolved aluminum and chemical weathering at the Hubbard Brook Experimental Forest, 17 New Hampshire. Geochim. Cosmochim. Acta45: 1421-1437. 18 Johnson, D. W.; Simonin, H. A.; Colquhoun, J. R.; Flack, F. M. (1987) In situ toxicity tests of 19 fishes in acid waters. Biogeochemistry 3: 181-208. 20 Johnson, C. E.; Johnson, A. H.; Siccama, T. G. (1991a) Whole-tree clear-cutting effects on 21 exchangeable cations and soil acidity. Soil Sci. Soc. Am. J. 55: 502-508. 22 Johnson, D. W.; Cresser, M. S.; Nilsson, S. L; Turner, J.; Ulrich, B.; Binkley, D.; Cole, D. W. 23 (1991b) Soil changes in forest ecosystems: evidence for and probable causes. Proc. R. 24 Soc. Edinburgh Sect. B: Biol. Sci. 97B: 81-116. 25 Johnson, D. W.; Van Miegroet, H.; Lindberg, S. E.; Todd, D. E.; Harrison, R. B. (1991c) 26 Nutrient cycling in red spruce forests of the Great Smoky Mountains. Can. J. For. Res. 27 21:769-787. 28 Johnson, A. H.; Andersen, S. B.; Siccama, T. G. (1994a) Acid rain and soils of the Adirondacks. 29 I. Changes in pH and available calcium, 1930-1984. Can. J. For. Res. 24: 39-45. 30 Johnson, A. H.; Friedland, A. J.; Miller, E. K.; Siccama, T. G. (1994b) Acid rain and soils of the 31 Adirondacks. III. Rates of soil acidification in a montane spruce-fir forest at Whiteface 32 Mountain, New York. Can. J. For. Res. 24: 663-669. 33 Joslin, J. D.; Wolfe, M. H. (1988) Responses of red spruce seedlings to changes to soil aluminum 34 in six amended forest soil horizons. Can. J. For. Res. 18: 1614-1623. 35 Joslin, J. D.; Wolfe, M. H. (1989) Aluminum effects on northern red oak seedling growth in six 36 forest soil horizons. Soil Sci. Soc. Am. J. 53: 274-281. 37 Joslin, J. D.; Wolfe, M. H. (1992) Red spruce soil chemistry and root distribution across a cloud 38 water deposition gradient. Can. J. For. Res. 22: 893-904. 39 Joslin, J. D.; Wolfe, M. H. (1994) Foliar deficiencies of mature Southern Appalachian red spruce 40 determined from fertilizer trials. Soil Sci. Soc. Am. J. 58: 1572-1579. August 2008 B-182 DRAFT-DO NOT QUOTE OR CITE ------- 1 Joslin, J. D.; Mays, P. A.; Wolfe, M. H.; Kelly, J. M.; Garber, R. W.; Brewer, P. F. (1987) 2 Chemistry of tension lysimeter water and lateral flow in spruce and hardwood stands. J. 3 Environ. Qual. 16: 152-160. 4 Joslin, J. D.; Kelly, J. M.; Van Miegroet, H. (1992) Soil chemistry and nutrition of North 5 American spruce-fir stands: evidence for recent change. J. Environ. Qual. 21: 12-30. 6 Kahl, S. (1999) Responses of Maine surface waters to the Clean Air Act Amendments of 1990. 7 Orono, ME: University of Maine, Water Research Institute. Final report; EPA project 8 CX826563-01-0. 9 Kahl, J. S.; Scott, M. (1994) High elevation lake monitoring in Maine: 1986-1989. August, ME: 10 Maine Department of Environmental Protection. 11 Kahl, J. S.; Norton, S. A.; Cronan, C. S.; Fernandez, I. J.; Bacon, L. C.; Haines, T. A. (1991) 12 Maine. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case 13 studies. New York, NY: Springer-Verlag; pp. 203-235. 14 Kahl, J. S.; Norton, S. A.; Haines, T. A.; Rochette, E. A.; Heath, R. H.; Nodvin, S. C. (1992) 15 Mechanisms of episodic acidification in low-order streams in Maine, USA. Environ. 16 Pollut. 78: 37-44. 17 Kahl, J. S.; Norton, S. A.; Fernandez, I. J.; Nadelhoffer, K. J.; Driscoll, C. T.; Aber, J. D. (1993a) 18 Experimental inducement of nitrogen saturation at the watershed scale. Environ. Sci. 19 Technol. 27: 565-568. 20 Kahl, J. S.; Haines, T. A.; Norton, S. A.; Davis, R. B. (1993b) Recent trends in the acid-base 21 status of surface waters in Maine, USA. Water Air Soil Pollut. 67: 281-300. 22 Kahl, J.; Norton, S.; Frenandez, I; Rustad, L.; Handley, M. (1999) Nitrogen and sulfur input- 23 output budgets in the experimental and reference watersheds, Bear Brook Watershed in 24 Maine (BBWM). Environ. Monit. Assess. 55: 113-131. 25 Kane, D. A.; Rabeni, C. F. (1987) Effects of aluminum and pH on the early life stages of 26 smallmouth bass (Micropterus dolemieui). Water Res. 21: 633-639. 27 Karr, J. R.; Chu, L. W. (1999) Restoring life in running rivers: better biological monitoring. 28 Washington, DC: Island Press. 29 Kaufmann, P. R.; Herlihy, A. T.; Elwood, J. W.; Mitch, M. E.; Overton, W. S.; Sale, M. J.; 30 Messer, J. J.; Cougan, K. A.; Peck, D. V.; Reckhow, K. H.; Kinney, A. J.; Christie, S. J.; 31 Brown, D. D.; Hagley, C. A.; Jager, H. I. (1988) Chemical characteristics of streams in 32 the mid-Atlantic and southeastern United States (national stream survey - phase I). 33 Volume I: population descriptions and physico-chemical relationships. Washington, DC: 34 U.S. Environmental Protection Agency, Office of Acid Deposition, Environmental 35 Monitoring and Quality Assurance; EPA report no. EPA-600/3-88-021a. 36 Kaufmann, P. R.; Herlihy, A. T.; Mitch, M. E.; Messer, J. J.; Overton, W. S. (1991) Stream 37 chemistry in the eastern United States: 1. synoptic survey design, acid-base status, and 38 regional patterns. Water Resour. Res. 27: 611-627. 39 Kauppi, P. E.; Mielikaeinen, K.; Kuusela, K. (1992) Biomass and carbon budget of European 40 forests, 1971 to 1990. Science (Washington, DC) 256: 70-74. 41 Keller, W. (1992) Introduction and overview to aquatic acidificaiton studies in the Sudbury, 42 Ontario, Canada, area. Can. J. Fish. Aquat. Sci. 49(suppl. 1): 3-7. August 2008 B-183 DRAFT-DO NOT QUOTE OR CITE ------- 1 Keller, W.; Gunn, J. M. (1995) Lake water quality improvements and recovering aquatic 2 communities. In: Gunn, J. M., ed. Restoration and recovery of an industrial region: 3 progress in restoring the smelter-damaged landscape near Sudbury, Canada. New York, 4 NY: Springer-Verlag; pp. 67-80. 5 Keller, W.; Pitblado, J. R. (1986) Water quality changes in Sudbury area lakes: a comparison of 6 synoptic surveys in 1974-1976 and 1981-1983. Water Air Soil Pollut. 29: 285-296. 7 Keller, W.; Yan, D. (1991) Recovery of crustacean zooplankton species richness in Sudbury area 8 lakes following water quality improvements. Can. J. Fish. Aquat. Sci. 48: 1635-1644. 9 Keller, W.; Pitblado, J. R.; Conroy, N. I. (1986) Water quality improvements in the Sudbury, 10 Ontario, Canada area related to reduced smelter emissions. Water Air Soil Pollut. 31: 11 765-774. 12 Kelly, C. A.; Rudd, J. W. M.; Hesslein, R. H.; Driscoll, C. T.; Gherini, S. A.; Hecky, R. E. 13 (1987) Prediction of biological acid neutralization in acid sensitive lakes. 14 Biogeochemistry 3: 129-140. 15 Kelso, J. R. M.; Jeffries, D. S. (1988) Response of headwater lakes to varying atmospheric 16 deposition in north-central Ontario. Can. J. Fish. Aquat. Sci. 45: 1905-1911. 17 Keltjens, W. G.; Van Loenen, E. (1989) Effects of aluminum and mineral nutrition on growth 18 and chemical composition of hydroponically grown seedlings of five different forest tree 19 species. Plant Soil. 119: 39-40. 20 Kim, E. Y.; Murakami, T.; Saeki, K.; Tatsukawa, R. (1996) Mercury levels and its chemical 21 form in tissues and organs of seabirds. Arch. Environ. Contam. Toxicol. 30: 259-266. 22 Kimmel, W. G.; Murphey, D. J.; Sharpe, W. E.; DeWalle, D. R. (1985) Macroinvetebrate 23 community structure and detritus processing rates in two southwestern Pennsylvania 24 streams acidified by atmospheric deposition. Hydrobiologia 124: 97-102. 25 Kingston, J. C.; Birks, H. J. B. (1990) Dissolved organic carbon reconstruction from diatom 26 assemblages in PIRLA project lakes, North America. Philos. Trans. R. Soc. London Ser. 27 B 327: 279-288. 28 Kingston, J. C.; Cook, R. B.; R.G. Kreis, J.; Camburn, K. E.; Norton, S. A.; Sweets, P. R.; 29 Binford, M. W.; Mitchell, M. J.; Schindler, S. C.; Shane, L. C. K.; King, G. A. (1990) 30 Paleoecological investigation of recent lake acidification in the northern Great Lakes 31 states. J. Paleolimnol. 4: 153-201. 32 Klauda, R. J.; Palmer, R. E. (1987) Responses of blueback herring eggs and larvae to pulses of 33 acid and aluminum. Trans. Am. Fish. Soc. 116: 561-569. 34 Klauda, R. J.; Palmer, R. E.; Lenkevich, M. J. (1987) Sensitivity of early life stages of blueback 35 herring to moderate acidity and aluminum in soft freshwater. Estuaries 10: 44-53. 36 Knights, J. S.; Zhao, F. J.; Spiro, B.; McGrath, S. P. (2000) Long-term effects of land use and 37 fertilizer treatments on sulfur cycling. J. Environ. Qual. 29: 1867-1874. 38 Kobe, R. K.; Likens, G. E.; Eagar, C. (2002) Tree seedling growth and mortality responses to 39 manipulations of calcium and aluminum in a northern hardwood forest. Can. J. Forest. 40 Res. 32: 954-966. August 2008 B-184 DRAFT-DO NOT QUOTE OR CITE ------- 1 Kochy, M.; Wilson, S. D. (2001) Nitrogen deposition and forest expansion in the Northern Great 2 Plains. J.Ecol. 89: 807-817. 3 Kozlowski, T. T. (1985) SC>2 effects on plant community structure. In: Winner, W. E.; Mooney, 4 H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation: physiology, ecology, and 5 policy issues. Stanford, CA: Stanford University Press; pp. 431-453. 6 Kramar, D.; Goodale, W. M.; Kennedy, L. M.; Carstensen, L. W.; Kaur, T. (2005) Relating land 7 cover characteristics and common loon mercury levels using geographic information 8 systems. Ecotoxicology 14: 253-262. 9 Kratz, K. W.; Cooper, S. D.; Melack, J. M. (1994) Effects of single and repeated experimental 10 acid pulses on invertebrates in a high altitude Sierra Nevada stream. Freshwater Biol. 32: 11 161-183. 12 Kretser, W.; Gallagher, J.; Nicolette, J. (1989) Adirondack lakes study, 1984-1987: an evaluation 13 offish communities and water chemistry. Ray Brook, NY: Adirondack Lakes Survey 14 Corporation. 15 Krug, E. C. (1989) Assessment of the theory and hypotheses of the acidification of watersheds. 16 Champaign, IL: Illinois State water Survey; contract report 457. 17 Krug, E. C. (1991) Review of acid-deposition-catchment interaction and comments on future 18 research needs. J. Hydrol. 128: 1-27. 19 Krug, E. C.; Frink, C. R. (1983) Acid rain on acid soil: a new perspective. Science (Washington, 20 DC) 221: 520-525. 21 Krug, E. C.; Isaacson, P. J.; Frink, C. R. (1985) Appraisal of some current hypotheses describing 22 acidification of watersheds. J. AirPollut. Control Assoc. 35: 109-114. 23 Lacroix, G. L. (1985a) Plasma ionic composition of the Atlantic salmon (Salmo salar), white 24 sucker (Catostomus commersoni), and alewife (Alosa pseudoharengus) in some acidic 25 rivers of Nova Scotia. Can. J. Zool. 63: 2254-2261. 26 Lacroix, G. L. (1985b) Survival of eggs and alevins of Atlantic salmon (Salmo salar) in relation 27 to the chemistry of interstitial water in redds in some acidic streams of Altantic Canada. 28 Can. J. Fish. Aquat. Sci. 42: 292-299. 29 Lacroix, G. L.; Townsend, D. R. (1987) Responses of juvenile Atlantic salmon (Salmo salar) to 30 episodic increases in acidity of Nova Scotia rivers. Can. J. Fish. Aquat. Sci. 44: 1475- 31 1484. 32 Lancaster, J.; Real, M.; Juggins, S.; Monteith, D. T.; Flower, R. J.; Beaumont, W. R. C. (1996) 33 Monitoring temporal changes in the biology of acid waters. Freshwater Biol. 36: 179- 34 201. 35 Landers, D. H.; Eilers, J. M.; Brakke, D. F.; Overton, W. S.; Kellar, P. E.; Silverstein, M. E.; 36 Schonbrod, R. D.; Crowe, R. E.; Linthurst, R. A.; Omernik, J. M.; league, S. A.; Meier, 37 E. P. (1987) Western lake survey phase I: characteristics of lakes in the western United 38 States, volume I: population descriptions and physico-chemical relationships. 39 Washington, DC: U.S. Environmental Protection Agency, Office of Acid Deposition, 40 Environmental Monitoring and Quality Assurance; EPA report no. EPA/600/3-86-054A. 41 Available from: NTIS, Springfield, VA; PB88-146824. August 2008 B-185 DRAFT-DO NOT QUOTE OR CITE ------- 1 Lapenis, A. G.; Lawrence, G. B.; Andreev, A. A.; Bobrov, A. A.; Torn, M. S.; Harden, J. W. 2 (2004) Acidification of forest soil in Russia: from 1893 to present. Glob. Biogeochem. 3 Cycles 18(GB1037): 10.1029/2003GB002107. 4 Larson, G. L.; Moore, S. E. (1985) Encroachment of exotic rainbow trout into stream populations 5 of native brook trout in the Southern Appalachian Mountains. Trans. Am. Fish. Soc. 114: 6 195-203. 7 Larsen, D. P.; Urquhart, N. S. (1993) A framework for assessing the sensitivity of the EMAP 8 design. In: Larsen, D. P.; Christie, S. J., eds. EMAP-surface waters 1991 pilot report. 9 Corvallis, OR: U.S. Environmental Protection Agency; pp. 4.1-4.37. 10 Larsen, D. P.; Thornton, K. W.; Urquhart, N. S.; Paulsen, S. G. (1994) The role of sample 11 surveys for monitoring the condition of the nation's lakes. Environ. Monit. Assess. 32: 12 101-134. 13 Laudon, H.; Clair, T. A.; Hemond, H. F. (2002) Long-term response in episodic acidification to 14 declining SC>42" deposition in two streams in Nova Scotia. Hydrol. Earth Syst. Sci. 6: 773- 15 782. 16 Lauenroth, W. K.; Milchunas, D. G. (1985) 862 effects on plant community function. In: Winter, 17 W. E.; Mooney, H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation: physiology, 18 ecology, and policy issues. Stanford, CA: Stanford University Press; pp. 454-477. 19 Lawrence, G. B. (2002) Persistent episodic acidification of streams linked to acid rain effects on 20 soil. Atmos. Environ. 36: 1589-1598. 21 Lawrence, G. B.; David, M. B. (1977) Response of aluminum solubility to elevated nitrification 22 in soil of a red spruce stand in eastern Maine. Environ. Sci. Technol. 31: 825-830. 23 Lawrence, G. B.; Huntington, T. G. (1999) Soil-calcium depletion linked to acid rain and forest 24 growth in the eastern United States. Reston, VA: U.S. Geological Survey. Available: 25 http://bqs.usgs.gov/acidrain/WRIR984267.pdf [29 September, 2004]. 26 Lawrence, G. B.; Fuller, R. D.; Driscoll, C. T. (1987) Release of aluminum following whole-tree 27 harvesting at the Hubbard Brook Experimental Forest, New Hampshire. J. Environ. Qual. 28 16: 383-390. 29 Lawrence, G. B.; David, M. B.; Shortle, W. C. (1995) A new mechanism for calcium loss in 30 forest-floor soils. Nature (London) 378: 162-165. 31 Lawrence, G. B.; David, M. B.; Bailey, S. W.; Shortle, W. C. (1997) Assessment of soil calcium 32 status in red spruce forests in the northeastern United States. Biogeochemistry 38: 19-39. 33 Lawrence, G. B.; David, M. B.; Shortle, W. S.; Bailey, S. W.; Lovett, G. M. (1999a) 34 Mechanisms of base-cation depletion by acid deposition in forest soils of the northeastern 35 US. In: Horsely, S. B.; Long, R. P., eds. Sugar maple ecology and health: proceedings of 36 an international symposium; June 2-4 1998. Radner, PA: U.S. Department of Agriculture, 37 Forest Service; pp. 75-87. 38 Lawrence, G. B.; David, M. B.; Lovett, G. M.; Murdoch, P. S.; Burns, D. A.; Stoddard, J. L.; 39 Baldigo, B. P.; Porter, J. H.; Thompson, A. W. (1999b) Soil calcium status and the 40 response of stream chemistry to changing acidic deposition rates. Ecol. Appl. 9: 1059- 41 1072. August 2008 B-186 DRAFT-DO NOT QUOTE OR CITE ------- 1 Lawrence, G. B.; Momen, B.; Roy, K. M. (2004) Use of stream chemistry for monitoring acidic 2 deposition effects in the Adirondack Region of New York. J. Environ. Qual. 33: 1002- 3 1009. 4 Lawrence, G. B.; Lapenis, A. G.; Berggren, D.; Aparin, B. F.; Smith, K. T.; Shortle, W. C.; 5 Bailey, S. W.; VarlyGuin, D. L.; Babikov, B. (2005) Climate dependency of tree growth 6 suppressed by acid deposition effects on soils in northwest Russia. Environ. Sci. Technol. 7 39: 2004-2010. 8 Lawrence, G. B.; Sutherland, J. W.; Boylen, C. W.; Nierzwicki-Bauer, S. A.; Momen, B.; 9 Baldigo, B. P.; Simonin, H. A. (2007) Acid rain effects on aluminum mobilization 10 clarified by inclusion of strong organic acids. Environ. Sci. Technol. 41: 93-98. 11 Leavitt, P. R.; Findlay, D. L.; Hall, R. I; Smol, J. P. (1999) Algal responses to dissolved organic 12 carbon loss and pH decline during whole-lake acidification: evidence from 13 paleolimnology. Limnol. Oceanogr. 44: 757-773. 14 Leino, R. L.; Wilkinson, P.; Anderson, J. G. (1987) Histopathlogical changes in the gills of pearl 15 dace, Semotilus margarita, and fathead minnows, Pimephales promleas, from 16 experimentally acidified Canadian lakes. Can. J. Fish. Aquat. Sci. 44(suppl. 1): 126-134. 17 Leivestad, H. (1982) Physiological effects of acid stress on fish. In: Haines, T. A.; Johnson, E., 18 eds. Acid rain/fisheries. Bethesda, MD: American Fisheries Society; pp. 157-164. 19 Levlin, S. A. (1998) Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1: 20 431-436. 21 Lewis, G. P.; Likens, G. E. (2007) Changes in stream chemistry associated with insect 22 defoliation in a Pennsylvania hemlock-hardwoods forest. For. Ecol. Manage. 238: 199- 23 211. 24 Ley, R. E.; Williams, M. W.; Schmidt, S. K. (2004) Microbial population dynamics in an 25 extreme environment: controlling factors in talus soils at 3750 m in the Colorado Rocky 26 Mountains. Biogeochemistry 68: 313-335. 27 Likens, G. E.; Bormann, F. H.; Johnson, N. M.; Fisher, D. W.; Pierce, R. S. (1970) Effects of 28 forest cutting and herbicide treatment on nutrient budgets in the Hubbard Brook 29 watershed-ecosystem. Ecol. Monogr. 40: 23-47. 30 Likens, G. E.; Driscoll, C. T.; Buso, D. C. (1996) Long-term effects of acid rain: response and 31 recovery of a forest ecosystem. Science (Washington, DC) 272: 244-246. 32 Likens, G. E.; Driscoll, C. T.; Buso, D. C.; Siccama, T. G.; Johnson, C. E.; Lovett, G. M.; Fahey, 33 T. J.; Reiners, W. A.; Ryan, D. F.; Martin, C. W.; Bailey, S. W. (1998) The 34 biogeochemistry of calcium at Hubbard Brook. Biogeochemistry 41: 89-173. 35 Likens, G. E.; Butler, T. J.; Buso, D. C. (2001) Long- and short-term changes in sulfate 36 deposition: effects of the 1990 Clean Air Act Amendments. Biogeochemistry 52: 1-11. 37 Likens, G. E.; Driscoll, C. T.; Buso, D. C.; Mitchell, M. J.; Lovett, G. M.; Bailey, S. W.; 38 Siccama, T. G.; Reiners, W. A.; Alewell, C. (2002) The biogeochemistry of sulfur at 39 Hubbard Brook. Biogeochemistry 60: 235-316. 40 Lilleskov, E. A.; Fahey, T. J.; Horton, T. R.; Lovett, G. M. (2002) Belowground ectomycorrhizal 41 fungal community change over a nitrogen deposition gradient in Alaska. Ecology 83: 42 104-115. August 2008 B-187 DRAFT-DO NOT QUOTE OR CITE ------- 1 Lind, C. J.; Hem, J. D. (1975) Effects of organic solutes on chemical reactions of aluminum. 2 U.S. Geological Survey Water Supply Paper 1827-G. 3 Linthurst, R. A.; Landers, D. H.; Eilers, J. M.; Brakke, D. F.; Overton, W. S.; Meier, E. P.; 4 Crowe, R. E. (1986a) Characteristics of lakes in the eastern United States. Volume I. 5 Population descriptions and physico-chemical relationships. Washington, DC: U.S. 6 Environmental Protection Agency, Office of Acid Deposition, Environmental 7 Monitoring, and Quality Assurance; EPA report no. EPA-600/4-86-007a. Available from: 8 NTIS, Springfield, VA; PB87-110383. 9 Linthurst, R. A.; Landers, D. H.; Eilers, J. M.; Kellar, P. E.; Brakke, D. F.; Overton, W. S.; 10 Crowe, R.; Meier, E. P.; Kanciruk, P.; Jeffries, D. S. (1986b) Regional chemical 11 characteristics of lakes in North America part II: eastern United States. Water Air Soil 12 Pollut. 31: 577-591. 13 Little, E. L., Jr. (1971) Atlas of United States trees, v. 1. conifers and important hardwoods. 14 Washington, DC: U.S. Department of Agriculture, Forest Service. [Miscellaneous 15 Publication, no. 1146]. 16 Locke, A.; Sprules, W. G. (1994) Effects of lake acidification and recovery on the stability of 17 zooplankton food webs. Ecology 75: 498-506. 18 Lofts, S.; Woof, C.; Tipping, E.; Clarke, N.; Mulder, J. (2001) Modelling pH buffering and 19 aluminium solubility in European forest soils. Eur. J. Soil Sci. 52: 189-204. 20 L0kke, H.; Bak, J.; Falkengren-Grerup, U.; Finlay, R. D.; Ilvesniemi, H.; Nygaard, P. H.; Starr, 21 M. (1996) Critical loads of acidic deposition for forest soils: is the current approach 22 adequate. Ambio 25: 510-516. 23 Longcore, J. R.; Gill, J. D. (1993) Acidic depositions: effects on wildlife and habitats. Bethesda, 24 MD: The Wildlife Society. (Wildlife Society technical review 93-1). 25 Lovett, G. M.; Ruesink, A. E. (1995) Carbon and nitrogen assimilation in red oaks (Quercus 26 rubra L.) subject to defoliation and nitrogen stress. Tree Physiol. 12: 259-269. 27 Lovett, G. M.; Rueth, H. (1999) Soil nitrogen transformations in beech and maple stands along a 28 nitrogen deposition gradient. Ecol. Appl. 9: 1330-1344. 29 Lovett, G. M.; Traynor, M. M.; Pouyat, R. V.; Carreiro, M. M.; Zhu, W.-X.; Baxter, J. W. 30 (2000a) Atmospheric deposition to oak forest along an urban-rural gradient. Environ. Sci. 31 Technol. 34: 4294-4300. 32 Lovett, G. M.; Weathers, K. C.; Sobczak, W. V. (2000b) Nitrogen saturation and retention in 33 forested watersheds of the Catskill Mountains, New York. Ecol. Appl. 10: 73-84. 34 Lovett, G. M.; Christenson, L. M.; Groffman, P. M.; Jones, C. G.; Hart, J. E; Mitchell, M. J. 35 (2002) Insect defoliation and nitrogen cycling in forests. BioScience 52: 335-341. 36 Lydersen, E.; Fjeld, E.; Gjessing, E. T. (1996) The Humic Lake Acidification Experiment 37 (HUMEX): main physico-chemical results after five years of artificial acidification. 38 Environ. Int. 22: 591-604. 39 Lynch, J. A.; Corbett, E. S. (1989) Hydrologic control of sulfate mobility in a forested 40 watershed. Water Resour. Res. 25: 1695-1703. August 2008 B-188 DRAFT-DO NOT QUOTE OR CITE ------- 1 MacAvoy, S. W.; Bulger, A. J. (1995) Survival of brook trout (Salvelinus fontinalis) embryos 2 and fry in streams of different acid sensitivity in Shenandoah National Park, USA. Water 3 Air Soil Pollut. 85: 445-450. 4 Magill, A. H.; Aber, J. D.; Berntson, G. M.; McDowell, W. H.; Nadelhoffer, K. J.; Melillo, J. M.; 5 Steudler, P. (2000) Long-term nitrogen additions and nitrogen saturation in two 6 temperate forests. Ecosystems 3: 238-253. 7 Magill, A. H.; Aber, J. D.; Currie, W. S.; Nadelhoffer, K. J.; Martin, M. E.; McDowell, W. H.; 8 Melillo, J. M.; Steudler, P. (2004) Ecosystem response to 15 years of chronic nitrogen 9 additions at the Harvard Forest LTER, Massachusetts, USA. For. Ecol. Manage. 196: 7- 10 28. 11 Malley, D. F.; Chang, P. S. (1995) Assessing the health of a zooplankton community in a small 12 Precambrian Shield lake during recovery from experimental acidification. J. Aquat. 13 Ecosyst. Health 3: 273-286. 14 Mansfield, T. A.; Jones, T. (1985) Growth/environment interactions in SCh responses of grasses. 15 In: Winter, W. E.; Mooney, H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation: 16 physiology, ecology, and policy issues. Stanford, CA: Stanford University Press; pp. 332- 17 345. 18 Markewitz, D.; Richter, D. D.; Allen, H. L.; Urrego, J. B. (1998) Three decades of observed soil 19 acidification in the Calhoun Experimental Forest: has acid rain made a difference? Soil 20 Sci. Soc. Am. J. 62: 1428-1439. 21 Martinson, L.; Lamersdorf, N.; Warfvinge, P. (2005) The Soiling roof revisited - slow recovery 22 from acidification observed and modeled despite a decade of "clean-rain" treatment. 23 Environ. Pollut. 135: 293-302. 24 Mason, J.; Seip, H. M. (1985) The current state of knowledge on acidification of surface waters 25 and guidelines for further research. Ambio 14: 45-51. 26 Mason, R. R.; Wickman, B. E.; Beckwith, R. C.; Paul, H. G. (1992) Thinning and nitrogen 27 fertilization in a grand fir stand infested with spruce budworm. part I: insect response. 28 For. Sci. 38:235-251. 29 Matuszek, J. E.; Beggs, G. L. (1988) Fish species richness in relation to lake area, pH, and other 30 abiotic factors in Ontario lakes. Can. J. Fish. Aquat. Sci. 45: 1931-1941. 31 Matzner, E.; Blanck, K.; Hartmann, G.; Stock, R. (1989) Needle chlorosis pattern in relation to 32 soil chemical properties in two Norway spruce (Picea abies, Karst) forests of the German 33 Harz Mountains. In: Bucher, J. B.; Bucher-Wallin, I, eds. Air pollution and forest 34 decline, proceedings of the 14th international meeting for specialists in air pollution 35 effects of forest ecosystems; October 1988; Interlaken, Switzerland. Birmensdorf, 36 Switzerland: International Union of Forestry Research Organizations; pp. 195-199. 37 McAuley, D. G.; Longcore, J. R. (1988a) Survival of juvenile ring-necked ducks on wetlands of 38 different pH. J. Wildlife Manage. 52: 169-176. 39 McAuley, D. G.; Longcore, J. R. (1988b) Foods of juvenile ring-necked ducks: relationship to 40 wetland pH. J. Wildlife Manage. 52: 177-185. 41 McClenahen, J. R. (1987) Effects of simulated throughfall pH and sulfate concentration on a 42 deciduous forest soil. Water Air Soil Pollut. 35:319-333. August 2008 B-189 DRAFT-DO NOT QUOTE OR CITE ------- 1 McCormick, L. H.; Steiner, K. C. (1978) Variation in aluminum tolerance among six genera of 2 trees. For. Sci. 24: 565-568. 3 McCormick, J. H.; Jensen, K. M.; Anderson, L. E. (1989) Chronic effects of low pH and 4 elevated aluminum on survival, maturation, spawning, and embryo-larval development of 5 the fathead minnow in soft water. Water Air Soil Pollut. 43: 293-307. 6 McCune, B. (1988) Lichen communities along Os and SCh gradients in Indianapolis. Bryologist 7 91:223-228. 8 McDonald, D. G. (1983) The effects of H+ upon the gills of freshwater fish. Can. J. Zool. 61: 9 691-703. 10 McDonald, D. G.; Milligan, C. L. (1988) Sodium transport in the brook trout, Salvelinus 11 fontinalis: effects of prolonged low pH exposure in the presence and absence of 12 aluminum. Can. J. Fish. Aquat. Sci. 45: 1606-1613. 13 McLaughlin, S. B.; Tjoelker, M. J. (1992) Growth and physiological changes in red spruce 14 saplings associated with acidic deposition at high elevations in the southern 15 Appalachians, USA. For. Ecol. Manage. 51: 43-51. 16 McLaughlin, S. B.; Wimmer, R. (1999) Tansley review no. 104: Calcium physiology and 17 terrestrial ecosystem processes. NewPhytol. 142: 373-417. 18 McNicol, D. K. (2002) Relation of lake acidification and recovery to fish, common loon and 19 common merganser occurrence in Algoma Lakes. Water Air Soil Pollut: Focus 2: 151- 20 168. 21 McNicol, D. K.; Mallory, M. L.; Wedeles, C. H. R. (1995) Assessing biological recovery of 22 acid-sensitive lakes in Ontario, Canada. Water Air Soil Pollut. 85: 457-462. 23 McNulty, S. G.; Aber, J. D.; Newman, S. D. (1996) Nitrogen saturation in a high elevation New 24 England spruce-fir stand. For. Ecol. Manage. 84: 109-121. 25 McWilliams, P. G.; Potts, W. T. W. (1978) The effects of pH and calcium concentrations on gill 26 potentials in the brown trout, Salmo trutta. J. Comp. Physiol. B 126: 277-286. 27 Melack, J. M.; Stoddard, J. L. (1991) Sierra Nevada, California. In: Charles, D. F., ed. Acidic 28 deposition and aquatic ecosystems: regional case studies. New York, NY: Springer- 29 Verlag; pp. 503-530. 30 Melack, J. M.; Stoddard, J. L.; Ochs, C. A. (1985) Major ion chemistry and sensitivity to acid 31 precipitation of Sierra Nevada lakes. Water Resour. Res. 21: 27-32. 32 Melack, J. M.; Cooper, S. D.; Holmes, R. W.; Sickman, J. O.; Kratz, K.; Hopkins, P.; 33 Hardenbergh, H.; Thieme, M.; Meeker, L. (1987) Chemical and biological survey of 34 lakes and streams located in the Emerald Lake watershed, Sequoia National Park. Final 35 report. Sacramento, CA: California Air Resources Board; contract no. A3-096-32. 36 Melack, J. M.; Sickman, J. O.; Leydecker, A. (1988) Comparative analyses of high-altitude lakes 37 and catchments in the Sierra Nevada: susceptibility to acidification. Final report. 38 Sacramento, CA: California Air Resources Board; contract A032-188. 39 Melack, J. M.; Cooper, S. C.; Jenkins, T. M.; Barmuta, L., Jr.; Hamilton, S.; Kratz, K.; Sickman, 40 J.; Soiseth, C. (1989) Chemical and biological characteristics of Emerald Lake and the August 2008 B-190 DRAFT-DO NOT QUOTE OR CITE ------- 1 streams in its watershed, and the response of the lake and streams to acidic deposition. 2 Final report. Sacramento, CA: California Air Resources Board; contract A6-184-32. 3 Melack, J. M.; Sickman, J. O.; Setaro, F.; Dawson, D. (1997) Monitoring of wet deposition in 4 alpine areas in the Sierra Nevada. Sacramento, CA: California Air Resources Board; 5 contract A932-081. 6 Meyer, M. W.; Evers, D. C.; Daulton, T.; Braselton, W. E. (1995) Common loons (Gavia immer) 7 nesting on low pH lakes in nothern Wisconsin have elevated blood mercury content. 8 Water Air Soil Pollut. 80: 871-880. 9 Miller, E. K.; Blum, J. D.; Friedland, A. J. (1993) Determination of soil exchangeable-cation loss 10 and weatheirng rates using Sr isotopes. Nature (London, U.K.) 362: 438-441. 11 Mills, K. H.; Chalanchuk, S. M.; Mohr, L. C.; Davies, I. J. (1987) Responses offish populations 12 in Lake 223 to 8 years of experimental acidification. Can. J. Fish. Aquat. Sci. 44(suppl. 13 1): 114-125. 14 Minocha, R.; Shortle, W. C.; Lawrence, G. B.; David, M. B.; Minocha, S. C. (1997) 15 Relationships among foliar chemistry, foliar polyamines, and soil chemistry in red spruce 16 trees growing across the northeastern United States. Plant Soil 191: 109-122. 17 Mitchell, M. J.; Driscoll, C. T.; Kahl, J. S.; Likens, G. E.; Murdoch, P. S.; Pardo, L. H. (1996) 18 Climatic control of nitrate loss from forested watersheds in the northeast United States. 19 Environ. Sci. Technol. 30: 2609-2612. 20 Mitchell, R. J.; Sutton, M. A.; Truscott, A.-M.; Leith, I. D.; Cape, J. N.; Pitcairn, C. E. R.; Van 21 Dijk, N. (2004) Growth and tissue nitrogen of epiphytic Atlantic bryophytes: effects of 22 increased and decreased atmospheric N deposition. Funct. Ecol. 18: 322-329. 23 Mitsch, W. J.; Gosselink, J. G. (2000) Wetlands. 3rd ed. New York, NY: John Wiley & Sons, 24 Inc. 25 Molot, L. A.; Dillon, P. J.; LaZerte, B. D. (1989) Changes in ionic composition of streamwater 26 during snowmelt in central Ontario. Can. J. Fish. Aquat. Sci. 46: 1658-1666. 27 Momen, B.; Lawrence, G. B.; Nierzwicki-Bauer, S. A.; Sutherland, J. W.; Eichler, L. W.; 28 Harrison, J. P.; Boylen, C. W. (2006) Trends in summer chemistry linked to productivity 29 in lakes recovering from acid deposition in the Adirondack region of New York. 30 Ecosystems 9: 1306-1317. 31 Moncoulon, D.; Probst, A.; Party, J. (2004) Weathering, atmospheric deposition and vegetation 32 uptake: role for ecosystem sensitivity to acid deposition and critical load. C.R. Geosci. 33 336: 1417-1426. 34 Morth, C. M.; Torssander, P.; Kj0naas, O. J.; Stuanes, A. O.; Moldan, F.; Giesler, R. (2005) 35 Mineralization of organic sulfur delays recovery from anthropogenic acidification. 36 Environ. Sci. Tech. 39: 5234-5240. 37 Mount, D. R.; Ingersoll, C. G.; Gulley, D. D.; Fernandez, J. D.; LaPoint, T. W.; Bergman, H. L. 38 (1988) Effect of long-term exposure to acid, aluminum, and low calcium on adult brook 39 trout (Salvelinus fontinalis). I. Survival, growth, fecundity, and progeny survival. Can. J. 40 Fish. Aquat. Sci. 45: 1623-1632. 41 Mulder, J.; Stein, A. (1994) The solubility of aluminum in acidic forest soils: long-term changes 42 due to acid deposition. Geochim. Cosmochim. Acta 58: 85-94. August 2008 B-191 DRAFT-DO NOT QUOTE OR CITE ------- 1 Mulder, J.; Van Breemen, N.; Eijck, H. C. (1989) Depletion of soil aluminium by acid deposition 2 and implications for acid neutralization. Nature (London, U.K.) 337: 247-249. 3 Muniz, I. P. (1991) Freshwater acidification: its effects on species and communities of 4 freshwater microbes, plants and animals. R. Soc. Edinburgh Sect. B: Biol. Sci. 97: 227- 5 254 6 Munson, R. K.; Gherini, S. A. (1993a) Analysis of the mineral acid-base components of acid- 7 neutralizing capacity in Adirondack Lakes. Water Resour. Res. 29: 881-890. 8 Munson, R. K.; Gherini, S. A. (1993b) Influence of organic acids on the pH and acid- 9 neutralizing capacity of Adirondack Lakes. Water Resour. Res. 29: 891-899. 10 Murdoch, P. S.; Shanley, J. B. (2006) Flow-specific trends in river-water quality resulting from 11 the effects of the Clean Air Act in three mesoscale, forested river basins in the 12 northeastern United States through 2002. Environ. Monit. Assess. 120: 1-25. 13 Murdoch, P. S.; Stoddard, J. L. (1992) The role of nitrate in the acidification of streams in the 14 Catskill Mountains of New York. Water Resour. Res. 28: 2707-2720. 15 Murdoch, P. S.; Stoddard, J. L. (1993) Chemical characteristics and temporal trends in eight 16 streams of the Catskill Mountains, New York. Water Air Soil Pollut. 67: 367-395. 17 Murdoch, P. S.; Burns, D. A.; Lawrence, G. B. (1998) Relation of climate change to the 18 acidification of surface waters by nitrogen deposition. Environ. Sci. Technol. 32: 1642- 19 1647. 20 Musselman, R. C.; Hudnell, L.; Williams, M. W.; Sommerfeld, R. A. (1996) Water chemistry of 21 Rocky Mountain Front Range aquatic ecosystems. Ft. Collins, CO: USDA Rocky 22 Mountain Forest and Range Experiment Station; research paper RM-RP-325. 23 National Acid Precipitation Assessment Program. (1991) National Acid Precipitation 24 Assessment Program 1990 integrated assessment report. Washington, DC: National Acid 25 Precipitation Assessment Program. 26 National Acid Precipitation Assessment Program. (1998) NAPAP biennial report to Congress: an 27 integrated assessment. Silver Spring, MD: National Science and Technology Council, 28 Committee on Environment and Natural Resources. 29 Neary, B. P.; Dillon, P. J. (1988) Effects of sulphur deposition on lake-water chemistry in 30 Ontario, Canada. Nature (London, U.K.) 333: 340-343. 31 Nelson, P. O. (1991) Cascade Mountains: lake chemistry and sensitivity of acid deposition. In: 32 Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case studies. New 33 York, NY: Springer-Verlag; pp. 531-563. 34 Newton, I; Hass, M. B. (1988) Pollutants in Merlin eggs and their effects on breeding. Br. Birds 35 81:258-269. 36 Niering, W. A. (1998) Forces that shaped the forests of the northeastern United States. Northeast. 37 Nat. 5:99-110. 38 Nilsson, S. I. (1993) Acidification of Swedish ologotrophic lakes - interactions between 39 deposition, forest growth and effects on lake-water quality. Ambio 22: 272-276. 40 Nilsson, S. I; Miller, J. G.; Miller, J. D. (1982) Forest growth as a possible cause of soil and 41 water acidification: an examination of the concepts. Oikos 39: 40-49. August 2008 B-192 DRAFT-DO NOT QUOTE OR CITE ------- 1 Nodvin, S. C.; Miegroet, H. V.; Lindberg, S. E.; Nicholas, N. S.; Johnson, D. W. (1995) Acidic 2 deposition, ecosystem processes, and nitrogen saturation in a high elevation southern 3 Appalachian watershed. Water Air Soil Pollut. 85: 1647-1652. 4 Norton, S. A.; Akielaszek, J. J.; Haines, T. A.; Stromborg, K. J.; Longcore, J. R. (1982) Bedrock 5 geologic control of sensitivity of aquatic ecosystems in the United States to acidic 6 deposition. Fort Collins, CO: National Atmospheric Deposition Program; pp. 1-13. 7 Norton, S. A.; Wright, R. F.; Kahl, J. S.; Scofield, J. P. (1992) The MAGIC simulation of surface 8 water acidification at, and first year results from, the Bear Brook Watershed 9 Manipulation, Maine, USA. Environ. Pollut. 77: 279-286. 10 Nosko, P.; Brassard, P.; Kramer, J. R.; Kershaw, K. A. (1988) The effect of aluminum on seed 11 germination and early seedling establishment, growth, and respiration of white spruce 12 (Picea glauca). Can. J. Bot. 66: 2305-2310. 13 Novak, M.; Bottrell, S. H.; Pfechova, E. (2001) Sulfur isotope inventories of atmospheric 14 deposition, spruce forest floor and living sphagnum along a NW-SE transect across 15 Europe. Biogeochemistry 53: 23-50. 16 Odman, M. T.; Boylan, J. W.; Wilkinson, J. G.; Russell, A. G; Mueller, S. F.; Imhoff, R. E.; 17 Doty, K. G.; Norris, W. B.; McNider, R. T. (2002) SAMI air quality modeling: final 18 report. Asheville, NC: Southern Appalachian Mountains Initiative. 19 Odum, E. P. (1989) Ecology and our endangered life-support systems. Sunderland, MA: Sinauer 20 Associates, Inc.; pp. 58-66, 108-118. 21 Ohno, T.; Sucoff, E. I; Erich, M. S.; Bloom, P. R.; Buschena, C. A.; Dixon, R. K. (1988) 22 Growth and nutrient content of red spruce seedlings in soil amended with aluminum. J. 23 Environ. Qual. 17: 666-672. 24 0kland, J.; 0kland, K. A. (1986) The effects of acid deposition on benthic animals in lakes and 25 streams. Experientia 42: 471-486. 26 Oliver, B. G.; Thurman, E. M.; Malcolm, R. L. (1983) The contribution of humic substances to 27 the acidity of colored natural waters. Geochim. Cosmochim. Acta 47: 2031-2035. 28 Ollinger, S. V.; Aber, J. D.; Lovett, G. M.; Millham, S. E.; Lathrop, R. G; Ellis, J. M. (1993) A 29 spatial model of atmospheric deposition for the northeastern U.S. Ecol. Appl. 3: 459-472. 30 Ormerod, S. J.; Tyler, S. J. (1987) Dippers (Cinclus cinclus) and grey wagtails (Motacilla 31 cinerea) as indicators of stream acidity in upland Wales. In: Diamond, A. W.; Filion, F. 32 L., eds. The value of birds. Cambridge, United Kingdom: International Council for Bird 33 Preservation; pp. 191-208. (ICBP technical publication no. 6). 34 Ormerod, S. J.; Tyler, S. J. (1991) Exploitation of prey by a river bird, the dipper Cinclus cinclus 35 (L.), along acidic and circumneutral streams in upland Wales. Freshwater Biol. 25: 105- 36 116. 37 Ormerod, S. J.; Tyler, S. J.; Lewis, J. M. S. (1985) Is the breeding distribution of dippers 38 influenced by stream acidity? Bird Study 32: 32-39. 39 Ormerod, S. J.; Allinson, N.; Hudson, D.; Tyler, S. J. (1986) The distribution of breeding 40 Dippers (Cinclus cinclus [L.]; Aves) in relation to stream acidity in upland Wales. 41 Freshwater Biol. 16: 501-507. August 2008 B-193 DRAFT-DO NOT QUOTE OR CITE ------- 1 Ormerod, S. I; Boole, P.; McCahon, C. P.; Weatherley, N. S.; Pascoe, D.; Edwards, R. W. 2 (1987) Short-term experimental acidification of a Welsh UK stream comparing the 3 biological effects of hydrogen ions and aluminum. Freshwater Biol. 17: 341-356. 4 Palmer, R. E.; Klauda, R. J.; Lewis, T. E. (1988) Comparative sensitivities of bluegill, channel 5 catfish and fathead minnow to pH and aluminum. Environ. Toxicol. Chem. 7: 505-516. 6 Palmer, S. M.; Driscoll, C. T.; Johnson, C. E. (2004) Long-term trends in soil solution and 7 stream water chemistry at the Hubbard Brrok Experiment Forest: relationship with 8 landscape position. Biogeochemistry 68: 51-70. 9 Parker, K. E. (1988) Common loon reproduction and chick feeding on acidified lakes in the 10 Adirondack Park, New York. Can. J. Zool. 66: 804-810. 11 Parker, D. R.; Zelazny, L. W.; Kinraide, T. B. (1989) Chemical speciation and plant toxicity of 12 aqueous aluminum. In: Lewis, T. E., ed. Environmental chemistry and toxicology of 13 aluminum. Washington, DC: American Chemical Society; pp. 117-145. 14 Perdue, E. M.; Reuter, J. H.; Parrish, R. S. (1984) A statistical model of proton binding by 15 humus. Geochim. Cosmochim. Acta48: 1257-1263. 16 Peterjohn, W. T.; Adams, M. B.; Gilliam, F. S. (1996) Symptoms of nitrogen saturation in two 17 central Appalachian hardwood forest ecosystems. Biogeochemistry 35: 507-522. 18 Peterson, R. H.; Martin-Robichaud, D. J. (1986) Growth and major inorganic cation budgets of 19 Atlantic salmon alevins at three ambient acidities. Trans. Am. Fish. Soc. 115: 220-226. 20 Peterson, R. H.; Van Eechhaute, L. (1992) Distributions of Ephemeroptera, Plecoptera, and 21 Trichoptera of three maritime catchments differing in pH. Freshwater Biol. 27: 65-78. 22 Peterson, R. H.; Daye, P. G.; Metcalfe, J. L. (1980) Inhibition of Atlantic salmon (Salmo salar) 23 hatching at low pH. Can. J. Fish. Aquat. Sci. 37: 770-774. 24 Peterson, D. L.; Sullivan, T. J.; Eilers, J. M.; Brace, S.; Horner, D. (1998) Assessment of air 25 quality and air pollutant impacts in national parks of the Rocky Mountains and northern 26 Great Plains. Denver, CO: U.S. Department of the Interior, National Park Service, Air 27 Resources Division. Available: http://www2.nature.nps.gov/air/Pubs/regionPark.cfm [16 28 June, 2006]. 29 Phillips, K. (1990) Where have all the frogs and toads gone? BioScience 40: 422-424. 30 Phillips, R. P.; Yanai, R. D. (2004) The effects of AlCb additions on rhizosphere soil and fine 31 root chemistry of sugar maple (Acer saccharum). Water Air Soil Pollut. 159: 339-356. 32 Plafkin, J. L.; Barbour, M. T.; Porter, K. D.; Gross, S. K.; Hughes, R. M. (1989) Rapid 33 bioassessment protocols for use in streams and rivers: benthic macroinvertebrates and 34 fish. Washington, DC: U.S. Environmental Protection Agency, Office of Water 35 Regulations and Standards; report no. EPA 440-89-001. 36 Pollman, C. D.; Canfield, D. E. (1991) Florida: hydrologic and biogeochemical controls on 37 seepage lake chemistry. In: Charles, D. F., ed. Acidic deposition and aquatic ecosystems: 38 regional case studies. New York, NY: Springer-Verlag, Inc. 39 Pollman, C. D.; Sweets, P. R. (1990) Hindcasting of pre-cultural ANC and pH of seepage lakes 40 in the Adirondacks, Upper Midwest, and Florida. Corvallis, OR: U.S. Environmental 41 Protection Agency; report no. EPA 89007B I/RES-1. August 2008 B-194 DRAFT-DO NOT QUOTE OR CITE ------- 1 Powell, J. F. F.; McKeown, B. A. (1986) The effects of acid exposure on the ion regulation and 2 seawater adaptation of Coho salmon (Oncorhynchus kisutch) parrs and smolts. Comp. 3 Biochem. Physiol. C: Comp. Pharmacol. 83: 45-52. 4 Power, S. A.; Green, E. R.; Barker, C. G.; Bell, J. N. B.; Ashmore, M. R. (2006) Ecosystem 5 recovery: heathland response to a reduction in nitrogen deposition. Glob. Change Biol. 6 12: 1241-1252. 7 Pregitzer, K. S.; Burton, A. J.; Mroz, G. D.; Leighty, H. O.; MacDonald, N. W. (1992) Foliar 8 sulfur and nitrogen along an 800-km pollution gradient. Can. J. Forest. Res. 22: 1761- 9 1769. 10 Raddum, G. G.; Fjellheim, A. (1984) Acidification and early warning organisms in freshwater in 11 western Norway. Verh. - Int. Ver. Theor. Angew. Limnol. 22: 1973-1980. 12 Raddum, G. G.; Brettum, P.; Matzow, D.; Nilssen, J. P.; Skov, A.; Svealy, T.; Wright, R. F. 13 (1986) Liming the acid lake Hovvatn, Norway: a whole lake study. Water Air Soil Pollut. 14 31:721-763. 15 Rago, P. J.; Wiener, J. G. (1986) Does pH affect fish species richness when lake area is 16 considered? Trans. Am. Fish. Soc. 115: 438-447. 17 Rapport, D. J.; Whitford, W. G. (1999) How ecosystems respond to stress: common properties of 18 arid and aquatic systems. BioScience 49: 193-203. 19 Rattner, B. A.; Haramis, G. M.; Chu, D. S.; Bunck, C. M.; Scanes, C. G. (1987) Growth and 20 physiological condition of black ducks reared on acidified wetlands. Can. J. Zool. 65: 21 2953-2958. 22 Reader, J. P.; Dalziel, T. R. K.; Morris, R. (1988) Growth, mineral uptake and skeletal calcium 23 deposition in brown trout, Salmo trutta L., yolk-sac fry exposed to aluminum and 24 manganese in soft acid water. J. Fish Biol. 32: 607-624. 25 Reckhow, K. H.; Black, R. W.; Stockton, T. B., Jr.; Vogt, J. D.; Wood, J. G. (1987) Empirical 26 models offish response to lake acidification. Can. J. Fish. Aquat. Sci. 44: 1432-1442. 27 Resh, V. H.; Norris, R. H.; Barbour, M. T. (1995) Design and implementation of rapid 28 assessment approaches for water resource monitoring using benthic macroinvertebrates. 29 Aust.J.Ecol. 20: 108-121. 30 Reuss, J. O. (1983) Implications of the calcium-aluminum exchange system for the effect of acid 31 precipitation on soils. J. Environ. Qual. 12: 591-595. 32 Reuss, J. O.; Johnson, D. W. (1985) Effect of soil processes on the acidification of water by acid 33 deposition. J. Environ. Qual. 14: 26-31. 34 Reuss, J. O.; Vertucci, F. A.; Musselman, R. C.; Sommerfeld, R. A. (1995) Chemical fluxes and 35 sensitivity to acidification of two high-elevation catchments in southern Wyoming. J. 36 Hydrol. 173: 165-189. 37 Richardson, D. H. S.; Cameron, R. P. (2004) Cyanolichens: their response to pollution and 38 possible management strategies for their conservation in notheastern North America. 39 Northeast. Nat. 11: 1-22. August 2008 B-195 DRAFT-DO NOT QUOTE OR CITE ------- 1 Richter, D. D.; Markewitz, D.; Wells, C. G.; Allen, H. L.; April, R.; Heine, P. R.; Urrego, B. 2 (1994) Soil chemical change during three decades in an old-field loblolly pine (Pinus 3 taeda L.) ecosystem. Ecology 75: 1463-1473. 4 Richter, D. D.; Markewitz, D.; Heine, P. R.; Jin, V.; Raikes, I; Tian, K.; Wells, C. G. (2000) 5 Legacies of agriculture and forest regrowth in the nitrogen of old-field soils. For. Ecol. 6 Manage. 138: 233-248. 7 Riggan, P. J.; Lockwood, R. N.; Lopez, E. N. (1985) Deposition and processing of airborne 8 nitrogen pollutants in Mediterranean-type ecosystems of southern California. Environ. 9 Sci. Technol. 19: 781-789. 10 Riggan, P. J.; Lockwood, R. N.; Jacks, P. M.; Colver, C. G.; Weirich, F.; DeBano, L. F.; Brass, J. 11 A. (1994) Effects of fire severity on nitrate mobilization in watersheds subject to chronic 12 atmospheric deposition. Environ. Sci. Technol. 28: 369-375. 13 Rochelle, B. P.; Church, M. R. (1987) Regional patterns of sulphur retention in watersheds of the 14 eastern U.S. Water Air Soil Pollut. 36(1-2): 61-73. 15 Rochelle, B. P.; Church, M. R.; David, M. B. (1987) Sulfur retention at intensively studied sites 16 in the U.S. and Canada. Water Air Soil Pollut. 33: 73-83. 17 Rosemond, A. D.; Reice, S. R.; Elwood, J. W.; Mulholland, P. J. (1992) The effects of stream 18 acidity on benthic invertebrate communities in the south-eastern United States. 19 Freshwater Biol. 27: 193-209. 20 Rosenqvist, I. (1978) Acid precipitation and other possible sources for acidification of rivers and 21 lakes. Sci. Total Environ. 10: 271-272. 22 Ross, D. S.; Lawrence, G. B.; Fredriksen, G. (2004) Mineralization and nitrification patterns at 23 eight northeastern USA forested research sites. For. Ecol. Manage. 188: 317-335. 24 Rosseland, B. O. (1986) Ecological effects of acidification on tertiary consumers: fish population 25 response. Water Air Soil Pollut. 30: 451-460. 26 Rosseland, B. O.; Skogheim, O. K. (1984) A comparative study on salmonid fish species in acid 27 aluminum-rich water. II. Physiological stress and mortality of one- and two-year-old fish. 28 Drottningholm, Sweden: Institute of Freshwater Research, report no. 61; pp. 186-194. 29 Rosseland, B. O.; Staurnes, M. (1994) Physiological mechanisms for toxic effects and resistance 30 to acidic water: an ecophysiological and ecotoxicological approach. In: Steinberg, C. E. 31 W.; Wright, R. F., eds. Acidification of freshwater ecosystems: implications for the 32 future. New York, NY: John Wiley & Sons Ltd.; pp. 227-246. 33 Rosseland, B. O.; Eldhuset, T. D.; Staurnes, M. (1990) Environmental effects of aluminum. 34 Environ. Geochem. Health 12: 17-27. 35 Rueth, H.; Baron, J. S. (2002) Differences in Englemann spruce forest biogeochemistry east and 36 west of the Continental Divide in Colorado, USA. Ecosystems 5: 45-57. 37 Rueth, H. M.; Baron, J. S.; Allstott, E. J. (2003) Responses of Engelmann spruce forests to 38 nitrogen fertilization in the Colorado Rocky Mountains. Ecol. Appl. 13: 664-673. 39 Rundle, S. D.; Hildrew, A. G. (1990) The distribution of micro-anthropods in some southern 40 English streams: the influence of physicochemistry. Freshwater Biol. 23: 411-431. August 2008 B-196 DRAFT-DO NOT QUOTE OR CITE ------- 1 Runn, P.; Johansson, N.; Milbrink, G. (1977) Some effects of low pH on the hatchability of eggs 2 of perch, Perca fluviatilis L. Zoon 5: 115-125. 3 Russell, C. A.; Kosola, K. R.; Paul, E. A.; Robertson, G. P. (2004) Nitrogen cycling in poplar 4 stands defoliated by insects. Biogeochemistry 68: 365-381. 5 Rustad, L. E.; Kahl, J. S.; Norton, S. A.; Fernandez, I. J. (1994) Underestimation of dry 6 deposition by throughfall in mixed northern hardwood forests. J. Hydrol. 162: 319-336. 7 Sadler, K.; Lynam, S. (1988) The influence of calcium on aluminium-induced changes in the 8 growth rate and mortality of brown trout, Salmo trutta L. J. Fish Biol. 33: 171-179. 9 Saros, J. E.; Interlandi, S. J.; Wolfe, A. P.; Engstrom, D. R. (2003) Recent changes in the diatom 10 community structure of lakes in the Beartooth Mountain Range, USA. Arct. Anarct. Alp. 11 Res. 35: 18-23. 12 Scheuhammer, A. M. (1988) Chronic dietary toxicity of methylmercury in the zebra finch, 13 Poephila guttata. Bull. Environ. Contam. Toxicol. 40: 123-130. 14 Schier, G. A. (1985) Response of red spruce and balsam fir seedlings to aluminum toxicity in 15 nutrient solutions. Can. J. For. Res. 15: 29-33. 16 Schimel, J. P.; Bennett, J. (2004) Nitrogen mineralization: challenges of a changing paradigm. 17 Ecology 85: 591-602. 18 Schindler, D. W. (1988) Effects of acid rain on freshwater ecosystems. Science (Washington, 19 DC) 239: 232-239. 20 Schindler, D. W. (1990) Experimental perturbations of whole lakes as tests of hypotheses 21 concerning ecosystem structure and function. Oikos 57: 25-41. 22 Schindler, D. W.; Mills, K. H.; Malley, D. F.; Findlay, D. L.; Schearer, J. A.; Davies, I. J.; 23 Turner, M. A.; Lindsey, G. A.; Cruikshank, D. R. (1985) Long-term ecosystem stress: 24 effects of years of experimental acidification on a small lake. Science (Washington, DC, 25 1979-1986)228:1395-1401. 26 Schlegel, H.; Amundson, R. G.; Hiittermann, A. (1992) Element distribution in red spruce (Picea 27 rubens) fine roots; evidence for aluminum toxicity at Whiteface Mountain. Can. J. Forest. 28 Res. 22: 1132-1138. 29 Schnitzer, M.; Skinner, S. I. M. (1963) Organo-metallic interactions in soils: 2. Reactions 30 between different forms of iron and aluminum and the organic matter of a podzol Bh 31 horizon. Soil Sci. 96: 181-186. 32 Schnoor, J. L.; Nikolaidis, N. P.; Glass, G. E. (1986) Lake resources at risk to acidic deposition 33 in the Upper Midwest. J. Water Pollut. Control Fed. 58: 139-148. 34 Schofield, C. L. (1993) Habitat suitability for Brook Trout (Salvelinus fontinalis) reproduction in 35 Adirondack lakes. Water Resour. Res. 29: 875-879. 36 Schofield, C. L.; Driscoll, C. T. (1987) Fish species distribution in relation to water quality 37 gradients in the North Branch of the Moose River Basin. Biogeochemistry 3: 63-85. 38 Schofield, C. L.; Keleher, C. (1996) Comparison of brook trout reproductive success and 39 recruitment in an acidic Adirondack lake following whole lake liming and watershed 40 liming. Biogeochemistry 32: 323-337. August 2008 B-197 DRAFT-DO NOT QUOTE OR CITE ------- 1 Schofield, C. L.; Trojnar, J. R. (1980) Aluminum toxicity to brook trout (Salvelinus fontinalis) in 2 acidified waters. In: Toribara, T. Y.; Miller, M. W; Morrow, P. E., eds. Polluted rain: 3 proceedings of the twelfth Rochester international conference on environmental toxicity; 4 May 1979; Rochester, NY. New York, NY: Plenum Press; pp. 341-366. (Hollaender, A.; 5 Probstein, R. F.; Welch, B. L., eds. Environmental science research: v. 17). 6 Schreck, C. B. (1981) Stress and rearing of salmonids. Aquaculture 28: 241-249. 7 Schreck, C. B. (1982) Stress and compensation in teleostean fishes: response to social and 8 physical factors. In: Pickering, A. D., ed. Stress and fish. New York, NY: Academic 9 Press; pp. 295-321. 10 Schroder, W. H.; Bauch, J.; Endeward, R. (1988) Microbeam analysis of Ca exchange and 11 uptake in the fine roots of spruce: influence of pH and aluminum. Trees (Heidelberg, 12 Ger.) 2: 96-103. 13 Scott, M. G.; Hutchinson, T. C.; Feth, M. J. (1989a) A comparison of the effects on Canadian 14 boreal forest lichens of nitric and sulphuric acids as sources of rain acidity. New Phytol. 15 111:663-671. 16 Scott, M. G; Hutchinson, T. C.; Feth, M. J. (1989b) Contrasting responses of lichens and 17 Vaccinium angustifolium to long-term acidification of a boreal forest ecosystem. Can. J. 18 Bot. 67: 579-588. 19 Seastedt, T. R.; Bowman, W. D.; Caine, T. N.; McKnight, D.; Townsend, A.; Williams, M. W. 20 (2004) The landscape continuum: a model for high elevation ecosystems. BioScience 54: 21 111-121. 22 Sharpe, W. E.; Kimmel, W. G.; Young, E. S., Jr.; DeWalle, D. R. (1983) In-situ bioassays offish 23 mortality in two Pennsylvania streams acidified by atmospheric deposition. Northeast. 24 Environ. Sci. 2: 171-178. 25 Shortle, W. C.; Smith, K. T. (1988) Aluminum-induced calcium deficiency syndrome in 26 declining red spruce. Science (Washington, DC) 240: 1017-1018. 27 Shortle, W. C.; Smith, K. T.; Minocha, R.; Lawrence, G. B.; David, M. B. (1997) Acidic 28 deposition, cation mobilization, and biochemical indicators of stress in healthy red 29 spruce. J. Environ. Qual. 26: 871-876. 30 Shugart, H. H.; McLaughlin, S. B. (1985) Modeling SCh effects on forest growth and community 31 dynamics. In: W.E. Winter, H. A. M., and R.A. Goldstein, ed. Sulfur dioxide and 32 vegetation: physiology, ecology, and policy issues. Stanford, CA: Stanford University 33 Press; pp. 478-491. 34 Siminon, H. A.; Kretser, W. A.; Bath, D. W.; Olson, M.; Gallagher, J. (1993) In situ bioassays of 35 brook trout (Salvelinus fontinalis) and blacknose dace (Rhinichthys atratulus) in 36 Adirondack streams affected by episodic acidification. Can. J. Forest. Res. 50: 902-912. 37 Simonsson, M.; Berggren, D. (1998) Aluminium solubility related to secondary solid phases in 38 upper B horizons with spodic characteristics. Eur. J. Soil Sci. 49: 317-326. 39 Simpson, K. W.; Bode, R. W.; Colquhoun, J. R. (1985) The macroinvertebrate fauna of an acid- 40 stressed headwater stream system in the Adirondack Mountains, New York. Freshwater 41 Biol. 15:671-681. August 2008 B-198 DRAFT-DO NOT QUOTE OR CITE ------- 1 Skjelkvale, B. L.; Wright, R. F.; Henriksen, A. (1998) Norwegian lakes show widespread 2 recovery from acidification; results from national surveys of lakewater chemistry 1986- 3 1997. Hydrol. Earth Syst. Sci. 2: 555-562. 4 Skjelkvale, B. L.; Stoddard, J. L.; Andersen, T. (2001) Trends in surface water acidification in 5 Europe and North America (1989-1998). Water Air Soil Pollut. 130: 787-792. 6 Skogheim, O. K.; Rosseland, B. O. (1986) Mortality of smolt of Atlantic salmon Salmo-salar L., 7 at low levels of aluminum in acidic softwater. Bull. Environ. Contam. Toxicol. 37: 258- 8 265. 9 Skyllberg, U. (1999) pH and solubility of aluminum in acidic forest soils: a consequence of 10 reactions between organic acidity and aluminum alkalinity. Eur. J. Soil Sci. 50: 95-106. 11 Smith, W. H. (1990) Forests as sinks for air contaminants: soil compartment. In: Air pollution 12 and forests: interactions between air contaminants and forest ecosystems. 2nd ed. New 13 York, NY: Springer-Verlag; pp. 113-146. (Springer series on environmental 14 management). 15 Smith, G. F.; Nicholas, N. S. (1999) Post disturbance spruce-fir forest stand dynamics at seven 16 disjunct sites. Castanea 64: 175-186. 17 Smock, L. A.; Gazzera, S. B. (1996) Effects of experimental episodic acidification on a 18 southeastern USA blackwater stream. J. Freshwater Ecol. 11: 81-90. 19 Soulsby, C.; Turnbull, D.; Langan, S. J.; Owen, R.; Hirst, D. (1995) Long-term trends in stream 20 chemistry and biology in northeast Scotland: evidence for recovery. Water Air Soil 21 Pollut. 85: 689-694. 22 Southern Appalachian Man and the Biosphere (SAMAB). (1996) The Southern Appalachian 23 Assessment. Aquatics technical report. Knoxville, TN: SAMAB Cooperative. Available: 24 http://samab.org/saa/reports/aquatic/aquatic.html [29 November, 2007]. 25 Spalding, M. G.; Forrester, D. J. (1991) Effects of parasitism and disease on the nesting success 26 of colonial wading birds (Ciconiiformes) in southern Florida. Tallahassee, FL: Florida 27 Game and Fresh Water Fish Commission, Nongame Wildlife Program; project NG88- 28 008. Prepared by: University of Florida, College of Veterinary Medicine, Department of 29 Infectious Diseases. 30 St. Louis, V. L.; Breebaart, L.; Barlow, J. C. (1990) Foraging behaviour of tree swallows over 31 acidified and nonacidic lakes. Can. J. Zool. 68: 2385-2392. 32 Stauffer, R. E. (1990) Granite weathering and the sensitivity of alpine lakes to acid deposition. 33 Limnol. Oceanogr. 35: 1112-1134. 34 Stauffer, R. E.; Wittchen, B. D. (1991) Effects of silicate weathering on water chemistry in 35 forested, upland, felsic terrain of the USA. Geochim. Cosmochim. Acta 55: 3253-3271. 36 Stienen, H.; Bauch, J. (1988) Element content in tissues of spruce seedlings from hydroponic 37 cultures simulating acidification and deacidification. Plant Soil 106: 231-238. 38 Stoddard, J. L. (1990) Plan for converting the NAPAP aquatic effects long-term monitoring 39 (LTM) project to the Temporally Integrated Monitoring of Ecosystems (TIME) project. 40 International report. Corvallis, OR: U.S. Environmental Protection Agency. August 2008 B-199 DRAFT-DO NOT QUOTE OR CITE ------- 1 Stoddard, J. L. (1991) Trends in Catskill stream water quality: evidence from historical data. 2 Water Resour. Res. 27: 2855-2864. 3 Stoddard, J. L. (1994) Long-term changes in watershed retention of nitrogen: its causes and 4 aquatic consequences. In: Baker, L. A., ed. Environmental chemistry of lakes and 5 reservoirs. Washington, DC: American Chemical Society; pp. 223-284. (Advances in 6 chemistry series no. 237). 7 Stoddard, J. L. (1995) Episodic acidification during snowmelt of high elevation lakes in the 8 Sierra Nevada mountains of California. Water Air Soil Pollut. 85: 353-358. 9 Stoddard, J. L.; Kellogg, J. H. (1993) Trends and patterns in lake acidification in the state of 10 Vermont: evidence from the long-term monitoring project. Water Air Soil Pollut. 67: 11 301-317. 12 Stoddard, J. L.; Murdoch, P. S. (1991) Catskill Mountains. In: Charles, D. F., ed. Acidic 13 deposition and aquatic ecosystems: regional case studies. New York, NY: Springer- 14 Verlag; pp. 237-271. 15 Stoddard, J. L.; Urquhart, N. S.; Newell, A. D.; Kugler, D. (1996) The Temporally Interated 16 Monitoring of Ecosystems (TIME) project design. 2. Detection of regional acidification 17 trends. Water Resour. Res. 32: 2529-2538. 18 Stoddard, J. L.; Driscoll, C. T.; Kahl, J. S.; Kellogg, J. H. (1998) A regional analysis of lake 19 acidification trends for the northeastern U.S., 1982-1994. Environ. Monit. Assess. 51: 20 399-413. 21 Stoddard, J. L.; Jeffries, D. S.; Lukewille, A.; Clair, T. A.; Dillon, P. J.; Driscoll, C. T.; Forsius, 22 M.; Johannessen, M.; Kahl, J. S.; Kellogg, J. H.; Kemp, A.; Mannio, J.; Monteith, D. T.; 23 Murdoch, P. S.; Patrick, S.; Rebsdorf, A.; Skjelkvale, B. L.; Stainton, M. P.; Traaen, T.; 24 Van Dam, H.; Webster, K. E.; Wieting, J.; Wilander, A. (1999) Regional trends in 25 aquatic recovery from acidification in North American and Europe. Nature (London, 26 U.K.) 401: 575-578. 27 Stoddard, J.; Kahl, J. S.; Deviney, F. A.; DeWalle, D. R.; Driscoll, C. T.; Herlihy, A. T.; 28 Kellogg, J. H.; Murdoch, P. S.; Webb, J. R.; Webster, K. E. (2003) Response of surface 29 water chemistry to the Clean Air Act Amendments of 1990. Research Triangle Park, NC: 30 U.S. Environmental Protection Agency, Office of Research and Development, National 31 Health and Environmental Effects Research Laboratory. EPA 620/R-03.001. 32 Stoermer, E. F.; Smol, J. P., eds. (1999) The diatoms: applications for the environmental and 33 earth sciences. New York, NY: Cambridge University Press. 34 Strengbom, J.; Nordin, A.; Nasholm, T.; Ericson, L. (2001) Slow recovery of boreal forest 35 ecosystem following decreased nitrogen input. Funct. Ecol. 15: 451-457. 36 Strickland, T. C.; Holdren, G. R., Jr.; Ringold, P. L.; Bernard, D.; Smythe, K.; Fallen, W. (1993) 37 A national critical loads framework for atmospheric deposition effects assessment: I. 38 Method summary. Environ. Manage. (N. Y.) 17: 329-334. 39 Sucoff, E. I; Thornton, F. C.; Joslin, J. D. (1990) Sensitivity of tree seedlings to aluminum: I. 40 Honeylocust. J. Environ. Qual. 19: 163-171. August 2008 B-200 DRAFT-DO NOT QUOTE OR CITE ------- 1 Sullivan, T. J. (1990) Historical changes in surface water acid-base chemistry in response to 2 acidic deposition. Washington, DC: National Acid Precipitation Assessment Program; 3 State of the Science; SOS/T11. 4 Sullivan, T. J. (1993) Whole ecosystem nitrogen effects research in Europe. Environ. Sci. 5 Technol. 27: 1482-1486. 6 Sullivan, T. J. (2000) Aquatic effects of acidic deposition. Boca Raton, FL: Lewis Publishers. 7 Sullivan, T. J.; Charles, D. F. (1994) The feasibility and utility of a paleolimnology/paleoclimate 8 data cooperative for North America. J. Paleolimnol. 10: 265-273. 9 Sullivan, T. J.; Cosby, B. J. (1998) Modeling the concentration of aluminium in surface waters. 10 Water Air Soil Pollut. 105: 643-659. 11 Sullivan, T. J.; Eilers, J. M.; Church, M. R.; Blick, D. J.; Eshleman, K. N.; Landers, D. H.; 12 DeHaan, M. S. (1988) Atmospheric wet sulphate deposition and lakewater chemistry. 13 Nature (London) 331: 607-609. 14 Sullivan, T. J.; Charles, D. F.; Smol, J. P.; Cumming, B. F.; Selle, A. R.; Thomas, D. R.; Bernert, 15 J. A.; Dixit, S. S. (1990) Quantification of changes in lakewater chemistry in response to 16 acidic deposition. Nature (London, U.K.) 345: 54-58. 17 Sullivan, T. J.; Turner, R. S.; Charles, D. F.; Cumming, B. F.; Smol, J. P.; Schofield, C. L.; 18 Driscoll, C. T.; Cosby, B. J.; Birks, H. J. B.; Uutala, A. J.; Kingston, J. C.; Dixit, S. S.; 19 Bernert, J. A.; Ryan, P. F.; Marmorek, D. R. (1992) Use of historical assessment for 20 evaluation of process-based model projections of future environmental change: lake 21 acidification in the Adirondack Mountains, New York, U.S.A. Environ. Pollut. 77: 253- 22 262. 23 Sullivan, T. J.; Cosby, B. J.; Driscoll, C. T.; Charles, D. F.; Hemond, H. F. (1996a) Influence of 24 organic acids on model projections of lake acidification. Water Air Soil Pollut. 91: 271- 25 282. 26 Sullivan, T. J.; McMartin, B.; Charles, D. F. (1996b) Re-examination of the role of landscape 27 change in the acidification of lakes in the Adirondack Mountains, New York. Sci. Total 28 Environ. 183:231-248. 29 Sullivan, T. J.; Eilers, J. M.; Cosby, B. J.; Vache, K. B. (1997) Increasing role of nitrogen in the 30 acidification of surface waters in the Adirondack Mountains, New York. Water Air Soil 31 Pollut. 95:313-336. 32 Sullivan, T. J.; Cosby, B. J.; Webb, J. R.; Snyder, K. U.; Herlihy, A. T.; Bulger, A. J.; Gilbert, E. 33 H.; Moore, D. (2002) Assessment of the effects of acidic deposition on aquatic resources 34 in the Southern Appalachian Mountains. Report prepared for the Southern Appalachian 35 Mountains Initiative (SAMI). Corvallis, OR: E&S Environmental Chemistry, Inc. 36 Sullivan, T. J.; Cosby, B. J.; Laurence, J. A.; Dennis, R. L.; Savig, K.; Webb, J. R.; Bulger, A. J.; 37 Scruggs, M.; Gordon, C.; Ray, J.; Lee, H.; Hogsett, W. E.; Wayne, H.; Miller, D.; Kern, 38 J. S. (2003) Assessment of air quality and related values in Shenandoah National Park. 39 Philadelphia, PA: U.S. Department of the Interior, National Park Service, Northeast 40 Region; technical report NPS/NERCHAL/NRTR-03/090. Available: 41 http://www.nps.gov/nero/science/FINAL/shen_air_quality/shen_airquality.html [19 June, 42 2006]. August 2008 B-201 DRAFT-DO NOT QUOTE OR CITE ------- 1 Sullivan, T. I; Cosby, B. I; Herlihy, A. T.; Webb, J. R.; Bulger, A. I; Snyder, K. U.; Brewer, P. 2 F.; Gilbert, E. H.; Moore, D. L. (2004) Regional model projections of future effects of 3 sulfur and nitrogen deposition on streams in the southern Appalachian Mountains. Water 4 Resour. Res. 40(W02101): 10.1029/2003WR001998. 5 Sullivan, T. J.; Cosby, B. J.; Tonnessen, K. A.; Clow, D. W. (2004) Surface water acidification 6 responses and critical loads of sulfur and nitrogen deposition in Loch Vale watershed, 7 Colorado. Water Resour. Res. 41(W01021): 10.1029/2004WR003414. 8 Sullivan, T. J.; Driscoll, C. T.; Cosby, B. J.; Fernandez, I. J.; Herlihy, A. T.; Zhai, J.; Stemberger, 9 R.; Snyder, K. U.; Sutherland, J. W.; Nierzwicki-Bauer, S. A.; Boylen, C. W.; 10 McDonnell, T. C.; Nowicki, N. A. (2006a) Assessment of the extent to which 11 intensively-studied lakes are representative of the Adirondack Mountain region. Final 12 report. Albany, NY: New York State Energy Research and Development Authority 13 (NYSERDA); report 06-17. Available: 14 http://nysl.nysed.gOv/uhtbin/cgisirsi/Qcwd6NzFby/NYSL/138650099/8/4298474 [1 15 November, 2007]. 16 Sullivan, T. J.; Fernandez, I. J.; Herlihy, A. T.; Driscoll, C. T.; McDonnell, T. C.; Nowicki, N. 17 A.; Snyder, K. U.; Sutherland, J. W. (2006b) Acid-base characteristics of soils in the 18 Adirondack Mountains, New York. Soil Sci. Soc. Am. J. 70: 141-152. 19 Sullivan, T. J.; Webb, J. R.; Snyder, K. U.; Herlihy, A. T.; Cosby, B. J. (2007) Spatial 20 distribution of acid-sensitive and acid-impacted streams in relation to watershed features 21 in the southern Appalachian mountains. Water Air Soil Pollut. 182: 57-71. 22 Swank, W. T. (1988) Stream chemistry responses to disturbance. In: Swank, W. T.; Crossley, D. 23 S., eds. Forest hydrology and ecology at Coweeta. New York, NY: Springer-Verlag, 24 Inc.; pp. 339-358. 25 Sweets, P. R. (1992) Diatom paleolimnological evidence for lake acidification in the Trail Ridge 26 region of Florida. Water Air Soil Pollut. 65: 43-57. 27 Sweets, P. R.; Binert, R. W.; Cusimono, T. L.; Binford, M. W. (1990) Paleoecological 28 investigations of recent lake acidification in northern Florida. J. Paleolimnol. 4: 103-137. 29 Swenson, W. A.; McCormick, J. H.; Simonson, T. D.; Jensen, K. M.; Eaton, J. G. (1989) 30 Experimental acidification of Little Rock Lake (Wisconsin): fish research approach and 31 early responses. Arch. Environ. Contam. Toxicol. 18: 167-174. 32 Taylor, G. E., Jr.; Pitelka, L. F. (1992) Genetic diversity of plant populations and the role of air 33 pollution. In: Barker, J. R.; Tingey, D. T., eds. Air pollution effects on biodiversity. New 34 York, NY: Van Nostrand Reinhold; pp. 111-130. 35 Taylor, G. E., Jr.; Hanson, P. J.; Baldocchi, D. D. (1988) Pollutant deposition to individual 36 leaves and plant canopies: sites of regulation and relationship to injury. In: Heck, W. W.; 37 Taylor, O. C.; Tingey, D. T., eds. Assessment of crop loss from air pollutants. New York, 38 NY: Elsevier Applied Science; pp. 227-257. 39 Temple, P. J.; Riechers, G. H.; Miller, P. R. (1992) Foliar injury responses of ponderosa pine 40 seedlings to ozone, wet and dry acidic deposition, and drought. Environ. Exp. Bot. 32: 41 101-113. 42 Thornton, F. C.; Schaedle, M.; Raynal, D. J. (1986a) Effect of aluminum on the growth of sugar 43 maple in solution culture. Can. J. For. Res. 16: 892-896. August 2008 B-202 DRAFT-DO NOT QUOTE OR CITE ------- 1 Thornton, F. C.; Schaedle, M.; Raynal, D. J. (1986b) Effects of aluminum on the growth, 2 development, and nutrient composition of honeylocust (Gleditia triacanthos L.) seedling. 3 TreePhysiol. 2: 307-316. 4 Thornton, F. C.; Schaedle, M.; Raynal, D. J.; Zipperer, C. (1986c) Effects of aluminum on 5 honeylocust (Gleditsia triacanthos L.) seedlings in solution culture. J. Exp. Bot. 37: 775- 6 785. 7 Thornton, F. C.; Schaedle, M.; Raynal, D. J. (1987) Effects of aluminum on red spruce seedlings 8 in solution culture. Environ. Exp. Bot. 27: 489-498. 9 Tietema, A.; Beier, C. (1995) A correlative evaluation of nitrogen cycling in the forest 10 ecosystems of the EC projects NITREX and EXMAN. For. Ecol. Manage. 71: 143-151. 11 Tietge, J. E.; Johnson, R. D.; Bergman, H. L. (1988) Morphometric changes in gill secondary 12 lamellae of brook trout (Salvelinus fontinalis) after long-term exposure to acid and 13 aluminum. Can. J. Fish. Aquat. Sci. 45: 1643-1648. 14 Tipping, E.; Berggren, D.; Mulder, J.; Woof, C. (1995) Modelling the solid-solution distributions 15 of protons, aluminium, base cations and humic substances in acid soils. Eur. J. Soil Sci. 16 46: 77-94. 17 Tipping, E.; Bass, J. A. B.; Hardie, D.; Haworth, E. Y.; Hurley, M. A.; Wills, G. (2002) 18 Biological responses to the reversal of acidification in surface waters of the English Lake 19 District. Environ. Pollut. 116: 137-146. 20 Tonnessen, K. A. (1991) The Emerald Lake Watershed Study: introduction and site description. 21 Water Resour. Res. 27: 1537-1539. 22 Townsend, C. R.; Hildrew, A. G.; Francis, J. (1983) Community structure in some southern 23 English streams: the influence of physiochemical factors. Freshw. Biol. 13: 521-544. 24 Treshow, M. (1980) Pollution effects on plant distribution. Environ. Conserv. 7: 279-286. 25 Trojnar, J. R. (1977) Egg and larval survival of white suckers (Catostomus commersoni) at low 26 pH. J. Fish. Res. Board Can. 34: 262-266. 27 Truman, R. A.; Humphreys, F. R.; Ryan, P. J. (1986) Effect of varying solution ratios of Al to Ca 28 and Mg on the uptake of phosphorus by Pinus radiata. Plant Soil 96: 109-123. 29 Turk, J. T.; Spahr, N. E. (1991) Rocky Mountains. In: Charles, D. F., ed. Acidic deposition and 30 aquatic ecosystems: regional case studies. New York, NY: Springer-Verlag, Inc.; pp. 31 471-501. 32 Turk, J. T.; Campbell, D. H.; Spahr, N. E. (1992) Initial findings of synoptic snowpack sampling 33 in Colorado Rocky Mountains. Denver, CO: U.S. Department of the Interior, Geologic 34 Survey; open-file report 92-645. 35 Turk, J. T.; Campbell, D. H.; Spahr, N. E. (1993) Use of chemistry and stable sulfur isotopes to 36 determine sources of trends in sulfate of Colorado lakes. Water Air Soil Pollut. 67: 415- 37 431. 38 Turner, R. S.; Cook, R. B.; Van Miegroet, H.; Johnson, D. W.; Elwood, J. W.; Bricker, O. P.; 39 Lindberg, S. E.; Hornberger, G. M. (1991) Watershed and lake processes affecting 40 surface water acid-base chemistry. In: Irving, P. M., ed. Acidic deposition: state of 41 science and technology, volume II, aquatic processes and effects. Washington, DC: The August 2008 B-203 DRAFT-DO NOT QUOTE OR CITE ------- 1 U.S. National Acid Precipitation Assessment Program. (Acidic deposition: state of 2 science and technology report 10). 3 Turner, R. S.; Ryan, P. F.; Marmorek, D. R.; Thornton, K. W.; Sullivan, T. I; Baker, J. P.; 4 Christensen, S. W.; Sale, M. J. (1992) Sensitivity to change for low-ANC eastern US 5 lakes and streams and brook trout populations under alternative sulfate deposition 6 scenarios. Environ. Pollut. 77: 269-277'. 7 Turnpenny, A. W. H.; Sadler, K.; Aston, R. J.; Milner, A. G. P.; Lynam, S. (1987) The fish 8 populations of some streams in Wales and northern England in relation to acidity and 9 associated factors. J. Fish Biol. 31: 415-434. 10 Tyler, S. J.; Ormerod, S. J. (1992) A review of the likely causal pathways relating the reduced 11 density of breeding dippers Cinclus cinclus to the acidification of upland streams. 12 Environ. Pollut. 78: 49-55. 13 U.S. Environmental Protection Agency. (1982) Air quality criteria for particulate matter and 14 sulfur oxides. Research Triangle Park, NC: Office of Health and Environmental 15 Assessment, Environmental Criteria and Assessment Office; EPA report no. EPA-600/8- 16 82-029aF-cF. 3v. Available from: NTIS, Springfield, VA; PB84-156777. 17 U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen. 18 Research Triangle Park, NC: Office of Health and Environmental Assessment, 19 Environmental Criteria and Assessment Office; report nos. EPA/600/8-91/049aF-cF. 3v. 20 Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517. 21 U.S. Environmental Protection Agency. (1995) Acid deposition standard feasibility study. Report 22 to Congress. Washington, DC: Office of Air and Radiation; report no. EPA 430-R-95- 23 00 la. 24 U.S. Environmental Protection Agency. (2004) Air quality criteria for particulate matter. 25 Research Triangle Park, NC: National Center for Environmental Assessment; report no. 26 EPA/600/P-99/002aF-bF. 2v. Available: http://cfpub.epa.gov/ncea/ [9 November, 2004]. 27 U.S. Environmental Protection Agency. (2006) Wadeable streams assessment: a collaborative 28 survey of the nation's streams. Washington, DC: Office of Research and Development, 29 Office of Water; report no. EPA 841-B-06-002. Available: 30 http://www.epa.gov/owow/streamsurvey/pdf/WS A_Assessment_May2007.pdf [30 31 November, 2007]. 32 Ulrich, B. (1983) Soil acidity and its relations to acid deposition. In: Ulrich, B.; Pankrath, J., eds. 33 Effects of accumulation of air pollutants in forest ecosystems: proceedings of a 34 workshop; May 1982; Gottingen, Federal Republic of Germany. Dordrecht, The 35 Netherlands: D. Reidel Publishing Company; pp. 127-146. 36 Ulrich, B.; Mayer, R.; Khanna, P. K. (1980) Chemical changes due to acid precipitation in loess- 37 derived soil in central Europe. Soil Sci. 130: 193-199. 38 Urquhart, N. S.; Paulsen, S. G.; Larsen, D. P. (1988) Monitoring for regional and policy-relevant 39 trends over time. Ecol. Appl. 8: 246-257. 40 Van Breemen, N.; Mulder, J.; Driscoll, C. T. (1983) Acidification and alkalinization of soils. 41 Plant Soil 75: 283-308. August 2008 B-204 DRAFT-DO NOT QUOTE OR CITE ------- 1 Van Breemen, N.; Giesler, R.; Olsson, M.; Finlay, R.; Lundstrom, U.; Jongmans, A. G. (2000) 2 Mycorrhizal weathering: A true case of mineral plant nutrition? Biogeochemistry 49: 53- 3 67. 4 Van Haluwyn, C.; van Herk, C. M. (2002) Bioindication: the community approach. In: Nimis, P. 5 L.; Scheidegger, C.; Wolseley, P. A., eds. Monitoring with lichens-monitoring lichens. 6 Dordrecht, The Netherlands: Kluwer Academic Publishers; pp. 39-64. 7 Van Herk, C. M. (2001) Bark pH and susceptibility to toxic air pollutants as independent causes 8 of changes in epiphytic lichen composition in space and time. Lichenologist 33(5): 419- 9 441. 10 Van Miegroet, H.; Cole, D. W.; Foster, N. W. (1992a) Nitrogen distribution and cycling. In: 11 Johnson, D. W.; Lindberg, S. E., eds. Atmospheric deposition and forest nutrient cycling: 12 a synthesis of the integrated forest study. New York, NY: Springer-Verlag; pp. 178-196. 13 (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. Ecological 14 studies: analysis and synthesis: v. 91). 15 Van Miegroet, H.; Johnson, D. W.; Cole, D. W. (1992b) Analysis of N cycles in polluted vs 16 unpolluted environment. In: Johnson, D. W.; Lindberg, S. E., eds. Atmospheric 17 deposition and forest nutrient cycling: a synthesis of the integrated forest study. New 18 York, NY: Springer-Verlag; pp. 199-202. (Billings, W. D.; Golley, F.; Lange, O. L.; 19 Olson, J. S.; Remmert, H., eds. Ecological studies: analysis and synthesis: v. 91). 20 Van Miegroet, H. V.; Johnson, D. W.; Burt, T. P.; Heathwaite, A. L.; Trudgill, S. T. (1993) 21 Nitrate dynamics in forest soils. In: Burt, T. P.; Heathwaite, A. L.; Trudgill, S. T., eds. 22 Nitrate: processes, patterns and management. New York, NY: John Wiley & Sons; pp. 23 75-97. 24 Van Miegroet, H.; Moore, P. T.; Tewksbury, C. E.; Nicholas, N. S. (2007) Carbon sources and 25 sinks in high-elevation spruce-fir forests of the southeastern US. For. Ecol. Manage. 238: 26 249-260. 27 Van Sickle, J.; Church, M. R. (1995) Nitrogen bounding study, methods for estimating the 28 relative effects of sulfur and nitrogen depostion on surface water chemistry. Corvallis, 29 OR: U.S. Environmental Protection Agency, National Health and Environment Effects 30 Research Laboratory; report no. EPA/600/R-95/172. Available from: NTIS, Springfield, 31 VA;PB96-133921. 32 Van Sickle, J.; Baker, J. P.; Simonin, H. A.; Baldigo, B. P.; Kretser, W. A.; Sharpe, W. E. (1996) 33 Episodic acidification of small streams in the northeastern United States: fish mortality in 34 field bioassays. Ecol. Appl. 6: 408-421. 35 Vertucci, F. A.; Eilers, J. M. (1993) Issues in monitoring wilderness lake chemistry: a case study 36 in the Sawtooth Mountains, Idaho. Environ. Monit. Assess. 28: 277-294. 37 Vitousek, P. M.; Aber, J. D.; Howarth, R. W.; Likens, G. E.; Matson, P. A.; Schindler, D. W.; 38 Schlesinger, W. H.; Tilman, D. G. (1997) Human alteration of the global nitrogen cycle: 39 sources and consequences. Ecol. Appl. 7: 737-750. 40 Vogt, K. A.; Dahlgren, R.; Ugolini, F.; Zabowski, D.; Moore, E. E.; Zasoski, R. (1987a) 41 Aluminum, Fe, Ca, Mg, K, Mn, Cu, Zn and P in above- and belowground biomass. I. 42 Abies amabilis and Tsuga mertensiana. Biogeochemistry 4: 277-294. August 2008 B-205 DRAFT-DO NOT QUOTE OR CITE ------- 1 Vogt, K. A.; Dahlgren, R.; Ugolini, F.; Zabowski, D.; Moore, E. E.; Zasoski, R. (1987b) 2 Aluminum, Fe, Ca, Mg, K, Mn, Cu, Zn and P in above- and belowground biomass. II. 3 Pools, and circulation in a subalpine Abies amabilis stand. Biogeochemistry 4: 295-311. 4 Waiwood, B. A.; Haya, K. (1983) Levels of chorionase activity during embryonic development 5 of Salmo salar under acid conditions. Bull. Environ. Contam. Toxicol. 30: 511-515. 6 Wake, D. B. (1991) Declining amphibian populations. Science (Washington, DC) 253: 860. 7 Wales, D. L.; Liimatainen, V. A. (1987) Preliminary assessment of the current impact and 8 potential risk of acidic deposition on walleye populations in Ontario. Toronto, ON, 9 Canada: Ministry of Natural Resources; Ontario Fisheries acidification report series no. 10 87-11. 11 Wallace, J. B.; Webster, J. R.; Lowe, R. L. (1992) High-gradient streams of the Appalachians. 12 In: Hackney, C. T.; Adams, S. M.; Martin, W. H., eds. Biodiversity of southeastern 13 United States aquatic communities. New York, NY: John Wiley & Sons; pp. 133-192. 14 Warby, R. A. F.; Johnson, C. E.; Driscoll, C. T. (2005) Chemical recovery of surface waters 15 across the northeastern U.S. from reduced inputs of acidic deposition: 1984-2001. 16 Environ. Sci. Tech. 39: 6548-6554. 17 Wargo, P. M.; Tilley, J.; Lawrence, G.; David, M.; Vogt, K.; Vogt, D.; Holifield, Q. (2003) 18 Vitality and chemistry of roots of red spruce in forest floors of stands with a gradient of 19 soil Al/Ca ratios in the northeastern United States. Can. J. Forest Res. 33: 635-652. 20 Wayland, M.; McNicol, D. K. (1990) Status report on the effects of acid precipitation on 21 common loon reproduction in Ontario: the Ontario Lakes loon survey. Ottawa, ON, 22 Canada: Canadian Wildlife Service. 23 Weathers, K. C.; Lovett, G. M.; Likens, G. E.; Caraco, N. F. M. (2000) Cloudwater inputs of 24 nitrogen to forest ecosystems in southern Chile: forms, fluxes, and sources. Ecosystems 25 3: 590-595. 26 Webb, J. R. (1999) Synoptic stream water chemistry. In: Bulger, A. J.; Cosby, B. J.; Dolloff, C. 27 A.; Eshleman, K. N.; Webb, J. R.; Galloway, J. N., eds. Shenandoah National Park: fish 28 in sensitive habitats (SNP:FISH); final report vol. II, Ch. 3. Prepared for: National Park 29 Service. Charlottesville, VA: University of Virginia, Department of Environmental 30 Sciences. 31 Webb, J. R.; Deviney, F. A.; Galloway, J. N.; Rinehart, C. A.; Thompson, P. A.; Wilson, S. 32 (1994) The acid-base status of native brook trout streams in the mountains of Virginia. 33 Charlottesville, VA: University of Virginia, Department of Environmental Sciences. 34 Webb, J. R.; Cosby, B. J.; Deviney, F. A., Jr.; Eshleman, K. N.; Galloway, J. N. (1995) Change 35 in the acid-base status of an Appalachian Mountain catchment following forest 36 defoliation by the gypsy moth. Water Air Soil Pollut. 85: 535-540. 37 Webb, J. R.; Cosby, B. J.; Deviney, F. A.; Galloway, J. N.; Maben, S. W.; Bulger, A. J. (2004) 38 Are brook trout streams in western Virginia and Shenandoah National Park recovering 39 from acidification? Environ. Sci. Technol. 38: 4091-4096. 40 Webster, K. E.; Newell, A. D.; Baker, L. A.; Brezonik, P. L. (1990) Climatically induced rapid 41 acidification of a softwater seepage lake. Nature (London, U.K.) 347: 374-376. August 2008 B-206 DRAFT-DO NOT QUOTE OR CITE ------- 1 Webster, K. E.; Brezonik, P. L.; Holdhusen, B. J. (1993) Temporal trends in low alkalinity lakes 2 of the Upper Midwest (1983-1989). Water Air Soil Pollut. 67: 397-414. 3 Webster, K. L.; Creed, I. F.; Nicholas, N. S.; Miegroet, H. V. (2004) Exploring interactions 4 between pollutant emissions and climatic variability in growth of red spruce in the Great 5 Smoky Mountains National Park. Water Air Soil Pollut. 159: 225-248. 6 Wedemeyer, G. A.; Barton, B. A.; MeLeay, D. J. (1990) Stress and acclimation. In: Schreck, C. 7 B.; Moyle, P. B., eds. Methods for fish biology. Bethesda, MD: American Fisheries 8 Society; pp. 178-196. 9 Westman, W. E.; Preston, K. P.; Weeks, L. B. (1985) SO2 effects on the growth of native plants. 10 In: Winter, W. E.; Mooney, H. A.; Goldstein, R. A., eds. Sulfur dioxide and vegetation: 11 physiology, ecology, and policy issues. Stanford, CA: Stanford University Press; pp. 264- 12 279. 13 Wetmore, C. M. (1985) Lichens and air quality in Isle Royale National Park. Denver, CO: U.S. 14 Department of the Interior, National Park Service. 15 Whiting, M. C.; Whitehead, D. R.; Holmes, R. W.; Norton, S. A. (1989) Paleolimnological 16 reconstruction of recent acidity changes in four Sierra Nevada lakes. J. Paleolimnol. 2: 17 285-304. 18 Wiemeyer, S. N.; Schmeling, S. K.; Anderson, A. (1987) Environmental pollutant and necropsy 19 data for ospreys from the eastern United States, 1975-1982. J. Wildl. Dis. 23: 279-291. 20 Wigington, P. J., Jr. (1999) Episodic acidification: causes, occurrence and significance to aquatic 21 resources. In: Drohan, J. R., ed. The effects of acidic deposition on aquatic ecosystems in 22 Pennsylvania: proceedings of the 1998 PA acidic deposition conference. University Park, 23 PA: Environmental Resources Research Institute; pp. 1-5. 24 25 Wigington, P. J., Jr.; Davies, T. D.; Tranter, M.; Eshleman, K. (1991) Episodic acidification of 26 surface waters due to acidic deposition. In: Irving, P. M., ed. Acidic deposition: state of 27 science and technology, volume II, aquatic processes and effects. Washington, DC: The 28 U.S. National Acid Precipitation Assessment Program. (State of science and technology 29 report 12). 30 Wigington, P. J.; Baker, J. P.; DeWalle, D. R.; Kretser, W. A.; Murdoch, P. S.; Siminon, H. A.; 31 Van Sickle, J.; McDowell, M. K.; Peck, D. V.; Barchet, W. R. (1993) Episodic 32 acidification of streams in the northeastern United States: chemical and biological results 33 of the Episodic Response Project. Washington, DC: U.S. Environmental Protection 34 Agency; report no. EPA/600/R-93/190. 35 Wigington, P. J., Jr.; DeWalle, D. R.; Murdoch, P. S.; Kretser, W. A.; Simonin, H. A.; Van 36 Sickle, J.; Baker, J. P. (1996) Episodic acidification of small streams in the northeastern 37 United States: ionic controls of episodes. Ecol. Appl. 6: 389-407. 38 Williams, M. W.; Melack, J. M. (1991) Precipitation chemistry in and ionic loading to an alpine 39 basin, Sierra Nevada. Water Resour. Res. 27: 1563-1574. 40 Williams, M.; Tonnessen, K. (2000) Critical loads for inorganic nitrogen deposition in the 41 Colorado Front Range, USA. Ecol. Appl. 10: 1648-1665. August 2008 B-207 DRAFT-DO NOT QUOTE OR CITE ------- 1 Williams, M. W.; Bales, R. C.; Brown, A. D.; Melack, J. M. (1995) Fluxes and transformation of 2 nitrogen in a high-elevation catchment, Sierra Nevada. Biogeochemistry 28: 1-31. 3 Williams, M. W.; Baron, J. S.; Caine, N.; Sommerfeld, R.; Sanford, R. (1996a) Nitrogen 4 saturation in the Rocky Mountains. Environ. Sci. Technol. 30: 640-646. 5 Williams, M. W.; Platts-Mills, T.; Caine, N. (1996b) Landscape controls on surface water nitrate 6 concentrations at catchment and regional scales in the Colorado Rocky Mountains. Sun 7 River, OR: Chapman conference: nitrogen cycling in forested catchments. 8 Winner, W. E.; Atkinson, C. J. (1986) Absorption of air pollution by plants, and consequences 9 for growth. Trends Ecol. Evol. 1: 15-18. 10 Winterbourn, M. J.; Collier, K. J. (1987) Distribution of benthic invertebrates in acid, brown 11 water streams in the South Island of New Zealand. Hydrobiologia 153: 255-286. 12 Wodzinski, R. S.; Labeda, D. P.; Alexander, M. (1977) Toxicity of SO2 and NOX: selective 13 inhibition of blue-green algae by bisulfite and nitrite. J. Air Pollut. Control Assoc. 27: 14 891-893. 15 Wolfe, M. H.; Joslin, J. D. (1989) Honeylocust (Gleditsia triacanthos L.) root response to 16 aluminum and calcium. Plant Soil 119: 181-185. 17 Wolfe, M. F.; Schwarzbach, S.; Sulaiman, R. A. (1998) Effects of mercury on wildlife: a 18 comprehensive review. Environ. Toxicol. Chem. 17: 146-160. 19 Wolfe, A. P.; Van Gorpe, A. C.; Baron, J. S. (2003) Recent ecological and biogeochemical 20 changes in alpine lakes of Rocky Mountain National Park (Colorado, USA): a response to 21 anthropogenic nitrogen deposition. Geobiology 1(2): 153-168. 22 Wood, C. M. (1989) The physiological problems offish in acid waters. In: Morris, R.; Taylor, E. 23 W.; Brown, D. J. A.; Brown, J. A., eds. Acid toxicity and aquatic animals. Cambridge, 24 United Kingdom: Cambridge University Press; pp. 125-152. 25 Wood, C. M.; McDonald, D. G. (1982) Physiological mechanisms of acid toxicity to fish. In: 26 Johnson, R. E., ed. Acid rain/fisheries. Bethesda, MD: American Fisheries Society; pp. 27 197-226. 28 Wood, C. M.; McDonald, D. G. (1987) The physiology of acid/aluminum stress in trout. Ann. 29 Soc. R. Zool. Belg. 177(suppl 1): 399-410. 30 Wood, C. M.; McDonald, D. G.; Ingersoll, C. G.; Mount, D. R.; Johannsson, O. E.; Landsberger, 31 S.; Bergman, H. L. (1990) Effects of water acidity, calcium, and aluminum on whole 32 body ions of brook trout (Salvelinus fontinalis) continuously exposed from fertilization to 33 swim-up: a study by instrumental neutron activation analysis. Can. J. Fish. Aquat. Sci. 34 47: 1593-1603. 35 Woodward, D. F. (1991) Sensitivity of greenback cutthroat trout to acidic pH and elevated 36 aluminum. Trans. Am. Fish. Soc. 120: 34-42. 37 Woodward, D. F.; Farag, A. M.; Mueller, M. E.; Little, E. E.; Vertucci, F. A. (1989) Sensitivity 38 of endemic Snake River cutthroat trout to acidity and elevated aluminum. Trans. Am. 39 Fish. Soc. 118: 630-641. August 2008 B-208 DRAFT-DO NOT QUOTE OR CITE ------- 1 Woodwell, G. M. (1970) Effects of pollution on the structure and physiology of ecosystems: 2 changes in natural ecosystems caused by many different types of disturbances are similar 3 and predictable. Science (Washington, DC) 168: 429-433. 4 Wright, R. F.; Snekvik, E. (1978) Acid precipitation: chemistry and fish populations in 700 lakes 5 in southernmost Norway. Verh. Int. Ver. Theor. Angew. Limnol. 20: 765-775. 6 Wright, R. F.; Lotse, E.; Semb, A. (1988a) Reversibility of acidification shown by whole- 7 catchment experiments. Nature (London, U.K.) 334: 670-675. 8 Wright, R. F.; Norton, S. A.; Brakke, D. F.; Frogner, T. (1988b) Experimental verification of 9 episodic acidification of freshwaters by sea salts. Nature (London) 334: 422-424. 10 Wright, R. F.; Lotse, E.; Semb, A. (1993) RAIN Project: results after 8 years of experimentally 11 reduced acid ceposition to a whole catchment. Can. J. Fish. Aquat. Sci. 50: 258-268. 12 Wright, R. F.; Larssen, T.; Camarero, L.; Cosby, B. J.; Ferrier, R. C.; Helliwell, R.; Forsius, M.; 13 Jenkins, A.; Kopacek, J.; Majer, V.; Moldan, F.; Posch, M.; Rogora, M.; Schopp, W. 14 (2005) Recovery of acidified European surface waters. Environ. Sci. Technol. 39: 64A- 15 72 A. 16 Yan, N. D.; Keller, W.; Somers, K. M.; Pawson, T. W.; Girard, R. E. (1996a) Recovery of 17 crustacean zooplankton communities from acid and metal contamination: comparing 18 manipulated and reference lakes. Can. J. Fish. Aquat. Sci. 53: 1301-1327. 19 Yan, N. D.; Welsh, P. G.; Lin, H.; Taylor, D. J.; Filion, J.-M. (1996b) Demographic and genetic 20 evidence of the long-term recovery of Daphnia galeata mendotae (Crustacea: Daphniidae) 21 in Sudbury lakes following additions of base: the role of metal toxicity. Can. J. Fish. 22 Aquat. Sci. 53: 1328-1344. 23 Yanai, R. D.; Siccama, T. G.; Arthur, M. A.; Federer, C. A.; Friedland, A. J. (1999) 24 Accumulation and depletion of base cations in forest floors in the northeastern United 25 States. Ecology 80: 2774-2787. 26 Yanai, R. D.; Phillips, R. P.; Arthur, M. A.; Siccama, T. G.; Hane, E. N. (2005) Spatial and 27 temporal variation in calcium and aluminum in northern hardwood forest floors. Water 28 Air Soil Pollut. 160: 109-118. 29 Zhai, J.; Driscoll, C. T.; Sullivan, T. J.; Cosby, B. J. (2008) Regional application of the PnET- 30 BGC model to assess historical acidification of Adirondack Lakes. Water Resour. Res.: 31 submitted. 32 Zillioux, E. J.; Porcella, D. B.; Benoit, J. M. (1993) Mercury cycling and effects in freshwater 33 wetland ecosystems. Environ. Toxicol. Chem. 12: 2245-2264. 34 Zulla, Y.; Billet, M. F. (1994) Long-term changes in chemical-weathering rates between 1949-50 35 and 1987 in forest soils from northeast Scotland. Eur. J. Soil Sci. 45: 327-335. August 2008 B-209 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-1 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-2 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-3 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-4 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-5 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 C-6 DRAFT-DO NOT QUOTE OR CITE ------- 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 ------- 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 August 2008 C-14 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-15 DRAFT-DO NOT QUOTE OR CITE ------- 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 C-16 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 C-17 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 C-18 DRAFT-DO NOT QUOTE OR CITE ------- (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 August 2008 C-19 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 C-20 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-21 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 C-22 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-23 DRAFT-DO NOT QUOTE OR CITE ------- 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.+. August 2008 C-24 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-25 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-26 DRAFT-DO NOT QUOTE OR CITE ------- 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., August 2008 C-27 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-28 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-29 DRAFT-DO NOT QUOTE OR CITE ------- 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., August 2008 C-30 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 C-31 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 C-32 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-33 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-34 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 C-35 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 C-36 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-37 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 C-38 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 C-39 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-40 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-41 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-42 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-43 DRAFT-DO NOT QUOTE OR CITE ------- 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., August 2008 C-44 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-45 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 C-46 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 C-47 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 C-48 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 C-49 DRAFT-DO NOT QUOTE OR CITE ------- ANNEX C - References 1 Aber, J. D. (1992) Nitrogen cycling and nitrogen saturation in temperate forest ecosystems. 2 Trends Ecol. Evol. 7: 220-224. 3 Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N deposition of N 4 cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11: 639- 5 648. 6 Aber, J. D.; Goodale, C. L.; Ollinger, S. V.; Smith, M.-L.; Magill, A. H.; Martin, M. E.; Hall, R. 7 A.; Stoddard, J. L. (2003) Is nitrogen deposition altering the nitrogen status of 8 northeastern forests? Bioscience 53: 375-389. 9 Aber, J. D.; Nadelhoffer, K. J.; Steudler, P.; Melillo, J. M. (1989) Nitrogen saturation in northern 10 forest ecosystems: excess nitrogen from fossil fuel combustion may stress the biosphere. 11 BioScience 39: 378-386. 12 Aber, J. D.; Ollinger, S. V.; Driscoll, C. T.; Likens, G. E.; Holmes, R. T.; Freuder, R. J.; 13 Goodale, C. L. (2002) Inorganic nitrogen losses from a forested ecosystem in response to 14 physical, chemical, biotic, and climatic perturbations. Ecosystems 5: 648-658. 15 Aber, J. D.; Ollinger, S. V.; Federer, C. A.; Driscoll, C. (1997) Modeling nitrogen saturation in 16 forest ecosystems in response to land use and atmospheric deposition. Ecol. Model. 101: 17 61-78. 18 Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.; McNulty, S.; 19 Currie, W.; Rustad, L.; Fernandez, I. (1998) Nitrogen saturation in temperate forest 20 ecosystems. BioScience 48: 921-934. 21 Adams, S. M.; Greeley, M. S.; Law, J. M.; Noga, E. J.; Zelikoff, J. T. (2003) Application of 22 multiple sublethal stress indicators to assess the health offish in Pamlico Sound 23 following extensive flooding. Estuaries 26: 1365-1382. 24 Adamsen, A. P. S. and G. M. King. 1993. Methane consumption in temperate and sub-arctic 25 forest soils - rates, vertical zonation, and responses to water and nitrogen. Applied and 26 Environmental Microbiology 59:485-490. 27 Aerts, R. (1990) Nutrient use efficiency in evergreen and deciduous species from heathland. 28 Oecologia 84: 391-397. 29 Aerts, R. and H. de Caluwe. 1999. Nitrogen deposition effects on carbon dioxide and methane 30 emissions from temperate peatland soils. Oikos 84:44-54. 31 Aerts, R. and S. Toet. 1997. Nutritional controls on carbon dioxide and methane emission from 32 Carex-dominated peat soils. Soil Biology & Biochemistry 29:1683-1690. 33 Aerts, R.; Berendse, F. (1988) The effect of increased nutrient availability on vegetation 34 dynamics in wet heathlands. Vegetatio 76: 63-69. 35 Aerts, R.; Berendse, F.; De Caluwe, H.; Schmits, M. (1990) Competition in heathland along an 36 experimental gradient of nutrient availability. Oikos 57: 310-318. 37 Aerts, R.; Verhoeven, J. T. A.; Whigham, D. F. (1999) Plant-mediated controls on nutrient 38 cycling in temperate fens and bogs. Ecology 80: 2170-2181. August 2008 C-50 DRAFT-DO NOT QUOTE OR CITE ------- 1 Aeschlimann, U., J. Nosberger, P. J. Edwards, M. K. Schneider, M. Richter, and H. Blum. 2005. 2 Responses of net ecosystem CC>2 exchange in managed grassland to long-term CC>2 3 enrichment, N fertilization and plant species. Plant Cell and Environment 28:823-833. 4 Alberti, M.; Booth, D.; Hill, K.; Coburn, B.; Avolio, C.; Coe, S.; Spirandelli, D. (2007) The 5 impact of urban patterns on aquatic ecosystems: an empirical analysis in Puget lowland 6 sub-basins. Landscape Urban Plan. 80: 345-361. 7 Alexander, R. B.; Boyer, E. W.; Smith, R. A.; Schwarz, G. E.; Moore, R. B. (2007) The role of 8 headwater streams in downstream water quality. J. Am. Water Resour. Assoc. 43: 41-59. 9 Alexander, R. B.; Johnes, R. J.; Boyer, E. W.; Smith, R. A. (2002) A comparison of models for 10 estimating the riverine export of nitrogen from large watersheds. Biogeochemistry 57/58: 11 295-339. 12 Alexander, R. B.; Smith, R. A.; Schwarz, G. E.; Preston, S. D.; Brakebill, J. W.; Srinivasan, R.; 13 Pacheco, P. A. (2001) Atmospheric nitrogen flux from the watersheds of major estuaries 14 of the United States: An application of the SPARROW watershed model. In: Valigura, 15 R.; Alexander, R.; Castro, M.; Meyers, T.; Paerl, H.; Stacey, P.; Turner, R. E., eds. 16 Nitrogen loading in coastal water bodies: an atmospheric perspective. Washington, DC: 17 American Geophysical Union; pp. 119-170. 18 Allen, E. B.; Rao, L. E.; Steers, R. J. (2008) Impacts of atmospheric nitrogen deposition on 19 vegetation and soils at Joshua Tree National Park. In: Webb, R. H.; Fenstermaker, J. S.; 20 Heaton, J. S.; Hughson, D. L.; McDonald, E. V.; Miller, D. M., eds. The Mojave Desert: 21 ecosystem processes and sustainability. Las Vegas, NV: University of Nevada Press (in 22 press). 23 Allen, N. S.; Hershey, A. E. (1996) Seasonal changes in chlorophyll a response to nutrient 24 amendments in a North Shore tributary of Lake Superior. J. N. Am. Benthol. Soc. 15: 25 170-178. 26 Ambus, P. and G. P. Robertson. 1999. Fluxes of CFL; and N2O in aspen stands grown under 27 ambient and twice-ambient CO2. Plant and Soil 209:1-8. 28 Ambus, P. and G. P. Robertson. 2006. The effect of increased n deposition on nitrous oxide, 29 methane and carbon dioxide fluxes from unmanaged forest and grassland communities in 30 Michigan. Biogeochemistry 79:315-337. 31 Ambus, P. and S. Christensen. 1995. Spatial and seasonal nitrous-oxide and methane fluxes in 32 danish forest-ecosystems, grassland-ecosystems, and agroecosystems. Journal of 33 Environmental Quality 24:993-1001. 34 Ambus, P., S. Zechmeister-Boltenstern, and K. Butterbach-Bahl. 2006. Sources of nitrous oxide 35 emitted from European forest soils. Biogeosciences 3:135-145. 36 Ambus, P.; Robertson, G. P. (2006) The effect of increased N deposition on nitrous oxide, 37 methane, and carbon dioxide fluxes from unmanaged forest and grassland communities in 38 Michigan. Biogeochemistry 79: 315-337. 39 Anderson, N. J.; Renberg, I; Segerstrom, U. (1995) Diatom production responses to the 40 development of early agriculture in a boreal forest lake-catchment (Kassjon, Northern 41 Sweden). J. Ecol. 83: 809-822. August 2008 C-51 DRAFT-DO NOT QUOTE OR CITE ------- 1 Arbaugh, M. J.; Peterson, D. L.; Miller, P. R. (1999) Air pollution effects on growth of 2 ponderosa pine, Jeffrey pine, and bigcone Douglas fir. In: Miller, P. R.; McBride, J. R., 3 eds. Oxidant air pollution impacts in the montane forests of southern California. New 4 York: Springer-Verlag; pp. 179-207. 5 Arheimer, B.; Wittgren, H. B. (1994) Modelling the effects of wetlands on regional nitrogen 6 transport. Ambio 23: 378-386. 7 Arnebrandt, K.; Baath, E.; Soderstrom, B. (1990) Changes in microfungal community structure 8 after fertilization of scots pine forest soils with ammonium nitrate or urea. Soil Biol. 9 Biogeochem. 22: 309-312. 10 Arnold, C. L.; Gibbons, C. J. (1996) Impervious surface coverage: emergence of a key 11 environmental indicator. J. Am. Planning Assoc. 62: 243-258. 12 Arrigo, K. R. (2005) Marine microorganisms and global nutrient cycles. Nature 437: 349-355. 13 Ashby, J. A.; Bowden, W. B.; Murdoch, P. S. (1998) Controls on denitrification in riparian soils 14 in headwater catchments of a hardwood forest in the Catskill Mountains, U. S. A. Soil 15 Biol. Biogeochem. 30: 853-864. 16 Asman, W. A. H.; Van Jaarsveld, J. A. (1992) A variable-resolution transport model applied for 17 NHX for Europe. Atmos. Environ. 26: 445-464. 18 Avis, P. G.; McLaughlin, D. J.; Dentinger, B. C.; Reich, P. B. (2003) Long-term increases in 19 nitrogen supply alters above and belowground ectomycorrhizal communities and 20 increases dominance of Russula species. NewPhytol. 160: 239-253. 21 Axler, R. P.; Reuter, J. E. (1996) Nitrate uptake by phytoplankton and periphyton: whole-lake 22 enrichments and mesocosm-N-15 experiments in an oligotrophic lake. Limnol. Oceanogr. 23 41:659-671. 24 Ayensu, E.; Van R. Claasen, D.; Collins, M.; Bearing, A.; Fresco, L.; Gadgil, M.; Gitay, H.; 25 Glaser, G.; Juma, C.; Krebs, J.; Lenton, R.; Lubchenco, J.; McNeely, J. A.; Mooney, H. 26 A.; Pinstrup-Andersen, P.; Ramos, M.; Raven, P.; Reid, W. V.; Samper, C.; Sarukhan, J.; 27 Schei, P.; Tundisi, J. G.; Watson, R. T.; Guanhua, X.; Zakri, A. H. (1999) International 28 ecosystem assessment. Science (Washington, DC) 286: 685-686. 29 Baddeley, J. A.; Thompson, D. B. A.; Lee, J. A. (1994) Regional and historic variation in the 30 nitrogen content of Racomitrium lanuginosum in Britain in relation to atmospheric 31 nitrogen deposition. Environ. Pollut. 84: 189-196. 32 Baez, S.; Fargione, J.; Moore, D. L; Collins, S. L.; Gosz, J. R. (2007) Atmospheric nitrogen 33 deposition in the northern Chihuahuan desert: temporal trends and potential 34 consequences. J. Arid Environ. 68: 640-651. 35 Baggs, E. M. and H. Blum. 2004. CFL; oxidation and emissions of CtL; andN2O from Lolium 36 perenne swards under elevated atmospheric CCh. Soil Biology & Biochemistry 36:713- 37 723. 38 Baggs, E. M., M. Richter, G. Cadisch, and U. A. Hartwig. 2003. Denitrification in grass swards 39 is increased under elevated atmospheric CC>2. Soil Biology & Biochemistry 35:729-732. 40 Bajwa, R.; Read, D. J. (1985) The biology of the mycorrhizae in the Ericaceae. IX. Peptides as 41 nitrogen sources for the ericoid endophyte and for mycorrhizal and non-mycorhizzal 42 plants. New Phytol. 101: 459-467. August 2008 C-52 DRAFT-DO NOT QUOTE OR CITE ------- 1 Baker, L. A. (1991) Regional estimates of atmospheric dry deposition. In: Charles, D. F., ed. 2 Acidic deposition and aquatic ecosystems: regional case studies. New York, NY: 3 Springer-Verlag; pp. 645-652. 4 Barker, D. H.; Vanier, C.; Naumburg, E.; Charlet, T. N; Nielsen, K. M.; Newingham, B. A.; 5 Smith, S. D. (2006) Enhanced monsoon precipitation and nitrogen deposition affect leaf 6 traits and photosynthesis differently in spring and summer in the desert shrub Larrea 7 tridentata. NewPhytol. 169: 799-808. 8 Baron, J. (1992) Surface waters. In: Baron, J.; Arthur, M. A.; Denning, S.; Harris, M. A.; Mast, 9 M. A.; Mcknight, D. M.; Mclaughlin, P.; Rosenlund, B. D.; Spaulding, S. A.; Walthall, P. 10 M., eds. Biogeochemistry of a subalpine ecosystem. Loch Vale watershed. New York: 11 Springer-Verlag; pp. 142-186. 12 Baron, J. S. (2006) Hindcasting nitrogen deposition to determine ecological critical load. Ecol. 13 Appl. 16: 433-439. 14 Baron, J. S.; Rueth, H. M.; Wolfe, A. M.; Nydick, K. R.; Allstott, E. J.; Minear, J. T.; Moraska, 15 B. (2000) Ecosystem responses to nitrogen deposition in the Colorado Front Range. 16 Ecosystems 3: 352-368. 17 Basiliko, N., T. R. Moore, R. Jeannotte, and J. L. Bubier. 2006. Nutrient input and carbon and 18 microbial dynamics in an ombrotrophic bog. Geomicrobiology Journal 23:531-543. 19 Bauer, G. A.; Bazaaz, F. A.; Minocha, R.; Long, S.; Magill, A. H.; Aber, J. D.; Berntson, G. M. 20 (2004) Effects of chronic N additions on tissue chemistry, photosynthetic capacity, and 21 carbon sequestration potential of a red pine (Pinus resinosa Ait.) stand in the NE United 22 States. For. Ecol. Manage. 196: 173-186. 23 Bedford, B. L.; Godwin, K. S. (2003) Fens of the United States: distribution, characteristics, and 24 scientific connection versus legal isolation. Wetlands 23: 608-629. 25 Bedford, B. L.; Walbridge, M. R.; Aldous, A. (1999) Patterns in nutrient availability and plant 26 diversity of temperature North American wetlands. Ecol. Soc. Am. 80: 2151-2169. 27 Benstead, J. P.; Deegan, L. A.; Peterson, B. J.; Huryn, A. D.; Bowden, W. B.; Suberkropp, K.; 28 Buzby, K. M.; Green, A. C.; Vacca, J. A. (2005) Responses of a beaded Arctic stream to 29 short-term N and P fertilisation. Freshwater Biol. 50: 277-290. 30 Berendse, F.; Breemen, N. V.; Rydin, H.; Buttler, A.; Heijmans, M.; Hoosbeek, M. R.; Lee, J. 31 A.; Mitchell, A.; Saarinen, T.; Vassander, H.; Wallen, B. (2001) Raised atmospheric CC>2 32 levels and increased N deposition cause shifts in plant species composition and 33 production in Sphagnum bogs. Glob. Change Biol. 7: 591-598. 34 Bergametti, G.; Remoudaki, E.; Losno, R.; Steiner, E.; Chatenet, B. (1992) Source, transport and 35 deposition of atmospheric phosphorus over the northwestern Mediterranean. J. Atmos. 36 Chem. 14: 501-513. 37 Bergstrom, A.; Blomqvist, P.; Jansson, M. (2005) Effects of atmospheric nitrogen deposition on 38 nutrient limitation and phytoplankton biomass in unproductive Swedish lakes. Limnol. 39 Oceanogr. 50: 987-994. 40 Bergstrom, A.-K.; Jansson, M. (2006) Atmospheric nitrogen deposition has caused nitrogen 41 enrichment and eutrophication of lakes in the northern hemisphere. Glob. Change Biol. 42 12: 635-643. August 2008 C-53 DRAFT-DO NOT QUOTE OR CITE ------- 1 Bernhardt, E. S.; Likens, G. E. (2002) Dissolved organic carbon enrichment alters nitrogen 2 dynamics in a forest stream. Ecology 83: 1689-1700. 3 Bernot, M. I; Dodds, W. K.; Gardner, W. S.; McCarthy, M. I; Sobolev, D.; Tank, J. L. (2003) 4 Comparing denitrification estimates for a Texas estuary by using acetylene inhibition and 5 membrane inlet mass spectrometry. Appl. Environ. Microbiol. 69: 5950-5956. 6 Bert, D.; Leavitt, S. W.; Dupouey, J.-L. (1997) Variations in wood 513C and water-use efficiency 7 of Abies alba during the last century. Ecology 78: 1588-1595. 8 Bloom, A. J.; Chapin, F. S., Ill; Mooney, H. A. (1985) Resource limitation in plants—an 9 economic analogy. Annu. Rev. Ecol. Syst. 16: 363-392. 10 Bobbink, R. (1998) Impacts of tropospheric ozone and airborne nitrogenous pollutants on natural 11 and semi-natural ecosystems: a commentary. New Phytol. 139: 161-168. 12 Bobbink, R.; Boxman, D.; Fremstad, E.; Heil, G.; Houdijk, A.; Roelofs, J. (1992) Critical loads 13 for nitrogen eutrophication of terrestrial and wetland ecosystems based upon changes in 14 vegetation and fauna. In: Grennfelt, P.; Thornelof, E., eds. Critical loads for nitrogen. 15 Nord 92:41. Nordic Council of Ministers, Copenhagen, pp. 111-159. 16 Bobbink, R.; Hornung, M.; Roelofs, J. G. M. (1998) The effects of air-borne nitrogen pollutants 17 on species diversity in natural and semi-natural European vegetation. J. Ecol. 86: 717- 18 738. 19 Bodelier, P. L. E.; Roslev, P.; Heckel, T.; Frenzel, P. (2000) Stimulation by ammonium-based 20 fertilizers of methane oxidation in soil around rice roots. Nature 403: 421-424. 21 Bohlke, J. K.; Denver, J. M. (1995) Combined use of ground-water dating, chemical and isotopic 22 analyses to resolve the history and fate of nitrate contamincation in 2 agricultural 23 watersheds, Atlantic coastal-plain, Maryland. Water Resour. Res. 31: 2319-2339. 24 Borken, W. and F. Beese. 2005. Control of nitrous oxide emissions in European beech, Norway 25 spruce and Scots pine forests. Biogeochemistry 76:141-159. 26 Borken, W., F. Beese, R. Brumme, and N. Lamersdorf 2002. Long-term reduction in nitrogen 27 and proton inputs did not affect atmospheric methane uptake and nitrous oxide emission 28 from a German spruce forest soil. Soil Biology & Biochemistry 34:1815-1819. 29 Bormann, F. H.; Likens, G. E. (1979) Pattern and process in a forested ecosystem. New York, 30 NY: Springer-Verlag. 31 Bormann, F. H.; Likens, G. E.; Fisher, D. W.; Pierce, R. S. (1968) Nutrient loss accelerated by 32 clear-cutting of a forest ecosystem. Science (Washington, DC) 159: 882-884. 33 Bormann, F. H.; Likens, G. E.; Melillo, J. M. (1977) Nitrogen budget for an aggrading northern 34 hardwood forest ecosystem. Science (Washington, DC) 196: 981-983. 35 Borum, J. (1996) Shallow waters and land/sea boundaries. In: Jorgensen, B. B.; Richardson, K., 36 eds. Eutrophi cation in coastal marine ecosystems. Washington DC: American 37 Geophysical Union; pp. 179-203. 38 Bouwman, A. F.; Van Drecht, G.; Knoop, J. M.; Beusen, A. H. W.; Meinardi, C. R. (2005) 39 Exploring changes in river nitrogen export the world's oceans. Glob. Biogeochem. Cycles 40 19(GB1002): 10.1029/2004GB002314. August 2008 C-54 DRAFT-DO NOT QUOTE OR CITE ------- 1 Bowden, R. D., G. Rullo, G. R. Stevens, and P. A. Steudler. 2000. Soil fluxes of carbon dioxide, 2 nitrous oxide, and methane at a productive temperate deciduous forest. Journal of 3 Environmental Quality 29:268-276. 4 Bowden, R. D., J. M. Melillo, P. A. Steudler, and J. D. Aber. 1991. Effects of nitrogen additions 5 on annual nitrous-oxide fluxes from temperate forest soils in the northeastern united- 6 states. Journal of Geophysical Research-Atmospheres 96:9321-9328. 7 Bowman, W. D. (1992) Inputs and storage of nitrogen in winter snowpack in an alpine 8 ecosystem. Arct. Alp. Res. 24: 211-215. 9 Bowman, W. D. (1994) Accumulation and use of nitrogen and phosphorus following fertilization 10 in two alpine tundra communities. Oikos 70: 261-270. 11 Bowman, W. D. (2000) Biotic controls over ecosystem response to environmental change in 12 alpine tundra of the Rocky Mountains. Ambio 29: 396-400. 13 Bowman, W. D.; Fisk, M. C. (2001) Primary production. In: Bowman, W. D.; Seastedt, T. R., 14 eds. Structure and function of an alpine ecosystem: Niwot Ridge, Colorado. Oxford, UK: 15 Oxford University Press; pp. 177-197. 16 Bowman, W. D.; Gartner, J. R.; Holland, K.; Wiedermann, M. (2006) Nitrogen critical loads for 17 alpine vegetation and terrestrial ecosystem response: are we there yet? Ecol. Appl. 16: 18 1183-1193. 19 Bowman, W. D.; Steltzer, H. (1998) Positive feedbacks to anthropogenic nitrogen deposition in 20 Rocky Mountain alpine tundra. Ambio 27: 514-517. 21 Bowman, W. D.; Theodose, T. A.; Fisk, M. C. (1995) Physiological and production responses of 22 plant growth forms to increases in limiting resources in alpine tundra: implications for 23 differential community response to environmental change. Oecologia 101: 217-227'. 24 Bowman, W. D.; Theodose, T. A.; Schardt, J. C.; Conant, R. T. (1993) Constraints of nutrient 25 availability on primary production in two alpine tundra communities. Ecology 74: 2085- 26 2097. 27 Boxman, A. W.; Blanck, K.; Brandrud, T.-E.; Emmett, B. A.; Gundersen, P.; Hogervorst, R. F.; 28 Kjonass, O. J.; Persson, H.; Timmermann, V. (1998a) Vegetation and soil biota response 29 to experimentally-changed nitrogen inputs in coniferous forest ecosystems of the 30 NITREX project. For. Ecol. Manage. 101: 65-79. 31 Boxman, A. W.; van der Ven, P. J. M.; Roelofs, J. G. M. (1998b) Ecosystem recovery after a 32 decrease in nitrogen input to a Scots pine stand at Ysselsteyn, the Netherlands. For. Ecol. 33 Manage. 101: 155-163. 34 Boyer, E. W.; Goodale, C. L.; Jaworski, N. A.; Howarth, R. W. (2002) Anthropogenic nitrogen 35 sources and relationships to riverine nitrogen export in the northeastern U.S.A. 36 Biogeochemistry 57/58: 137-169. 37 Boyer, E.; Howarth, R. W.; Galloway, J. N.; Dentener, F. J.; Green, P. A.; Vorosmarty, C. J. 38 (2006) Riverine nitrogen export from the continents to the coasts. Glob. Biogeochem. 39 Cycles 20. 40 Boynton, W. R.; Garber, J. H.; Summers, R.; Kemp, W. M. (1995) Inputs, transformations, and 41 transport of nitrogen and phosphorus in Chesapeake Bay and selected tributaries. 42 Estuaries 18: 285-314. August 2008 C-55 DRAFT-DO NOT QUOTE OR CITE ------- 1 Bradford, M. A., P. Ineson, P. A. Wookey, and H. M. Lappin-Scott. 2001. The effects of acid 2 nitrogen and acid sulphur deposition on CH4 oxidation in a forest soil: a laboratory study. 3 Soil Biology & Biochemistry 33:1695-1702. 4 Bredemeier, M.; Blanck, K.; Xu, Y. J.; Tietema, A.; Boxman, A. W.; Emmett, B.; Moldan, F.; 5 Gundersen, P.; Schleppi, P.; Wright, R. F. (1998) Input-output budgets at the NITREX 6 sites. For. Ecol. Manage. 101: 57-64. 7 Bricker, S. B.; Clement, C. G.; Pirhalla, D. E.; Orlando, S. P.; Farrow, D. G. G. (1999) National 8 estuarine eutrophication assessment: effects of nutrient enrichment in the nation's 9 estuaries. Silver Spring, MD: Special Projects Office and the National Centers for Coastal 10 Ocean Science, National Ocean Service, National Oceanic and Atmospheric 11 Administration. 12 Broderick, S. J.; Cullen, P.; Maher, W. (1988) Denitrification in a natural wetland receiving 13 secondary treated effluent. Water Res. 4: 431-439. 14 Brooks, M. L. (1999) Alien annual grasses and fire in the Mojave Desert. Madrono 46: 13-19. 15 Brooks, M. L. (2003) Effects of increased soil nitrogen on the dominance of alien annual plants 16 in the Mojave Desert. J. Appl. Ecol. 40: 344-353. 17 Brooks, M. L.; D'Antonio, C. M.; Richardson, D. M.; Grace, J. B.; Keeley, J. E.; DiTomaso, J. 18 M.; Hobbs, R. J.; Pellant, M.; Pyke, D. (2004) Effects of invasive alien plants on fire 19 regimes. BioScience 54: 677-688. 20 Brooks, M. L.; Esque, T. C. (2002) Alien annual plants and wildfire in desert tortoise habitat: 21 status, ecological effects, and management. Chelonian Conserv. Biol. 4: 330-340. 22 Brumme, R. and F. Beese. 1992. Effects of liming and nitrogen-fertilization on emissions of CO2 23 and N2O from a temperate forest. Journal of Geophysical Research-Atmospheres 24 97:12851-12858. 25 Brumme, R., W. Borken, and S. Finke. 1999. Hierarchical control on nitrous oxide emission in 26 forest ecosystems. Global Biogeochemical Cycles 13:1137-1148. 27 Brunet, J.; Diekmann, M.; Falkengren-Grerup, U. (1998) Effects of nitrogen deposition on field 28 layer vegetation in south Swedish oak forests. Environ. Pollut. (Oxford, U.K.) 102(suppl. 29 1): 35-40. 30 Bubier, J. L., T. R. Moore, and L. A. Bledzki. 2007. Effects of nutrient addition on vegetation 31 and carbon cycling in an ombrotrophic bog. Global Change Biology 13:1168-1186. 32 Burges, S. J.; Wigmosta, M. S.; Meena, J. M. (1998) Hydrological effects of landuse change in a 33 zero-order catchment. J. Hydro. Engr. 3: 86-97. 34 Burkholder, J. M.; Dickey, D. A.; Kinder, C. A.; Reed, R. E.; Mallin, M. A.; Mclver, M. R.; 35 Cahoon, L. B.; Melia, G.; Brownie, C.; Smith, J.; Deamer, N.; Springer, J.; Glasgow, H. 36 B.; Toms, D. (2006) Comprehensive trend analysis of nutrients and related variables in a 37 large eutrophic estuary: a decadal study of anthropogenic and climatic influences. 38 Limnol. Oceanogr. 51(1, pt. 2): 463-487. 39 Burns, D. A. (2004) The effects of atmospheric nitrogen deposition in the Rocky Mountains of 40 Colorado and southern Wyoming, USA—a critical review. Environ. Pollut. 127: 257- 41 269. August 2008 C-56 DRAFT-DO NOT QUOTE OR CITE ------- 1 Burns, D. A.; Kendall, C. (2002) Analysis of 615N and 618O to differentiate NO3" sources in 2 runoff at two watersheds in the Catskill Mountains of New York. Water Resour. Res. 38: 3 1051. 4 Burton, A. J., K. S. Pregitzer, and R. L. Hendrick. 2000. Relationships between fine root 5 dynamics and nitrogen availability in Michigan northern hardwood forests. Oecologia 6 125:389-399. 7 Bushong, S. J.; Bachmann, R. W. (1989) In situ nutrient enrichment experiments with periphyton 8 in agricultural streams. Hydrobiologia 178: 1-10. 9 Butterbach-Bahl, K., L. Breuer, R. Gasche, G. Willibald, and H. Papen. 2002. Exchange of trace 10 gases between soils and the atmosphere in Scots pine forest ecosystems of the 11 northeastern German lowlands 1. Fluxes of N2O, NO/NO2 and CFLi at forest sites with 12 different N-deposition. Forest Ecology and Management 167:123-134. 13 Butterbach-Bahl, K.; Breuer, L.; Gasche, R.; Willibald, G.; Papen, H. (2002a) Exchange of trace 14 gases between soils and the atmosphere in Scots pine forest ecosystems of the 15 northeastern German lowlands. 1. Fluxes of N2O, NO/NO2 and CFLi at forest sites with 16 different N-deposition. For. Ecol. Manage. 167: 123-134. 17 Butterbach-Bahl, K.; Gasche, R.; Willibald, G.; Papen, H. (2002b) Exchange of N-gases at the 18 Hoglwald forest: a summary. Plant Soil 240: 117-123. 19 Bytnerowicz, A. (2002) Physiological/ecological interactions between ozone and nitrogen 20 deposition in forest ecosystems. Phyton 42: 13-28. 21 Bytnerowicz, A.; Fenn, M. E. (1996) Nitrogen deposition in California forests: a review. 22 Environ. Pollut. 92: 127-146. 23 Bytnerowicz, A.; Miller, P. R.; Olszyk, D. M. (1987) Dry deposition of nitrate, ammonium and 24 sulfate to a Ceanothus crassifolius canopy and surrogate surfaces. Atmos. Environ. 21: 25 1749-1757 26 Caddy, J. F. (1993) Toward a comparative evaluation of human impacts on fishery ecosystems of 27 enclosed and semi-enclosed seas. Rev. Fish. Sci. 1: 57-95. 28 Camargo, J. A.; Alonso, A.; Salamanca, A. (2005) Nitrate toxicity to aquatic animals: a review 29 with new data for freshwater invertebrates. Chemosphere 58: 1255-1267. 30 Camargo, J. A.; Ward, J. V. (1995) Nitrate (NOs-N) toxicity to aquatic life: a proposal of safe 31 concentrations for two species of nearctic freshwater invertebrates. Chemosphere 31: 32 3211-3216. 33 Campbell, D. H.; Clow, D. W.; Ingersoll, G. P.; Mast, M. A.; Spahr, N. E.; Turk, J. T. (1995) 34 Processes controlling the chemistry of two snowmelt-dominated streams in the Rocky 3 5 Mountains. Water Resour. Res. 31: 2811 -2821. 36 Campbell, D. H.; Kendall, C.; Chang, C. C. Y.; Silva, S. R.; Tonnessen, K. A. (2002) Pathways 37 for nitrate release from an alpine watershed: determination using 515N and 51 O. Water 38 Resour. Res. 31:2811-2821. 39 Campbell, J. L.; Hornbeck, J. W.; McDowell, W. H.; Buso, D. C.; Shanley, J. B.; Likens, G. E. 40 (2000) Dissolved organic nitrogen budgets for upland, forested ecosystems in New 41 England. Biogeochemistry 49: 123-142. August 2008 C-57 DRAFT-DO NOT QUOTE OR CITE ------- 1 Canary, J. D., R. B. Harrison, J. E. Compton, and H. N. Chappell. 2000. Additional carbon 2 sequestration following repeated urea fertilization of second-growth Douglas-fir stands in 3 western Washington. Forest Ecology and Management 138:225-232. 4 Cape, J. N.; Leith, I. D.; Fowler, D.; Murray, M. B.; Sheppard, L. J. Eamus, D.; Wilson, R. H. F. 5 (1991) Sulphate and ammonium in mist impair the frost hardening of red spruce 6 seedlings. New Phytol. 118: 119-126. 7 Carreiro, M. M.; Sinsabaugh, R. L.; Repert, D. A.; Parkhurst, D. F. (2000) Microbial enzyme 8 shifts explain litter decay responses to simulated nitrogen deposition. Ecology 81: 2359- 9 2365. 10 Carroll, J. J.; Miller, P. R.; Pronos, J. (2003) Historical perspectives on ambient ozone and its 11 effects on the Sierra Nevada. In: Bytnerowicz, A.; Arbaugh, M. J.; Alonso, R., eds. 12 Ozone air pollution in the Sierra Nevada: distribution and effects on forests; pp. 33-54. 13 New York, NY: Elsevier. (Developments in Environmental Science: v. 2). 14 Caspersen, J. P.; Pacala, S. W.; Jenkins, J. C.; Hurtt, G. C.; Moorcroft, P. R.; Birdsey, R. A. 15 (2000) Contributions of land-use history to carbon accumulation in U.S. forests. Science 16 290:1148-1151. 17 Castaldi, S. and K. A. Smith. 1998. The effect of different N substrates on biological N2O 18 production from forest and agricultural light textured soils. Plant and Soil 199:229-238. 19 Castro, M. S., P. A. Steudler, J. M. Melillo, J. D. Aber, and S. Millham. 1992. Exchange of N2O 20 and CH4 between the atmosphere and soils in spruce-fir forests in the northeastern united- 21 states. Biogeochemistry 18:119-135. 22 Castro, M. S., W. T. Peterjohn, J. M. Melillo, P. A. Steudler, H. L. Gholz, and D. Lewis. 1994. 23 Effects of nitrogen-fertilization on the fluxes of N2O, CH/i, and CO2 from soils in a florida 24 slash pine plantation. Canadian Journal of Forest Research-Revue Canadienne De 25 Recherche Forestiere 24:9-13. 26 Castro, M. S.; Driscoll, C. T. (2002) Atmospheric nitrogen deposition has caused nitrogen 27 enrichment and eutrophication of lakes in the northern hemisphere. Environ. Sci. 28 Technol. 36: 3242-3249. 29 Castro, M. S.; Driscoll, C. T.; Jordan, T. E.; Reay, W. G.; Boynton, W. R.; Seitzinger, S. P.; 30 Styles, R. V.; Cable, J. E. (2001) Contribution of atmospheric depostition to the total 31 nitrogen loads to thirty-four estuaries on the Atlantic and Gulf Coasts of the United 32 States. In: Valigura, R. A.; Alexander, R. B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; 33 Stacey, P. E.; Turner, R. E., eds. Nitrogen loading in coastal water bodies: an atmospheric 34 perspective. Washington, DC: American Geophysical Union; pp. 77-106. 35 Castro, M. S.; Driscoll, C. T.; Jordan, T. E.; Reay, W. G.; Boynton, W. R. (2003) Sources of 36 nitrogen to estuaries in the United States. Estuaries 26: 803-814. 37 Chadwick, M. A.; Huryn, A. D. (2003) Effect of a whole-catchment N addition on stream 38 detritus processing. J. North Am. Benthol. Soc. 22: 194-206. 39 Chadwick, M. A.; Huryn, A. D. (2005) Response of stream macroinvertebrate production to 40 atmospheric N deposition and channel drying. Limnol. Oceanogr. 50: 228-236. August 2008 C-58 DRAFT-DO NOT QUOTE OR CITE ------- 1 Chan, A. S. K., P. A. Steudler, R. D. Bowden, J. Gulledge, and C. M. Cavanaugh. 2005. 2 Consequences of nitrogen fertilization on soil methane consumption in a productive 3 temperate deciduous forest. Biology and Fertility of Soils 41:182-189. 4 Chapin, F. S,. Ill; Moilanen, L.; Kielland, K. (1993) Preferential use of organic nitrogen for 5 growth by a non-mycorrhizal arctic sedge. Nature 361: 150-153. 6 Chapin, F. S., III. (1980) The mineral nutrition of wild plants. Annu. Rev. Ecol. Syst. 11: 233- 7 260. 8 Chapin, F. S., III. (1991) Integrated responses of plants to stress: a centralized system of 9 physiological responses. BioScience 41: 29-36. 10 Chapin, F. S., Ill; Bloom, A. J.; Field, C. B.; Waring, R. H. (1987) Plant responses to multiple 11 environmental factors. BioScience 37: 49-57. 12 Chapin, F. S., Ill; Sala, O. E.; Burke, I. C.; Grime, J. P.; Hooper, D. U.; Lauenroth, W. K.; 13 Lombard, A.; Mooney, H. A.; Mosier, A. R.; Naeem, S.; Pacala, S. W.; Roy, J.; Steffen, 14 W. L.; Tilman, D. (1998) Ecosystem consequences of changing biodiversity. BioScience 15 48: 45-52. 16 Chappelka, A. H.; Samuelson, L. J. (1998) Ambient ozone effects on forest trees of the eastern 17 United States: a review. New Phytol. 139: 91-108. 18 Charbonneau, R.; Kondolf, G. M. (1993) Land use change in California: nonpoint source water 19 quality impacts. Environ. Manage. 17: 453-460. 20 Chen, L.; Driscoll, C. T. (2004) Modeling the response of soil and surface waters in the 21 Adirondack and Catskill regions of New York to changes in atmospheric deposition and 22 historical land disturbance. Atmos. Environ. 38: 4099-4109. 23 Christensen, T. R., A. Michelsen, S. Jonasson, and I. K. Schmidt. 1997. Carbon dioxide and 24 methane exchange of a subarctic heath in response to climate change related 25 environmental manipulations. Oikos 79:34-44. 26 Christie, C. E.; Smol, J. P. (1993) Diatom assemblages as indicators of lake rophic status in 27 southeastern Ontario lakes. J. Phycol. 29: 575-586. 28 Clone, N. K.; Padgett, P. E.; Allen, E. B. (2002) Restoration of a native shrubland impacted by 29 exotic grasses, frequent fire and nitrogen deposition in southern California. Restor. Ecol. 30 10: 376-384. 31 Cloern, J. E. (2001) Our evolving conceptual model of the coastal eutrophication problem. Mar. 32 Ecol. Prog. Ser. 210: 223-253. 33 Cole, D. W.; Rapp, M. (1981) Elemental cycling in forest ecosystems. In: Reichle, D. E., ed. 34 Dynamic properties of forest ecosystems. Cambridge, United Kingdom: Cambridge 35 University Press; pp. 341-409. (International Biological Programme, 23). 36 Compton, J. E.; Watrud, L. S.; Porteous, A.; DeGrood, S. (2004) Response of soil microbial 37 biomass and community composition to chronic nitrogen additions at Harvard forest. For. 38 Ecol. Manage. 196:143-158. 39 Conley, D. J.; Markager, S.; Andersen, J.; Ellermann, T.; Svendsen, L. M. (2002) Coastal 40 eutrophi cation and the Danish National Aquatic Monitoring and Assessment Program. 41 Estuaries 25: 848-861. August 2008 C-59 DRAFT-DO NOT QUOTE OR CITE ------- 1 Conley, D. J.; Schelske, C. L.; Stoermer, E. F. (1993) Modification of the biogeochemical cycle 2 of silica with eutrophication. Mar. Ecol. Prog. Ser. 101: 179-192. 3 Cook, R. B.; Elwood, J. W.; Turner, R. R.; Bogle, M. A.; Mulholland, P. J.; Palumbo, A. V. 4 (1994) Acid-base chemistry of high-elevation streams in the Great Smoky Mountains. 5 Water Air Soil Pollut. 72: 331-356. 6 Cooper, A. B. (1990) Nitrate depletion in the riparian zone and stream channel of a small 7 headwater catchment. Hydrobiologia 202: 13-26. 8 Costanza, R.; d'Arge, R.; De Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; 9 Naeem, S.; O'Neill, R. V.; Paruelo, J.; Raskin, R. G.; Sutton, P.; Van Den Belt, M. (1997) 10 The value of the world's ecosystem services and natural capital. Nature (London) 387: 11 253-259. 12 Crill, P. M., P. J. Martikainen, H. Nykanen, and J. Silvola. 1994. Temperature and n-fertilization 13 effects on methane oxidation in a drained peatland soil. Soil Biology & Biochemistry 14 26:1331-1339. 15 Curtis, C. J., B. A. Emmett, B. Reynolds, and J. Shilland. 2006. How important is N2O 16 production in removing atmospherically deposited nitrogen from UK moorland 17 catchments? Soil Biology & Biochemistry 38:2081-2091. 18 Dahlgren, R. A.; Driscoll, C. T. (1994) The effects of whole-tree clear cutting on soil processes 19 at Hubbard Brook Experimental Forest, New Hampshire, USA. Plant Soil 58: 239-262. 20 Daily, G. C. (1997) Introduction: what are ecosystem services? In: Daily, G. C., ed. Nature's 21 services: societal dependence on natural ecosystems. Washington, DC: Island Press; pp. 22 1-10. 23 Daily, G. C.; Ehrlich, P. R. (1999) Managing Earth's ecosystems: an interdisciplinary challenge. 24 Ecosystems 2: 277-280. 25 Dalsgaard, T.; Tharndrup, B.; Canfield, D. E. (2005) Anaerobic ammonium oxidation 26 (anammox) in the marine environment. Res. Microbiol. 156: 457-464. 27 D'Antonio, C. M.; Vitousek, P. M. (1992) Biological invasions by exotic grasses, the grass/fire 28 cycle, and global change. Annu. Rev. Ecol. Syst. 23: 63-87. 29 Das, B.; Vinebrooke, R. D.; Sanchez-Azofeifa, A.; Rivard, B.; Wolfe, A. P. (2005) Inferring 30 sedimentary chlorophyll concentrations with reflectance spectroscopy: a novel approach 31 to reconstructing historical changes in the trophic status of mountain lakes. Can. J. Fish. 32 Aquat. Sci. 62: 1067-1078. 33 Davidson, E. A.; Keller, M.; Erickson, H. E.; Verchot, L. V.; Veldkamp, E. (2000) Testing a 34 conceptual model of soil emissions of nitrous and nitric oxides. BioScience 50: 667-680. 35 Davidson, E. A.; Seitzinger, S. (2006) The enigma of progress in denitrification research. Ecol. 36 Appl. 16: 2057-2063. 37 De Bakker, A. J. (1989) Effects of ammonia emission on epiphytic lichen vegetation. Acta Bot. 38 Neerl. 38: 337-342. 39 Del Giorgio, P. A.; Cole, J. J.; Cimberlic, A. (1997) Respiration rates in bacteria exceed 40 phytoplankton production in unproductive aquatic system. Nature 385: 148-151. August 2008 C-60 DRAFT-DO NOT QUOTE OR CITE ------- 1 Delaune, R. D., C. W. Lindau, E. Sulaeman, and A. Jugsujinda. 1998. Nitrification and 2 denitrification estimates in a Louisiana swamp forest soil as assessed BY N-15 isotope 3 dilution and direct gaseous measurements. Water Air and Soil Pollution 106:149-161. 4 Delgado, J. A., A. R. Mosier, R. H. Follett, R. F. Follett, D. G. Westfall, L. K. Klemedtsson, and 5 J. Vermeulen. 1996. Effects of N management on N2O and CFL; fluxes and N15 Recovery 6 in an irrigated mountain meadow. Nutrient Cycling in Agroecosystems 46:127-134. 7 D'Elia, C. F.; Sanders, J. G.; Boynton, W. R. (1986) Nutrient enrichment studies in a coastal 8 plain estuary: phytoplankton growth in large-scale, continuous cultures. Can. J. Fish. 9 Aquat. Sci. 43: 397-406. 10 Devito, K. J.; Dillon, P. J.; LaZerte, B. D. (1989) Phosphorus and nitrogen retention in five 11 Precambrian shield wetlands. Biogeochemistry 8: 185-204. 12 Devol, A. (2003) Solution to a marine mystery. Nature 422: 575-576. 13 DeWalle, D. R.; Kochenderfer, J. N.; Adams, M. B.; Miller, G. W.; Gilliam, F. S.; Wood, F.; 14 Odenwald-Clemens, S. S.; Sharpe, W. E. (2006) Vegetation and acidification. In: Adams, 15 M. B.; DeWalle, D. R.; Horn, J. L., eds. The Fernow watershed acidification study. 16 Dordrecht, The Netherlands: Springer; pp. 137-188. 17 Diemer, M. 1997. Effects of elevated CO2 on gas exchange characteristics of alpine grassland. 18 Acta Oecologica-International Journal of Ecology 18:177-182. 19 Dierberg, F. E.; Brezonik, P. L. (1983) Nitrogen and phosphorus mass balances in natural and 20 sewage-enriched cypress domes. J. Appl. Ecol. 20: 323-337. 21 Dijkstra, F. A.; Smits, M. M. (2002) Tree species effects on calcium cycling: the role of calcium 22 uptake in deep soils. Ecosystems 5: 385-398. 23 Dise, N. B.; Wright, R. F. (1995) Nitrogen leaching from European forests in relation to nitrogen 24 deposition. For. Ecol. Manage. 71: 153-161. 25 Dodds, W. K. (2006) Eutrophication and trophic state in rivers and streams. Limnol. Oceanogr. 26 51(1 pt. 2): 671-680. 27 Dortch, Q.; Whitledge, T. E. (1992) Does nitrogen or silicon limit phytoplankton production in 28 the Mississippi River plume and nearby regions? Cont. Shelf. Res. 12: 1293-1309. 29 Downing, J. A. (1997) Marine nitrogen: phosphorus stoichiometry and the global N:P cycle. 30 Biogeochemistry 37: 237-252. 31 Downing, J. A.; McCauley, E. (1992) The nitrogen-phosphorus relationship in lakes. Limnol. 32 Oceanogr. 37: 936-945. 33 Drenovsky, R. H.; Richards, J. H. (2005) Nitrogen addition increases fecundity in the desert 34 shrub Sarcobatus vermiculatus. Oecologia 143: 349-356. 35 Driscoll, C. T.; Driscoll, K. M.; Roy, K. M.; Mitchell, M. J. (2003b) Chemical response of lakes 36 in the Adirondack region of New York to declines in acidic deposition. Environ. Sci. 37 Technol. 37: 2036-2042. 38 Driscoll, C. T.; Van Dreason, R. (1993) Seasonal and long-term temporal patterns in the 39 chemistry of Adirondack lakes. Water Air Soil Pollut. 67: 319-344. 40 Driscoll, C. T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C. L.; 41 Groffman, P.; Hopkinson, C.; lambert, K.; Lawrence, G.; Ollinger, S. (2003c) Nitrogen August 2008 C-61 DRAFT-DO NOT QUOTE OR CITE ------- 1 pollution in the northeastern United States: sources, effects, and management options. 2 BioScience 53: 357-374. 3 Driscoll, C.; Whitall, D.; Aber, I; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C.; Groffman, P.; 4 Hopkinson, C.; Lambert, K.; Lawrence, G.; Ollinger, S. (2003a) Nitrogen pollution: 5 sources and consequences in the U.S. Northeast. Environment 45: 8-22. 6 Dueck, T. A.; Dorel, F. G.; Ter Horst, R.; Van der Eerden, L. J. M. (1990) Effects of ammonia, 7 ammonium sulphate and sulphur dioxide on the frost sensitivity of Scots pine (Pinus 8 sylvestris L.). Water Air Soil Pollut. 54: 35-49. 9 Dueck, T. A.; Van der Eerden, L. J. M.; Breemsterboer, B.; Elderson, J. (1991) Nitrogen uptake 10 and allocation by Calluna vulgaris (L.) Hull and Deschampsia flexuosa (L.) Trin. exposed 11 to 15NH3. Acta Bot. Neerl. 40: 257-267. 12 Dumont, E.; Harrison, J. A.; Kroeze, C.; Bakker, E. J.; Seitzinger, S. P. (2005) Global 13 distribution and sources of dissolved inorganic nitrogen export to the coastal zone: results 14 from a spatially explicit, global model. Glob. Biogeochem. Cycles 19. 15 Duriscoe, D. M.; Stolte, K. W. (1989) Photochemical oxidant injury to ponderosa pine (Pinus 16 ponderosa Laws.) and Jeffrey pine (Pinus jeffreyi Grev. and Balf) in the national parks 17 of the Sierra Nevada of California. In: Olson, R. K.; Lefohn, A. S., eds. Effects of air 18 pollution on western forests [an A&WMA symposium; June; Anaheim, CA]. Pittsburgh, 19 PA: Air and Waste Management Asssociation; pp. 261-278. (APCA transactions series: 20 no. 16). 21 Eadie, B. J.; McKee, B. A.; Lansing, M. B.; Robbins, J. A.; Metz, S.; Trefry, J. H. (1994) 22 Records of nutrient-enhanced coastal productivity in sediments from the Louisiana 23 continental shelf. Estuaries 17: 754-765. 24 Edmondson, W. T. (1969) Eutrophication in North America. Washington, D.C.: National 25 Academy of Sciences, Printing and Publishing Office. 26 Edmondson, W. T. (1991) The uses of ecology: Lake Washington and beyond. Seattle, WA: 27 University of Washington Press. 28 Edwards, P. J.; Wood, F.; Kochenderfer, J. N. (2002) Baseflow and peakflow chemical responses 29 to experimental applications of ammonium sulphate to forested watersheds in north- 30 central West Virginia, USA. Hydrol. Process. 16: 2287-2310. 31 Egerton-Warburton, L. M.; Allen, E. B. (2000) Shifts in arbuscular mycorrhizal communities 32 along an anthropogenic nitrogen deposition gradient. Ecol. Appl. 10: 484-496. 33 Egerton-Warburton, L. M.; Graham, R. C.; Allen, E. B.; Allen, M. F. (2001) Reconstruction of 34 the historical changes in mycorrhizal fungal communities under anthropogenic nitrogen 35 deposition. Proc. R. Soc. London B 268: 2479-2484. 36 Eilers, J. M.; Kanciruk, P.; McCord, R. A.; Overton, W. S.; Hook, L.; Blick, D. J.; Brakke, D. F.; 37 Kellar, P. E.; DeHaan, M. S.; Silverstein, M. E.; Landers, D. H. (1987) Western lake 38 survey phase I: characteristics of lakes in the western United States, volume II: data 39 compendium for selected physical and chemical variables. Washington, DC: U.S. 40 Environmental Protection Agency, Office of Acid Deposition, Environmental Monitoring 41 and Quality Assurance; EPA report no. EPA/600/3-86/054B. Available from: NTIS, 42 Springfield, VA; PB88-146832/REB. August 2008 C-62 DRAFT-DO NOT QUOTE OR CITE ------- 1 Eliason, S. A.; Allen, E. B. (1997) Exotic grass competition in suppressing native shrubland re- 2 establishment. Restor. Ecol. 5: 245-255. 3 Ellenberg, H. (1987) Floristic changes due to eutrophication. In: Asman, W. A. H.; Diederen, S. 4 M. A., eds. Ammonia and acidification: proceedings of a symposium of the European 5 Association for the Science of Air Pollution (EURASAP); April; Bilthoven, The 6 Netherlands. European Association for the Science of Air Pollution; pp. 301-308. 7 Elser, J. I; Bracken, M. E. S.; Cleland, E. E.; Gruner, D. S.; Harpole, W. S.; Hillebrand, H., II; 8 Ngai, J. T.; Seabloom, E. W.; Shurin, J. B.; Smith, J. E. (2007) Global analysis of 9 nitrogen and phosphorus limitation of primary producers in freshwater, marine, and 10 terrestrial ecosystems. Ecol. Lett.: submitted. 11 Elser, J. J.; Hayakawa, K.; Urabe, J. (2001) Nutrient limitation reduces food quality for 12 zooplankton: Daphnia response to seston phosphorus enrichment. Ecology 82: 898-903. 13 Elser, J. J.; Marzolf, E. R.; Goldman, C. R. (1990) Phosphorus and nitrogen limitation of 14 phytoplankton growth in the freshwaters of North America: a review and critique of 15 experimental enrichments. Can. J. Fish. Aquat. Sci. 47: 1468-1477. 16 Elvir, J. A., G. B. Wiersma, M. E. Day, M. S. Greenwood, and I. J. Fernandez. 2006. Effects of 17 enhanced nitrogen deposition on foliar chemistry and physiological processes of forest 18 trees at the Bear Brook Watershed in Maine. Forest Ecology and Management 221:207- 19 214. 20 Elvir, J. A.; Wiersma, G. B.; White, A. S.; Fernandez, I. J. (2003) Effects of chronic ammonium 21 sulfate treatment on basal area increment in red spruce and sugar maple at the Bear Brook 22 Watershed in Maine. Can. J. Forest. Res. 33: 862-869. 23 Emmett, B. A. (1999) The impact of nitrogen on forest soils and feedbacks on tree growth. Water 24 Air Soil Pollut. 116: 65-74. 25 Emmett, B. A.; Boxman, D.; Bredemeier, M.; Gunderson, P.; Kj0naas, O. J.; Moldan, F.; 26 Schleppi, P.; Tietema, A.; Wright, R. F. (1998) Predicting the effects of atmospheric 27 nitrogen deposition in conifer stands: evidence from the NITREX ecosystem-scale 28 experiments. Ecosystems 1: 352-360. 29 Engstrom, P.; Dalsgaard, T.; Hulth, S.; Aller, R. C. (2005) Anaerobic ammonium oxidation by 30 nitrate (anammox): implications for N2 production in coastal marine sediments. Geochim. 31 Cosmochim. Acta 69: 2057-2065. 32 Erickson, H., M. Keller, and E. A. Davidson. 2001. Nitrogen oxide fluxes and nitrogen cycling 33 during postagricultural succession and forest fertilization in the humid tropics. 34 Ecosystems 4:67-84. 35 Eshleman, K. N.; Fiscus, D. A.; Castro, N. M.; Webb, J. R.; Herlihy, A. T. (2004) 36 Regionalization of disturbance-induced nitrogen leakage from mid-Appalachian forests 37 using a linear systems model. Hydrol. Process. 18: 2713-2725. 38 Eshleman, K. N.; Gardner, R. H.; Seagle, S. W.; Castro, N. M.; Fiscus, D. A.; Webb, J. R.; 39 Galloway, J. N.; Deviney, F. A.; Herlihy, A. T. (2000) Effects of disturbance on nitrogen 40 export from forested lands of the Chesapeake Bay watershed. Environ. Monit. Assess. 63: 41 187-197. August 2008 C-63 DRAFT-DO NOT QUOTE OR CITE ------- 1 Eshleman, K. N.; Morgan II, R. P.; Webb, J. R.; Deviney, F. A.; Galloway, J. N. (1998) 2 Temporal patterns of nitrogen leakage from mid-Appalachian forested watersheds: role of 3 insect defoliation. Water Resour. Res. 34: 2005-2116. 4 Everett, K. R.; Brown, J. (1982) Some recent trends in the physical and chemical 5 characterization and mapping of tundra soils, arctic slope of Alaska. Soil Sci. 133: 264- 6 280. 7 Falkengren-Grerup, U. (1986) Soil acidification and vegetation changes in deciduous forest in 8 southern Sweden. Oecologia 70: 339-347. 9 Falkengren-Grerup, U. (1989) Soil acidification and its impact on ground vegetation. Ambio 18: 10 179-183. 11 Falkengren-Grerup, U. (1998) Nitrogen response of herbs and graminoids in experiments with 12 simulated acid soil solution. Environ. Pollut. 102(suppl. 1): 93-99. 13 Fangmeier, A.; Hadwiger-Fangmeier, A.; Van der Eerden, L.; Jager, H.-J. (1994) Effects of 14 atmospheric ammonia on vegetation—a review. Environ. Pollut. 86: 43-82. 15 Fenn, M. E.; Baron, J. S.; Allen, E. B.; Rueth, H. M.; Nydick, K. R.; Geiser, L.; Bowman, W. D.; 16 Sickman, J. O.; Meixner, T.; Johnson, D. W.; Neitlich, P. (2003a) Ecological effects of 17 nitrogen deposition in the western United States. BioScience 53: 404-420. 18 Fenn, M. E.; Bytnerowicz, A. (1997) Summer throughfall and winter deposition in the San 19 Bernardino Mountains in southern California. Atmos. Environ. 31: 673-683. 20 Fenn, M. E.; De Bauer, L. L; Hernandez-Tejeda, T. (2002) Urban air pollution and forests: 21 resources at risk in the Mexico City air basin. New York, NY: Springer-Verlag. 22 (Ecological studies: v. 156). 23 Fenn, M. E.; Haeuber, R.; Tonnesen, G. S.; Baron, J. S.; Grossman-Clarke, S.; Hope, D.; Jaffe, 24 D. A.; Copeland, S.; Geiser, L.; Rueth, H. M.; Sickman, J. O. (2003c) Nitrogen 25 emissions, deposition, and monitoring in the western United States. BioScience 53: 391- 26 403. 27 Fenn, M. E.; Poth, M. A. (1999) Temporal and spatial trends in streamwater nitrate 28 concentrations in the San Bernardino Mountains, southern California. J. Environ. Qual. 29 28: 822-836. 30 Fenn, M. E.; Poth, M. A.; Aber, J. D.; Baron, J. S.; Bormann, B. T.; Johnson, D. W.; Lemly, A. 31 D.; McNulty, S. G.; Ryan, D. F.; Stottlemyer, R. (1998) Nitrogen excess in North 32 American ecosystems: predisposing factors, ecosystem responses, and management 33 strategies. Ecol. Appl. 8: 706-733. 34 Fenn, M. E.; Poth, M. A.; Bytnerowicz, A.; Sickman, J. O.; Takemoto, B. K. (2003b) Effects of 35 ozone, nitrogen deposition, and other stressors on montane ecosystems in the Sierra 36 Nevada. In: Bytnerowicz, A.; Arbaugh, M. J.; Alonso, R., eds. Ozone air pollution in the 37 Sierra Nevada: distribution and effects on forests; pp. 111-155. New York, NY: Elsevier. 38 (Developments in Environmental Science: v. 2). 39 Fenn, M. E.; Poth, M. A.; Johnson, D. W. (1996) Evidence for nitrogen saturation in the San 40 Bernardino Mountains in southern California. For. Ecol. Manage. 82: 211-230. 41 Ferrari, J. B. (1999) Fine-scale patterns of leaf litterfall and nitrogen cycling in an old-growth 42 forest. Can. J. Forest. Res. 29: 291-302. August 2008 C-64 DRAFT-DO NOT QUOTE OR CITE ------- 1 Field, C. B.; Mooney, H. A. (1986) The photosynthesis-nitrogen relationship in wild plants. In: 2 Givnish, T. J., ed. On the economy of plant form and function. Cambridge, UK: 3 Cambridge University Press. 4 Findlay, D. L.; Hecky, R. E.; Kasian, S. E. M.; Stainton, M. P.; Hendzel, L. L.; Schindler, E. U. 5 (1999) Effects on phytoplankton of nutrients added in conjunction with acidification. 6 Freshwater Biol. 41: 131-145. 7 Finzi, A. C.; Van Breemen, N.; Canham, C. D. (1998) Canopy tree-soil interactions within 8 temperate forests: species effects on soil carbon and nitrogen. Ecol. Appl. 8: 440-446. 9 Fisher, D. C.; Oppenheimer, M. (1991) Atmospheric nitrogen deposition and the Chesapeake 10 Bay estuary. Ambio 20: 102-108. 11 Fisher, T. R.; Hagy, J. D., Ill; Boynton, W. R.; Williams, M. R. (2006) Cultural eutrophication in 12 the Choptank and Patuxent estuaries of Chesapeake Bay. Limnol. Oceanogr. 51(1 pt. 2): 13 435-447. 14 Fisher, T. R.; Harding, L. W., Jr.; Stanley, D. W.; Ward, L. G. (1988) Phytoplankton, nutrients, 15 and turbidity in the Chesapeake, Delaware, and Hudson estuaries. Estuarine Coastal Shelf 16 Sci. 27: 61-93. 17 Fisher, T. R.; Lee, K.-Y.; Berndy, H.; Benitez, J. A.; Norton, M. M. (1998) Hydrology and 18 chemistry of the Choptank River basin in the Chesapeake Bay drainage. Water Air Soil 19 Pollut. 105: 387-397. 20 Fisk, M. C.; Schmidt, S. K. (1996) Microbial responses to nitrogen additions in alpine tundra 21 soil. Soil Biol. Biogeochem. 28: 751-755. 22 Fisk, M. C.; Schmidt, S. K.; Seastedt, T. R. (1998) Topographic patterns of above- and 23 belowground production and nitrogen cycling in Alpine tundra. Ecology 79: 2253-2266. 24 Fisk, M. C.; Zak, D. R.; Crow, T. R. (2002) Nitrogen storage and cycling in old- and second- 25 growth northern hardwood forests. Ecology 83: 73-87. 26 Fore, L. S.; Karr, J. R.; Wisseman, R. W. (1996) Assessing invertebrate responses to human 27 activities: evaluating alternative approaches. J. North Am. Benthol. Soc. 15: 212-231. 28 Forkner, R. E.; Hunter, M. D. (2000) What goes up must come down? Nutrient addition and 29 predation pressure on oak herbivores. Ecology 81: 1588-1600. 30 Fraterrigo, J. M.; Turner, M. G.; Pearson, S. M.; Dixon, P. (2005) Effects of past land use on 31 spatial heterogeneity of soil nutrients in southern Appalachian forests. Ecol. Monogr. 75: 32 215-230. 33 Frey, S. D.; Knorr, M.; Parrent, J. L.; Simpson, R. T. (2004) Chronic nitrogen enrichment affects 34 the structure and function of the soil microbial community in temperate hardwood and 35 pine forests. For. Ecol. Manage. 196: 159-171. 36 Frost, P. C.; Stelzer, R. S.; Lamberti, G. A.; Elser, J. J. (2002) Ecological stoichiometry of 37 trophic interactions in the benthos: understanding the role of C:N:P ratios in lentic and 38 lotic habitats. J. N. Am. Benthol. Soc. 21: 515-528. 39 Galloway, J. N. (1998) The global nitrogen cycle: changes and consequences. Environ. Pollut. 40 102(suppl. 1): 15-24. August 2008 C-65 DRAFT-DO NOT QUOTE OR CITE ------- 1 Galloway, J. N.; Aber, J. D.; Erisman, J. W.; Seitzinger, S. P.; Howarth, R. W.; Cowling, E. B.; 2 Cosby, B. J. (2003) The nitrogen cascade. BioScience 53: 341-356. 3 Galloway, J. N.; Cowling, E. B. (2002) Reactive nitrogen and the world: 200 years of change. 4 Ambio 31: 64-71. 5 Galloway, J. N.; Schlesinger, W. H.; Levy, H.; Michaels, A. F.; Schnoor, J. L. Nitrogen- 6 fixation: anthropogenic enhancement, environmental response. Glob. Biogeochem. 7 Cycles 9: 235-252. (1995) Nitrogen-fixation: anthropogenic enhancement, 8 environmental response. Glob. Biogeochem. Cycles 9: 235-252. 9 Garner, J. H. B. (1994) Nitrogen oxides, plant metabolism, and forest ecosystem response. In: 10 Alscher, R. G.; Wellburn, A. R., eds. Plant responses to the gaseous environment: 11 molecular, metabolic and physiological aspects, [3rd international symposium on air 12 pollutants and plant metabolism]; June 1992; Blacksburg, VA. London, United Kingdom: 13 Chapman & Hall; pp. 301-314. 14 Gauslaa, Y. (1995) Lobarion, an epiphytic community of ancient forests, threatened by acid rain. 15 Lichenologist 27: 59-76. 16 Gebauer, R. L. E.; Reynolds, J. F.; Tenhunen, J. D. (1995) Growth and allocation of the arctic 17 sedges Eriophorum angustifolium and E. vaginatum: effects of variable soil oxygen and 18 nutrient availability. Oecologia 104: 330-339. 19 Geiser, L.; Neitlich, P. (2007) Air pollution and climate gradients in western Oregon and 20 Washington indicated by epiphytic macrolichens. Environ. Pollut. 145: 203-218. 21 Gilliam, F. S. (2006) Response of the herbaceous layer of forest ecosystems to excess nitrogen 22 deposition. J. Ecol. 94: 1176-1191. 23 Gilliam, F. S.; Hockenberry, A. W.; Adams, M. B. (2006) Effects of atmospheric deposition on 24 the herbaceous layer of a central Appalachian hardwood forest. J. Torrey Bot. Soc. 133: 25 240-254. 26 Gobler, C. J.; Davis, T. W.; Coyne, K. J.; Boyer, G. L. (2007) Interactive influences of nutrient 27 loading, zooplankton grazing, and microcystin synthetase gene expression on 28 cyanobactedal bloom dynamics in a eutrophic New York lake. Harmful Algae 6: 119- 29 133. 30 Goldman, C. R. (1988) Primary productivity, nutrients, and transparency during the early onset 31 of eutrophication in ultra-oligotrophic Lake Tahoe, California-Nevada. Limnol. 32 Oceanogr. 33: 1321-1333. 33 Goodale, C. L.; Aber, J. D. (2001) The long-term effects of land-use history on nitrogen cycling 34 in northern hardwood forests. Ecol. Appl. 11: 253-267. 35 Goodale, C. L.; Aber, J. D.; McDowell, W. H. (2000) The long-term effects of disturbance on 36 organic and inorganic nitrogen export in the White Mountains, New Hampshire. 37 Ecosystems 3: 433-450. 38 Goodale, C. L.; Aber, J. D.; Vitousek, P. M. (2003) An unexpected nitrate decline in New 39 Hampshire streams. Ecosystems 6: 75-86. 40 Goodale, C. L.; Aber, J. D.; Vitousek, P. M.; McDowell, W. H. (2005) Long-term decreases in 41 stream nitrate: successional causes unlikely; possible links to DOC? Ecosystems 8: 334- 42 337. August 2008 C-66 DRAFT-DO NOT QUOTE OR CITE ------- 1 Goodroad, L. L. and D. R. Keeney. 1984. Nitrous-oxide emission from forest, marsh, and prairie 2 ecosystems. Journal of Environmental Quality 13:448-452. 3 Gotelli, N. J.; Ellison, A. M. (2002) Nitrogen deposition and extinction risk in the northern 4 pitcher plant, Sarracenia purpurea. Ecology 83: 2758-2765. 5 Goulder, L. H.; Kennedy, D. (1997) Valuing ecosystem services: philosophical bases and 6 empirical methods. In: Daily, G. C., ed. Nature's services: societal dependence on natural 7 ecosystems. Washington, DC: Island Press; pp. 23-47. 8 Granberg, G.; Sundh, L; Svensson, B. H.; Nilsson, M. (2001) Effects of temperature and nitrogen 9 and sulfur deposition, on methane emission from a boreal mire. Ecology 82: 1982-1998. 10 Green, P. A.; Vorosmarty, C. J.; Meybeck, M.; Galloway, J. N.; Peterson, B. J.; Boyer, E. W. 11 (2004) Pre-industrial and contemporary fluxes of nitrogen through rivers: a global 12 assessment based on typology. Biogeochemistry 68: 71-105. 13 Grenon, F.; Bradley, R. L.; Joanisse, G.; Titus, B. D.; Prescott, C. E. (2004) Mineral N 14 availability for conifer growth following clearcutting: responsive versus non-responsive 15 ecosystems. For. Ecol. Manage. 188: 305-316. 16 Grimm, N. B.; Fisher, S. G. (1986) Nitrogen limitation in a Sonoran Desert stream. J. North Am. 17 Benthol. Soc. 5:2-15. 18 Groffman, P. M. (1994) Denitrification in freshwater wetlands. Curr. Topics Wetland 19 Biogeochem. 1: 15-35. 20 Groffman, P. M.; Altabet, A. M.; Bohlke, J. K.; Butterbach-Bahl, K.; David, M. B.; Firestone, 21 M. K.; Giblin, A. E.; Kana, T. M.; Nielsen, L. P.; Voytek, M. A. (2006) Methods for 22 measuring denitrification: diverse approaches to a difficult problem. Ecol. Appl. 16: 23 2091-2122. 24 Grulke, N. E.; Andersen, C. P.; Fenn, M. E.; Miller, P. R. (1998) Ozone exposure and nitrogen 25 deposition lowers root biomass of ponderosa pine in the San Bernardino Mountains, 26 California. Environ. Pollut. 103: 63-73. 27 Grulke, N. E.; Balduman, L. (1999) Deciduous conifers: high N deposition and Os exposure 28 effects on growth and biomass allocation in ponderosa pine. Water Air Soil Pollut. 116: 29 235-248. 30 Griinhage, L.; Dammgen, U.; Haenel, H. D.; Jager, H. J. (1992) Vertikale fliisse von spurengasen 31 in der bodennahen atmosphare. Landbauforsch. Voelkenrode 128: 201-245. 32 Gulledge, J. and J. P. Schimel. 2000. Controls on soil carbon dioxide and methane fluxes in a 33 variety of taiga forest stands in interior Alaska. Ecosystems 3:269-282. 34 Gulledge, J.; Doyle, A. P.; Schimel, J. P. (1997) Different NH4+-inhibition patterns of soil CH4 - 35 oxidizer populations across sites. Soil Biol. Biochem 29: 13-21. 36 Gundersen, P.; Callesen, L; De Vries, W. (1998) Nitrate leaching in forest ecosystems is related 37 to forest floor C/N ratios. Environ. Pollut. 102(suppl. 1): 403-407. 38 Gundhardt-Goerg, M. S.; McQuattie, C. J.; Maurer, S.; Frey, B. (2000) Visible and microscopic 39 injury in leaves of five deciduous tree species related to current critical ozone levels. 40 Environ. Pollut. 109: 489-500. August 2008 C-67 DRAFT-DO NOT QUOTE OR CITE ------- 1 Hachmoller, B.; Matthews, R. A.; Brakke, D. F. (1991) Effects of riparian community structure, 2 sediment size, and water-quality on the macroinvertebrate communities in a small, 3 suburban stream. Northwest Sci. 65: 125-132. 4 Hagy, J. D.; Boynton, W. R.; Keefe, C. W.; Wood, K. V. (2004) Hypoxia in Chesapeake Bay, 5 1950-2001: long-term change in relation to nutrient loading and river flow. Estuaries 27: 6 634-658. 7 Hall, R. I; Leavit, P. R.; Quinlan, R.; Dixit, A. S.; Smol, J. P. (1999) Effects of agriculture, 8 urbanization, and climate on water quality in the northern Great Plains. Limnol. 9 Oceanogr. 44: 739-756. 10 Hall, S. J. and P. A. Matson. 1999. Nitrogen oxide emissions after nitrogen additions in tropical 11 forests. Nature 400:152-155. 12 Hallingback, T. (1991) Blue-green algae and cyanophilic lichens threatened by air pollution and 13 fertilization. Sven. Bot. Tidskr. 85: 87-104. 14 Harding, L. W. J.; Mallonee, M. E.; Perry, E. S. (2002) Toward a predictive understanding of 15 primary productivity in a temperate, partially stratified estuary. Estuar. Coast. Shelf Sci. 16 55: 437-463. 17 18 Harpole, W. S., D. L. Potts, and K. N. Suding. 2007. Ecosystem responses to water and nitrogen 19 amendment in a California grassland. Global Change Biology 13:2341-2348. 20 Harwell, M. A.; Myers, V.; Young, T.; Bartuska, A.; Gassman, N.; Gentile, J. H.; Harwell, C. C.; 21 Appelbaum, S.; Barko, J.; Causey, B.; Johnson, C.; McLean, A.; Smola, R.; Templet, P.; 22 Tosini, S. (1999) A framework for an ecosystem integrity report card. BioScience 49: 23 543-556. 24 Hasler, A. D. (1947) Eutrophication of lakes by domestic drainage. Ecology 28: 383-395. 25 Hayden, M. J.; Ross, D. S. (2005) Denitrification as a nitrogen removal mechanism in a Vermont 26 peatland. J. Environ. Qual. 34: 2052-2061. 27 Hazlett, P. W.; Gordon, A. M.; Voroney, R. P.; Sibley, P. K. (2007) Impact of harvesting and 28 logging slash on nitrogen and carbon dynamics in soils from upland spruce forests in 29 northeastern Ontario. Soil Biol. Biogeochem. 39: 43-57. 30 Hecnar, S. J. (1995) Acute and chronic toxicity of ammonium-nitrate fertilizer to amphibians 31 from southern Ontario. Environ. Toxicol. Chem. 14: 2131-2137. 32 Hedin, L. O.; Von Fischer, J. C.; Ostrom, N. E.; Kennedy, B. P.; Brown, M. G.; Robertson, G. P. 33 (1998) Thermodynamic constraints on nitrogen transformations and other 34 biogeochemical processes at soil-stream interfaces. Ecology 79: 684-703. 35 Hefting, M. M.; Bobbink, R.; De Caluwe, H. (2003) Nitrous oxide emission and denitrification 36 in chronically nitrate-loaded riparian buffer zones. J. Environ. Qual. 32: 1194-1203. 37 Heil, G. W.; Bruggink, M. (1987) Competition for nutrients between Calluna vulgaris (L.) Hull 38 and Molinia caerulea (L.) Moench. Oecologia 73: 105-108. 39 Heil, G. W.; Diemont, W. H. (1983) Raised nutrient levels change heathland in grassland. 40 Vegetation 53: 113-120. August 2008 C-68 DRAFT-DO NOT QUOTE OR CITE ------- 1 Hicke, J. A.; Asner, G. A.; Randerson, J. T.; Tucker, C. J.; Los, S.; Birdsey, R. A.; Jenkins, J. C.; 2 Field, C. B. (2002) Trends in North American net primary productivity derived from 3 satellite observations, 1982-1998. Glob. Biogeochem. Cycles 16(1018): 4 10.1029/2001GB001550. 5 Higley, B.; Carrick, H. J.; Brett, M. T.; Luecke, C.; Goldman, C. R. (2001) The effects of 6 ultraviolet radiation and nutrient additions on periphyton biomass and composition in a 7 sub-alpine lake (Castle Lake, USA). Int. Rev. Hydrobiol. 86: 147-163. 8 Hill, A. R. (1996) Nitrate removal in stream riparian zones. J. Environ. Qual. 25: 743-755. 9 Hill, B. H.; Elonen, C. M.; Jicha, T. M.; Cotter, A. M.; Trebitz, A. S.; Danz, N. P. (2006) 10 Sediment microbial enzyme activity as an indicator of nutrient limitation in Great Lakes 11 coastal wetlands. Freshwater Biol. 51: 1670-1683. 12 Hill, W. R.; Knight, A. W. (1988) Nutrient and light limitation of algae in 2 northern California 13 streams. J. Phycol. 24: 125-132. 14 Hinga, K. R.; Keller, A. A.; Ovlatt, C. A. (1991) Atmospheric deposition and nitrogen inputs to 15 coastal waters. Ambio 20: 256-260. 16 Hogberg, P.; Fan, H.; Quist, M.; Binkleys, D.; Oloftamm, C. (2006) Tree growth and soil 17 acidification in response to 30 years of experimental nitrogen loading on boreal forest. 18 Glob. Change Biol. 12: 489-499. 19 Holland, E. A.; Braswell, B. H.; Lamarque, J. F.; Townsend, A.; Sulzman, J.; Muller, J. F.; 20 Dentener, F.; Brasseur, G.; Levy, H.; Penner, J. E.; Roelofs, G. J. (1997) Variations in the 21 predicted spatial distribution of atmospheric nitrogen deposition and their impact on 22 carbon uptake by terrestrial ecosystems. J. Geophys. Res. 102: 15849-15866. 23 Holland, E. A.; Dentener, F. J.; Braswell, B. H.; Sulzman, J. M. (1999) Contemporary and pre- 24 industrial global reactive nitrogen budgets. Biogeochemistry 46: 7-43. 25 Hooper, D. U.; Vitousek, P. M. (1997) The effects of plant composition and diversity on 26 ecosystem processes. Science (Washington, DC) 277: 1302-1305. 27 Horvath, L., E. Fuhrer, and K. Lajtha. 2006. Nitric oxide and nitrous oxide emission from 28 Hungarian forest soils; linked with atmospheric N-deposition. Atmospheric Environment 29 40: 7786-7795. 30 Houdijk, A.; Verbeek, P. J. M.; Van Dijk, H. F. G; Roelofs, J. G. M. (1993) Distribution and 31 decline of endangered herbaceous heathland species in relation to the chemical 32 composition of the soil. Plant Soil 148: 137-143. 33 Houston, D. R. (1994) Major new tree epidemics: beech bark disease. Annu. Rev. Phytopathol. 34 32: 75-87. 35 Howarth, R. W.; Billen, G.; Swaney, D.; Twonsend, A.; Jaworski, N.; Lajtha, K.; Downing, J. 36 A.; Elmgren, R.; Caraco, N.; Jordan, T.; Ferendse, F.; Freney, J.; Kudeyarov, V.; 37 Murdoch, P.; Zhu, Z.-L. (1996) Regional nitrogen budgets and riverine N & P fluxes for 38 the drainages to the North Atlantic Ocean: natural and human influences. 39 Biogeochemistry 35: 75-139. 40 Howarth, R. W.; Boyer, E. W.; Pabich, W. J.; Galloway, J. N. (2002) Nitrogen use in the United 41 States from 1961-2000 and potential future trends. Ambio 31: 88-96. August 2008 C-69 DRAFT-DO NOT QUOTE OR CITE ------- 1 Howarth, R. W.; Marino, R. (2006) Nitrogen as the limiting nutrient for eutrophication in coastal 2 marine ecosystems: evolving views over three decades. Limnol. Oceanogr. 51: 364-376. 3 Huberty, L. E.; Gross, K. L.; Miller, C. J. (1998) Effects of nitrogen addition on successional 4 dynamics and species diversity in Michigan old-fields. J. Ecol. 86: 794-803. 5 Hungate, B. A., C. P. Lund, H. L. Pearson, and F. S. Chapin. 1997. Elevated CO2 and nutrient 6 addition alter soil N cycling and N trace gas fluxes with early season wet-up in a 7 California annual grassland. Biogeochemistry 37:89-109. 8 Hyvarinen, M.; Crittenden, P. D. (1998) Relationships between atmospheric nitrogen inputs and 9 the vertical nitrogen and phosphorus concentration gradients in the lichen Cladonia 10 portentosa. New Phytol. 140:519-530. 11 Ineson, P., P. Coward, D. G. Benham, and S. M. C. Robertson. 1998. Coniferous forests as 12 "secondary agricultural" sources of nitrous oxide. Atmospheric Environment 32:3321- 13 3330. 14 Inouye, R. S. (2006) Effects of shrub removal and nitrogen addition on soil moisture in 15 sagebrush steppe. J. Arid Environ. 65: 604-618. 16 Interlandi, S. J.; Kilham, S. S. (1998) Assessing the effects of nitrogen deposition on mountain 17 waters: a study of phytoplankton community dynamics. Water Sci. Technol. 38: 139-146. 18 Interlandi, S. J.; Kilham, S. S. (2001) Limiting resources and the regulation of diversity in 19 phytoplankton communities. Ecology 82: 1270-1282. 20 Interlandi, S. J.; Kilham, S. S.; Theriot, E. C. (1999) Responses of phytoplankton to varied 21 resource availability in large lakes of the Greater Yellowstone ecosystem. Limnol. 22 Oceanogr. 44: 668-682. 23 Ittekot, V. (2003) Carbon-silicon interactions. In: Melillo, J. M.; Field, C. B.; Moldan, B., eds. 24 Interactions of the major biogeochemical cycles: global change and human impacts. 25 Washington, D.C.: Island Press; pp. 311-322. 26 Jackson, J. B. C.; Kirby, M. X.; Berger, W. H.; Bjorndal, K. A.; Botsford, L. W.; Bourque, B. J.; 27 Bradbury, R. H.; Cooke, R.; Erlandson, J.; Estes, J. A.; Huges, T. P.; Kidwell, S.; Lange, 28 C. B.; Lenihan, H. S.; Pandolfi, J. M.; Peterson, C. H.; Steneck, R. S.; Tegner, M. J.; 29 Warner, R. R. (2001) Historical overfishing and recent collapse of coastal ecosystems. 30 Science 293: 629-637. 31 Jansson, M.; Bergstrom, A. K.; Drakare, S.; Blomqvist, P. (2001) Nutrient limitation of 32 bacterioplankton and phytoplankton in humic lakes in northern Sweden. Freshwater Biol. 33 46: 653-666. 34 Jassby, A. D.; Reuter, J. E.; Axler, R. P.; Goldman, C. R.; Hackley, S. H. (1994) Atmospheric 35 deposition of N and phosphorus in the annual nutrient load of Lake Tahoe (California- 36 Nevada). Water Resour. Res. 30: 2207-2216. 37 Jaworski, N. A.; Howarth, R. W.; Hetling, L. J. (1997) Atmospheric deposition of nitrogen 38 oxides onto the landscape contributes to coastal eutrophi cation in the northeast United 39 States. Environ. Sci. Technol. 31: 1995-2004. 40 Jetten, M. S. M.; Cirpus, I; Kartal, B.; van Niftrik, L.; van de Pas-Schoonen, K. T.; Sliekers, O.; 41 Haaijer, S. C. M.; Van der Star, W. R. L.; Schmid, M.; Van de Vossenberg, J.; Schmidt, 42 I; Harhangi, H. R.; van Loosdrecht, M.; Gijs Kuenen, J.; Op den Camp, H.; Strous, M. 1 August 2008 C-70 DRAFT-DO NOT QUOTE OR CITE ------- 1 (2005) 1994-2004: 10 years of research on the anaerobic oxidation of ammonium. 2 Biochem. Soc. Trans. 33: 119-123. 3 Jetten, M. S. M.; Sliekers, O.; Kuypers, M. M. M.; Dalsgaard, T.; Van Niftrik, L.; Cirpus, I; Van 4 de Pas-Schoonen, K. T.; Lavik, G.; Thamdrup, B.; Le Paslier, D.; Op Den Camp, H. J. 5 M.; Hulth, S.; Nielsen, L. P.; Abma, W.; Third, K.; Engstrom, P.; Kuenen, J. G; 6 Jorgensen, B. B.; Canfield, D. E.; Damste, J. S. S.; Revsbech, N. P.; Fuerst, J.; 7 Weissenbach, J.; Wagner, M.; Schmidt, L; Schmid, M.; Strous, M. (2003) Anaerobic 8 ammonium oxidation by marine and freshwater planctomycete-like bacteria. Appl. 9 Microbiol. Biotechnol. 63: 107-114. 10 Johansson, M.; Rasanen, K.; Merila, J. (2001) Comparison of nitrate tolerance between different 11 populations of the common frog, Rana temporaria. Aquat. Toxicol. 54: 1-14. 12 Johnson, C. E. (1995) Soil nitrogen status 8 years after whole-tree clear-cutting. Can. J. Forest. 13 Res. 25: 1346-1355. 14 Johnson, D. W., A. M. Hoylman, J. T. Ball, and R. F. Walker. 2006. Ponderosa pine responses to 15 elevated CCh and nitrogen fertilization. Biogeochemistry 77:157-175. 16 Johnson, D. W.; Susfalk, R. B.; Caldwell, T. G; Murphy, J. D.; Miller, W. W.; Walker, R. F. 17 (2004) Fire effects on carbon and nitrogen budgets in forests. Water Air Soil Pollut: 18 Focus 4: 263-275. 19 Johnson, L. B.; Richards, C.; Host, G. E.; Arthur, J. W. (1997) Landscape influences on water 20 chemistry in Midwestern stream ecosystems. Freshwater Biol. 37: 193-208. 21 Jones, J. A.; Swanson, F. J.; Wemple, B. C.; Snyder, K. U. (2000) Effects of roads on hydrology, 22 geomorphology, and disturbances patches in stream networks. Conserv. Biol. 14: 76-85. 23 Joos, F.; Prentice, I. C.; House, J. I. (2002) Growth enhancement due to global atmospheric 24 change as predicted by terrestrial ecosystem models: consistent with US forest inventory 25 data. Glob. Change Biol. 8: 299-303. 26 Jovan, S.; McCune, B. (2005) Air-quality bioindication in the greater central valley of California, 27 with epiphytic macrolichen communities. Ecol. Appl. 15: 1712-1726. 28 Jovan, S.; McCune, B. (2006) Using epiphytic macrolichen communities for biomonitoring 29 ammonia in forests of the greater Sierra Nevada, California. Water Air Soil Pollut. 170: 30 69-93. 31 Justic, D.; Rabalais, N. N.; Turner, R. E. (1995a) Stoichiometric nutrient balance and origin of 32 coastal eutrophication. Mar. Pollut. Bull. 30: 41-46. 33 Justic, D.; Rabalais, N. N.; Turner, R. E. (1997) Impacts of climate change on net productivity of 34 coastal waters: implications for carbon budget and hypoxia. Clim. Res. 8: 225-237. 35 Justic, D.; Rabalais, N. N.; Turner, R. E.; Dortch, Q. (1995b) Changes in nutrient structure of 36 river-dominated coastal waters: Stoichiometric nutrient balance and its conseqeuences. 37 Estuar. Coast. Shelf Sci. 40: 339-356. 38 Justic, D.; Rabalais, N. N.; Turner, R. E.; Wiseman Jr., W. J. (1993) Seasonal coupling between 39 riverborne nutrients, net productivity and hypoxia. Mar. Pollut. Bull. 26: 184-189. August 2008 C-71 DRAFT-DO NOT QUOTE OR CITE ------- 1 Kahl, J.; Norton, S.; Frenandez, I; Rustad, L.; Handley, M. (1999) Nitrogen and sulfur input- 2 output budgets in the experimental and reference watersheds, Bear Brook Watershed in 3 Maine (BBWM). Environ. Monit. Assess. 55: 113-131. 4 Kana, T. M.; Sullivan, M. B.; Cornwell, J. C.; Groszkowski. (1998) Denitrification in estuarine 5 sediments determined by membrane inlet mass spectrometry. Limnol. Oceanogr. 43: 334- 6 339. 7 Kauppi, P. E.; Mielikaeinen, K.; Kuusela, K. (1992) Biomass and carbon budget of European 8 forests, 1971 to 1990. Science (Washington, DC) 256: 70-74. 9 Keller, J. K., S. D. Bridgham, C. T. Chapin, and C. M. Iversen. 2005. Limited effects of six years 10 of fertilization on carbon mineralization dynamics in a Minnesota fen. Soil Biology & 11 Biochemistry 37:1197-1204. 12 Keller, M., W. A. Kaplan, S. C. Wofsy, and J. M. Dacosta. 1988. Emissions of N2O from tropical 13 forest soils - response to fertilization with NH4+, NCV, and PC>43". Journal of Geophysical 14 Research-Atmospheres 93:1600-1604. 15 Kemp, W. M.; Sampou, P.; Caffrey, J.; Mayer, M.; Henriksen, K.; Boynton, W. R. (1990) 16 Ammonium recycling versus denitrification in Cheasapeake Bay sediments. Limnol. 17 Oceanogr. 35: 1545-1563. 18 Kenk, G.; Fischer, H. (1988) Evidence from nitrogen fertilisation in the forests of Germany. 19 Environ. Pollut. 54: 199-218. 20 Kielland, K. (1994) Amino acid absorption by Arctic plants: implications for plant nutrition and 21 nitrogen cycling. Ecology 75: 2373-2383. 22 Kielland, K. (1995) Landscape patterns of free amino acids ina rctic tundra soils. 23 Biogeochemistry 31: 85-98. 24 Killingbeck, K. T. (1996) Nutrients in senesced leaves: keys to the search for potential resorption 25 and resorption proficiency. Ecology 77: 1716-1727. 26 Kincheloe, J. W.; Wedemeyer, G. A.; Koch, D. L. (1979) Tolerance of developing salmonid eggs 27 and fry to nitrate exposure. Bull. Environ. Contam. Toxicol. 23: 575-578. 28 King, G. M.; Schnell, S. (1994) Ammonium and nitrate inhibition of methane oxidation by 29 Methylobacter albus BG8 and Methylosinus trichosporium OB3b atlow methane 30 concentrations. Appl. Environ. Microbiol. 60: 3508-3513. 31 Kirschbaum, M. U. F. (1994) The temperature dependence of soil organic matter decomposition, 32 and the effect of global warming on soil organic C storage. Soil. Biol. Biochem. 27: 753- 33 760. 34 Kitzler, B., S. Zechmeister-Boltenstern, C. Holtermann, U. Skiba, and K. Butterbach-Bah. 2006. 35 Controls over N2O, NOx and CC>2 fluxes in a calcareous mountain forest soil. 36 Biogeosciences 3:383-395. 37 Kitzler, B., S. Zechmeister-Boltenstern, C. Holtermann, U. Skiba, and K. Butterbach-Bahl. 2006. 38 Nitrogen oxides emission from two beech forests subjected to different nitrogen loads. 39 Biogeosciences 3:293-310. 40 Kleb, H. R.; Wilson, S. D. (1997) Vegetation effects on soil resource heterogeneity in prairie and 41 forest. American Naturalist 150: 283-298. August 2008 C-72 DRAFT-DO NOT QUOTE OR CITE ------- 1 Klemedtsson, L., A. K. Klemedtsson, F. Moldan, and P. Weslien. 1997. Nitrous oxide emission 2 from Swedish forest soils in relation to liming and simulated increased N-deposition. 3 Biology and Fertility of Soils 25:290-295. 4 Klopatek, J. M.; Barry, M. J.; Johnson, D. W. (2006) Potential canopy interception of nitrogen in 5 the Pacific Northwest, USA. For. Ecol. Manage. 234: 344-354. 6 Kochy, M.; Wilson, S. D. (2001) Nitrogen deposition and forest expansion in the Northern Great 7 Plains. J. Ecol. 89: 807-817. 8 Koerselman, W.; Van Kerkhoven, M. B.; Verhoeven, J. T. A. (1993) Release of inorganic N, P, 9 and K in peat soils; effect of temperature, water chemistry and water level. 10 Biogeochemistry 20: 63-81. 11 Kooijman, A. M.; Bakker, C. (1994) The acidification capacity of wetland bryophytes as 12 influenced by simulated clean and polluted rain. Aquat. Bot. 48: 133-144. 13 Korb, J. E.; Ranker, T. A. (2001) Changes in stand composition and structure between 1981 and 14 1996 in four Front Range plant communities in Colorado. Plant Ecol. 157: 1-11. 15 Kristensen, H. L.; Gundersen, P.; Callesen, I; Reinds, G. J. (2004) Throughfall nitrogen 16 deposition has different impacts on soil solution nitrate concentration in European 17 coniferous and deciduous forests. Ecosystems 7: 180-192. 18 Krupa, S. V. (2003) Effects of atmospheric ammonia (NFL?) on terrestrial vegetation: a review. 19 Environ. Pollut. 124: 179-221. 20 Kuypers, M. M. M.; Lavik, G.; Woebken, D.; Schmid, M.; Fuchs, B.; Amann, R.; Jorgensen, B. 21 B.; Jetten, M. S. M. (2005) Massive nitrogen loss from the Benguela upwelling system 22 through anaerobic ammonium oxidation. Proc. Natl. Acad. Sci. 102: 6478-3483. 23 Lafrancois, B. M.; Nydick, K. R.; Caruso, B. (2003) Influence of nitrogen on phytoplankton 24 biomass and community composition in fifteen Snowy Range lakes (Wyoming, U.S.A.). 25 Arct. Anarct. Alp. Res. 35: 499-508. 26 Lafrancois, B. M.; Nydick, K. R.; Johnson, B. M.; Baron, J. S. (2004) Cumulative effects of 27 nutrients and pH on the plankton of two mountain lakes. Can. J. Fish. Aquat. Sci. 61: 28 1153-1165. 29 Lane, D. R.; BassiriRad, H. (2002) Differential responses of tallgrass prairie species to nitrogen 30 loading and varying ratios of NO3" to NH4+. Funct. Plant Biol. 29: 1227-1235. 31 Laposata, M. M.; Dunson, W. A. (1998) Effects of boron and nitrate on hatching success of 32 amphibian eggs. Arch. Environ. Contam. Toxicol. 35: 615-619. 33 Larson, G. L.; Moore, S. E. (1985) Encroachment of exotic rainbow trout into stream populations 34 of native brook trout in the Southern Appalachian Mountains. Trans. Am. Fish. Soc. 114: 35 195-203. 36 Latty, E. F.; Canham, C. D.; Marks, P. L. (2004) The effects of land-use history on soil 37 properties and nutrient dynamics in northern hardwood forests of the Adirondack 38 Mountains. Ecosystems 7: 193-207. 39 Laurence, J. A.; Amundson, R. G.; Friend, A. L.; Pell, E. J.; Temple, P. J. (1994) Allocation of 40 carbon in plants under stress: an analysis of the ROPIS experiments. J. Environ. Qual. 23: 41 412-417. August 2008 C-73 DRAFT-DO NOT QUOTE OR CITE ------- 1 Lawrence, G. B.; David, M. B.; Lovett, G. M.; Murdoch, P. S.; Burns, D. A.; Stoddard, J. L.; 2 Baldigo, B. P.; Porter, J. H.; Thompson, A. W. (1999) Soil calcium status and the 3 response of stream chemistry to changing acidic deposition rates. Ecol. Appl. 9: 1059- 4 1072. 5 Leggett, Z. H. and D. L. Kelting. 2006. Fertilization effects on carbon pools in loblolly pine 6 plantations on two upland sites. Soil Science Society of America Journal 70:279-286. 7 Lesack, L. F. W.; Melack, J. M. (1996) Mass balance of major solutes in a rainforest catchment 8 in the central Amazon: implications for nutrient budgets in tropical rainforests. 9 Biogeochemistry 32: 115-142. 10 Levine, M. A.; Whalen, S. C. (2001) Nutrient limitation of phytoplankton production in Alaskan 11 arctic foothill lakes. Hydrobiologia 455: 189-201. 12 Levine, S. N.; Shambaugh, A. D.; Pomeroy, S. E.; Braner, M. (1997) Phosphorus, nitrogen, and 13 silica as controls on phytoplankton biomass and species composition in Lake Champlain 14 (USA-Canada). Intern. Assoc. for Great Lakes Res. 23: 131-148. 15 Levlin, S. A. (1998) Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1: 16 431-436. 17 Likens, G. E.; Bormann, F. H.; Johnson, N. M.; Fisher, D. W.; Pierce, R. S. (1970) Effects of 18 forest cutting and herbicide treatment on nutrient budgets in the Hubbard Brook 19 watershed-ecosystem. Ecol. Monogr. 40: 23-47. 20 Likens, G. E.; Bormann, F. H.; Pierce, R. S.; Reiners, W. A. (1978) Recovery of a deforested 21 ecosystem. Science 199: 492-496. 22 Lilleskov, E. A.; Fahey, T. J.; Horton, T. R.; Lovett, G. M. (2002) Belowground ectomycorrhizal 23 fungal community change over a nitrogen deposition gradient in Alaska. Ecology 83: 24 104-115. 25 Lilleskov, E. A.; Fahey, T. J.; Lovett, G. M. (2001) Ectomycorrhizal fungal aboveground 26 community change over an atmospheric nitrogen deposition gradient. Ecol. Appl. 11: 27 397-410. 28 Lindau, C. W., R. D. Delaune, and J. H. Pardue. 1994. Inorganic nitrogen processing and 29 assimilation in a forested wetland. Hydrobiologia 277:171-178. 30 Lohman, K.; Jones, J. R.; Baysinger-Daniel, C. (1991) Experimental evidence for nitrogen 31 limitation in a northern Ozark stream. J. North Am. Benthol. Soc. 10: 14-23. 32 Lohman, K.; Priscu, J. C. (1992) Physiological indicators of nutrient deficiency in cladophora 33 (chlorophyta) in the Clark Fork of the Columbia River, Montana. J. Phycol. 28: 443-448. 34 Lohrenz, S. E.; Fahnenstiel, G. L.; Redakje, D. G.; Lang, G. A.; Chen, X.; Dagg, M. J. (1997) 35 Variations in primary production of northern Gulf of Mexico continental shelf waters 36 linked to nutrient inputs from the Mississippi River. Mar. Ecol. Prog. Ser. 155: 445-454. 37 Letter, A. F. (1998) The recent eutrophication of Baldeggersee (Switzerland) as assessed by 38 fossil diatom assemblages. Holocene 8: 395-405. 39 Lovett, G. M. (1992) Atmospheric deposition and canopy interactions of nitrogen. In: Johnson, 40 D. W.; Lindberg, S. E., eds. Atmospheric deposition and forest nutrient cycling: a 41 synthesis of the integrated forest study. New York, NY: Springer-Verlag, Inc.; pp. 152- August 2008 C-74 DRAFT-DO NOT QUOTE OR CITE ------- 1 166. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. 2 Ecological studies: analysis and synthesis: v. 91). 3 Lovett, G. M.; Weathers, K. C.; Arthur, M. A. (2002) Control of nitrogen loss from forested 4 watersheds by carbon: nitrogen ratio and tree species composition. Ecosystems 5: 712- 5 718. 6 Lovett, G. M.; Weathers, K. C.; Sobczak, W. V. (2000) Nitrogen saturation and retention in 7 forested watersheds of the Catskill Mountains, New York. Ecol. Appl. 10: 73-84. 8 Lowe, P. N.; Lauenroth, W. K.; Burke, I. C. (2002) Effects of nitrogen availability on the growth 9 of native grasses and exotic weeds. J. Range Manage. 55: 94-98. 10 Lowrance, R. (1992) Groundwater nitrate and denitrification in a coastal plain riparian forest. J. 11 Environ. Qual. 21: 401-405. 12 Macdonald, J. A., U. Skiba, L. J. Sheppard, B. Ball, J. D. Roberts, K. A. Smith, and D. Fowler. 13 1997. The effect of nitrogen deposition and seasonal variability on methane oxidation and 14 nitrous oxide emission rates in an upland spruce plantation and moorland. Atmospheric 15 Environment 31:3693-3706. 16 MacDonald, J. A.; Dise, N. B.; Matzner, E.; Arbruster, M.; Gundersen, P.; Forsius, M. (2002) 17 Nitrogen input together with ecosystem nitrogen enrichment predict nitrate leaching from 18 European forests. Glob. Change Biol. 8: 1028-1033. 19 Mack, M. C., E. A. G. Schuur, M. S. Bret-Harte, G. R. Shaver, and F. S. Chapin. 2004. 20 Ecosystem carbon storage in arctic tundra reduced by long-term nutrient fertilization. 21 Nature 431:440-443. 22 Magill, A. H., J. D. Aber, J. J. Hendricks, R. D. Bowden, J. M. Melillo, and P. A. Steudler. 1997. 23 Biogeochemical response of forest ecosystems to simulated chronic nitrogen deposition. 24 Ecological Applications 7:402-415. 25 Magill, A. H., J. D. Aber, W. S. Currie, K. J. Nadelhoffer, M. E. Martin, W. H. McDowell, J. M. 26 Melillo, and P. Steudler. 2004. Ecosystem response to 15 years of chronic nitrogen 27 additions at the Harvard Forest LTER, Massachusetts, USA. Forest Ecology and 28 Management 196:7-28. 29 Magill, A. H.; Aber, J. D.; Berntson, G. M.; McDowell, W. H.; Nadelhoffer, K. J.; Melillo, J. M.; 30 Steudler, P. (2000) Long-term nitrogen additions and nitrogen saturation in two 31 temperate forests. Ecosystems 3: 238-253. 32 Magill, A. H.; Aber, J. D.; Currie, W. S.; Nadelhoffer, K. J.; Martin, M. E.; McDowell, W. H.; 33 Melillo, J. M.; Steudler, P. (2004) Ecosystem response to 15 years of chronic nitrogen 34 additions at the Harvard Forest LTER, Massachusetts, USA. For. Ecol. Manage. 196: 7- 35 28. 36 Makipaa, R.; Karjalainen, T.; Pussinen, A.; Kellomaki, S. (1999) Effects of climate change and 37 nitrogen deposition on the carbon sequestration of a forest ecosystem in the boreal zone. 38 Can. J. Forest. Res. 29: 1490-1501. 39 Maljanen, M., H. Jokinen, A. Saari, R. Strommer, and P. J. Martikainen. 2006. Methane and 40 nitrous oxide fluxes, and carbon dioxide production in boreal forest soil fertilized with 41 wood ash and nitrogen. Soil Use and Management 22:151-157. August 2008 C-75 DRAFT-DO NOT QUOTE OR CITE ------- 1 Malone, D. I; Conley, T. R.; Fisher, P. M.; Gilbert, L. W.; Harding, J.; Dellner, K. G. (1996) 2 Scales of nutrient-limited phytoplankton productivity in Chesapeake Bay. Estuaries 19: 3 371-385. 4 Malone, T. C.; Crocker, L. H.; Pike, S. E.; Wendler, B. W. (1988) Influence of river flow on the 5 dynamics of phytoplankton in a partially stratified estuary. Mar. Ecol. Progr. Ser. 48: 6 235-249. 7 Mann, L. K.; Johnson, D. W.; West, D. C.; Cole, D. W.; Hornbeck, J. W.; Martin, C. W.; 8 Riekerk, H.; Smith, C. T.; Swank, W. T.; Tritton, L. M.; Van Lear, D. H. (1988) Effects 9 on whole-tree and stem-only clearcutting on postharvest hydrologic losses, nutrient 10 capital, and regrowth. For. Sci. 34: 412-428. 11 Marler, R. J.; Stromberg, J. C.; Pattern, D. T. (2001) Growth response of Populus fremontii, 12 Salix gooddingii, and Tamarix ramosissimi seedlings under different nitrogen and 13 phosphorus concentrations. J. Arid Environ. 49: 133-146. 14 Marshall, H. G.; Lacouture, R. (1986) Seasonal patterns of growth and composition of 15 phytoplankton in the lower Chesapeake Bay and vicinity. Estuar. Coast. Shelf Sci. 23: 16 115-130. 17 Martin, C. W.; Noel, D. S.; Federer, C. A. (1984) Effects of forest clearcutting in New England 18 on stream chemistry. J. Environ. Qual. 13: 204-210. 19 Mattson, W. J., Jr. (1980) Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Syst. 20 11:119-161. 21 McClain, M. E.; Boyer, E. W.; Dent, C. L.; Gergel, S. E.; Grimm, N. B.; Groffman, P. M.; Hart, 22 S. C.; Harvey, J. W.; Johnston, C. A.; Mayorga, E.; McDowell, W. H.; Pinay, G. (2003) 23 Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic 24 ecosystems. Ecosystems 6: 301-312. 25 McCune, B. (1988) Lichen communities along Oj, and SCh gradients in Indianapolis. Bryologist 26 91:223-228. 27 McCune, B.; Geiser, L. (1997) Macrolichens of the Pacific Northwest. Corvallis, OR: Oregon 28 State University Press. 29 McDowell, W. H.; Magill, A. H.; Aitkenhead-Peterson, J. A.; Aber, J. D.; Merriam, J. L.; 30 Kaushal, S. S. (2004) Effects of chronic nitrogen amendment on dissolved organic matter 31 and inorganic nitrogen in soil solution. For. Ecol. Manage. 196: 29-41. 32 McGurk, M. D.; Landry, F.; Tang, A.; Hanks, C. C. (2006) Acute and chronic toxicity of nitrate 33 to early life stages of lake trout (Salvelinus namaycush) and lake whitefish (Coregonus 34 clupeaformis). Environ. Toxicol. Chem. 25: 2187-2196. 35 McHale, M. R.; Cirmo, C. P.; Mitchell, M. J.; McDonnell, J. J. (2004) Wetland nitrogen 36 dynamics in an Adirondack forested watershed. Hydrol. Process. 18: 1853-1870. 37 McKane, R. B.; Johnson, L. C.; Shaver, G. R.; Nadelhoffer, K. J.; Rastetter, E. B.; Fry, B.; 38 Giblin, A. E.; Kielland, K.; Kwiatkowski, B. L.; Laundre, J. A.; Murray, G. (2002) 39 Resource-based niches provide a basis for plant species diversity and dominance in arctic 40 tundra. Nature 415: 68-72. 41 McKnight, D. M.; Smith, R. L.; Bradbury, J. P.; Baron, J. S.; Spaulding, S. (1990) Phytoplankton 42 dynamics in 3 Rocky Mountain Lakes, Colorado USA. Arct. Alp. Res. 22: 264-274. August 2008 C-76 DRAFT-DO NOT QUOTE OR CITE ------- 1 McNeil, B. E.; Read, J. M.; Sullivan, T. J.; McDonnell, T. C.; Fernandez, 1.1; Driscoll, C. T. 2 (2008) The spatial pattern of nitrogen cycling in the Adirondack Park, New York. Ecol. 3 Appl.: in press. 4 McNulty, S. G.; Aber, J. D.; Newman, S. D. (1996) Nitrogen saturation in a high elevation New 5 England spruce-fir stand. For. Ecol. Manage. 84: 109-121. 6 McNulty, S. G.; Boggs, J.; Aber, J. D.; Rustad, L.; Magill, A. (2005) Red spruce ecosystem level 7 changes following 14 years of chronic N fertilization. For. Ecol. Manage. 219: 279-291. 8 Meyers, T.; Sickles, J.; Dennis, R.; Russell, K.; Galloway, J.; Church, T. (2001) Atmospheric 9 nitrogen deposition to coastal estuaries and their watersheds. In: Valigura, R. A.; 10 Alexander, R. B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; Stacey, P. E.; Turner, R. E., 11 eds. Nitrogen Loading in Coastal Water Bodies: an Atmospheric Perspective. 12 Washington, DC: AGU Press. (Coastal and Estuarine Studies, v.57). 13 Michel, T. J.; Saros, J. E.; Interlandi, S. J.; Wolfe, A. P. (2006) Resource requirements of four 14 freshwater diatom taxa determined by in situ growth bioassays using natural populations 15 from alpine lakes. Hydrobiologia 568: 235-243. 16 Milberg, P.; Lament, B. B.; Perez-Fernandez, M. A. (1999) Survival and growth of native and 17 exotic composites in response to a nutrient gradient. Plant Ecol. 145: 125-132. 18 Milchunas, D. G.; Lauenroth, W. K. (1995) Inertia in plant community structure: state changes 19 after cessation of nutrient-enrichment stress. Ecol. Appl. 5: 452-458. 20 Miller, A. E.; Bowman, W. D. (2002) Variation in nitrogen-15 natural abundance and nitrogen 21 uptake traits among co-occuring alpine species: do species partition by nitrogen form? 22 Oecologia 130: 609-616. 23 Miller, P. R.; Longbotham, G. J.; Longbotham, C. R. (1983) Sensitivity of selected western 24 conifers to ozone. Plant Dis. 67: 1113-1115. 25 Miller, P. R.; McBride, J. R., eds. (1999) Oxidant air pollution impacts in the Montane forests of 26 southern California: a case study of the San Bernardino Mountains. New York, NY: 27 Springer-Verlag. (Ecological Studies: v. 134.) 28 Minnich, R. A.; Barbour, M. G.; Burk, J. H.; Fernau, R. F. (1995) Sixty years of change in 29 California conifer forests of the San Bernardino mountains. Conserv. Biol. 9: 902-914. 30 Minnich, R. A.; Dezzani, R. J. (1998) Historical decline of coastal sage scrub in the Riverside- 31 Ferris Plain, California. Western Birds 29: 366-391. 32 Mitchell, M. J.; Driscoll, C. T.; Kahl, J. S.; Likens, G. E.; Murdoch, P. S.; Pardo, L. H. (1996) 33 Climatic control of nitrate loss from forested watersheds in the northeast United States. 34 Environ. Sci. Technol. 30: 2609-2612. 35 Mitchell, M. J.; Piatek, K. B.; Christopher, S.; Mayer, B.; Kendall, C.; McHale, P. J. (2006) 36 Solute sources in stream water during consecutive fall storms in a northern hardwood 37 forest watershed: a combined hydrological, chemical and isotopic approach. 38 Biogeochemistry 78: 217-246. 39 Mitchell, R. J.; Truscot, A. M.; Leith, I. D.; Cape, J. N; Dijk, N. V.; Tang, Y. S.; Fowler, D.; 40 Sutton, M. A. (2005) A study of the epiphytic communities of Atlantic oak woods along 41 an atmospheric nitrogen deposition gradient. J. Ecol. 93: 482-492. August 2008 C-77 DRAFT-DO NOT QUOTE OR CITE ------- 1 Mohn, J., A. Schumann, F. Hagedorn, P. Schleppi, and R. Bachofen. 2000. Increased rates of 2 denitrification in nitrogen-treated forest soils. Forest Ecology and Management 137:113- 3 119. 4 Montgomery, D. R.; Buffington, J. M. (1998) Channel processes, classification, and response. In: 5 Naiman, R. J.; Bilby, R., eds. River ecology and management: lessons from the Pacific 6 coastal ecoregion. New York: Springer-Verlag; pp. 13-42. 7 Moore, D. R. J.; Keddy, P. A.; Gaudet, C. L.; Wisheu, I. C. (1989) Conservation of wetlands: do 8 infertile wetlands deserve a higher priority? Biol. Conserv. 47: 203-217. 9 Morley, S. A.; Karr, J. R. (2002) Assessing and restoring the health of urban streams in the Puget 10 Sound Basin. Conserv. Biol. 16: 1498-1509. 11 Morris, D. P.; Lewis, W. M., Jr. (1988) Phytoplankton nutrient limitation in Colorado mountain 12 lakes. Freshwater Biol. 20: 315-327. 13 Morris, J. T. (1991) Effects of nitrogen loading on wetland ecosystems with particular reference 14 to atmospheric deposition. Annu. Rev. Ecol. Syst. 22: 257-279. 15 Mosier, A., D. Schimel, D. Valentine, K. Bronson, and W. Parton. 1991. Methane and nitrous- 16 oxide fluxes in native, fertilized and cultivated grasslands. Nature 350:330-332. 17 Mulholland, P. J.; Valett, H. M.; Webster, J. R.; Thomas, S. A.; Cooper, L. W.; Hamilton, S. K.; 18 Peterson, B. (2004) Stream denitrification and total nitrate uptake rates measured using a 19 field 15N tracer addition approach. Limnol. Oceanogr. 49: 809-820. 20 Munoz-Hincapie, M., J. M. Morell, and J. E. Corredor. 2002. Increase of nitrous oxide flux to the 21 atmosphere upon nitrogen addition to red mangroves sediments. Marine Pollution 22 Bulletin 44:992-996. 23 Nadelhoffer, K. J.; Colman, B. P.; Currie, W. S.; Magill, A. H.; Aber, J. D. (2004) Decadal-scale 24 fates of 15N tracers to oak and pine stands under ambient and elevated N inputs at the 25 Harvard Forest (USA). For. Ecol. Manage. 196: 89-107. 26 Nadelhoffer, K.; Downs, M.; Fry, B.; Magill, A.; Aber, J. (1999) Controls on N retention and 27 exports in a forested watershed. Environ. Monit. Assess. 55: 187-210. 28 Nagy, K. A.; Henen, B. T.; Vyas, D. B. (1998) Nutritional quality of native and introduced food 29 plants of wild desert tortoises. J. Herpetol. 32: 260-267. 30 Nash, T. H., Ill; Sigal, L. L. (1999) Epiphytic lichens in the San Bernardino mountains in 31 relation to oxidant gradients. In: Miller, P.R.; McBride, J.R. ,eds. Oxidant air pollution 32 impacts in the montane forests of southern California: a case study of the San Bernardino 33 Mountains. New York, NY: Springer-Verlag; pp 223-234. (Ecological studies: v. 134). 34 Nasholm, T.; Ekblad, A.; Nordin, A.; Giesler, R.; Hogberg, M.; Hogberg, P. (1998) Boreal forest 35 plants take up organic nitrogen. Nature (London) 392: 914-916. 36 National Atmospheric Deposition Program/National Trends Network. (2006) Isopleth maps. 37 Champaign, IL: Program Office, Illinois State Water Survey. Available: 38 http://nadp.sws.uiuc.edu/isopleths/annualmaps.asp [21 November, 2007]. 39 National Research Council (NRC). (1991) Rethinking the ozone problem in urban and regional 40 air pollution. Washington, DC: National Academy Press. Available: 41 http://www.nap.edu/openbook.php?isbn=0309046319 [21 November, 2007]. August 2008 C-78 DRAFT-DO NOT QUOTE OR CITE ------- 1 National Research Council (NRC). (2000) Clean coastal waters: understanding and reducing the 2 effects of nutrient pollution. Washington, DC: National Academy Press. Available: 3 http://www.nap.edu/openbook.php?isbn=0309069483 [21 November, 2007]. 4 5 Neff, J. S.; Bowman, W. D.; Holland, E. A.; Fisk, M. C.; Schmidt, S. K. (1994) Fluxes of nitrous 6 oxide and methane from nitrogen-amended soils in a Colorado alpine ecosystem. 7 Biogeochemistry 27: 23-33. 8 Nellemann, C.; Thomsen, M. G. (2001) Long-term changes in forest growth: potential effects of 9 nitrogen deposition and acidification. Water Air Soil Pollut. 128: 197-205. 10 Nicholls, K. H.; Hopkins, G. J.; Standke, S. J.; Nakamoto, L. (2001) Trends in total phosphorus 11 in Canadian nearshore waters of Laurentian Great Lakes: 1976-1999. Great Lakes Res. 12 27:402-422. 13 Nixon, S. W. (1995) Coastal marine eutrophication: a definition, social causes, and future 14 concerns. Ophelia 41: 199-219. 15 Nordin, A.; Nasholm, T.; Ericson, L. (1998) Effects of simulated N deposition on understorey 16 vegetation of a boreal coniferous forest. Funct. Ecol. 12: 691-699. 17 Nordin, A.; Strengbom, J.; Witzell, J.; Nasholm, T.; Ericson, L. (2005) Nitrogen depositon and 18 the biodiversity of boreal forests: implications for the nitrogen critical load. Ambio 34: 19 20-24. 20 Norton, S.; Kahl, J.; Fernandez, I. (1999) Altered soil-soil water interactions inferred from 21 stream water chemistry at an artificially acidified watershed at Bear Brook Watershed, 22 Maine USA. Environ. Monit. Assess. 55: 97-111. 23 Nydick, K. R.; Lafrancois, B. M.; Baron, J. S. (2004a) NO3 uptake in shallow, oligotrophic, 24 mountain lakes: the influence of elevated NO3 concentrations. J. North Am. Benthol. Soc. 25 23:397-415. 26 Nydick, K. R.; Lafrancois, B. M.; Baron, J. S.; Johnson, B. M. (2003) Lake-specific responses to 27 elevated atmospheric nitrogen deposition in the Colorado Rocky Mountains, U.S.A. 28 Hydrobiologia 510: 103-114. 29 Nydick, K. R.; Lafrancois, B. M.; Baron, J. S.; Johnson, B. M. (2004b) Nitrogen regulation of 30 algal biomass, productivity, and composition in shallow mountain lakes, Snowy Range, 31 Wyoming, USA. Can. J. Fish. Aquat. Sci. 61: 1256-1268. 32 Nykanen, H., H. Vasander, J. T. Huttunen, and P. J. Martikainen. 2002. Effect of experimental 33 nitrogen load on methane and nitrous oxide fluxes on ombrotrophic boreal peatland. Plant 34 and Soil 242:147-155. 35 Officer, C. B.; Biggs, R. B.; Taft, J. L.; Cronin, L. E.; Tyler, M. A.; Boynton, W. R. (1984) 36 Chesapeake Bay anoxia: origin, development, and significance. Science (Washington, 37 DC) 223: 22-27. 38 Officer, C. B.; Ryther, J. H. (1980) The possible importance of silicon in marine eutrophication. 39 Mar. Ecol. Prog. Ser. 3: 83-91. 40 Ollinger, S. V.; Aber, J. D.; Lovett, G. M.; Millham, S. E.; Lathrop, R. G; Ellis, J. M. (1993) A 41 spatial model of atmospheric deposition for the northeastern U.S. Ecol. Appl. 3: 459-472. August 2008 C-79 DRAFT-DO NOT QUOTE OR CITE ------- 1 Ollinger, S. V.; Aber, J. D.; Reich, P. B. (1997) Simulating ozone effects on forest productivity: 2 interactions among leaf-, canopy-, and stand-level processes. Ecol. Appl. 7: 1237-1251. 3 Ollinger, S. V.; Smith, M. L.; Martin, M. E.; Hallett, R. A.; Goodale, C. L.; Aber, J. D. (2002) 4 Regional variation in foliar chemistry and N cycling among forests of diverse history and 5 composition. Ecology 83: 339-355. 6 Olsen, Y.; Agusti, S.; Andersen, T.; Duarte, C. M.; Gasol, J. M.; Gismervik, L; Heiskanen, A.-S.; 7 Hoell, E.; Kuuppo, P.; Lignell, R.; Reinertsen, H.; Sommer, U.; Stibor, H.; Tamminen, 8 T.; Vadstein, O.; Vaque, D.; Vidal, M. (2006) A comparative study of responses in 9 planktonic food web structure and function in contrasting European coastal waters 10 exposed to experimental nutrient addition. Limnol. Oceanogr. 51(1 pt 2): 488-503. 11 Op Den Camp, H. J. M.; Kartal, B.; Guven, D.; Van Niftrik, L. A. M. P.; Haaijer, S. C. M.; Van 12 Der Star, W. R. L.; Pas-Schoonen, K. T. V. D.; Cabezas, A.; Ying, Z.; Schmid, M. C.; 13 Kuypers, M. M. M.; Van de Vossenberg, J.; Harhangi, H. R.; Picioreanu, C.; Van 14 Loosdrecht, M. C. M.; Kuenen, J. G.; Strous, M.; Jetten, M. S. M. (2006) Global impact 15 and application of the anaerobic ammonium-oxidizing (anammox) bacteria. Biochem. 16 Soc. Trans. 34: 174-178. 17 Oura, N., J. Shindo, T. Fumoto, H. Toda, and H. Kawashima. 2001. Effects of nitrogen 18 deposition on nitrous oxide emissions from the forest floor. Water Air and Soil Pollution 19 130:673-678. 20 Padgett, P. E.; Allen, E. B.; Bytnerowicz, A.; Minich, R. A. (1999) Changes in soil inorganic 21 nitrogen as related to atmospheric nitrogenous pollutants in southern California. Atmos. 22 Environ. 33:769-781. 23 Padgett, P.; Allen, E. B. (1999) Differential responses to nitrogen fertilization in native shrubs 24 and exotic annuals common to Mediterranean coastal sage scrub of California. Plant 25 Ecol. 144: 93-101. 26 Paerl, H. (1995) Coastal eutrophication in relation to atmospheric nitrogen deposition: current 27 perspectives. Ophelia 41: 237-259. 28 Paerl, H. (1997) Coastal eutrophi cation and harmful algal blooms: importance of atmospheric 29 deposition and groundwater as "new" nitrogen and other nutrient sources. Limnol. 30 Oceanogr. 42: 1154-1162. 31 Paerl, H. W. (1985) Enhancement of marine primary production by nitrogen-enriched acid rain. 32 Nature (London) 315: 747-749. 33 Paerl, H. W. (2002) Connecting atmospheric nitrogen deposition to coastal eutrophi cation. 34 Environ. Sci. Technol. 36: 323A-326A. 35 Paerl, H. W.; Boynton, W. R.; Dennis, R. L.; Driscoll, C. T.; Greening, H. S.; Kremer, J. N.; 36 Rabalais, N. N.; Seitzinger, S. P. (2001) Atmospheric deposition of nitrogen in coastal 37 waters: biogeochemical and ecological implications. In: Valigura, R. W.; Alexander, R. 38 B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; Stacey, P. E.; Turner, R. E., eds. Nitrogen 39 loading in coastal water bodies: an atmospheric perspective. Washington, DC: American 40 Geophysical Union; pp. 11-52. 41 Paerl, H. W.; Dennis, R. L.; Whitall, D. R. (2002) Atmospheric deposition of nitrogen: 42 implications for nutrient over-enrichment of coastal waters. Estuaries 25: 677-693. August 2008 C-80 DRAFT-DO NOT QUOTE OR CITE ------- 1 Paerl, H. W.; Valdes, L. M.; Peierls, B. L.; Adolf, J. E.; Harding, L. W. (2006) Anthropogenic 2 and climatic influences on the eutrophication of large estuarine ecosystems. Limnol. 3 Oceanogr. 51(lpt 2): 448-462. 4 Paerl, H.; Pinckney, J.; Fear, J.; Peierls, B. (1998) Ecosystem responses to internal and watershed 5 organic matter loading: Consequences for hypoxia in the eutrophying Neuse River 6 Estuary, NC, USA. Mar. Ecol. Prog. Ser. 166: 17-25. 7 Papen, H. and K. Butterbach-Bahl. 1999. A 3-year continuous record of nitrogen trace gas fluxes 8 from untreated and limed soil of a N-saturated spruce and beech forest ecosystem in 9 Germany - 1. N2O emissions. Journal of Geophysical Research-Atmospheres 104:18487- 10 18503. 11 Papen, H., M. Daum, R. Steinkamp, and K. Butterbach-Bahl. 2001. N2O and CH4-fluxes from 12 soils of a N-limited and N-fertilized spruce forest ecosystem of the temperate zone. 13 Journal of Applied Botany-Angewandte Botanik 75:159-163. 14 Pardo, L. H.; Driscoll, C. T.; Likens, G. E. (1995) Patterns of nitrate loss from a chronosequence 15 of clear-cut watersheds. Water Air Soil Pollut. 85: 1659-1664. 16 Pardo, L. H.; Kendall, C.; Pett-Ridge, J. P.; Change, C. C. Y. (2004) Evaluating the source of 17 streamwater nitrate using 5 5N and 518O in nitrate in two watersheds in New Hampshire, 18 USA. Hydrol. Process. 18: 2699-2712. 19 Parker, J. L., I. J. Fernandez, L. E. Rustad, and S. A. Norton. 2001. Effects of nitrogen 20 enrichment, wildfire, and harvesting on forest-soil carbon and nitrogen. Soil Science 21 Society of America Journal 65:1248-1255. 22 Paschke, M. W.; McLendon, T.; Redente, E. F. (2000) Nitrogen availability and old-field 23 succession in a shortgrass steppe. Ecosystems 3: 144-158. 24 Paul, A. J.; Leavitt, P. R.; Schindler, D. W.; Hardie, A. K. (1995) Direct and indirect effects of 25 predation by a calanoid copepod (subgenus: Hesperodiaptomus) and of nutrients in a 26 fishless alpine lake. Can. J. Fish. Aquat. Sci. 52: 2628-2638. 27 Paz-Gonzalez, A.; Taboada, M. T. (2000) Nutrient variability from point sampling on 2 meter 28 grid in cultivated and adjacent forest land. Comm. Soil Sci. Plant Anal. 31: 2135-2146. 29 Pearcy, R. W.; Bjorkman, O.; Caldwell, M. M.; Keeley, J. E.; Monson, R. K.; Strain, B. R. 30 (1987) Carbon gain by plants in natural environments. BioScience 37: 21-29. 31 Pearson, J.; Stewart, R. G. (1993) Atmospheric ammonia deposition and its effects on plants. 32 Tansley Review No. 56. New Phytol. 125: 283-305. 33 Peierls, B. L.; Caraco, N. F.; Pace, M. L.; Cole, J. J. (1991) Human influence on river nitrogen. 34 Nature 350: 386-387. 35 Peterjohn, W. T.; Adams, M. B.; Gilliam, F. S. (1996) Symptoms of nitrogen saturation in two 36 central Appalachian hardwood forest ecosystems. Biogeochemistry 35: 507-522. 37 Peterjohn, W. T.; Correll, D. L. (1984) Nutrient dynamics in an agricultural watershed: 38 observations on the role of a riparian forest. Ecology 65: 1466-1475. 39 Peterson, B. J.; Wollheim, W. M.; Mulholland, P. J.; Webster, J. W.; Meyer, J. L.; Tank, J. L.; 40 Marti, E.; Bowden, W. B.; Valett, H. M.; Hershey, A. E.; McDowell, W. H.; Dodds, W. August 2008 C-81 DRAFT-DO NOT QUOTE OR CITE ------- 1 K.; Hamilton, S. K.; Gregory, S. V.; Morrall, D. D. (2001) Control of nitrogen export 2 from watersheds by headwater streams. Science 292: 86-89. 3 Peterson, D. L.; Arbaugh, M. J.; Robinson, L. J. (1991) Regional growth changes in ozone- 4 stressed ponderosa pine (Pinus ponderosa) in the Sierra Nevada, California, USA. 5 Holocene 1: 50-61. 6 Peterson, D. L.; Arbaugh, M. J.; Wakefield, V. A.; Miller, P. R. (1987) Evidence of growth 7 reduction in ozone-injured Jeffrey pine (Pinus Jeffrey! Grev. and Balf) in Sequoia and 8 Kings Canyon National Parks. JAPCA 37: 906-912. 9 Peterson, G.; Allen, C. R.; Rolling, C. S. (1998) Ecological resilience, biodiversity, and scale. 10 Ecosystems 1: 6-18. 11 Phillips, R. L., S. C. Whalen, and W. H. Schlesinger. 2001. Influence of atmospheric CO2 12 enrichment on nitrous oxide flux in a temperate forest ecosystem. Global Biogeochemical 13 Cycles 15:741-752. 14 Pike, L. H. (1978) The importance of epiphytic lichens in mineral cycling. Bryologist 81: 247- 15 257. 16 Pilcher, H. (2005) Pipe Dreams. Nature 437: 1227-1228. 17 Pilegaard, K., U. Skiba, P. Ambus, C. Beier, N. Bruggemann, K. Butterbach-Bahl, J. Dick, J. 18 Dorsey, J. Duyzer, M. Gallagher, R. Gasche, L. Horvath, B. Kitzler, A. Leip, M. K. 19 Pihlatie, P. Rosenkranz, G. Seufert, T. Vesala, H. Westrate, and S. Zechmeister- 20 Boltenstern. 2006. Factors controlling regional differences in forest soil emission of 21 nitrogen oxides (NO and N2O). Biogeosciences 3:651-661. 22 Pimentel, D.; Wilson, C.; McCullum, C.; Huang, R.; Dwen, P.; Flack, J.; Iran, Q.; Saltman, T.; 23 Cliff, B. (1997) Economic and environmental benefits of biodiversity. BioScience 47: 24 747-757. 25 Pimm, S. L. (1984) The complexity and stability of ecosystems. Nature (London) 307: 321-326. 26 Pinay, G.; Black, V. J.; Planty-Tabacchi, A. M.; Gumiero, B.; Decamps, H. (2000) Geomorphic 27 control of denitrification in large river floodplain soils. Biogeochemistry 50: 163-182. 28 Pinay, G.; Roques, L.; Fabre, A. (1993) Spatial and temporal patterns of denitrification in a 29 riparian forest. J. Appl. Ecol. 30: 581-591. 30 Pitcairn, C. E. R.; Fowler, D.; Leith, I. D.; Sheppard, L. J.; Sutton, M. A.; Kennedy, V.; Okello, 31 E. (2003) Bioindicators of enhanced nitrogen depositon. Environ. Pollut. 126: 353-361. 32 Poff, N. L.; Allan, J. D.; Bain, M. B.; Karr, J. R.; Prestegaard, K. L.; Richter, B. D.; Sparks, R. 33 E.; Stromberg, J. C. (1997) The natural flow regime: a paradigm for river conservation 34 and restoration. BioScience 47: 769-784. 35 Ponnamperuma, F. N. (1972) The chemistry of submerged soils. Adv. Agron. 24: 29-96. 36 Pregitzer, K. S., A. J. Burton, D. R. Zak, and A. F. Talhelm. 2008. Simulated chronic nitrogen 37 deposition increases carbon storage in Northern Temperate forests. Global Change 38 Biology 14:142-153. 39 Priscu, J. C.; Axler, R. P.; Goldman, C. R. (1985) Nitrogen metabolism of the shallow and deep 40 water phytoplankton in a subalpine lake. Oikos 45: 137-147. August 2008 C-82 DRAFT-DO NOT QUOTE OR CITE ------- 1 Providoli, I; Bugmann, H.; Siegwolf, R.; Buchmann, N.; Schleppi, P. (2005) Flow of deposited 2 inorganic N in two Gleysol-dominated mountain catchments traced with 15NC>3" and 3 15NH4+. Biogeochemistry 76: 453-475. 4 Purbopuspito, J., E. Veldkamp, R. Brumme, and D. Murdiyarso. 2006. Trace gas fluxes and 5 nitrogen cycling along an elevation sequence of tropical montane forests in Central 6 Sulawesi, Indonesia. Global Biogeochemical Cycles 20:11. 7 Quist, M. E.; Nasholm, T.; Lindeberg, J.; Johannisson, C.; Hogbom, L.; Hogberg, P. (1999) 8 Responses of a nitrogen-saturated forest to a sharp decrease in nitrogen input. J. Environ. 9 Qual. 28: 1970-1977. 10 Rabalais, N. N. (1998) Oxygen depletion in coastal waters. NOAA state of the coast report. 11 Silver Spring, MD: National Oceanic and Atmospheric Administration. Available: 12 http://oceanservice.noaa.gov/websites/retiredsites/sotc_pdf/HYP.PDF [21 November, 13 2007]. 14 Rabalais, N. N. (2002) Nitrogen in aquatic ecosystems. Ambio 31: 102-112. 15 Rabalais, N. N.; Turner, R. E.; Justic, D.; Dortch, Q.; Wiseman, W. J., Jr.; Sen Gupta, B. K. 16 (1996) Nutrient changes in the Mississippi River and system responses on the adjacent 17 continental shelf. Estuaries 19: 386-407. 18 Redbo-Torstensson, P. (1994) The demographic consequences of nitrogen fertilization of a 19 population of sundew, Drosera rotundifolia. Acta Bot. Neerl. 43: 175-188. 20 Redfield, A. C. (1958) The biological control of chemical factors in the environment. Am. Sci. 21 46:205-221. 22 Regina, K., H. Nykanen, M. Maljanen, J. Silvola, and P. J. Martikainen. 1998. Emissions of N2O 23 and NO and net nitrogen mineralization in a boreal forested peatland treated with 24 different nitrogen compounds. Canadian Journal of Forest Research-Revue Canadienne 25 De Recherche Forestiere 28:132-140. 26 Reich, P. B. (1987) Quantifying plant response to ozone: a unifying theory. Tree Physiol. 3: 63- 27 91. 28 Reich, P. B.; Grigal, D. F.; Aber, J. D.; Gower, S. T. (1997a) Nitrogen mineralization and 29 productivity in 50 hardwood and conifer stands on diverse soils. Ecology 78: 335-347. 30 Reich, P. B.; Walters, M. B.; Ellsworth, D. S. (1997b) From tropics to tundra: global 31 convergence in plant functioning. Proc. Nat. Acad. Sci. 94: 13730-13734. 32 Reiners, W. A. (1986) Complementary models in ecology. Am. Nat. 27: 59-73. 33 Renberg, I; Korsman, T.; Anderson, N. J. (1993) A temporal perspective of lake acidification in 34 Sweden. Ambio 22: 264-271. 35 Reuss, J. O.; Johnson, D. W. (1985) Effect of soil processes on the acidification of water by acid 36 deposition. J. Environ. Qual. 14: 26-31. 37 Reuss, J. O.; Johnson, D. W. (1986) Acid deposition and the acidification of soils and waters. 38 New York, NY: Springer-Verlag. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; 39 Remmert, H., eds. Ecological studies: analysis and synthesis: v. 59). August 2008 C-83 DRAFT-DO NOT QUOTE OR CITE ------- 1 Reuter, J. E.; Axler, R. P. (1992) Physiological characterisisics of inorganic nitrogen uptake by 2 spatially separate algal communities in a nitrogen-deficient lake. Freshwater Biol. 27: 3 227-236. 4 Reuter, J. E.; Loeb, S. L.; Axler, R. P.; Carlton, R. G.; Goldman, C. R. (1985) Transformation of 5 nitrogen following an epilimnetic nitrogen fertilization in Castle Lake, C A. 1. epilithic 6 periphyton responses. Arch. Hydrobiol. 102: 425-433. 7 Reuter, J. E.; Loeb, S. L.; Goldman, C. R. (1986) Inorganic nitrogen uptake by epilithic 8 periphyton in a N-deficient lake. Limnol. Oceanogr. 31: 149-160. 9 Riegman, R. (1992) Phaeocystis blooms and eutrophication of the continental coastal zones of 10 the North Sea. Mar. Biol. 112: 479-484. 11 Riggan, P. J.; Lockwood, R. N.; Lopez, E. N. (1985) Deposition and processing of airborne 12 nitrogen pollutants in Mediterranean-type ecosystems of southern California. Environ. 13 Sci. Technol. 19: 781-789. 14 Roberts, B. J.; Howarth, R. W. (2006) Nutrient and light availability regulate the relative 15 contribution of autotrophs and heterotrophs to respiration in freshwater pelagic 16 ecosystems. Limnol. Oceanogr. 51: 288-298. 17 Robertson, G. P.; Crum, J. R.; Ellis, B. G. (1993) The spatial variability of soil resources 18 following long-term disturbance. Oecologia 96: 451-456. 19 Roelofs, J. G. M. (1986) The effect of airborne sulphur and nitrogen deposition on aquatic and 20 terrestrial heathland vegetation. Experientia 42: 372-377. 21 Roem, W. J.; Berendse, F. (2000) Soil acidity and nutrient supply ratio as possible factors 22 determining changes in plant species diversity in grassland and heathland communities. 23 Biol. Cons. 92: 151-161. 24 Romansic, J. M.; Diez, K. A.; Higashi, E. M.; Blaustein, A. R. (2006) Effects of nitrate and the 25 pathogenic water mold Saprolegnia on survival of amphibian larvae. Dis. Aquat. Org. 68: 26 235-243. 27 Rose, C.; Axler, R. P. (1998) Uses of alkaline phosphatase activity in evaluating phytoplankton 28 community phosphorous deficiency. Hydrobiologia 361: 145-156. 29 Ross, D. S.; Lawrence, G. B.; Fredriksen, G. (2004) Mineralization and nitrification patterns at 30 eight northeastern USA forested research sites. For. Ecol. Manage. 188: 317-335. 31 Rudek, J.; Paerl, H. W.; Mallin, M. A.; Bates, P. W. (1991) Seasonal and hydrological control of 32 phytoplankton nutrient limitation in the lower Neuse River Estuary, North Carolina. 33 Mar. Ecol. Progr. Ser. 75: 133-142. 34 Rueth, H.; Baron, J. S. (2002) Differences in Englemann spruce forest biogeochemistry east and 35 west of the Continental Divide in Colorado, USA. Ecosystems 5: 45-57. 36 Ryerson, T. B.; Trainer, M.; Holloway, J. S.; Parrish, D. D.; Huey, L. G.; Sueper, D. T.; Frost, G. 37 J.; Donnelly, S. G.; Schauffler, S.; Atlas, E. L.; Kuster, W. C.; Goldan, P. D.; Hubler, G.; 38 Meagher, J. F.; Fehsenfeld, F. C. (2001) Observations of ozone formation in power plant 39 plumes and implications for ozone control strategies. Science (Washington, DC) 292: 40 719-723. August 2008 C-84 DRAFT-DO NOT QUOTE OR CITE ------- 1 Rysgaard, S.; Glud, R. N.; Risgaard-Petersen, N.; Dalsgaard, T. (2004) Denitrification and 2 anammox activity in Arctic marine sediments. Limnol. Oceanogr. 49: 1493-1502. 3 Rysgaard, S.; Risgaard-Petersen, N.; Sloth, N. P.; Jensen, K.; Nielsen, L. P. (1994) Oxygen 4 regulation of nitrification and denitrification in sediments. Limnol. Oceanogr. 39: 1643- 5 1652. 6 Saarnio, S. and J. Silvola. 1999. Effects of increased CO2 and N on CH4 efflux from a boreal 7 mire: a growth chamber experiment. Oecologia 119:349-356. 8 Saarnio, S.; Jarvio, S.; Saarinen, T.; Vasander, H.; Silvola, J. (2003) Minor changes in vegetation 9 and carbon gas balance in a boreal mire under a raised CO2 or NfLJSTOs supply. 10 Ecosystems 6: 46-60. 11 Saarnio, S.; Saarinen, T.; Vasander, H.; Silvola, J. (2000) A moderate increase in the annual CH4 12 efflux by raised CO2 or MLjNOs supply in a boreal oligotrophic mire. Glob. Change 13 Biol. 6: 137-144. 14 Saros, J. E.; Interlandi, S. J.; Wolfe, A. P.; Engstrom, D. R. (2003) Recent changes in the diatom 15 community structure of lakes in the Beartooth Mountain Range, USA. Arct. Anarct. Alp. 16 Res. 35: 18-23. 17 Saros, J. E.; Michel, T. J.; Interlandi, S. J.; Wolfe, A. P. (2005) Resource requirements of 18 Asterionella formosa and Fragilaria crotonensis in oligotrophic alpine lakes: implications 19 for recent phytoplankton community reorganizations. Can. J. Fish. Aquat. Sci. 62: 1681- 20 1689. 21 Schaeffer, S. M., S. A. Billings, and R. D. Evans. 2003. Responses of soil nitrogen dynamics in a 22 Mojave Desert ecosystem to manipulations in soil carbon and nitrogen availability. 23 Oecologia 134:547-553. 24 Schelske, C. L. (1991) Historical nutrient enrichment of Lake Ontario: paleolimnological 25 evidence. Can. J. Fish. Aquat. Sci. 48: 1529-1538. 26 Schimel, J. P.; Bennett, J. (2004) Nitrogen mineralization: challenges of a changing paradigm. 27 Ecology 85: 591-602. 28 Schindler, D. W. (1974) Eutrophication and recovery in experimental lakes: implications for lake 29 management. Science 184: 897-899. 30 Schindler, D. W. (1980) The effect of fertilization with phosphorous and nitrogen versus 31 phosphorus alone on eutrophication of experimental lakes. Limnol. Oceanogr. 25: 1149- 32 1152. 33 Schindler, D. W.; Armstrong, F. A. J.; Brunskill, G. J.; Holmgren, S. K. (1971) Eutrophi cation of 34 Lake 227, Experimental Lakes Area, northwestern Ontario, by addition of phosphate and 35 nitrate. J. Fish. Res. Board Can. 28: 1763-1782. 36 Schlesinger, W. H.; Ward, T. J.; Anderson, J. (2000) Nutrient losses in runoff from grassland and 37 shrubland habitats in southern New Mexico: II. Field plots. Biogeochemistry 49: 69-86. 38 Schwinning, S.; Starr, B. I; Wojcik, N. J.; Miller, M. E.; Ehleringer, J. E.; Sanford, R. S. (2005) 39 Effects of nitrogen deposition on an arid grassland in the Colorado Plateau cold desert. 40 Rangeland Ecol. Manage. 58: 565-574. August 2008 C-85 DRAFT-DO NOT QUOTE OR CITE ------- 1 Scott, G.; Crunkilton, R. L. (2000) Acute and chronic toxicity of nitrate to fathead minnows 2 (Pimephales promelas), Ceriodaphnia dubia, and Daphnia magna. Environ. Toxicol. 3 Chem. 19: 2918-2922. 4 Scudlark, J. R.; Church, T. M. (1993) Atmospheric input of inorganic nitrogen to Delaware Bay. 5 Estuaries 16: 747-759. 6 Seastedt, T. R.; Bowman, W. D.; Caine, T. N.; McKnight, D.; Townsend, A.; Williams, M. W. 7 (2004) The landscape continuum: a model for high elevation ecosystems. BioScience 54: 8 111-121. 9 Seastedt, T. R.; Vaccaro, L. (2001) Plant species richness, productivity, and nitrogen and 10 phosphorus limitations across a snowpack gradient in alpine tundra, Colorado, USA. 11 Arct. Anarct. Alp. Res. 33: 100-106. 12 Seely, B.; Lajtha, K. (1997) Application of a 15N tracer to simulate and track the fate of 13 atmospherically deposited N in the coastal forests of the Waquoit Bay watershed, Cape 14 Cod, Massachusetts. Oecologia 112: 393-402. 15 Seitzinger, S. P. (1988) Denitrification in freshwater and coastal marine ecosystems: ecological 16 and geochemical significance. Limnol. Oceanogr. 33: 702-724. 17 Seitzinger, S. P.; Nielsen, L. P.; Caffrey, J.; Christensen, P. B. (1993) Denitrification 18 measurements in aquatic sediments: a comparison of three methods. Biogeochemistry 23: 19 147-167. 20 Seitzinger, S. P.; Styles, R. V.; Boyer, E. W.; Alexander, R. B.; Billen, G.; Howarth, R. W.; 21 Mayer, B.; Van Breemen, N. (2002) Nitrogen retention in rivers: model development and 22 application to watersheds in the northeastern U.S.A. Biogeochemistry 57/58: 199-237. 23 Seitzinger, S.; Harrison, J. A.; Bohlke, J. K.; Bouwman, A. F.; Lowrance, R.; Peterson, B.; 24 Tobias, C.; Van Drecht, G. (2006) Denitrification across landscapes and waterscapes: a 25 synthesis. Ecol. Appl. 16: 2064-2090. 26 Shan, J. P., L. A. Morris, and R. L. Hendrick. 2001. The effects of management on soil and plant 27 carbon sequestration in slash pine plantations. Journal of Applied Ecology 38:932-941. 28 Shaver, G. R., L. C. Johnson, D. H. Cades, G. Murray, J. A. Laundre, E. B. Rastetter, K. J. 29 Nadelhoffer, and A. E. Giblin. 1998. Biomass and CC>2 flux in wet sedge tundras: 30 Responses to nutrients, temperature, and light. Ecological Monographs 68:75-97. 31 Shaver, G. R.; Billings, W. D. (1975) Root production and root turnover in a wet tundra 32 ecosystem, Barrow, Alaska. Ecology 56: 401-409. 33 Shaver, G. R.; Chapin, F. S. (1980) Response to fertilization by various plant growth forms in an 34 Alaskan tundra: nutrient accumulation and growth. Ecology 61: 662-675. 35 Sherrod, S. K.; Seastedt, T. R. (2001) Effects of the northern pocket gopher (Thomomys 36 talpoides) on alpine soil characteristics, Niwot Ridge, CO. Biogeochemistry 55: 195-218. 37 Sickman, J. O.; Leydecker, A.; Chang, C. C. Y.; Kendall, C.; Melack, J. M.; Lucero, D. M.; 38 Schimel, J. (2003b) Mechanisms underlying export of N from high-elevation catchments 39 during seasonal transitions. Biogeochemistry 64: 1-24. 40 Sickman, J. O.; Melack, J. M.; Clow, D. W. (2003a) Evidence for nutrient enrichment of high- 41 elevation lakes in the Sierra Nevada, California. Limnol. Oceanogr. 48: 1885-1892. August 2008 C-86 DRAFT-DO NOT QUOTE OR CITE ------- 1 Sickman, J.; Melack, J.; Stoddard, J. (2002) Regional analysis of inorganic nitrogen yield and 2 retention in high-elevation ecosystems of the Sierra Nevada and Rocky Mountains. 3 Biogeochemistry 57/58: 341-374. 4 Sievering, H. (1999) Nitrogen deposition and carbon sequestration. Nature 400: 629-630. 5 Sievering, H.; Fernandez, L; Lee, J.; Horn, J.; Rustad, L. (2000) Forest canopy uptake of 6 atmospheric nitrogen deposition at eastern U.S. conifer sites: carbon storage 7 implications? Glob. Biogeochem. Cycles 14: 1153-1159. 8 Silvola, J.; Saarnio, S.; Foot, J.; Sundh, L; Greenup, A.; Heijmans, M.; Ekberg, A.; Mitchell, E.; 9 van Breeman, N. (2003) Effects of elevated CCh and N deposition on CFL; emissions 10 from European mires. Glob. Biogeochem. Cycles 17: 1068-1079. 11 Sitaula, B. K., L. R. Bakken, and G. Abrahamsen. 1995. Nutrient balance in Scots pine (Pinus 12 sylvestris L) forest .3. Fluxes of N2O from lysimeter as influenced by nitrogen input. 13 Water Air and Soil Pollution 85:1155-1159. 14 Skeffmgton, R. A.; Wilson, E. J. (1988) Excess nitrogen deposition: issues for consideration. 15 Environ. Pollut. 54: 159-184. 16 Skiba, U., L. Sheppard, C. E. R. Pitcairn, I. Leith, A. Crossley, S. van Dijk, V. H. Kennedy, and 17 D. Fowler. 1998. Soil nitrous oxide and nitric oxide emissions as indicators of elevated 18 atmospheric N deposition rates in seminatural ecosystems. Environmental Pollution 19 102:457-461. 20 Skinner, M. W.; Pavlik, B. M., eds. (1994) California Native Plant Society's inventory of rare 21 and endangered vascular plants of California. Sacramento, CA: California Native Plant 22 Society. 23 Smith, G. F.; Nicholas, N. S. (1999) Post disturbance spruce-fir forest stand dynamics at seven 24 disjunct sites. Castanea 64: 175-186. 25 Smith, L. K.; Voytek, M. A.; Bohlke, J. K., Harvey, J. W. (2006) Denitrification in nitrate-rich 26 streams: application of N2:Ar and 1 N-tracer methods in intact cores. Ecol. Appl. 16: 27 2191-2207. 28 Smith, M.-L.; Ollinger, S. V.; Martin, M. E.; Aber, J. D.; Hallett, R. A.; Goodale, C. L. (2002) 29 Direct estimation of aboveground forest productivity through hyperspectral remote 30 sensing of canopy nitrogen. Ecol. Appl. 12: 1286-1302. 31 Smith, R. A.; Schwarz, G. E.; Alexander, R. B. (1997) Regional interpretation of water-quality 32 monitoring data. Water Resour. Res. 33: 2781-2798. 33 Smith, S. M.; Lee, K. D. (2006) Responses of periphyton to artificial nutrient enrichment in 34 freshwater kettle ponds of Cape Cod National Seashore. Hydrobiologia 571: 201-211. 35 Smith, V. H. (2006) Responses of estuarine and coastal marine phytoplankton to nitrogen and 36 phosphorus enrichment. Limnol. Oceanogr. 51(1 pt2): 377-384. 37 Smith, V. H.; Tilman, G. D.; Nekola, J. C. (1999) Eutrophication: impacts of excess nutrient 38 inputs on freshwater, marine, and terrestrial ecosystems. Environ. Pollut. 100: 179-196. 39 Sobczak, W. V.; Findlay, S.; Dye, S. (2003) Relationship between DOC bioavailability and 40 nitrate removal in an upland stream: an experimental approach. Biogeochemistry 62: 309- 41 327. August 2008 C-87 DRAFT-DO NOT QUOTE OR CITE ------- 1 Sollins, P.; Grier, C. C.; McCorison, F. M.; Cromack, K., Jr.; Fogel, R.; Fredriksen, R. L. (1980) 2 The internal element cycles of an old-growth Douglas-fir ecosystem in western Oregon. 3 Ecol. Monogr. 50: 261-285. 4 Soussana, J. F., V. Allard, K. Pilegaard, P. Ambus, C. Amman, C. Campbell, E. Ceschia, J. 5 Clifton-Brown, S. Czobel, R. Domingues, C. Flechard, J. Fuhrer, A. Hensen, L. Horvath, 6 M. Jones, G. Kasper, C. Martin, Z. Nagy, A. Neftel, A. Raschi, S. Baronti, R. M. Rees, 7 U. Skiba, P. Stefani, G. Manca, M. Sutton, Z. Tubaf, and R. Valentini. 2007. Full 8 accounting of the greenhouse gas (CC>2, N2O, CH/i) budget of nine European grassland 9 sites. Agriculture Ecosystems & Environment 121:121-134. 10 Spiecker, H.; Meielikainen, K.; Kohl, M.; Skovsgaard, J. (1996) Growth trends in European 11 forests: studies from 12 countries. New York, NY: Springer-Verlag. [European Forest 12 Research Institute Report 5]. 13 Spoelstra, J.; Schiff, S. L.; Elgood, R. J.; Semkin, R. G; Jeffries, D. S. (2001) Tracing the 14 sources of exported nitrate in the Turkey Lakes watershed using 15N/14N and 8O/16O 15 isotopic ratios. Ecosystems 4: 536-544. 16 Stacey, P. E.; Greening, J. S.; Kremer, J. N.; Peterson, D.; Tomasko, D. A. (2001) Contribution 17 of atmospheric nitrogen deposition to U.S. estuaries: summary and conclusions. In: 18 Valigura, R. W.; Alexander, R. B.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; Stacey, P. 19 E.; Turner, R. E., eds. Nitrogen loading in coastal water bodies: An atmospheric 20 perspective. Washington, DC: American Geophysical Union; pp. 187-226. 21 Stanley, E. H.; Short, R. A.; Harrison, J. W.; Hall, R. L; Wiedenfeld, R. C. (1990) Variation in 22 nutrient limitation of lotic and lentic algal communities in a Texas (USA) river. 23 Hydrobiologia 206: 61-71. 24 Steltzer, H.; Bowman, W. D. (1998) Differential influence of plant species on soil nitrogen 25 transformations within moist meadow alpine tundra. Ecosystems 1: 464-474. 26 Sterner, R. W.; Elser, J. J. (2002) Ecological stoichiometry: the biology of elements from 27 molecules to the biosphere. Princeton, NJ: Princeton University Press. 28 Steudler, P. A.; Bowden, R. D.; Mellilo, J. M.; Aber, J. D. (1989) Influence of nitrogen 29 fertilization on methane uptake in temperate forest soil. Nature 341:314-316. 30 Stevens, C. J.; Dise, N. B.; Mountford, J. O.; Gowing, D. J. (2004) Impact of nitrogen deposition 31 on the species richness of grasslands. Science 303: 1876-1879. 32 Stevens, R. J.; Laughlin, R. J.; Malone, J. P. (1997) Measuring the contributions of nitrification 33 and denitrification to the flux of nitrous oxide from soil. Soil Biol. Biogeochem. 29: 139- 34 151. 35 Stoddard, J. L. (1994) Long-term changes in watershed retention of nitrogen: its causes and 36 aquatic consequences. In: Baker, L. A., ed. Environmental chemistry of lakes and 37 reservoirs. Washington, DC: American Chemical Society; pp. 223-284. (Advances in 38 chemistry series no. 237). 39 Stoddard, J.; Kahl, J. S.; Deviney, F. A.; DeWalle, D. R.; Driscoll, C. T.; Herlihy, A. T.; 40 Kellogg, J. H.; Murdoch, P. S.; Webb, J. R.; Webster, K. E. (2003) Response of surface 41 water chemistry to the Clean Air Act Amendments of 1990. Research Triangle Park, NC: 42 U.S. Environmental Protection Agency, Office of Research and Development, National 43 Health and Environmental Effects Research Laboratory. EPA 620/R-03.001. August 2008 C-88 DRAFT-DO NOT QUOTE OR CITE ------- 1 Stohlgren, T. I; Binkley, D.; Chong, G. W.; Kalkhan, M. A.; Schell, L. D.; Bull, K. A.; Otsuki, 2 Y.; Newman, G.; Bashkin, M.; Son, Y. (1999) Exotic plant species invade hot spots of 3 native plant diversity. Ecol. Monogr. 69: 25-46. 4 Stolte, W.; McCollin, T.; Noordeloos, A.; Riegman, R. (1994) Effects of nitrogen source on the 5 size distribution within marine phytoplankton populations. J. Exp. Mar. Biol. Ecol. 184: 6 83-97. 7 Stribley, D. P.; Read, D. J. (1980) The biology of mycorrhiza in the Ericacae. VII. The 8 relationship between mycorrhizal infection and the capacity to utilize simple and 9 complex organic nitrogen sources. NewPhytol. 86: 365-371. 10 Suding, K. N.; Collins, S. L.; Gough, L.; Clark, C.; Cleland, E. E.; Gross, K. L.; Milchunas, D. 11 G.; Pennings, S. (2005) Functional- and abundance-based mechanisms explain diversity 12 loss due to N fertilization. Proc. Natl. Acad. Sci. U. S. A. 102: 4387-4392. 13 Suding, K. N.; Miller, A. E.; Bechtold, H.; Bowman, W. D. (2006) The consequence of species 14 loss on ecosystem nitrogen cycling depends on community compensation. Ocecologia 15 149:141-149. 16 Sullivan, T. J. (2000) Aquatic effects of acidic deposition. Boca Raton, FL: Lewis Publishers. 17 Sullivan, T. J.; Cosby, B. J.; Laurence, J. A.; Dennis, R. L.; Savig, K.; Webb, J. R.; Bulger, A. J.; 18 Scruggs, M.; Gordon, C.; Ray, J.; Lee, H.; Hogsett, W. E.; Wayne, H.; Miller, D.; Kern, 19 J. S. (2003) Assessment of air quality and related values in Shenandoah National Park. 20 Philadelphia, PA: U.S. Department of the Interior, National Park Service, Northeast 21 Region; technical report NPS/NERCHAL/NRTR-03/090. Available: 22 http://www.nps.gov/nero/science/FINAL/shen_air_quality/shen_airquality.html [19 June, 23 2006]. 24 Swank, W. T. (1988) Stream chemistry responses to disturbance. In: Swank, W. T.; Crossley, D. 25 S., eds. Forest hydrology and ecology at Coweeta. New York, NY: Springer-Verlag, 26 Inc.; pp. 339-358. 27 Takemoto, B. K.; Bytnerowicz, A.; Fenn, M. E. (2001) Current and future effects of ozone and 28 atmospheric nitrogen deposition on California's mixed conifer forests. For. Ecol. Manage. 29 144:159-173. 30 Tank, J. L.; Dodds, W. K. (2003) Nutrient limitation of epilithic and epixylic biofilms in ten 31 North American streams. Freshwater Biol. 48: 1031-1049. 32 Taylor, G. E., Jr.; Hanson, P. J. (1992) Forest trees and tropospheric ozone: role of canopy 33 deposition and leaf uptake in developing exposure-response relationships. Agric. Ecosyst. 34 Environ. 42: 255-273. 35 Templer, P. H.; Lovett, G. M.; Weathers, K. C.; Findlay, S. E.; Dawson, T. E. (2005) Influence 36 of tree species on forest nitrogen retention in the Catskill Mountains, New York, USA. 37 Ecosystems 8: 1-16. 38 Tester, P. A.; Varnam, S. M.; Culver, M. E.; Eslinger, D. L.; Stumpf, R. P.; Swift, R. N.; Yungel, 39 J. K.; Black, M. D.; Litaker, R. W. (2003) Airborne detection of ecosystem responses to 40 an extreme event: phytoplankton displacement and abundance after hurricane induced 41 flooding in the Albemarle-Pamlico Sound System, North Carolina. Estuaries 26: 1353- 42 1364. August 2008 C-89 DRAFT-DO NOT QUOTE OR CITE ------- 1 Theodose, T. A.; Bowman, W. D. (1997) Nutrient availability, plant abundance, and species 2 diversity in two alpine tundra communities. Ecology 78: 1861-1872. 3 Thorne, R. S. J.; Williams, W. P.; Gordon, C. (2000) The macroinvertebrates of a polluted 4 stream in Ghana. J. Freshwater Ecol. 15: 209-217. 5 Throop, H. L. (2005) Nitrogen deposition and herbivory affect biomass production and 6 allocation in an annual plant. Oikos 111: 91-100. 7 Throop, H. L.; Lerdau, M. T. (2004) Effects of nitrogen deposition on insect herbivory: 8 implications for community and ecosystem processes. Ecosystems 7: 109-133. 9 Tietema, A.; Emmett, B. A.; Gundersen, P.; Kj0naas, O. J.; Koopmans, C. J. (1998) The fate of 10 15N-labelled nitrogen deposition in coniferous forest ecosystems. For. Ecol. Manage. 101: 11 19-27. 12 Tilman, D. (1981) Tests of resource competition theory using four species of Lake Michigan 13 algae. Ecology 62: 802-815. 14 Tilman, D. (1987) Secondary succession and the pattern of plant dominance along experimental 15 nitrogen gradients. Ecol. Monogr. 57: 189-214. 16 Tilman, D. (1996) Biodiversity: population versus ecosystem stability. Ecology 77: 350-363. 17 Tilman, D. (2000) Causes, consequences and ethics of biodiversity. Nature (London) 405: 208- 18 211. 19 Tilman, D.; Downing, J. A. (1994) Biodiversity and stability in grasslands. Nature (London) 367: 20 363-365. 21 Tilman, D.; Wedin, D. (1991) Dynamics of nitrogen competition between successional grasses. 22 Ecology 72: 1038-1049. 23 Tomassen, H. B. M.; Smolders, A. J. P.; Lamers, L. P. M.; Roelofs, J. G. M. (2003) Stimulated 24 growth of Betula pubescens and Molinia caerulea on ombrotrophic bogs: role of high 25 levels of atmospheric nitrogen deposition. J. Ecol. 91: 357-370. 26 Tomassen, H. B. M.; Smolders, A. J. P.; Limpens, J.; Lamers, L. P. M.; Roelofs, J. G. M. (2004) 27 Expansion of invasive species on ombrotrophic bogs: dessication or high N deposition? J. 28 Appl. Ecol. 41: 139-150. 29 Townsend, A. R.; Braswell, B. H.; Holland, E. A.; Penner, J. E. (1996) Spatial and temporal 30 patterns in terrestrial carbon storage due to deposition of fossil fuel nitrogen. Ecol. Appl. 31 6:806-814. 32 Townsend, A. R.; Howarth, R. W.; Bazzaz, F. A.; Booth, M. S.; Cleveland, C. C.; Collinge, S. 33 K.; Dobson, A. P.; Epstein, P. R.; Holland, E. A.; Keeney, D. R.; Mallin, M. A.; Rogers, 34 C. A.; Wayne, P.; Wolfe, A. H. (2003) Human health effects of a changing global 35 nitrogen cycle. Front. Ecol. Environ. 1: 240-246. 36 Treseder, K. K. (2004) The meta-analysis of mycorrhizal response to nitrogen, phosphorus, and 37 atmospheric CO2 in field studies. New Phytol. 164: 347-355. 38 Tritton, L. M.; Martin, C. W.; Hornbeck, J. W.; Pierce, R. S. (1987) Biomass and nutrient 39 removals from commercial thinning and whole-tree clearcutting of central hardwoods. 40 Environ. Manage. 11: 659-666. August 2008 C-90 DRAFT-DO NOT QUOTE OR CITE ------- 1 Trombulak, S. C.; Frissell, C. A. (2000) Review of ecological effects of roads on terrestrial and 2 aquatic communities. Conserv. Biol. 14: 18-30. 3 Turner, R. E. (2002) Element ratios and aquatic food webs. Estuaries 25: 694-703. 4 Turner, R. E.; Qureshi, N.; Rabalais, N. N.; Dortch, Q.; Justic, D.; Shaw, R. F.; Cope, J. (1998) 5 Fluctuating silicate:nitrate ratios and coastal plankton food webs. Proc. Nat. Acad. Sci. 6 95: 13048-13051. 7 Turner, R. E.; Stanley, D.; Brock, D.; Pennock, J.; Rabalais, N. N. (2001) A comparison of 8 independent N-loading estimates for U.S. estuaries. In: Valigura, R.; Alexander, R.; 9 Castro, M.; Meyers, T.; Paerl, H.; Stacey, P.; Turner, R. E., eds. Nitrogen loading in 10 coastal water bodies: an atmospheric perspective. Washington, DC.: American 11 Geophysical Union. 12 U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen. 13 Research Triangle Park, NC: Office of Health and Environmental Assessment, 14 Environmental Criteria and Assessment Office; report nos. EPA/600/8-9!/049aF-cF. 3v. 15 Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517. 16 U.S. Environmental Protection Agency. (1999a) The benefits and costs of the Clean Air Act 17 1990 to 2010: EPA report to Congress. Washington, DC: Office of Air and Radiation; 18 EPA report no. 410-R-99-001. Available: http://www.epa.gov/air/sect812/1990- 19 2010/fullrept.pdf 20 U.S. Environmental Protection Agency. (1999b) Deposition of air pollutants to the great 21 waters—third report to Congress. Washington, DC: U.S. Government Printing Office. 22 http://www.epa.gov/air/oaqps/gr8water/3rdrpt/index.html 23 U.S. Environmental Protection Agency. (2000a) Nutrient Criteria Technical Guidance Manual: 24 Rivers and Streams. Washington, DC: U.S. Environmental Protection Agency. EPA-822- 25 BOO-022. http://www.epa.gov/waterscience/criteria/nutrient/guidance/rivers/index.html 26 U.S. Environmental Protection Agency. (2000b) Nutrient Criteria Technical Guidance Manual: 27 Lakes and Reservoirs. Washington, DC: U.S. Environmental Protection Agency. EPA- 28 822-BOO-001. http://www.epa.gov/waterscience/criteria/nutrient/guidance/lakes/lakes.pdf 29 U.S. Environmental Protection Agency. (2003) Economic analyses of nutrient and sediment 30 reduction actions to restore Chesapeake bay water quality. Annapolis, MD: 31 U.S. Environmental Protection Agency Region III, Chesapeake Bay Program Office. 32 http://www.chesapeakebay.net/ecoanalyses.htm. 33 U.S. Environmental Protection Agency. (2004) Air quality criteria for particulate matter. 34 Research Triangle Park, NC: National Center for Environmental Assessment; report no. 35 EPA/600/P-99/002aF-bF. 2v. http://cfpub.epa.gov/ncea/ 36 U.S. Environmental Protection Agency. (2005) Review of the national ambient air quality 37 standards for particulate matter: policy assessment of scientific and technical information. 38 OAQPS staff paper. Research Triangle Park, NC: Office of Air Quality Planning and 39 Standards; report no. EPA/452/R-05-005a. 40 http://www.epa.gov/ttn/naaqs/standards/pm/data/pmstaffpaper_20051221 .pdf August 2008 C-91 DRAFT-DO NOT QUOTE OR CITE ------- 1 Valiela, I; Costa, J. E. (1988) Eutrophi cation of Buttermilk Bay, a Cape Cod coastal 2 embayment: concentrations of nutrients and watershed nutrient budgets. Environ. 3 Manage. 12: 539-553. 4 Valiela, I; Costa, J. E.; Foreman, K. (1990) Transport of groundwater-borne nutrients from 5 watersheds and their effects on coastal waters. Biogeochemistry 10: 177-197. 6 Valiela, I; Foreman, K.; LaMontagne, M.; Hersh, D.; Costa, J.; Peckol, P.; DeMeo-Andreson, 7 B.; D'Avanzo, C.; Babione, M.; Sham, C.; Brawley, J.; Lajtha, K. (1992) Couplings of 8 watersheds and coastal waters: sources and consequences of nutrient enrichment in 9 Waquoit Bay, Massachusetts. Estuaries 15: 443-457. 10 Valigura, R. A.; Alexander, R. B.; Brock, D. A.; Castro, M. S.; Meyers, T. P.; Paerl, H. W.; 11 Stacey, P. E.; Stanley, D., eds. (2000) An assessment of nitrogen inputs to coastal areas 12 with an atmospheric perspective. In: Nitrogen loading in coastal water bodies: an 13 atmospheric perspective. Washington, DC: American Geophysical Union. [AGU Coastal 14 Estuaries Series, no. 57]. 15 Van Breemen, N.; Boyer, E. W.; Goodale, C. L.; Jaworski, N. A.; Paustian, K.; Seitzinger, S. P.; 16 Lajtha, K.; Mayer, B.; Van Dam, D.; Howarth, R. W.; Nadelhoffer, K. J.; Eve, M.; Billen, 17 G. (2002) Where did all the nitrogen go? Fate of nitrogen inputs to large watersheds in 18 the northeastern U.S.A. Biogeochemistry 57/58: 267-293. 19 Van Breemen, N.; Van Dijk, H. F. G. (1988) Ecosystem effects of atmospheric deposition of 20 nitrogen in The Netherlands. In: Dempster, J. P.; Manning, W. J., eds. Excess nitrogen 21 deposition. Environ. Pollut. 54: 249-274. 22 Van De Graaf, A.; Mulder, A.; De Brujin, P.; Jetten, M. S. M.; Robertson, L. A.; Kuenen, J. G. 23 (1995) Anaerobic oxidation of ammonium is a biologically mediated process. Appl. 24 Environ. Microbiol. 61: 1246-1251. 25 Van Dijk, H. F. G.; Roelofs, J. G. M. (1988) Effects of excessive ammonium deposition on the 26 nutritional status and condition of pine needles. Physiol. Plant. 73: 494-501. 27 Van Dobben, H. F.; de Bakker, A. J. (1996) Re-mapping of lichen biodiversity in The 28 Netherlands: effects of decreasing SCh and increasing NHa. Acta Bot. Neerl. 45: 55-71. 29 Van Dobben, H. F.; Wolterbeek, T.; Wamelink, G. W. W.; ter Braak, C. J. F. (2001) 30 Relationship between epiphytic lichens, trace elements and gaseous atmospheric 31 pollutants. Environ. Pollut. 112: 163-169. 32 Van Drecht, G.; Bouwman, A. F.; Knoop, J. M.; Beusen, A. H. W.; Meinardi, C. R. (2003) 33 Global modeling of the fate of nitrogen from point and nonpoint sources in soils, 34 groundwater, and surface water. Global Biogeochem. Cycles 17(art. no. 1115): 35 10.1029/2003GB002060. 36 Van Egmond, K.; Bresser, T.; Bouwman, L. (2002) The European nitrogen case. Ambio 31: 72- 37 78. 38 Van Haluwyn, C.; van Herk, C. M. (2002) Bioindication: the community approach. In: Nimis, P. 39 L.; Scheidegger, C.; Wolseley, P. A., eds. Monitoring with lichens: monitoring lichens. 40 Dordrecht, The Netherlands: Kluwer Academic Publishers; pp. 39-64. 41 Van Herk, C. M. (1999) Mapping of ammonia pollution with epiphytic lichens in the 42 Netherlands. Lichenologist 31: 9-20. August 2008 C-92 DRAFT-DO NOT QUOTE OR CITE ------- 1 Van Herk, C. M. (2001) Bark pH and susceptibility to toxic air pollutants as independent causes 2 of changes in epiphytic lichen composition in space and time. Lichenologist 33: 419-441. 3 vanderNat, F., J. F. C. deBrouwer, J. J. Middelburg, and H. J. Laanbroek. 1997. Spatial 4 distribution and inhibition by ammonium of methane oxidation in intertidal freshwater 5 marshes. Applied and Environmental Microbiology 63:4734-4740. 6 Venterea, R. T.; Groffman, P. M.; Verchot, L. V.; Magill, A. H.; Aber, J. D.; Steudler, P. A. 7 (2003) Nitrogen oxide gas emissions from temperate forest soils receiving long-term 8 nitrogen inputs. Glob. Change Biol. 9: 346-357. 9 Venterea, R. T.; Groffman, P. M.; Verchot, L. V.; Magill, A. H.; Aber, J. D. (2004) Gross 10 nitrogen process rates in temperate forest soils exhibiting symptoms of nitrogen 11 saturation. For. Ecol. Manage. 196: 129-142. 12 Vitousek, P. M.; Aber, J. D.; Howarth, R. W.; Likens, G. E.; Matson, P. A.; Schindler, D. W.; 13 Schlesinger, W. H.; Tilman, D. G. (1997a) Human alteration of the global nitrogen cycle: 14 sources and consequences. Ecol. Appl. 7: 737-750. 15 Vitousek, P. M.; Howarth, R. W. (1991) Nitrogen limitation on land and in the sea: how can it 16 occur? Biogeochemistry 13: 87-115. 17 Vitousek, P. M.; Mooney, H. A.; Lubchenco, J.; Melillo, J. M. (1997b) Human domination of 18 Earth's ecosystems. Science (Washington, DC) 277: 494-499. 19 Vollenweider, R. A. (1968) Water management research: scientific fundamentals of the 20 eutrophication of lakes and flowing waters, with particular reference to nitrogen and 21 phosphorus as factors in eutrophi cation. Paris, France: Organisation for Economic Co- 22 operation and Development; report DAS/CSI/68.27. 23 Wall, D. H. (1999) Biodiversity and ecosystem functioning. BioScience 49: 107-108. 24 Wall, D. H.; Moore, J. C. (1999) Interactions underground: soil biodiversity, mutualism, and 25 ecosystem processes. BioScience 49: 109-117. 26 Ward, B. B. (2003) Significance of anaerobic ammonium oxidation in the ocean. Trends 27 Microbiol. 11:408-410. 28 Wargo, P. M. (1988) Amino nitrogen and phenolic constituents of bark of American beech, 29 Fagus grandifolia, and infestation by beech scale, Cryptococcus fagisuga. Eur. J. Forest 30 Pathol. 18: 279-290. 31 Watts, S. H.; Seitzinger, S. P. (2001) Denitrification rates in organic and mineral soils from 32 riparian sites: a comparison of N2 flux and acetylene inhibition methods. Soil Biol. 33 Biogeochem. 22: 331-335. 34 Webb, J. R. (1999) Synoptic stream water chemistry. In: Bulger, A. J.; Cosby, B. J.; Dolloff, C. 35 A.; Eshleman, K. N.; Webb, J. R.; Galloway, J. N., eds. Shenandoah National Park: fish 36 in sensitive habitats (SNP:FISH); final report vol. II, Ch. 3. Prepared for: National Park 37 Service. Charlottesville, VA: University of Virginia, Department of Environmental 38 Sciences. 39 Webb, J. R.; Cosby, B. J.; Deviney, F. A., Jr.; Eshleman, K. N.; Galloway, J. N. (1995) Change 40 in the acid-base status of an Appalachian Mountain catchment following forest 41 defoliation by the gypsy moth. Water Air Soil Pollut. 85: 535-540. August 2008 C-93 DRAFT-DO NOT QUOTE OR CITE ------- 1 Wedin, D. A.; Tilman, D. (1996) Influence of nitrogen loading and species composition on the 2 carbon balance of grasslands. Science (Washington, DC) 274: 1720-1723. 3 Weintraub, M. N.; Schimel, J. P. (2005) Nitrogen cycling and the spread of shrubs control 4 changes in the carbon balance of Arctic tundra ecosystems. BioScience 55: 408-415. 5 Weis, W.; Rotter, V.; Gottlein, A. (2006) Water and element fluxes during the regeneration of 6 Norway spruce with European beech: effects of shelterwood-cut and clear-cut. Water Air 7 SoilPollut. 224: 304-317. 8 Weiss, S. B. (1999) Cars, cows, and checkerspot butterflies: nitrogen deposition and 9 management of nutrient-poor grasslands for a threatened species. Conserv. Biol. 13: 10 1476-1486. 11 Weitz, A. M., M. Keller, E. Linder, and P. M. Crill. 1999. Spatial and temporal variability of 12 nitrogen oxide and methane fluxes from a fertilized tree plantation in Costa Rica. Journal 13 of Geophysical Research-Atmospheres 104:30097-30107. 14 Westman, W. E. (1977) How much are nature's services worth? Measuring the social benefits of 15 ecosystem functioning is both controversial and illuminating. Science (Washington, DC) 16 197: 960-964. 17 Wetzel, R. G. (2001) Limnology: lake and river ecosystems. San Diego, CA: London: Academic 18 Press. 19 Wilcox, G.; Decosta, J. (1982) The effect of phosphorus and nitrogen addition on the algal 20 biomass and species composition of an acidic lake. Arch. Hydrobiol. 94: 393-424. 21 Williams, M. W.; Baron, J. S.; Caine, N.; Sommerfeld, R.; Sanford, R. (1996) Nitrogen 22 saturation in the Rocky Mountains. Environ. Sci. Technol. 30: 640-646. 23 Wilson, S. D.; Tillman, D. (1991) Components of plant competition along an experimental 24 gradient of nitrogen availability. Ecology 72: 1050-1065. 25 Wold, A. P.; Hershey, A. E. (1999) Spatial and temporal variability of nutrient limitation in 6 26 North Shore tributaries to Lake Superior. J. North Am. Benthol. Soc. 18: 2-14. 27 Wolfe, A. P.; Baron, J. S.; Cornett, R. J. (2001) Anthropogenic nitrogen deposition induces rapid 28 ecological changes in alpine lakes of the Colorado Front Range (USA). J. Paleolimnol. 29 25: 1-7. 30 Wolfe, A. P.; Cooke, C. A.; Hobbs, W. O. (2006) Are current rates of atmospheric nitrogen 31 deposition influencing lakes in the eastern Canadian Arctic? Arct. Anarct. Alp. Res. 38: 32 465-476. 33 Wolfe, A. P.; Van Gorpe, A. C.; Baron, J. S. (2003) Recent ecological and biogeochemical 34 changes in alpine lakes of Rocky Mountain National Park (Colorado, USA): a response to 35 anthropogenic nitrogen deposition. Geobiology 1: 153-168. 36 Wood, Y. A.; Meixner, T.; Shouse, P. J.; Allen, E. B. (2006) Altered ecohydrologic response 37 drives native shrub loss under conditions of elevated nitrogen deposition. J. Environ. 38 Qual. 35: 76-92. 39 Woodwell, G. M. (1970) Effects of pollution on the structure and physiology of ecosystems: 40 changes in natural ecosystems caused by many different types of disturbances are similar 41 and predictable. Science (Washington, DC) 168: 429-433. August 2008 C-94 DRAFT-DO NOT QUOTE OR CITE ------- 1 World Resources Institute. (2000) World resources 2000-2001: people and ecosystems: the 2 fraying web of life. Washington, DC: World Resources Institute. 3 Yeakley, J. A.; Coleman, D. C.; Raines, B. L.; Kloeppel, B. D.; Meyer, J. L.; Swank, W. T.; 4 Argo, B. W.; Deal, J. M.; Taylor, S. F. (2003) Hillslope nutrient dynamics following 5 upland riparian vegetation disturbance. Ecosystems 6: 154-167. 6 Ylimartimo, A. (1991) Effects of foliar nitrogen, potassium and magnesium concentrations on 7 the resistance of Scots pine seedlings to Scleroderris canker infection. Eur. J. Forest 8 Pathol. 21:414-423. 9 Yoshida, L. C.; Allen, E. B. (2001) Response to ammonium and nitrate by a mycorrhizal annual 10 invasive grass and native shrub in southern California. Am. J. Bot. 88: 1430-1436. 11 Young, T. F.; Sanzone, S., eds. (2002) A framework for assessing and reporting on ecological 12 condition: an SAB report. Washington, DC: U.S. Environmental Protection Agency, 13 Science Advisory Board; report no. EPA-SAB-EPEC-02-009. Available: 14 http://www.epa.gov/sab/pdf/epec02009.pdf [9 December, 2003]. 15 Zavaleta, E. S.; Shaw, M. R.; Chiariello, N. R.; Thomas, B. D.; Cleland, E. E.; Field, C. B.; 16 Mooney, H. A. (2003) Grassland responses to three years of elevated temperature, CC>2, 17 precipitation, and N deposition. Ecol. Monogr. 73: 585-604. 18 Zhang, L. H., C. C. Song, X. H. Zheng, D. X. Wang, and Y. Y. Wang. 2007. Effects of nitrogen 19 on the ecosystem respiration, CFLi and N2O emissions to the atmosphere from the fresh 20 water marshes in northeast China. Environmental Geology 52:529-539. 21 Zhang, L.; Song, C.; Zheng, X.; Wang, D.; Wang, Y. (2007) Effects of nitrogen on the 22 ecosystem respiration, CFLi and N20 emissions to the atmosphere from the freshwater 23 marshes in northeast China. Environ. Geol. 52: 529-539. August 2008 C-95 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-1 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-2 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-3 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-4 DRAFT-DO NOT QUOTE OR CITE ------- 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." August 2008 D-5 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-6 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-7 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-8 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-9 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-10 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-11 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-12 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-13 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-14 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-15 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-16 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-17 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-18 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-19 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-20 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-21 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 D-22 DRAFT-DO NOT QUOTE OR CITE ------- 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, August 2008 D-23 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 D-24 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-25 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-26 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-27 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-28 DRAFT-DO NOT QUOTE OR CITE ------- 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 D-29 DRAFT-DO NOT QUOTE OR CITE ------- ANNEX D - References 1 Aber, J. D.; Driscoll, C. T. (1997) Effects of land use, climate variation, and N deposition of N 2 cycling and C storage in northern hardwood forests. Glob. Biogeochem. Cycles 11(4): 3 639-648. 4 Aber, J. D.; Federer, C. A. (1992) A generalized, lumped-parameter model of photosynthesis, 5 evapotranspiration and net primary production in temperate and boreal forest ecosystems. 6 Oecologia 92: 463-474. 7 Aber, J. D.; Nadelhoffer, K. J.; Steudler, P.; Melillo, J. M. (1989) Nitrogen saturation in northern 8 forest ecosystems: excess nitrogen from fossil fuel combustion may stress the biosphere. 9 BioScience 39: 378-386. 10 Aber, J. D.; Mellilo, J. M.; Nadelhoffer, K. J.; Pastor, J.; Boone, R. D. (1991) Factors controlling 11 nitrogen cycling and nitrogen saturation in northern temperate forest ecosystems. Ecol. 12 Appl. 1:303-315. 13 Aber, J. D.; Ollinger, S. V.; Federer, C. A.; Driscoll, C. (1997) Modeling nitrogen saturation in 14 forest ecosystems in response to land use and atmospheric deposition. Ecol. Model. 101: 15 61-78. 16 Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.; McNulty, S.; 17 Currie, W.; Rustad, L.; Fernandez, I. (1998) Nitrogen saturation in temperate forest 18 ecosystems. BioScience 48: 921-934. 19 Aber, J. D.; Goodale, C. L.; Ollinger, S. V.; Smith, M.-L.; Magill, A. H.; Martin, M. E.; Hall, R. 20 A.; Stoddard, J. L. (2003) Is nitrogen deposition altering the nitrogen status of 21 northeastern forests? Bioscience 53: 375-389. 22 Achermann, B.; Bobbink, R. (2003) Empirical critical loads for nitrogen: proceedings of an 23 expert workshop held under the UNECE Convention on Long-range Transboundary Air 24 Pollution; November, 2002; Berne, Switzerland. Berne, Switzerland: Swiss Agency for 25 the Environment, Forests, and Landscape (SAEFL), Environmental Documentation No. 26 164, pp. 327. 27 Adams, M. B.; Burger, J. A.; Jenkins, A. B.; Zelazny, L. (2000) Impact of harvesting and 28 atmospheric pollution on nutrient depletion of eastern U.S. hardwood forests. For. Ecol. 29 Manage. 138:301-319. 30 Aherne, J.; Larssen, T.; Dillon, P. J.; Cosby, B. J. (2004) Effects of climate events on elemental 31 fluxes from forested catchments in Ontario, Canada: modelling drought-induced redox 32 processes. Water Air Soil Pollut. Focus 4: 37-48. 33 Alveteg, M.; Sverdrup, H. (2002) Manual for regional assessments using the SAFE model 34 (draft). Lund, Sweden: Department of Chemical Engineering II, Lund University. 35 Arp, P. A.; Oja, T.; Marsh, M. (1996) Calculating critical S and N loads and current exceedances 36 for upland forests in southern Ontario, Canada. Can. J. For. Res. 26: 696-709. 37 Baker, L. A. (1991) Appendix A: Ion enrichment analysis for the Regional Case Studies Project. 38 In: Charles, D. F., Christie, S., eds. Acidic deposition and aquatic ecosystems: regional 39 case studies. New York, NY: Springer-Verlag; pp. 641-644. August 2008 D-30 DRAFT-DO NOT QUOTE OR CITE ------- 1 Baker, L. A.; Christensen, S. W. (1991) Effects of acidification on biological communities. In: 2 Charles, D. F., ed. Acidic deposition and aquatic ecosystems: regional case studies. New 3 York, NY: Springer-Verlag; pp. 83-106. 4 Baker, L. S.; Kaufmann, P. R.; Herlihy, A. T.; Eilers, J. M. (1991) Current status of surface 5 water acid-base chemistry. In: Irving, P. M., ed. Acidic deposition: state of science and 6 technology, v. II, aquatic processes and effects. Washington, DC: National Acid 7 Precipitation Assessment Program; NAPAP State of Science/Technology report 9; pp. 9- 8 5 - 9-367, 9-A1, 9-B1, 9-C1, 9-D1, and 9-CP1. 9 Baker, J. P.; Van Sickle, J.; Gagen, C. J.; DeWalle, D. R.; Sharpe, W. E.; Carline, R. F.; Baldigo, 10 B. P.; Murdoch, P. S.; Bath, D. W.; Kretser, W. A.; Simonin, H. A.; Wigington, P. J., Jr. 11 (1996) Episodic acidification of small streams in the northeastern United States: effects 12 on fish populations. Ecol. Appl. 6: 423-437. 13 Baron, J. S. (2006) Hindcasting nitrogen deposition to determine ecological critical load. Ecol. 14 Appl. 16: 433-439. 15 Baron, J. S.; Ojima, D. S.; Holland, E. A.; Parton, W. J. (1994) Analysis of nitrogen saturation 16 potential in Rocky Mountain tundra and forest: implications for aquatic systems. 17 Biogeochemistry 27: 61-82. 18 Baron, J. S.; Rueth, H. M.; Wolfe, A. M.; Nydick, K. R.; Allstott, E. J.; Minear, J. T.; Moraska, 19 B. (2000) Ecosystem responses to nitrogen deposition in the Colorado Front Range. 20 Ecosystems 3: 352-368. 21 Bashkin, V. N.; Kozlov, M. Y.; Priputina, I. V.; Avrumuchev, A. V.; Dedkova, I. S. (1995) 22 Calculation and mapping of critical loads of S, N, and acidity of ecosystems of northern 23 Asia. Water Air Soil Pollut. 85: 2395-2400 24 Bobbink, R.; Boxman, D.; Fremstad, E.; Heil, G.; Houdijk, A.; Roelofs, J. (1992) Critical loads 25 for nitrogen eutrophication of terrestrial and wetland ecosystems based upon changes in 26 vegetation and fauna. In: Grennfelt, P.; Thornelof, E., eds. Critical loads for nitrogen. 27 Nord 92:41. Nordic Council of Ministers, Copenhagen, pp. 111-159. 28 Bobbink, R.; Hornung, M.; Roelofs, J. G. M. (1996) Empirical nitrogen critical loads for natural 29 and semi-natural ecosystems. Annex III. In: Manual on methodologies and criteria for 30 mapping critical levels/loads and geographical areas where they are exceeded. UNECE 31 Convention on Long-Range Transboundary Air Pollution. Umvelt Bundes Arnt, Berlin. 32 Bobbink, R.; Ashmore, M.; Braun, S.; Fluckiger, W.; Van Den Wyngaert, I. J. J. (2003) 33 Empirical nitrogen critical loads for natural and semi-natural ecosystems: 2002 update. 34 In: Achermann, B.; Bobbink, R., eds. Empirical Critical Loads for Nitrogen. Berne: 35 Swiss Agency for Environment, Forest and Landscape SAEFL; pp. 43-170. 36 Bowman, W. D.; Gartner, J. R.; Holland, K.; Wiedermann, M. (2006) Nitrogen critical loads for 37 alpine vegetation and terrestrial ecosystem response: are we there yet? Ecol. Appl. 16: 38 1183-1193. 39 Brooks, M. L. (2003) Effects of increased soil nitrogen on the dominance of alien annual plants 40 in the Mojave Desert. J. Appl. Ecol. 40: 344-353. 41 Bulger, A. J.; Lien, L.; Cosby, B. J.; Henriksen, A. (1993) Trout status and chemistry from the 42 Norwegian thousand lake survey: statistical analysis. Can. J. Fish. Aquat. Sci. 50: 575- 43 585. August 2008 D-31 DRAFT-DO NOT QUOTE OR CITE ------- 1 Bulger, A. J.; Cosby, B. J.; Webb, J. R. (2000) Current, reconstructed past, and projected future 2 status of brook trout (Salvelinus fontinalis) streams in Virginia. Can. J. Fish. Aquat. Sci. 3 57:1515-1523. 4 Burns, D. A. (2004) The effects of atmospheric nitrogen deposition in the Rocky Mountains of 5 Colorado and southern Wyoming, USA—a critical review. Environ. Pollut. 127: 257- 6 269. 7 Camargo, J. A.; Alonso, A.; Salamanca, A. (2005) Nitrate toxicity to aquatic animals: a review 8 with new data for freshwater invertebrates. Chemosphere 58: 1255-1267. 9 Campbell, D. H.; Baron, J. S.; Tonnessen, K. A.; Brooks, P. D.; Schuster, P. F. (2000) Controls 10 on nitrogen flux in alpine/subalpine watersheds. Water Resour. Res. 36: 37-48. 11 Campbell, D. H.; Muths, E.; Turk, J. T.; Corn, P. S. (2004) Sensitivity to acidification of 12 subalpine ponds and lakes in north-western Colorado. Hydrol. Processes 18: 2817-2834. 13 Centre for Ecology and Hydrology. (2003) Status of UK critical loads: critical loads methods, 14 data & maps. Monks Wood, UK: UK National Focal Centre. 15 Chen, L.; Driscoll, C. T. (2005a) A two-layer model to simulate variations in surface water 16 chemistry draining a northern forest watershed. Water Resour. Res. 41(W09425): 17 10.1029/2004WR003625. 18 Chen, L.; Driscoll, C. T. (2005b) Regional assessment of the response of the acid-base status of 19 lake-watersheds in the Adirondack region of New York to changes in atmospheric 20 deposition using PnET-BGC. Environ. Sci. Technol. 39: 787-794. 21 Chen, L.; Driscoll, C. T. (2005c) Regional application of an integrated biogeochemical model to 22 northern New England and Maine. Ecol. Appl. 15: 1783-1797. 23 Church, M. R.; Shaffer, P. W.; Thornton, K. W.; Cassell, D. L.; Liff, C. I; Johnson, M. G.; 24 Lammers, D. A.; Lee, J. J.; Holdren, G. R.; Kern, J. S.; Liegel, L. H.; Pierson, S. M.; 25 Stevens, D. L.; Rochelle, B. P.; Turner, R. S. (1992) Direct/delayed response project: 26 future effects of long-term sulfur deposition on stream chemistry in the mid-Appalachian 27 region of the eastern United States. Corvallis, OR: U.S. Environmental Protection 28 Agency; report no. EPA/600/R-92/186. 29 Clean Air Act Advisory Committee. (2005) Recommendations to the Clear Air Act Advisory 30 Committee, Air Quality Management Work Group, phase I and next ateps, January 2005. 31 Washington, DC: Clean Air Act Advisory Committee. 32 Clow, D. W.; Sueker, J. K. (2000) Relations between basin characteristics and stream water 33 chemistry in alpine/subalpine basins in Rocky Mountain National Park, Colorado. Water 34 Resour. Res. 36: 49-62. 35 Clow, D. W.; Sickman, J. O.; Striegl, R. G; Krabbenhoft, D. P.; Elliot, J. G; Dornblaser, M.; 36 Roth, D. A.; Campbell, D. H. (2003) Changes in the chemistry of lakes and precipitation 37 in high-elevation national parks in the western United States, 1985-1999. Water Resour. 38 Res. 39(6) 1171: 10.1029/2002WR001533. 39 Cosby, B. J.; Hornberger, G. M.; Galloway, J. N.; Wright, R. F. (1985a) Modeling the effects of 40 acid deposition: assessment of a lumped parameter model of soil water and streamwater 41 chemistry. Water Resour. Res. 21: 51-63. August 2008 D-32 DRAFT-DO NOT QUOTE OR CITE ------- 1 Cosby, B. I; Wright, R. F.; Hornberger, G. M.; Galloway, J. N. (1985b) Modeling the effects of 2 acid deposition: estimation of long-term water quality responses in a small forested 3 catchment. Water Resour. Res. 21: 1591-1601. 4 Cosby, B. J.; Ferrier, R. C.; Jenkins, A.; Wright, R. F. (2001) Modelling the effects of acid 5 deposition: refinements, adjustments and inclusion of nitrogen dynamics in the MAGIC 6 model. Hydrol. Earth Syst. Sci. 5: 499-517. 7 Cosby, B. J.; Webb, J. R.; Galloway, J. N.; Deviney, F. A. (2006) Acidic deposition impacts on 8 natural resources in Shenandoah National Park. Philadelphia: U.S. Department of the 9 Interior, National Park Service, Northeast Region. Technical Report NPS/NER/NRTR- 10 2006/066. Available: 11 http://www.nps.gov/nero/science/FINAL/SHEN_acid_dep/SHEN_acid_dep.htm (3 12 December, 2007). 13 Craig, B. W.; Friedland, A. J. (1991) Spatial patterns in forest composition and standing dead red 14 spruce in montane forests of the Adirondacks and northern Appalachians. Environ. 15 Monit. Assess. 18: 129-140. 16 Cronan, C. S.; Grigal, D. F. (1995) Use of calcium/aluminum ratios as indicators of stress in 17 forest ecosystems. J. Environ. Qual. 24: 209-226. 18 Cronan, C. S.; Schofield, C. L. (1990) Relationships between aqueous aluminum and acidic 19 deposition in forested watersheds of North America and northern Europe. Environ. Sci. 20 Tech. 24: 1100-1105. 21 De Vries, W. (1988) Critical deposition levels for nitrogen and sulphur on Dutch forest 22 ecosystems. Water Air Soil Pollut. 42: 221-239. 23 De Vries, W.; Reinds, G. J.; Vel, E. (2003) Intensive monitoring of forest ecosystems in Europe: 24 2. Atmospheric deposition and its impacts on soil solution chemistry. For. Ecol. Manage. 25 174:97-115. 26 DeHayes, D. H.; Schaberg, P. G.; Hawley, G. J.; Strimbeck, G. R. (1999) Acid rain impacts on 27 calcium nutrition and forest health. BioScience 49: 789-800. 28 Dennis, T. E.; Bulger, A. J. (1995) Condition factor and whole-body sodium concentrations in a 29 freshwater fish: evidence for acidification stress and possible ionoregulatory over- 30 compensation. Water Air Soil Pollut. 85: 377-382. 31 Driscoll, C. T.; Postek, K. M. (1995) The chemistry of aluminum in surface waters. In: Sposito, 32 G., ed. The environmental chemistry of aluminum. Chelsea, MI: Lewis Publishers; pp. 33 363-418. 34 Driscoll, C. T.; Lawrence, G. B.; Bulger, A. J.; Butler, T. J.; Cronan, C. S.; Eagar, C.; Lambert, 35 K. F.; Likens, G. E.; Stoddard, J. L.; Weathers, K. C. (2001) Acidic deposition in the 36 northeastern United States: sources and inputs, ecosystem effects, and management 37 strategies. BioScience 51: 180-198. 38 Driscoll, C.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; Goodale, C.; Groffman, P.; 39 Hopkinson, C.; Lambert, K.; Lawrence, G.; Ollinger, S. (2003) Nitrogen pollution: 40 sources and consequences in the U.S. Northeast. Environment 45: 8-22. 41 Drohan, P.; Stout, S.; Petersen, G. (1999) Spatial relationships between sugar maple (Acer 42 sacharum Marsh.), sugar maple decline, slope, aspect, and atmospheric deposition in August 2008 D-33 DRAFT-DO NOT QUOTE OR CITE ------- 1 northern Pennsylvania. In: Horsley, S. B.; Long, R. P., eds. Sugar Maple Ecology and 2 Health: Proceedings of an International Symposium; June, 1998; Warren, PA. Radnor, 3 PA: US Department of Agriculture, Forest Service, General Technical Report NE-261. 4 Duan, L.; Hao, J. M.; Xie, S. D.; Du, K. (2000a) Critical loads of acidity for surface waters in 5 China. Sci. Total Environ. 246: 1-10. 6 Duan, L.; Xie, S. D.; Zhou, Z. P.; Hao, J. M. (2000b) Critical loads of acid deposition on soil in 7 China. Water Air Soil Pollut. 118: 35-51. 8 Duan, L.; Xie, S. D.; Zhou, Z. P.; Ye, X. M.; Hao, J. M. (2001) Calculation and mapping of 9 critical loads for S, N and acidity in China. Water Air Soil Pollut. 130: 1199-1204. 10 Dupont, J.; Clair, T. A.; Gagnon, C.; Jeffries, D. S.; Kahl, J. S.; Nelson, S. J.; Peckenham, J. M. 11 (2005) Estimation of critical loads of acidity for lakes in northeastern United States and 12 eastern Canada. Environ. Monit. Assess. 109: 275-291. 13 Ellis, H.; Bowman, M. (1994) Critical loads and development of acid rain control options. J. 14 Environ. Eng. 120: 273-290. 15 Ellis, H.; Ringold, P. L.; Holdren, G. R., Jr. (1996) Emissions reductions and ecological 16 response: management models for acid rain control. Socioecon. Plann. Sci. 30: 15-26. 17 Federal/Provincial Research and Monitoring Coordinating Committee (RMCC). (1990) The 18 1990 Canadian long-range transport of air pollutants and acid deposition assessment 19 report, part 4: aquatic effects. Ottawa, Ontario, Canada: Federal/Provincial Research and 20 Monitoring Coordinating Committee. 21 Federer, C. A.; Hornbeck, J. W.; Tritton, L. M.; Martin, C. W.; Pierce, R. S. (1989) Long-term 22 depletion of calcium and other nutrients in eastern US forests. Environ. Manage. (N. Y.) 23 13: 593-601. 24 Fenn, M. E.; Dunn, P. H. (1989) Litter decomposition across an air-pollution gradient in the San 25 Bernardino Mountains. Soil Sci. Soc. Am. J. 53: 1560-1567. 26 Fenn, M. E.; Poth, M. A.; Aber, J. D.; Baron, J. S.; Bormann, B. T.; Johnson, D. W.; Lemly, A. 27 D.; McNulty, S. G.; Ryan, D. F.; Stottlemyer, R. (1998) Nitrogen excess in North 28 American ecosystems: predisposing factors, ecosystem responses, and management 29 strategies. Ecol. Appl. 8: 706-733. 30 Fenn, M. E.; Baron, J. S.; Allen, E. B.; Rueth, H. M.; Nydick, K. R.; Geiser, L.; Bowman, W. D.; 31 Sickman, J. O.; Meixner, T.; Johnson, D. W.; Neitlich, P. (2003) Ecological effects of 32 nitrogen deposition in the western United States. BioScience 53: 404-420. 33 Galloway, J. N.; Aber, J. D.; Erisman, J. W.; Seitzinger, S. P.; Howarth, R. W.; Cowling, E. B.; 34 Cosby, B. J. (2003) The nitrogen cascade. BioScience 53: 341-356. 35 Gbondo-Tugbawa, S. S.; Driscoll, C. T.; Aber, J. D.; Likens, G. E. (2001) Evaluation of an 36 integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem. 37 Water Resour. Res. 37: 10578-1070. 38 Geiser, L.; Neitlich, P. (2007) Air pollution and climate gradients in western Oregon and 39 Washington indicated by epiphytic macrolichens. Environ. Pollut. 145: 203-218. 40 Groffman, P. M.; Altabet, A. M.; Bohlke, J. K.; Butterbach-Bahl, K.; David, M. B.; Firestone, 41 M. K.; Giblin, A. E.; Kana, T. M.; Nielsen, L. P.; Voytek, M. A. (2006) Methods for August 2008 D-34 DRAFT-DO NOT QUOTE OR CITE ------- 1 measuring denitrification: diverse approaches to a difficult problem. Ecol. Appl. 16: 2 2091-2122. 3 Gundersen, P.; Callesen, I; De Vries, W. (1998) Nitrate leaching in forest ecosystems is related 4 to forest floor C/N ratios. Environ. Pollut. 102(suppl. 1): 403-407. 5 Hao, J. M.; Ye, X. M.; Duan, L.; Zhou, Z. P. (2001) Calculating critical loads of sulfur 6 deposition for 100 surface waters in China using the MAGIC model. Water Air Soil 7 Pollut. 130: 1157-1162. 8 Henriksen, A.; Dillon, P. J. (2001) Critical load of acidity to surface waters in south-central 9 Ontario, Canada. I. Application of the Steady-State Water Chemistry SSWC model. Oslo, 10 Norway: Norwegian Institute for Water Research (NIVA). Acid Rain Research Report 11 2001:52. 12 Henriksen, A.; Posch, M. (2001) Steady-state models for calculating critical loads of acidity for 13 surface waters. Water Air Soil Pollut. Focus 1: 375-398. 14 Henriksen, A.; Kamari, J.; Posch, M.; Wilander, A. (1992) Critical loads of acidity: Nordic 15 surface waters. Ambio 21: 356-363. 16 Henriksen, A.; Dillon, P. J.; Aherne, J. (2002) Critical loads of acidity for surface waters in 17 south-central Ontario, Canada: regional application of the Steady-State Water Chemistry 18 model. Can. J. Fish. Aquat. Sci. 59: 1287-1295. 19 Hicks, B. B.; McMillen, R. T.; Turner, R. S.; Holdren, J.; Strickland, T. C. (1993) A national 20 critical loads framework for atmospheric deposition effects assessment: III Deposition 21 characterization. Environ. Manage. 17: 343-353. 22 Hindar, A.; Posch, M.; Henriksen, A.; Gunn, J.; Snucins, E. (2000) Application of the FAB 23 model to calculate critical loads of S and N for lakes in the Killarney Provincial Park 24 (Ontario, Canada). Oslo, Norway: Norwegian Institute for Water Research; NIVA report 25 SNO 4202-2000. 26 Hindar, A.; Posch, M.; Henriksen, A. (2001) Effects of in-lake retention of nitrogen on critical 27 load calculations. Water Air Soil Pollut. 130: 1403-1408. 28 Holdren, G. R., Jr.; Strickland, T. C.; Cosby, B. J.; Marmorek, D.; Bernard, D.; Santore, R.; 29 Driscoll, C. T.; Pardo, L.; Hunsaker, C.; Turner, R. S.; Aber, J. (1993) A national critical 30 loads framework for atmospheric deposition effects assessment: IV. Model selection, 31 applications and critical loads mapping. Environ. Manage. 17: 355-363. 32 Holdren, G. R.; Strickland, T. C.; Rosenbaum, B. J.; Turner, R. E.; Ryan, P. F.; McDowell, M. 33 K.; Bishop, G. D. (1993) Comparison of selected critical loads estimation approaches for 34 assessing the effects of sulfate deposition on lakes in the northeastern United States. 35 Washington, D.C.: U.S. Environmental Protection Agency; EPA/600/R93/074. Available 36 from: NTIS, Springfield, VA; PB92-119015. 37 Holdren, G. R.; Strickland, T. C.; Shaffer, P. W.; Ryan, P. F.; Ringold, P. L.; Turner, R. S. 38 (1993) Sensitivity of critical load estimates for surface waters to model selection and 39 regionalization schemes. J. Environ. Qual. 22: 279-289. 40 Hornung, M.; Bull, K. R.; Cresser, M.; Hall, J.; Langan, S. J.; Loveland, P.; Smith, C. (1995) An 41 empirical map of critical loads of acidity for soils in Great Britain. Environ. Pollut. 90: 42 301-310. August 2008 D-35 DRAFT-DO NOT QUOTE OR CITE ------- 1 Horsley, S. B.; Long, R. P.; Bailey, S. W.; Hallett, R. A.; Hall, T. J. (2000) Factors associated 2 with the decline disease of sugar maple on the Allegheny Plateau. Can. J. Forest. Res. 30: 3 1365-1378. 4 Hunsaker, C.; Graham, R.; Turner, R. S.; Ringold, P. L.; Holdren, G. R., Jr.; Strickland, T. C. 5 (1993) A national critical loads framework for atmospheric deposition effects assessment: 6 II. Defining assessment end points, indicators, and functional subregions. Environ. 7 Manage. (N.Y.) 17: 335-341. 8 Interlandi, S. J.; Kilham, S. S. (1998) Assessing the effects of nitrogen deposition on mountain 9 waters: a study of phytoplankton community dynamics. Water Sci. Technol. 38: 139-146. 10 Jeffries, D. S., ed. (1997) Canadian acid rain assessment. Volume 3. The effects on Canada's 11 lakes, rivers and wetlands. Burlington, Ontario, Canada: Environment Canada. 12 Jeffries, D. S.; Lam, C. C. L. (1993) Assessment of the effect of acidic deposition on Canadian 13 lakes: determination of critical loads for sulphate deposition. Water Sci. Technol. 28: 14 183-187. 15 Jeffries, D. S.; Ouimet, R.; Aherne, J.; Arp, P. A.; Balland, V.; Demerchant, L; Dupont, J.; 16 Franklyn, J.; Lam, C. C. L.; Norouzian, F.; Watmough, S. A.; Wong, I. (2005) Critical 17 load values and exceedances. In: 2004 Canadian acid deposition science assessment. 18 Ottawa: Meteorological Service of Canada. Environment Canada. 19 Jenkins, A.; Cosby, B. J.; Ferrier, R. C.; Larssen, T.; Posch, M. (2003) Assessing emission 20 reduction targets with dynamic models: deriving target load functions for use in 21 integrated assessment. Hydrol. Earth Syst. Sci. 7: 609-617. 22 Johnson, D. W.; Lindberg, S. E., eds. (1992) Atmospheric deposition and forest nutrient cycling: 23 a synthesis of the integrated forest study. New York, NY: Springer-Verlag, Inc. (Billings, 24 W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; Remmert, H., eds. Ecological studies: 25 analysis and synthesis: v. 91). 26 Johnson, D. W.; Todd, D. E. (1990) Nutrient cycling in forests of Walker Branch Watershed, 27 Tennessee: roles of uptake and leaching in causing soil changes. J. Environ. Qual. 19: 97- 28 104. 29 Johnson, D. W.; Kelly, J. M.; Swank, W. T.; Cole, D. W.; Van Miegroet, H.; Hornbeck, J. W.; 30 Pierce, R. S.; Van Lear, D. (1988) The effects of leaching and whole-tree harvesting on 31 cation budgets of several forests. J. Environ. Qual. 17: 418-424. 32 Juggins, S.; Omerod, S. J.; Harriman, R. (1995) Relating critical loads to aquatic biota. In: 33 Critical loads of acid deposition for UK freshwaters. London UK: Department of the 34 Environment; pp. 9-13. 35 Kauppi, P. E.; Mielikaeinen, K.; Kuusela, K. (1992) Biomass and carbon budget of European 36 forests, 1971 to 1990. Science (Washington, DC) 256: 70-74. 37 Kristensen, H. L.; Gundersen, P.; Callesen, I; Reinds, G. J. (2004) Throughfall nitrogen 38 deposition has different impacts on soil solution nitrate concentration in European 39 coniferous and deciduous forests. Ecosystems 7: 180-192. 40 Kros, J.; Reinds, G. J.; de Vries, W.; Latour, J. B.; Bollen, M. J. S. (1995) Modelling abiotic site 41 factors in response to atmospheric deposition and upward seepage. In: Schoute, J. F. T.; 42 Finke, P. A.; Veeneklaas, F. R.; Wolfert, H. P., eds. Scenario studies for the rural August 2008 D-36 DRAFT-DO NOT QUOTE OR CITE ------- 1 environment: selected and edited proceedings of the symposium Scenario Studies for the 2 Rural Environment; September, 1994; Wageningen, the Netherlands. Dordrecht, The 3 Netherlands: Kluwer; pp. 445-448. 4 Lafrancois, B. M.; Nydick, K. R.; Caruso, B. (2003) Influence of nitrogen on phytoplankton 5 biomass and community composition in fifteen Snowy Range lakes (Wyoming, U.S.A.). 6 Arct. Anarct. Alp. Res. 35(4): 499-508. 7 Lafrancois, B. M.; Nydick, K. R.; Johnson, B. M.; Baron, J. S. (2004) Cumulative effects of 8 nutrients and pH on the plankton of two mountain lakes. Can. J. Fish. Aquat. Sci. 61: 9 1153-1165. 10 Li, J. H.; Tang, H. X.; Bai, Q. Z.; Me, Y. F.; Luan, Z. K. (2000) Aquatic acidification sensitivity 11 for regional environment: a multi-indicator evaluation approach. Water Air Soil Pollut. 12 11:251-261. 13 Lien, L.; Raddum, G. G.; Fjellheim, A. (1992) Critical loads for surface waters: invertebrates and 14 fish. Oslo, Norway: Norwegian Institute for Water Research; acid rain research report no. 15 21. 16 Lien, L.; Raddum, G. G.; Fjellheim, A.; Henriksen, A. (1996) A critical limit for acid 17 neutralizing capacity in Norwegian surface waters, based on new analyses offish and 18 invertebrate responses. Sci. Total Environ. 177: 173-193. 19 MacAvoy, S. W.; Bulger, A. J. (1995) Survival of brook trout (Salvelinus fontinalis) embryos 20 and fry in streams of different acid sensitivity in Shenandoah National Park, USA. Water 21 Air Soil Pollut. 85: 445-450. 22 Marco, A.; Blaustein, A. R. (1999) The effects of nitrite on behavior and metamorphosis in 23 cascades frogs (Rana cascadae). Environ. Toxicol. Chem. 18: 949-949. 24 Martinson, L.; Alveteg, M.; Warfvinge, P. (2003) Parameterization and evaluation of sulfate 25 adsorption in a dynamic soil chemistry model. Environ. Pollut. 124: 119-125. 26 McLaughlin, S. B.; Wimmer, R. (1999) Tansley review no. 104: Calcium physiology and 27 terrestrial ecosystem processes. NewPhytol. 142: 373-417. 28 Milindalekha, J.; Vashkin, V. N.; Towpprayoon, S. (2001) Calculation and mapping of sulfur 29 critical loads for terrestrial ecosystems of Thailand. Water Air Soil Pollut. 130: 1265- 30 1270. 31 Miller, E. K. (2006) Assessment of forest sensitivity to nitrogen and sulfur deposition in Maine. 32 Conference of New England Governors and Eastern Canadian Premiers, Forest Mapping 33 Group. Augusta, ME: Maine Department of Environmental Protection, Bureau of Air 34 Quality. Available: http://www.maine.gov/dep/air/acidrain/ME-Forest-Mapping-Report- 35 2007-01-26.pdf [3 December, 2007]. 36 Moayeri, M.; Meng, F. R.; Arp, P. A.; Foster, N. W. (2001) Evaluating critical soil acidification 37 loads and exceedances for a deciduous forest at the Turkey Lakes watershed. Ecosystems 38 4: 555-567. 39 Munson, R. K.; Gherini, S. A. (1991) Processes influencing the acid-base chemistry of surface 40 waters. In: Charles, D. F.; Christie, S., eds. Acidic deposition and aquatic ecosystems: 41 regional case studies. New York: Springer-Verlag; pp. 9-34. August 2008 D-37 DRAFT-DO NOT QUOTE OR CITE ------- 1 NEG/ECP Forest Mapping Group. (2001) Protocol for assessment and mapping of forest 2 sensitivity to atmospheric S and N deposition. Boston, MA: New England 3 Governors/Eastern Canadian Premiers: acid rain action plan. Action item 4: forest 4 mapping research project. Available: http://www.ecosystems- 5 research.com/fmi/Protocol.pdf [3 December, 2007]. 6 National Acid Precipitation Assessment Program. (1991) National Acid Precipitation 7 Assessment Program 1990 integrated assessment report. Washington, DC: National Acid 8 Precipitation Assessment Program. 9 National Research Council. (2004) Air quality management in the United States. Washington, 10 DC: The National Academies Press. 11 Nilsson, J., ed. (1986) Critical loads of nitrogen and sulphur. Copenhagen, Denmark: Council of 12 Ministers; environmental report 1986:11. 13 Nilsson, J.; Grennfelt, P., eds. (1988) Critical loads for sulphur and nitrogen: report from a 14 workshop; March; Skokloster, Sweden. Copenhagen, Denmark: Nordic Council of 15 Ministers; Milj0rapport 1988:15. 16 National Park Service (NFS). (2000) Federal land manager's air quality related values workgroup 17 (FLAG) phase I report. Lakewood, CO: U.S. Department of the Interior, National Park 18 Service, Air Resources Division. Available: http://199.128.173.141/Flag2000.pdf [8 19 January, 2008]. 20 National Park Service (NFS). (2004) Federal land manager's critical loads for sulfur and nitrogen 21 workshop. Denver, CO: National Park Service Air Resources Division; U.S. Fish and 22 Wildlife Service Air Quality Branch; U.S. Forest Service, Air Resource Management 23 Program. Available: 24 http://www2.nature.nps.gov/air/Pubs/pdf/FinalCL_WorkshopSummary092904.pdf [8 25 January, 2008]. 26 Nydick, K. R.; Lafrancois, B. M.; Baron, J. S.; Johnson, B. M. (2003) Lake-specific responses to 27 elevated atmospheric nitrogen deposition in the Colorado Rocky Mountains, U.S.A. 28 Hydrobiologia 510: 103-114. 29 Nydick, K. R.; Lafrancois, B. M.; Baron, J. S. (2004a) NOs uptake in shallow, oligotrophic, 30 mountain lakes: the influence of elevated NOs concentrations. J. North Am. Benthol. Soc. 31 23:397-415. 32 Nydick, K. R.; Lafrancois, B. M.; Baron, J. S.; Johnson, B. M. (2004b) Nitrogen regulation of 33 algal biomass, productivity, and composition in shallow mountain lakes, Snowy Range, 34 Wyoming, USA. Can. J. Fish. Aquat. Sci. 61: 1256-1268. 35 Ouimet, R.; Arp, P. A.; Watmough, S. A.; Aherne, J.; Demerchant, I. (2006) Determination and 36 mapping critical loads of acidity and exceedances for upland forest soils in eastern 37 Canada. Water Air Soil Pollut. 172: 57-66. 38 Pardo, L. H.; Driscoll, C. T. (1993) A critical review of mass balance methods for calculating 39 critical loads of nitrogen for forested ecosystems. Environ. Rev. 1: 145-156. 40 Pardo, L. H.; Driscoll, C. T. (1996) Critical loads for nitrogen deposition: case studies at two 41 northern hardwood forests. Water Air Soil Pollut. 89: 105-128. August 2008 D-38 DRAFT-DO NOT QUOTE OR CITE ------- 1 Pardo, L. H.; Duarte, N. (2007) Assessment of effects of acidic deposition on forested 2 ecosystems in Great Smoky Mountains National Park using critical loads for sulfur and 3 nitrogen. Lakewood, CO: U.S. Department of the Interior, National Park Service, Air 4 Resources Division. Available: 5 http://www2.nature.nps.gov/air/Pubs/pdf/GSMN_CL_Report_080830.pdf [3 December, 6 2007]. 7 Pembrook, H. (2004) 2004 update: calculating critical loads of acidity and exceedances for acid- 8 impaired lakes in Vermont using the steady-state water chemistry (SSWC) model. 9 Porter, E.; Blett, T.; Potter, D. U.; Huber, C. (2003) Protecting resources on federal lands: 10 implications of critical loads for atmospheric deposition of nitrogen and sulfur. 11 Bioscience 55: 603-612. 12 Posch, M.; De Smet, P. A. M.; Hettelingh, J.-P.; Downing, R. J., eds. (1995) Calculation and 13 mapping of critical thresholds in Europe. Status report 1995. Bilthoven, The Netherlands: 14 National Institute of Public Health and the Environment (RIVM), Coordination Center 15 for Effects; RIVM report no. 259101004. Available: 16 http://www.mnp.nl/bibliotheek/rapporten/259101004.pdf [4 December, 2007]. 17 Posch, M.; Kamari, J.; Forsius, J.; Henriksen, A.; Wilander, A. (1997) Environmental auditing: 18 exceedance of critical loads for lakes in Finland, Norway and Sweden: reduction 19 requirements for acidifying nitrogen and sulfur deposition. Environ. Manage. 21: 291- 20 304. 21 Posch, M.; DeSmet, P. A. M.; Hettelingh, J. P.; Downing, R. J. (2001) Calculation and mapping 22 of critical thresholds in Europe. Status report 2001. Bilthoven, The Netherlands: 23 National Institute of Public Health and the Environment (RIVM), Coordination Center 24 for Effects; RIVM report no. 259101010. Available: 25 http://www.mnp.nl/cce/Images/SROI_tcm42-16441.pdf [4 December, 2007]. 26 Posch, M.; Hettelingh, J.-P.; Slootweg, J., eds. (2003) Manual for dynamic modelling of soil 27 response to atmospheric deposition. Bilthoven, The Netherlands: National Institute for 28 Public Health and the Environment (RIVM), Working Group on Effects of the 29 Convention on Long-range Transboundary Air Pollution; report 259101012. Available: 30 http://www.mnp.nl/cce/Images/ModManI_tcm42-16497.pdf [4 December, 2007]. 31 Raddum, G. G.; Fjellheim, A. (1984) Acidification and early warning organisms in freshwater in 32 western Norway. Verh. - Int. Ver. Theor. Angew. Limnol. 22: 1973-1980. 33 Raddum, G. G.; Skjelvale, B. L. (2001) Critical limit of acidifying compounds to invertebrates in 34 different regions of Europe. Water Air Soil Pollut. 130: 825-830. 35 Reuss, J. O.; Johnson, D. W. (1986) Acid deposition and the acidification of soils and waters. 36 New York, NY: Springer-Verlag. (Billings, W. D.; Golley, F.; Lange, O. L.; Olson, J. S.; 37 Remmert, H., eds. Ecological studies: analysis and synthesis: v. 59). 38 Rochelle, B. P.; Church, M. R. (1987) Regional patterns of sulphur retention in watersheds of the 39 eastern U.S. Water Air Soil Pollut. 36(1-2): 61-73. 40 Rouse, J. D., Bishop, C. A., Struger, J. (1999) Nitrogen pollution: an assessment of its threat to 41 amphibian survival. Environ. Health Perspect. 107: 799-803. 42 Rowe, E. C.; Moldan, F.; Emmett, B. A.; Evans, C. D.; Hellsten, S. (2005) Model chains for 43 assessing impacts of nitrogen on soils, waters and biodiversity: a review. Brighton, UK: August 2008 D-39 DRAFT-DO NOT QUOTE OR CITE ------- 1 Centre for Ecology and Hydrology, Natural Environment Research Council; report 2 project C02887. 3 Rueth, H. M.; Baron, J. S. (2001) Englemann spruce nitrogen dynamics across a nitrogen 4 deposition gradient in Colorado, USA. Ekologia (Bratislava, Slovakia) 20: 43-49. 5 Rueth, H.; Baron, J. S. (2002) Differences in Englemann spruce forest biogeochemistry east and 6 west of the Continental Divide in Colorado, USA. Ecosystems 5: 45-57. 7 Saros, J. E.; Interlandi, S. J.; Wolfe, A. P.; Engstrom, D. R. (2003) Recent changes in the diatom 8 community structure of lakes in the Beartooth Mountain Range, USA. Arct. Anarct. Alp. 9 Res. 35: 18-23. 10 Schaberg, P. G.; DeHayes, D. H.; Hawley, G. J.; Strimbeck, G. R.; Gumming, J. R.; Murakami, 11 P. F.; Borer, C. H. (2000) Acid mist, soil Ca and Al alter the mineral nutrition and 12 physiology of red spruce. Tree Physiol. (Victoria, BC, Can.) 20: 73-85. 13 Schaberg, P. G.; DeHayes, D. H.; Hawley, G. J.; Murakami, P. F.; Strimbeck, G. R.; McNulty, S. 14 G. (2002) Effects of chronic N fertilization on foliar membranes, cold tolerance, and 15 carbon storage in montane red spruce. Can. J. Forest Res. 32: 1351-1359. 16 Shortle, W. C.; Smith, K. T. (1988) Aluminum-induced calcium deficiency syndrome in 17 declining red spruce. Science (Washington, DC) 240: 1017-1018. 18 Skjelkvale, B. L.; Andersen, T.; Halvorsen, G. A.; Raddum, G. G.; Heegaard, E.; Stoddard, J.; 19 Wright, R. F. (2000) The 12-year report: acidification of surface water in Europe and 20 North America; trends, biological recovery and heavy metals. Oslo, Norway: Norwegian 21 Institute for Water Research; ICP Waters report 52/2000. 22 http://www.niva.no/symfoni/RappArkiv5.nsf/URL/C125730900463888C1256FB80053D 23 51D/$FILE/4208_200dpi.pdf 24 Skjelkvale, B. L.; Evans, C. D.; Larssen, T.; Hindar, A.; Raddum, G. G. (2003) Recovery from 25 acidification in European surface waters: a view to the future. Ambio 30: 170-175. 26 Stevens, C. J.; Dise, N. B.; Mountford, J. O.; Gowing, D. J. (2004) Impact of nitrogen deposition 27 on the species richness of grasslands. Science 303: 1876-1879. 28 Stoddard, J. L. (1994) Long-term changes in watershed retention of nitrogen: its causes and 29 aquatic consequences. In: Baker, L. A., ed. Environmental chemistry of lakes and 30 reservoirs. Washington, DC: American Chemical Society; pp. 223-284. (Advances in 31 chemistry series no. 237). 32 Strickland, T. C.; Holdren, G. R., Jr.; Ringold, P. L.; Bernard, D.; Smythe, K.; Fallen, W. (1993) 33 A national critical loads framework for atmospheric deposition effects assessment: I. 34 Method summary. Environ. Manage. (N. Y.) 17: 329-334. 35 Sullivan, T. J. (2000) Aquatic effects of acidic deposition. Boca Raton, FL: Lewis Publishers. 36 Sullivan, T. J.; Cosby, B. J.; Webb, J. R.; Snyder, K. U.; Herlihy, A. T.; Bulger, A. J.; Gilbert, E. 37 H.; Moore, D. (2002) Assessment of the effects of acidic deposition on aquatic resources 38 in the Southern Appalachian Mountains. Report prepared for the Southern Appalachian 39 Mountains Initiative (SAMI). Corvallis, OR: E&S Environmental Chemistry, Inc. 40 Sullivan, T. J.; Cosby, B. J.; Laurence, J. A.; Dennis, R. L.; Savig, K.; Webb, J. R.; Bulger, A. J.; 41 Scruggs, M.; Gordon, C.; Ray, J.; Lee, H.; Hogsett, W. E.; Wayne, H.; Miller, D.; Kern, 42 J. S. (2003) Assessment of air quality and related values in Shenandoah National Park. August 2008 D-40 DRAFT-DO NOT QUOTE OR CITE ------- 1 Philadelphia, PA: U.S. Department of the Interior, National Park Service, Northeast 2 Region; technical report NPS/NERCHAL/NRTR-03/090. Available: 3 http://www.nps.gov/nero/science/FINAL/shen_air_quality/shen_airquality.html [19 June, 4 2006]. 5 Sullivan, T. I; Cosby, B. I; Herlihy, A. T.; Webb, J. R.; Bulger, A. I; Snyder, K. U.; Brewer, P. 6 F.; Gilbert, E. H.; Moore, D. L. (2004) Regional model projections of future effects of 7 sulfur and nitrogen deposition on streams in the southern Appalachian Mountains. Water 8 Resour. Res. 40(W02101): 10.1029/2003WR001998. 9 Sullivan, T. J.; Driscoll, C. T.; Cosby, B. J.; Fernandez, I. J.; Herlihy, A. T.; Zhai, J.; Stemberger, 10 R.; Snyder, K. U.; Sutherland, J. W.; Nierzwicki-Bauer, S. A.; Boylen, C. W.; 11 McDonnell, T. C.; Nowicki, N. A. (2006) Assessment of the extent to which intensively- 12 studied lakes are representative of the Adirondack Mountain region. Final report. Albany, 13 NY: New York State Energy Research and Development Authority (NYSERDA); report 14 06-17. Available: 15 http://nysl.nysed.gOv/uhtbin/cgisirsi/Qcwd6NzFby/NYSL/138650099/8/4298474fl 16 November, 2007]. 17 Sverdrup, H.; De Vries, W. (1994) Calculating critical loads for acidity with the simple mass 18 balance method. Water Air Soil Pollut. 72: 143-162. 19 Sverdrup, H.; Warfvinge, P. (1988) Weathering of primary silicate minerals in the natural soil 20 environment in relation to a chemical weathering model. Water Air Soil Pollut. 38: 387- 21 408. 22 Sverdrup, H.; Warfvinge, P. (1993) The effect of soil acidification on the growth of trees, grass 23 and herbs as expressed by the (Ca+ Mg+ K)/A1 ratio. Lund, Sweden: Lund University, 24 Department of Chemical Engineering (Reports in ecology and environmental engineering 25 2). 26 Sverdrup, H.; De Vries, W.; Henriksen, A. (1990) Mapping critical loads: a guidance to the 27 criteria, calculations, data collection and mapping of critical loads. Copenhagen, 28 Denmark: Nordic Council of Ministers; environmental report 1990:14; NORD 1990:98. 29 Sverdrup, H.; Warfvinge, P.; Rabenhorst, M.; Janicki, A.; Morgan, R.; Bowman, M. (1992) 30 Critical loads and steady-state chemistry for streams in the state of Maryland. Environ. 31 Pollut. 77: 195-203. 32 Takemoto, B. K.; Bytnerowicz, A.; Fenn, M. E. (2001) Current and future effects of ozone and 33 atmospheric nitrogen deposition on California's mixed conifer forests. For. Ecol. Manage. 34 144:159-173. 35 Turner, R. S.; Cook, R. B.; Van Miegroet, H.; Johnson, D. W.; Elwood, J. W.; Bricker, O. P.; 36 Lindberg, S. E.; Hornberger, G. M. (1991) Watershed and lake processes affecting 37 surface water acid-base chemistry. In: Irving, P. M., ed. Acidic deposition: state of 38 science and technology, volume II, aquatic processes and effects. Washington, DC: The 39 U.S. National Acid Precipitation Assessment Program. (Acidic deposition: state of 40 science and technology report 10). 41 U.S. Environmental Protection Agency. (1995) Acid deposition standard feasibility study. Report 42 to Congress. Washington, DC: Office of Air and Radiation; report no. EPA 430-R-95- 43 00 la. August 2008 D-41 DRAFT-DO NOT QUOTE OR CITE ------- 1 U.S. Environmental Protection Agency. (2006) Multi-agency critical loads workshop: sulfur & 2 nitrogen deposition effects on freshwater and terrestrial ecosystems. Final report. 3 Washington, DC: Clear Air Markets Division; contract no. EPA 68-W-03-02. Available: 4 http://nadp.sws.uiuc.edu/cladws/fmalreport.pdf [4 December, 2007]. 5 United Nations Economic Commission for Europe (UNECE). (2004) Manual on methodologies 6 and criteria for modeling and mapping critical loads and levels and air pollution effects, 7 risks, and trends. Geneva, Switzerland: Convention on Long-Range Transboundary Air 8 Pollution. Available: http://www.icpmapping.org [16 August, 2006]. 9 Van Tienhoven, A. M.; Olbrich, K. A.; Skoroszewski, R.; Taljaard, I; Zunckel, M. (1995) 10 Application of the critical loads approach in South Africa. Water Air Soil Pollut. 85: 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. August 2008 D-42 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 D-43 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 E-1 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 E-2 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 E-3 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 E-4 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 E-5 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 E-6 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 E-7 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 E-8 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 E-9 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 E-10 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 E-11 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 E-12 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 E-13 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 E-14 DRAFT-DO NOT QUOTE OR CITE ------- Materials Exposure Conditions Findings Reference Mild Steel Steel Copper Zinc Nickel Iron Aluminum Copper Zinc Steel Steel samples were exposed to the atmosphere in 47 marine atmospheres with varying levels of chloride and SO2. Atmospheres were separated for data analysis based on chloride and SO2 deposition rates. Two types of steel were exposed to 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 August 2008 E-15 DRAFT-DO NOT QUOTE OR CITE ------- Materials Exposure Conditions Comments Reference Marble Limestone Marble Marble Brick Marble Limestone Mortars Jaumont limestone Calcium carbonate Mortars Mortars Concrete Marble Laboratory exposure to HNO3 ranging from 54 to 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. August 2008 E-16 DRAFT-DO NOT QUOTE OR CITE ------- ANNEX E - References 1 Aastrup, T.; Wadsak, M.; Leygraf, C.; Schreinerb, M. (2000) In situ studies of the initial 2 atmospheric corrosion of copper influence of humidity, sulfur dioxide, ozone, and 3 nitrogen dioxide. J. Electrochem. Soc. 147: 2543-2551. 4 Agelakopoulou, T.; Bassiotis, I; Metax, E.; Roubani-Kalantzopoulou, F. (2007) Benzene and 5 toluene influence with or without nitrogen dioxide on inorganic pigments of works of 6 art—Part II. Atmos. Environ. 41: 2009-2018. 7 Almeida, E.; Morcillo, M.; Resales, B. (2000) Atmospheric corrosion of mild steel. Part II - 8 Marine atmospheres. Mater. Corros. 51: 865-874. 9 Apsimon, H. M.; Cowell, D. (1996) The benefits of reduced damage to buildings from abatement 10 of sulphur dioxide emissions. Energy Policy 24: 651-654. 11 Arbizzani, R.; Casellatoa, U.; Fiorina, E.; Nodarib, L.; Russob, U.; Vigato, P. A. (2004) Decay 12 markers for the preventative conservation and maintenance of paintings. J. Cult. Heritage 13 5: 167-182. 14 Askey, A.; Lyon, S. B.; Thompson, G. E.; Johnson, J. B.; Wood, G. C.; Sage, P. W.; Cooke, M. 15 J. (1993) The effect of fly-ash particulates on the atmospheric corrosion of zinc and mild 16 steel. Corros. Sci. 34: 1055-1081. 17 Ausset, P.; Del Monte, M.; Lefevre, R. A. (1999) Embryonic sulphated black crusts on carbonate 18 rocks in atmospheric simulation chamber and in the field: role of carbonaceous fly-ash. 19 Atmos. Environ. 33: 1525-1534. 20 Blucher, D. B.; Svensson, J. E.; Johansson, L. G. (2005) Influence of ppb levels of SC>2 on the 21 atmospheric corrosion of aluminum in the presence of NaCl. J. Electrochem. Soc. 152: 22 B397-B404. 23 Boke, H.; Gokturk, E. H.; Caner-Saltik, E. N.; Demirci, S. (1999) Effect of airborne particle on 24 SO2-calcite reaction. Appl. Surface Sci. 140: 70-82. 25 Colombini, M. P.; Modugno, F.; Fuoco, R.; Tognazzi; A. (2002) A GC-MS study on the 26 deterioration of lipidic paint binders. Microchem. J. 73: 175-185. 27 Cowell, D.; Apsimon, H. (1996) Estimating the cost of damage to buildings by acidifying 28 atmospheric pollution in Europe. Atmos. Environ. 30: 2959-2968. 29 Damian, L.; Fako, R. (2000) Weathering structural steels corrosion in atmospheres of various 30 degrees of pollution in Romania. Mater. Corros. 51: 574-578. 31 Dante, J. F.; Kelly, R. G. (1993) The evolution of the adsorbed solution layer during atmospheric 32 corrosion and its effect on the corrosion rate of copper. J. Electrochem. Soc. 140: 1890- 33 1897. 34 Dehri, I; Yazici, B.; Erbil, M.; Galip, H. (1994) The effects of SO2 and NH3 on the atmospheric 35 corrosion of galvanized iron sheet. Corros. Sci. 36: 2181-2191. 36 Del Monte, M.; Rossi, P. (1997) Fog and gypsum crystals on building materials. Atmos. 37 Environ. 31: 1637-1646. August 2008 E-17 DRAFT-DO NOT QUOTE OR CITE ------- 1 Dubowski, Y.; Sumner, A. L.; Menke, E. J.; Gaspar, D. J.; Newberg, J. T.; Hoffman, R. C.; 2 Penner, R. M.; Hemminger, J. C.; Finlayson-Pitts, B. J. (2004) Interactions of gaseous 3 nitric acid with surfaces of environmental interest. Phys. Chem. Chem. Phys. 6: 3879- 4 3888. 5 Grosjean, D.; Grosjean, E.; Williams, E. L., II. (1993) Fading of artists' colorants by a mixture of 6 photochemical oxidants. Atmos. Environ. Part A 27: 765-772. 7 Grosjean, D.; Grosjean, E.; Williams, E. L., II. (1994) Fading of colorants by atmospheric 8 pollutants: reflectance spectroscopy studies. Sci. Total Environ. 151: 213-226. 9 Gysels, K.; Delalieuxa, F.; Deutscha, F.; Grieken, R. V.; Camuffob, D.; Bernardib, A.; Sturarob, 10 G.; Bussec, H-J.; Wieserc, M. (2004) Indoor environment and conservation in the Royal 11 Museum of Fine Arts, Antwerp, Belgium. J. Cult. Heritage 5: 221-230. 12 Johansson, A.; Lennholm, H. (2000) Influences of SCh and 63 on the ageing of paper 13 investigated by in situ diffuse reflectance FTIR and time-resolved trace gas analysis. 14 Appl. Surface Sci. 161: 163-169. 15 Jouen, S.; Jean, M.; Hannoyer, B. (2004) Atmospheric corrosion of nickel in various outdoor 16 environments. Corros. Sci. 46: 499-514. 17 Kim, S.-T.; Maedab, Y.; Tsujino, Y. (2004) Assessment of the effect of air pollution on material 18 damages in northeast Asia. Atmos. Environ. 38: 37-48. 19 Lan, T. T. N.; Thoaa, N. T. P.; Nishimurab, R.; Tsujinoc, Y.; Yokoid, M.; Maeda, Y. (2005) 20 New model for the sulfation of marble by dry deposition sheltered marble—the indicator 21 of air pollution by sulfur dioxide. Atmos. Environ. 39: 913-920. 22 Leuenberger-Minger, A. U.; Faller, M.; Richner, P. (2002) Runoff of copper and zinc caused by 23 atmospheric corrosion. Mater. Corros. 53: 157-164. 24 Marinoni, N.; Birelli, M. P.; Rostagno, C.; Pavese, A. (2003) The effects of atmospheric 25 multipollutants on modern concrete. Atmos. Environ. 37: 4701-4712. 26 Martinez-Arkarazo, L; Angulo, M.; Bartolome, L.; Etxebarria, N.; Olazabal, M. A.; Madariaga, 27 J. M. (2007) An integrated analytical approach to diagnose the conservation state of 28 building materials of a palace house in the metropolitan Bilbao (Basque Country, north of 29 Spain). Anal. Chim. Acta 584: 350-359. 30 National Acid Precipitation Assessment Program. (1991) National Acid Precipitation 31 Assessment Program 1990 integrated assessment report. Washington, DC: National Acid 32 Precipitation Assessment Program. 33 Oesch, S.; Faller, M. (1997) Environmental effects on materials: the efect of the air pollutants 34 862, NO2, NO and 63 on the corrosion of copper, zinc and aluminum. A short literature 35 survey and results of laboratory exposures. Corros. Sci. 39: 1505-1530. 36 Sabbioni, C.; Zappia, G.; Riontinoa, C.; Blanco-Varela, M. T.; Aguilerac, J.; Puertasc, F.; Van 37 Balen, K.; Toumbakari, E. E. (2001) Atmospheric deterioration of ancient and modern 38 hydraulic mortars. Atmos. Environ. 35: 539-548. 39 Sabbioni, C.; Bonazzaa, A.; Zappia, G. (2002) Damage on hydraulic mortars: the Venice 40 arsenal. J. Cult. Heritage 3: 83-88. August 2008 E-18 DRAFT-DO NOT QUOTE OR CITE ------- 1 Sarnie, F.; Tidblad, J.; Kucera, V.; Leygraf, C. (2007) Atmospheric corrosion effects 2 comparison of laboratory-exposed copper, zinc and carbon steel. Atmos. Environ. 41: 3 4888-4896. 4 Shashoua, Y. (2006) Inhibiting the inevitable; current approaches to slowing the deterioration of 5 plastics. Macromol. Symp. 238: 61-11. 6 Sikiotis, D.; Kirkitsos, P. (1995) The adverse effects of nitrates on stone monuments. Sci. Total 7 Environ. 171: 173-182. 8 Smith, G. D.; Clark, R. J. H. (2002) The role of H2S in pigment blackening. J. Cult. Heritage 3: 9 101-105. 10 Smith, L. V.; DeVries, K. L. (1993) Mechanical properties of polymeric fibres exposed to stress 11 in a NOX environment. Polymer 34: 546-550. 12 Strandberg, H. (1998) Reactions of copper patina compounds--!. Influence of some air 13 pollutants. Atmos. Environ. 32: 3511-3520. 14 Svensson, J.-E.; Johansson, L.-G. (1993a) A laboratory study of the effect of ozone, nitrogen 15 dioxide, and sulfur dioxide on the atmospheric corrosion of zinc. J. Electrochem. Soc. 16 140: 2210-2216. 17 Svensson, J.-E.; Johansson, L.-G. (1993b) A laboratory study of the initial stages of the 18 atmospheric corrosion of zinc in the presence of NaCl; influence of SC>2 and NC>2. Corros. 19 Sci. 34: 721-740. 20 Svensson, J.-E.; Johansson, L.-G. (1996) The temperature-dependence of the SCh-induced 21 atmospheric corrosion of zinc; a laboratory study. Corros. Sci. 38: 2225-2233. 22 Torfs, K.; Van Grieken, R. (1996) Effect of stone thickness on run-off water composition and 23 derived damage functions in ambient exposure experiments. Atmos. Environ. 30: 1-8. 24 U.S. Environmental Protection Agency. (1982) Air quality criteria for particulate matter and 25 sulfur oxides. Research Triangle Park, NC: Office of Health and Environmental 26 Assessment, Environmental Criteria and Assessment Office; EPA report no. EPA-600/8- 27 82-029aF-cF. 3v. Available from: NTIS, Springfield, VA; PB84-156777. 28 U.S. Environmental Protection Agency. (1993) Air quality criteria for oxides of nitrogen. 29 Research Triangle Park, NC: Office of Health and Environmental Assessment, 30 Environmental Criteria and Assessment Office; report nos. EPA/600/8-9!/049aF-cF. 3v. 31 Available from: NTIS, Springfield, VA; PB95-124533, PB95-124525, and PB95-124517. 32 U.S. Environmental Protection Agency. (2004) Air quality criteria for particulate matter. 33 Research Triangle Park, NC: National Center for Environmental Assessment; report no. 34 EPA/600/P-99/002aF-bF. 2v. Available: http://cfpub.epa.gov/ncea/ [9 November, 2004]. 35 Vilche, J. R.; Varela F. E.; Acuna G.; Codaro E. N.; Resales B. M.; Fernandez A.; Moriena G. 36 (1995) A survey of Argentinean atmospheric corrosion: I. Aluminum and zinc samples. 37 Corros. Sci. 37: 941-961. 38 Weissenrieder, J.; Kleber, C.; Schreiner, M.; Leygrafa, M. (2004) In situ studies of sulfate nest 39 formation on iron. J. Electrochem. Soc. 151: B497-B504. August 2008 E-19 DRAFT-DO NOT QUOTE OR CITE ------- 1 Yerrapragada, S. S.; Chirra, S. R.; Jaynes, J. H.; Bandyopadhyay, J. K.; Gauri, K. L. (1996) 2 Weathering rates of marble in laboratory and outdoor conditions. J. Environ. Eng. 122: 3 856-863. 4 Zappia, G.; Sabbioni, C.; Riontino, C.; Gobbi, G.; Favoni, O. (1998) Exposure tests of building 5 materials in urban atmosphere. Sci. Total Environ. 224: 235-244. August 2008 E-20 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-1 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-2 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-3 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-4 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-5 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 F-6 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-7 DRAFT-DO NOT QUOTE OR CITE ------- bu T3 CU I jn .^ 40 cc Ł 30 - TJ *^ in t/> 20 o 1_ CU j rt . _Q 10 E z o - u c CO '^ ^ CD O" CO CD 1 I I I I ^~~- c ""—^ ~ ^— CD CD O ° -2 0> c -J3 "•" tn -Q ^ Z re 5 CB E 3 .2 Ł ^ o - S 0 CD LL. 'C ° K o % •*^L* d isheries LL ' i i i ^ "S CO « CD -S1 "S | co-g-g re =5 2 » g JCD Ł g 3 - ^ '> "S ™ o : o 5 -2 o w » g 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 August 2008 F-8 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-9 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-10 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-11 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-12 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-13 DRAFT-DO NOT QUOTE OR CITE ------- 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, August2008 F-14 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-15 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 F-16 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-17 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-18 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-19 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-20 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-21 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 F-22 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-23 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-24 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-25 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-26 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-27 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-28 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-29 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-30 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-31 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 F-32 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-33 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-34 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-35 DRAFT-DO NOT QUOTE OR CITE ------- 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) August 2008 F-36 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-37 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-38 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 F-39 DRAFT-DO NOT QUOTE OR CITE ------- 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). August 2008 F-40 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-41 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-42 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-43 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-44 DRAFT-DO NOT QUOTE OR CITE ------- 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%. August 2008 F-45 DRAFT-DO NOT QUOTE OR CITE ------- 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 August 2008 F-46 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-47 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-48 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-49 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-50 DRAFT-DO NOT QUOTE OR CITE ------- 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. August 2008 F-51 DRAFT-DO NOT QUOTE OR CITE ------- ANNEX F - References 1 Adams, R. M.; Crocker, T. D. (1989) The agricultural economics of environmental change: some 2 lessons from air pollution. J. Environ. Manage. 28: 295-307. 3 Adams, R. M.; Horst, R. L., Jr. (2003) Future directions in air quality research: economic issues. 4 Environ. Int. 29: 289-302. 5 Adams, R. M.; Hamilton, S. A.; McCarl, B. A. (1986a) The benefits of pollution control: the 6 case of ozone and U.S. agriculture. Am. J. Agric. Econ. 68: 886-893. 7 Adams, R. M.; Callaway, J. M.; McCarl, B. A. (1986b) Pollution, agriculture and social welfare: 8 the case of acid deposition. Can. J. Agric. Econ. 34: 3-19. 9 Banzhaf, H. S.; Burtraw, D.; Evans, D.; Krupnick, A. J. (2006) Valuation of natural resource 10 improvements in the Adirondacks. Land Econ. 82: 445-464. 11 Barbier, E. B. (1991) An approach to economic evaluation of tropical wetlands: with examples 12 from Guatemala and Nicaragua. In: Girvan, N. P.; Simons, D. eds. Caribbean Ecology 13 and Economics. St. Michael, Barbados: Caribbean Conservation Association; pp. 207- 14 231. 15 Barbier, E. B.; Acreman, M.; Knowler, D. (1997) Economic valuation of wetlands. A guide for 16 policy makers and planners. Gland, Switzerland: Ramsar Convention Bureau. 17 Bentley, J.; Horst, R. (1998) Commercial forestry benefits of alternative emission controls for 18 the NOX SIP Call. Research Triangle Park, NC: U.S. Environmental Protection Agency, 19 Office of Air Quality Planning and Standards, Science Applications International 20 Corporation (SAIC) Contract #68-D-98-113. 21 Bergstrom, J. C.; Taylor, L. O. (2006) Using meta-analysis for benefits transfer: theory and 22 practice. Ecol. Econ. 60: 351-360. 23 Bockstael, N. E.; McConnell, K. E.; Strand, I. E. (1988) Benefits from improvements in 24 Chesapeake Bay water quality, v. Ill of Benefit analysis using indirect or imputed market 25 methods. Washington, DC: U.S. Environmental Protection Agency Cooperative 26 Agreement CR-811043-01-0. 27 Bockstael, N. E.; McConnell, K. E.; Strand, I. E. (1989a) Measuring the benefits of 28 improvements in water quality: the Chesapeake Bay. Mar. Resour. Econ. 6: 1-18. 29 Bockstael, N.; McConnell, K.; Strand, I. (1989b) A random utility model for sportfishing: Some 30 preliminary results for Florida. Mar. Resour. Econ. 6: 245-260. 31 Bouwes, N. W.; Schneider, R. (1979) Procedures in estimating benefits of water quality change. 32 Am. J. Agric. Econ. 61: 535-539. 33 Boyd, J.; Banzhaf, S. (2007) What are ecosystem services? The need for standardized 34 environmental accounting units. Ecol. Econ. 63: 616-626. 35 Boyle, K. J.; Poor, P. J.; Taylor, L. O. (1999) Estimating the demand for protecting freshwater 36 lakes from eutrophication. Am. J. Agric. Econ. 81:1118-1122. August 2008 F-52 DRAFT-DO NOT QUOTE OR CITE ------- 1 Brander, L. M.; Florax, R. J. G. M.; Vermaat, J. E. (2006) The empirics of wetland valuation: a 2 comprehensive summary and a meta-analysis of the literature. Environ. Res. Econ. 33: 3 223-250. 4 Brouwer, R.; Langford, I. H.; Bateman, I. J.; Turner, R. K. (1999) A meta-analysis of wetland 5 contingent valuation studies. Reg. Environ. Change 1: 47-57. 6 Buhyoff, G. J.; Wellman, J. D. (1980) The specification of a non-linear psychophysical function 7 for visual landscape dimensions. J. Leisure Res. 12: 257-272. 8 Buhyoff, G. J., Leuschner, W. A.; Wellman, J. D. (1979) Aesthetic impacts of southern pine 9 beetle damage. J. Environ. Manage. 8: 261-267. 10 Buhyoff, G. J., Wellman, J. D.; Daniel, T. C. (1982) Predicting scenic quality for mountain pine 11 beetle and western spruce budworm damaged forests. For. Sci. 28: 827-838. 12 Burtraw, D.; Krupnick, A.; Mansur, E.; Austin, D.; Farrell, D. (1997) The costs and benefits of 13 reducing acid rain. Washington, DC: Resources for the Future. Discussion Paper 97-31- 14 REV. 15 Callaway, J. M.; Darwin, R. F.; Nesse, R. J. (1986) Economic valuation of acidic deposition 16 damages: preliminary results from the 1985 NAPAP assessment. In: Proceedings of 17 International Symposium on Acidic Precipitation; September; Muskoka, Ontario, 18 Canada. Richland, WA: U. S. Environmental Protection Agency; Pacific Northwest 19 Laboratory. Water Air Soil Pollut. 31: 1019-1034. 20 Cameron, T. A.; Englin, J. (1997) Welfare effects of changes in environmental quality under 21 individual uncertainty about use. Rand J. Econ. 28: S45-S70. 22 Carson, R. T.; Hanemann, M. W. (2005) Contingent valuation. In: Maler, K.-G.; Vincent, J. R., 23 eds. Handbook of Environmental Economics. Vol. 2. Valuation of Environmental 24 Changes. Amsterdam, The Netherlands: Elsevier; pp. 821-936. 25 Carson, R. T.; Mitchell, R. C. (1993) The value of clean water: The public's willingness to pay 26 for boatable, fishable, and swimmable quality water. Water Resour. Res. 29: 2445-2454. 27 Carson, R. T.; Flores, N. E.; Martin, K. M.; Wright., J. L. (1996) Contingent valuation and 28 revealed preference methodologies: comparing the estimates for quasi-public goods. 29 Land Econ. 72: 80-99. 30 Costanza, R.; d'Arge, R.; De Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; 31 Naeem, S.; O'Neill, R. V.; Paruelo, J.; Raskin, R. G.; Sutton, P.; Van Den Belt, M. (1997) 32 The value of the world's ecosystem services and natural capital. Nature (London) 387: 33 253-259. 34 Crocker, T. D. (1985) On the value of the condition of a forest stock. Land Econ. 61: 244-254. 35 Crocker, T. D.; Forster, B. A. (1986) Atmospheric deposition and forest decline. Water Air Soil 36 Pollut. 31: 1007-1017. 37 De Steiguer, J. E.; Pye, J. M. (1988) Using scientific opinion to conduct forestry air pollution 38 economic analyses. In: Jobstl, A., ed. Proceedings of symposium on the economic 39 assessment: of damage caused to forests by air pollutants; pp. 133-142. IUFRO Working 40 Party S4.04-02, September; Gmunden, Austria. August 2008 F-53 DRAFT-DO NOT QUOTE OR CITE ------- 1 De Steiguer, J. E.; Pye, J. M.; Love, C. S. (1990) Air pollution damage to U.S. forests: a survey 2 of perceptions and estimates of scientists. J. For. 88: 17-22. 3 Diaz, R. J.; Solow, A. (1992) Ecological and economic consequences of hypoxia. Topic 2 report 4 for the integrated assessment on hypoxia in the Gulf of Mexico. In: Hypoxia in the Gulf 5 of Mexico. Progress towards the completion of an integrated assessment. Silver Spring, 6 MD: National Oceanic and Atmospheric Administration. 7 Englin, J. E.; Cameron, T. A.; Mendelsohn, R. E.; Parsons, G. A.; Shankle, S. A. (1991) 8 Valuation of damages to recreational trout fishing in the upper northeast due to acidic 9 deposition. Richland, WA: Pacific Northwest Laboratory, report no. PNL-7683. 10 Epp, D. J.; Al-Ani, K. S. (1979) The effect of water quality on rural nonfarm residential property 11 values. Am. J. Agric. Econ. 61: 529-534. 12 Federal Register. (1993) Report of the NOAA Panel on contingent valuation. F. R. (January 15) 13 58: 4601-4614. 14 Flowers, P. J.; Vaux, H. J.; Gardner, P. D.; Mills, T. J. (1985) Changes in recreation values after 15 fire in the northern Rocky Mountains. Berkeley, CA: U.S. Department of Agriculture 16 Forest Service, Pacific Southwest Forest and Range Experiment Station; Research Note 17 PSW-373. 18 Freeman, M. A. (2003) The measurement of environmental and resource values theory and 19 methods. Washington, D.C.: RFF Press. 20 Gramlich, F. W. (1977) The demand for clean water: the case of the Charles River. Natl. Tax J. 21 30(2): 183-194. 22 Haefele, M.; Kramer, R. A.; Holmes, T. P. (1992) Estimating the total value of forest quality in 23 high-elevation spruce-fir forests. In: The Economic Value of Wilderness: Proceedings of 24 the Conference; May, 1991; Jackson, WY. Asheville, NC: USD A Forest Service, 25 Southeastern Forest Experiment Station; General Technical Report SE-78, pp. 91-96. 26 Hammitt, W. E.; Patterson, M. E.; Noe, F. P. (1994) Identifying and predicting visual preference 27 of southern Appalachian forest recreation vistas. Landscape Urban Plan. 29: 171-183. 28 Hanemann, M. (1991) Willingness to pay and willingness to accept: How much can they differ? 29 T Am. Econ. Rev. 81(3): 635-647. 30 Hanley, N.; Shogren, J.; White, B. (1997) Environmental economics in theory and practice. New 31 York: Oxford University Press. 32 Haynes, R. W.; Adams, D. M. (1992) Assessing economic impacts of air pollution damage to 33 U.S. forests. In: de Steiguer, J. E., ed. The economic impact of air pollution on timber 34 markets: studies from North America and Europe. Asheville, NC: U.S. Department of 35 Agriculture, Southeastern Forest Experiment Station; general technical report no. SE-75. 36 Heal, G. M.; Barbier, E. B.; Boyle, K. J. (2005) Valuing ecosystem services: toward better 37 environmental decision making. Washington, DC: The National Academies Press. 38 Available: http://www.nap.edu/openbook.php?isbn=030909318X [6 November, 2007]. 39 Heck, W. W.; Adams, R. M.; Cure, W. W.; Heagle, A. S.; Heggestad, H. E.; Kohut, R. J.; Kress, 40 L. W.; Rawlings, J. O.; Taylor, O. C. (1983) A reassessment of crop loss from ozone. 41 Environ. Sci. Technol. 17: 573A-581A. August 2008 F-54 DRAFT-DO NOT QUOTE OR CITE ------- 1 Hollenhorst, S. I; Brock, S. M.; Freimund, W. A.; Twery, M. J. (1993) Predicting the effects of 2 gypsy moth on near-view aesthetic preferences and recreation appeal. For. Sci. 39(1): 28- 3 40. 4 Holmes, T. P. (1992) Economic welfare impacts of air pollution damage to forests in the 5 southern United States. In: de Steiguer, J. E., ed. The Economic Impact of Air Pollution 6 on Timber Markets: Studies from North America and Europe. Asheville, NC: U.S. Dept. 7 of Agriculture, Forest Service, Southeastern Forest Experiment Station. General 8 Technical Report SE-75; pp. 19-26. 9 Holmes, T. P.; Kramer, R. A. (1996) Contingent valuation of ecosystem health. Ecosyst. Health 10 2: 56-60. 11 Holmes, T. P.; Murphy, E. A.; Bell, K. P. (2006) Exotic forest insects and residential property 12 values. Agric. Res. Econ. Rev. 35: 155-166. 13 Industrial Economics, Inc. (1999a) Characterizing the commercial timber benefits from 14 tropospheric ozone reduction attributable to the 1990 Clean Air Act Amendments, 1990- 15 2010. Washington, DC: U.S. Environmental Protection Agency, Office of Policy. 16 Industrial Economics, Inc. (1999b) Characterizing the forest aesthetics benefits attributable to the 17 1990 Clean Air Act Amendments, 1990-2010. Washington, DC: U.S. Environmental 18 Protection Agency, Office of Policy. 19 Industrial Economics, Inc. (1999c) Benefits assessment of decreased nitrogen deposition to 20 estuaries in the United States attributable to the 1990 Clean Air Act Amendments, 1990- 21 2010. Washington, DC: U.S. Environmental Protection Agency, Office of Policy. 22 Jakus, P.; Smith, V. K. (1991) Measuring use and nonuse values for landscape amenities: a 23 contingent behavior analysis of gypsy moth control. Washington, DC: Resources for the 24 Future; Discussion Paper no. QE92-07. 25 Jenkins, D. H.; Sullivan, J.; Amacher, G. S.; Nicholas, N. S.; Reaves, D. W. (2002) Valuing high 26 altitude spruce-fir forest improvements: importance of forest condition and recreation 27 activity. J. For. Econ. 8: 77-99. 28 Johnston, R. J.; Besedin, E. Y.; Ranson, M. H.; Helm, E. C. (2005) What determines willingness 29 to pay per fish? A meta-analysis of recreational fishing values. Mar. Resour. Econ. 21: l- 30 32. 31 Kaoru, Y. (1995) Measuring marine recreation benefits of water quality improvements by the 32 nested random utility model. Resour. Energy Econ. 17: 119-136. 33 Kaoru, Y.; Smith, V. K.; Liu, J. L. (1995) Using random utility models to estimate the 34 recreational value of estuarine resources. Am. J. Agric. Econ. 77: 141-151. 35 Kim, H. J.; Helfand, G. E.; Howitt, R. E. (1998) An economic analysis of ozone control in 36 California's San Joaquin Valley. J. Agric. Resource Econ. 23: 55-70. 37 Kopp, R. J.; Vaughn, W. J.; Hazilla, M.; Carson, R. (1985) Implications of environmental policy 38 for U.S. agriculture: the case of ambient ozone standards. J. Environ. Manage. 20: 321- 39 331. 40 Kramer, R. A.; Eisen-Hecht, J. I. (2002) Estimating the economic value of water quality 41 protection in the Catawba River basin. Water Resour. Res. 38: 10.1029/2001WR000755. August 2008 F-55 DRAFT-DO NOT QUOTE OR CITE ------- 1 Kramer, R. A.; Mercer, D. E. (1997) Valuing a global environmental good: U.S. residents' 2 willingness to pay to protect tropical rain forests. Land Econ. 73: 196-210. 3 Kramer, R. A.; Holmes, T. P.; Haefele, M. (2003) Contingent valuation of forest ecosystem 4 protection. In: Sills, E. O.; Abt, K. L., eds. Forests in a Market Economy. New York, NY: 5 Kluwer Academic Publishers; pp. 303-320. 6 Kuik, O. J.; Helming, J. F. M.; Borland, C.; Spaninks, F. A. (2000) The economic benefits to 7 agriculture of a reduction of low-level ozone pollution in The Netherlands. Eur. Rev. 8 Agric Econ. 27: 75-90. 9 Lefohn, A. S.; Oltmans, S. J.; Dann, T.; Singh, H. B. (2001) Present-day variability of 10 background ozone in the lower troposphere. J. Geophys. Res. [Atmos.] 106: 9945-9958. 11 Leuschner, W. A.; Young, R. L. (1978) Estimating southern pine beetle's impact on reservoir 12 recreation. For. Sci. 24: 527-537. 13 Leuschner, W. A.; Young., R. L. (1978) Estimating the southern pine beetle's impact on reservoir 14 campsites. For. Sci. 43: 46-55. 15 Lipton, D. (2004) The value of improved water quality to Chesapeake Bay boaters. Mar. Resour. 16 Econ. 19: 265-270. 17 Loomis, J. B.; Gonzalez-Caban, A.; Gregory, R. (1996) A contingent valuation study of the 18 value of reducing fire hazards to old-growth forests in the Pacific Northwest. Albany, 19 CA: U.S. Department of Agriculture Forest Service, Pacific Southwest Research Station; 20 research paper PSW-RP-229-Web. 21 MacMillan, D.; Ferrier, B.; Hanley, N. (2001) Valuation of air pollution effects on ecosystems: a 22 scoping study. Aberdeen, Scotland: University of Aberdeen, Department for 23 Environment, Food and Rural Affairs. 24 Maler, K.-G.; Vincent, J. R., eds. (2005) Handbook of environmental economics, vol. 2. 25 Valuation of environmental changes. Amsterdam, The Netherlands: Elsevier. 26 Mathtech. (1994) The Regional Model Farm (RMF): an agricultural sector benefits assessment 27 model: Version 3.0 for personal computers. Washington, DC: Office of Air Quality 28 Planning and Standards, pp. 1-83. 29 McConnell, V.; Walls, M. (2005) The value of open space: evidence from studies of nonmarket 30 benefits. Washington, DC: Resources for the Future; RFF Report. 31 McLaughlin, S.; Percy, K. (1999) Forest health in North America: some perspectives on actual 32 and potential roles of climate and air pollution. Water Air Soil Pollut. 116: 151-197. 33 Millennium Ecosystem Assessment. (2003) Ecosystems and human well-being: a framework for 34 assessment. Washington, DC: Island Press. 35 Miller, J. D.; Lindsay, B. E (1993) Willingness to pay for a state gypsy moth control program in 36 New Hampshire: a contingent valuation case study. J. Econ. Entomol. 86: 828-837. 37 Morey, E. R.; Shaw, W. D. (1990) An economic model to assess the impact of acid rain: a 38 characteristics approach to estimating the demand for and benefits from recreational 39 fishing. In: Link, A. N.; Smith, V. K., eds. Recent Developments in the Modeling of 40 Technical Change and Modeling Demand for and Valuation of Recreation Resources. August 2008 F-56 DRAFT-DO NOT QUOTE OR CITE ------- 1 Greenwich, CT: JAI Press Inc., pp. 195-216. (Advances in Applied Microeconomics, 2 v. 5). 3 Morgan, C.; Owens, N. (2001) Benefits of water quality policies: the Chesapeake Bay. Ecol. 4 Econ. 39: 271-284. 5 Mullen, J. K.; Menz, F. C. (1985) The effect of acidification damages on the economic value of 6 the Adirondack Fishery to New York anglers. Am. J. Agric. Econ. 67: 112-119. 7 Murphy, J. J.; Deluki, M. A.; McCubbin, D. R.; Kim, H. J. (1999) The cost of crop damage 8 caused by ozone air pollution from motor vehicles. J. Environ. Manage. 55: 273-289. 9 National Acid Precipitation Assessment Program. (1991) National Acid Precipitation 10 Assessment Program 1990 integrated assessment report. Washington, DC: National Acid 11 Precipitation Assessment Program. 12 National Science and Technology Council (NSTC). (2000) Integrated assessment of hypoxia in 13 the northern Gulf of Mexico [draft]. Washington, DC: U.S. National Science and 14 Technology Council, Committee on Environment and Natural Resources. 15 Needelman, M.; Kealy, M. J. (1995) Recreational swimming benefits of New Hampshire lake 16 water quality policies: An application of a repeated discrete choice model. Agric. Resour. 17 Econ. Rev. 24: 78-87. 18 Newman, D. H. (1987) An econometric analysis of the southern softwood stumpage 19 market: 1950-1980. For. Sci. 33: 932-945. 20 Paquet, J.; Belanger, L. (1997) Public acceptability thresholds of clearcutting to maintain visual 21 quality of boreal balsam fir landscapes. For. Sci. 43: 46-55. 22 Park, T. B., Bowker, J. M.; Leeworthy, V. P. (2002) Valuing snorkeling visits to the Florida 23 Keys with stated and revealed preference models. J. Environ. Manage. 65: 301-312. 24 Peterson, D. G.; Rowe, R. D.; Schulze, W. D.; Russell, G. W.; Boyce, R. R.; Elliott, S. R.; Kurd, 25 B. (1987) Valuation of visual forest damages from ozone, a volume of improving 26 accuracy and reducing costs of environmental benefit assessments. In: Improving 27 Accuracy and Reducing Costs of Environmental Benefit Assessments. Report to U.S. 28 Environmental Protection Agency. Boulder, CO: University of Colorado, Center for 29 Economic Analysis. 30 Poe, G. L.; Boyle, K. J.; Bergstrom, J. C. (2000) A meta-analysis of contingent values for 31 groundwater quality in the United States. Presented at: American Agricultural Economics 32 Association Conference. Tampa, FL. 33 Polasky, S.; Costello, C.; Solow, A. (2005) Economics of biodiversity. In: Maler, K.-G.; Vincent, 34 J. R., eds. Handbook of Environmental Economics. Amsterdam, The Netherlands: 35 Elsevier; pp. 1517-1560. 36 Poor, J. P.; Pessagno, K. L.; Paul, R. W. (2006) Exploring the hedonic value of ambient water 37 quality: A local watershed-based study. Ecol. Econ. 60: 797-806. 38 Randall, A.; Kriesel, W. (1990) Evaluating national policy proposals by contingent valuation. In: 39 Johnson, G. V., Johnson, R. L., eds. Economic Valuation of Natural Resources: issues, 40 theory and applications. Boulder, CO: Westview Press, pp. 153-178. August 2008 F-57 DRAFT-DO NOT QUOTE OR CITE ------- 1 Rosenberger, R.; Loomis, J. (2001) Benefit transfer of outdoor recreation use values: a technical 2 document supporting the Forest Service Strategic Plan, (2000 revision). Fort Collins, CO: 3 U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 4 General Technical Report RMRS-GTR-72. 5 Ruddell, E. J.; Gramann, J. H.; Rudis, V. A.; Westphal, J. M. (1989) The psychological utility of 6 visual penetration in near-view forest scenic-beauty models. Environ. Behav. 21: 393- 7 412. 8 Shaw, W. D. (1989) Valuing the effect of acidification damages on the Adirondack Fishery: 9 Comment. Am. J. Agric. Econ. 71: 217-220. 10 Shogren, J. F.; Shin, S. Y.; Hayes, D. J.; Kliebenstein, J. B. (1994) Resolving differences in 11 willingness to pay and willingness to accept. Am. Econ. Rev. 84: 255-270. 12 Smith, V. K. (2007) Reflections on the literature. Rev. Environ. Econ. Policy 1: 152-165. 13 Smith, V. K.; Desvousges, W. H. (1986) Measuring water quality benefits. Boston, MA: Kluwer- 14 Nijhoff 15 Smith, V. K.; Huang, J.-C. (1995) Can markets value air quality? A meta-analysis of hedonic 16 property value models. J. Polit. Econ. 103: 209-27. 17 Smith, V. K.; Pattanayak, S. K. (2002) Is meta-analysis a Noah's Ark for non-market valuation? 18 Environ. Resour. Econ. 22: 271-296. 19 Smith, V. K.; Palmquist, R. B.; Jakus, P. (1991) Combining Farrell frontier and hedonic travel 20 cost models for valuing estuarine quality. Rev. Econ. Stat. 73: 694-699. 21 Smith, M. D.; Crowder, L. B. (2005) Valuing ecosystem services with fishery rents: a lumped- 22 parameter approach to hypoxia in the Neuse River Estuary. Milan, Italy: Fondazione Eni 23 Enrico Mattel (FEEM), NRM Nota di Lavoro (Natural Resources Management Working 24 Papers), 115.05. Available at SSRN: http://ssrn.com/abstract=825587 [5 November, 25 2007]. 26 Smith, V. K.; Pattanayak, S.; Houtven, G. V. (2006) Structural benefits transfer: an example 27 using VSL estimates. Ecol. Econ. 60(2): 361-371. 28 Spash, C. L. (1997) Assessing the economic benefits to agriculture from air pollution control. J. 29 Econ. Surv. 11:47-70. 30 Toman, M. A. (1998) Why not to calculate the value of the world's ecosystem services and 31 natural capital. Ecol. Econ. 25: 57-60. 32 Treiman, T.; Gartner, J. (2006) Are residents willing to pay for their community forests? Results 33 of a contingent valuation survey in Missouri, USA. Urban Stud. 43: 1537-1547. 34 U.S. Environmental Protection Agency. (1999) The benefits and costs of the Clean Air Act 1990 35 to 2010: EPA report to Congress. Washington, DC: Office of Air and Radiation; EPA 36 report no. 410-R-99-001. Available: http://www.epa.gov/air/sect812/1990- 37 2010/fullrept.pdf (19 December 2003). 38 U.S. Environmental Protection Agency. (2002) A framework for the economic assessment of 39 ecological benefits. Washington, DC: Science Policy Council, Social Sciences Discussion 40 Group, Ecological Benefit Assessment Workgroup. August 2008 F-58 DRAFT-DO NOT QUOTE OR CITE ------- 1 U.S. Environmental Protection Agency. (2006) Air quality criteria for ozone and related 2 photochemical oxidants, volume I. Research Triangle Park, NC: National Center for 3 Environmental Assessment; report no. EPA/600/R-05/004aF. Available: 4 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=149923 [19 March, 2007]. 5 Van Houtven, G.; Powers, J.; Pattanayak, S. (2007) Valuing water quality improvements in the 6 United States using meta-analysis: Is the glass half-full or half-empty for national policy 7 analysis? Resour. Energy Econ. 29: 206-228. 8 Vaux, H. J., Jr.; Gardner, P. D.; Mills, T. J. (1984) Methods for assessing the impact of fire on 9 forest recreation. Berkeley, CA: U.S. Department of Agriculture Forest Service. Pacific 10 Southwest Forest and Range Experiment Station, General Technical Report PSW-79. 11 Walsh, R. G.; Ward, F. A.; Olienyk, J. P. (1989) Recreational demand for trees in national 12 forests. J. Environ. Manage. 28: 255-268. 13 Walsh, R. G; Bjonback, R. D.; Aiken, R. A.; Rosenthal, D. H. (1990) Estimating the public 14 benefits of protecting forest quality. J. Environ. Manage. 30: 175-189. 15 Westenbarger, D. V.; Frisvold, G. B. (1995) Air pollution and farm-level crop yields: An 16 empirical analysis of corn and soybeans. Agric. Resour. Econ. Rev. 24: 156-165. 17 White, P. S.; Cogbill, C. V. (1992) Spruce-fir forests of eastern North America. In: Eagar, C.; 18 Adams, M.B., eds. Ecology and decline of red spruce in the eastern United States. New 19 York, NY: Springer-Verlag, pp. 3-39. 20 Whitehead, J. C.; Groothuis, P. A. (1992) Economic benefits of improved water quality: a case 21 of North Carolina's Tar-Pamlico River. Rivers 3: 170-178. 22 Whitehead, J. C.; Haab, T. C.; Huang, J.-C. (2000) Measuring recreation benefits of quality 23 improvements with revealed and stated behavior data. Resour. Energy Econ. 22: 339-354. 24 Willig, R. D. (1976) Consumer's surplus without apology. Am. Econ. Rev. 66: 589-597. 25 Wilson, M. A.; Carpenter, S. R. (1999) Economic valuation of freshwater ecosystem services in 26 the United States: 1971-1997. Ecol. Appl. 9: 772-783. 27 Woodward, R. T.; Wui, Y.-S. (2001) The economic value of wetland services: a meta-analysis. 28 Ecol. Econ. 37: 257-270. 29 August 2008 F-59 DRAFT-DO NOT QUOTE OR CITE ------- |