Grantee Research Project Results
Final Report: Developing Regional-scale Stressor Models for Managing Eutrophication in Coastal Marine Ecosystems, Including Interactions of Nutrients, Sediments, Land-use Change, and Climate Variability and Change
EPA Grant Number: R830882Title: Developing Regional-scale Stressor Models for Managing Eutrophication in Coastal Marine Ecosystems, Including Interactions of Nutrients, Sediments, Land-use Change, and Climate Variability and Change
Investigators: Howarth, Robert W. , Marino, Roxanne M. , Swaney, Dennis P. , Boyer, Elizabeth W. , Scavia, Donald , Alber, Merryl
Institution: Cornell University
EPA Project Officer: Packard, Benjamin H
Project Period: March 1, 2003 through June 8, 2007
Project Amount: $749,644
RFA: Developing Regional-Scale Stressor-Response Models for Use in Environmental Decision-making (2002) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Aquatic Ecosystems , Climate Change
Objective:
In the original statement of our approach, we proposed two interacting components. The first was primarily a model-development and testing program, in which we planned to continue to work on analysis of watershed nitrogen transport, ie the relationships between net anthropogenic nitrogen inputs (NANI) and observed nitrogen fluxes, and to develop and test the Regional Nutrient Management Model (ReNuMa), a model designed to be used by managers to evaluate sources and magnitude nutrient and sediment fluxes from regions and large watersheds to coastal marine ecosystems, and to be responsive to watershed management practices. A specific aim was to refine and modify this model to increase its effectiveness as a tool to investigate the interacting effects of climate variability, potential future climate change, and land-use change on fluxes of water, sediments, and nutrients from regions and watersheds. We planned to develop the model for the Hudson/Mohawk watershed due to our prior experience modeling this system, and then to extend the scope to other watersheds of the Northeastern US.
The second component was a program of statistical analysis of coastal data sets, (eg such as those collected by NOAA and LOICZ) in an attempt to classify coastal marine ecosystems based on their physical and ecological aspects as a step towards developing a typology for evaluating the sensitivity of coastal marine ecosystems to nutrient enrichment, and to predict how climate change and other stressors such as sediment loading and water diversions interact with this sensitivity.
The structure of the first component of the project remained largely as originally stated. The second component of the project changed from its initial conception, based on discussions at a project meeting in 2004. We developed a simple nutrient-phytoplankton-zooplankton (NPZ) estuarine response model to be used as a basis for estuarine classification. Outcomes of these project components are outlined below, and in more detail in the accompanying publications and websites.
Summary/Accomplishments (Outputs/Outcomes):
Watershed nutrient analysis and modeling: NANI
The NANI accounting system (Howarth et al. 1996; Boyer et al. 2002) estimates the net anthropogenic nitrogen inputs across watershed boundaries and correlates these with riverine fluxes from the watershed. NANI originally included four general categories: nitrogen content of net food and feed transport across watershed boundaries, atmospheric N deposition, fertilizer applications, and nitrogen fixation within the watershed. The methodology has evolved somewhat in response to the availability of datasets and improved data quality. For example, future estimates of NANI will include newer model-based estimates of atmospheric N deposition based on more refined, highly resolved models, such as CMAQ. Recent work (Howarth et al., 2006) examined the relationships between NANI, climatic drivers and riverine N fluxes in northeastern US rivers. Results of relatively simple regression models show that either average precipitation or river discharge explain much of the variation between NANI and nitrogen export in rivers from watersheds in the Northeastern US (figure 1). Planned future work will extend this to other regions. Ongoing work will also consider the sensitivity and uncertainty of NANI estimates to its components and parameters.
Figure 1. Regression models using NANI and climatic variables agree well with independent estimates of riverine N flux over northeastern US watersheds. a) using average watershed precipitation; b) using average discharge measurements. (Howarth et al., 2006b)
Watershed nutrient analysis and modeling: ReNuMa
Over the course of the project, weather, land cover, and other landscape data were obtained and analyzed in order to characterize watersheds and provide data to parameterize and drive models. Hydrological and water quality data were obtained and analyzed to validate the model for the watersheds studied, simulating river discharge and nitrogen. Version 1.0 of ReNuMa has been completed, together with a user’s guide and supplementary information, and published on a project website. Simulations of annual variation of river discharge and dissolved inorganic nitrogen for the period 1988-93 have been completed for the 16 northeastern US watersheds described in Boyer et al (2002). Basic features of the model and simulation results are described in the poster presented in the 2005 meeting of American Society of Limnology and Oceanography and in a manuscript currently in preparation (Swaney et al., in prep). The poster (and others related to project outcomes) is available on the project website: http://www.eeb.cornell.edu/biogeo/epa-star/pubs.htm Exit
The ReNuMa model is a hydrologically-driven, quasi-empirical model designed to estimate nutrient fluxes at the scale of large watersheds (i.e., several thousand km2). It is quasi-empirical in the sense that it makes no attempt to model detailed mechanisms; most relationships in the model are based on empirical observations and mass balance. It is a lumped-parameter model in the sense that it does not deal with spatially explicit details of watershed processes, but rather aggregates areas of similar land use/land cover into categories which can be modeled as independent sources of nutrients and runoff. Categories of nutrient inputs to the watershed are used to estimate runoff and groundwater concentrations following some empirical relationships rather than theoretical mechanisms. These relationships are described in detail in Section 2 of the ReNuMa user’s manual. At this stage of its development (version 1.0), the model should be regarded as a work in progress, whose structure and parameterization are subject to change, depending upon scale of problem, region of application, and available data for parameterization and calibration. The most recent version of the model is available at http://www.eeb.cornell.edu/biogeo/nanc/usda/renuma.htm Exit .
ReNuMa is based on two lines of earlier work: GWLF (Generalized Watershed Loading Functions; Haith and Shoemaker 1987), which is a lumped-parameter, watershed-scale hydrology, sediment, and nutrient transport model, and NANI, the accounting methodology for nitrogen inputs to watersheds.
The GWLF model (Haith and Shoemaker (1987; Haith et al. 1996, included in the ReNuMa model package), which simulates monthly and annual streamflow, sediment transport, and associated nutrient fluxes, was designed to be used in mixed-use watersheds (urban, multiple agricultural land uses and forested land use). Daily runoff, groundwater flows, and sediment fluxes are summed over time to estimate monthly and annual fluxes. Runoff from each landuse category is parameterized using a variation of the SCS (Soil Conservation Service) curve number formulation; erosion is generated using the USLE (Universal Soil Loss Equation). The model has been developed on different platforms over the years (Figure 2) and has been revised for some circumstances (Schneiderman et al. 2002). Various versions have been used successfully to estimate nutrient loads into the Delaware River (Haith and Shoemaker 1987), the Tar-Pamlico estuary (Dodd and Tippett 1994), and the Choptank River drainage of Chesapeake Bay (Lee et al. 2000, 2001), and several Pennsylvania watersheds (Evans et al. 2000; Chang 2004). It has proven to be a good estimator of freshwater discharge, sediment and organic carbon load on seasonal and annual time scales in the Hudson River and its tributaries (Howarth et al. 1991; Swaney et al. 1996). The model has also been employed outside the US, including the Kao-Ping watershed of Taiwan (Ning et al. 2003), and several northern Swedish watersheds (Smedberg et al. 2006).
Figure 2. Genealogy of the GWLF family of models. (Hong and Swaney, 2007)
ReNuMa uses a variant of the NANI accounting system (Howarth et al. 1996; Boyer et al. 2002), ie, the net anthropogenic nitrogen inputs across watershed boundaries, as a determinant of nitrogen concentration in runoff and subsurface flows. NANI originally included four general categories: nitrogen content of net food and feed transport across watershed boundaries, atmospheric N deposition, fertilizer applications, and nitrogen fixation within the watershed. The methodology continues to evolve somewhat in response to the availability of datasets and improved data quality. ReNuMa currently uses estimates of human sewage and septic system effluents and manure production instead of net food and feed across boundaries, because this formulation is more consistent with a hydrological model.
Current features of ReNuMa (version 1.0) which distinguish it from the earlier GWLF model include:
- N flux calculations: ReNuMa relates runoff and groundwater N flux concentrations to the NANI accounting system directly. Other N flux calculations newly introduced in ReNuMa include wet and dry N deposition, and denitrification losses (see Section 2 of the user’s manual for detailed descriptions of these processes).
- Excel user interface: ReNuMa, which runs in Microsoft Excel, is implemented as a workbook macro consisting of several worksheets and a pulldown menu (Figure 3). The user can perform all the usual worksheet functions in addition to running the model. Section 4 of the user’s manual includes descriptions of all the worksheets and menu items used in ReNuMa.
- Batch mode simulation: Instead of running the model for one watershed at a time, the user can set up the batch input worksheets and run the simulation for multiple watersheds.
- Land use trajectories: Land use trajectory simulation permits evaluation of the effects of land use change over time in a simple manner. In the current version (1.0) of the model, simple linear changes in land uses are permitted to occur over time. If this option is chosen, instead of specifying a single area for each land use, an initial and final area for each landuse can be specified and the program will interpolate the areas between these limiting beginning and ending land use distributions for each year of the simulation. Properties of each land use category will be used, and thus the relative importance of these properties will vary in each year of the simulation.
- Spin-up simulation: The “spin-up” option is designed to specify the initial conditions for the simulation. The intent of spin-up period is to simulate the model for a specified period before actually starting to produce results for output. The idea is to use an initial period to allow the model to equilibrate, so that initial conditions will not matter so much in the simulation.
- Special purpose simulations: The special purpose simulations include calibration, uncertainty analysis, and sensitivity analysis. When making special purpose simulations, the model will run multiple times with varying parameter values to produce a specific type of analysis useful in analyzing model behavior or fitting the model to observations.
Figure 3. ReNuMa user interface. (Hong and Swaney, 2007)
Coastal ecosystem response model The process-based NPZ model, a result of the second objective, while structurally simple, provides a rich repertoire of responses to variations in residence time, driven by hydrological inputs, and nutrient level, driven primarily by terrestrial nitrogen loads. The model includes three interconnected compartments: nutrients, phytoplankton and zooplankton, which represent a parsimonious, aggregated picture of a coastal ecosystem subject to varying levels of hydrological and nutrient loads (figure 4). By evaluating several levels of loading or varying other processes operating within the model (eg denitrification), the overall hypothetical response of a representative coastal ecosystem can be evaluated and compared to established measures of trophic status (figure 5). Also, sensitivity of the model to change at varying degrees of loading can be assessed; preliminary results suggest strongly nonlinear responses to nutrient loads and sensitivity to residence time (figure 5). The model was presented in poster form at the 2005 Estuarine Research Federation meeting (downloadable at http://www.eeb.cornell.edu/biogeo/epa-star/pubs.htm Exit ) and has been published in the peer-reviewed literature (Swaney et al., in press). Current versions are being adapted to incorporate additional details, including oxygen dynamics and multiple layers.
Figure 4. Structure of the NPZ response model model. (Swaney et al., in press)
Figure 5. Some NPZ model results: Simulated steady-state response of phytoplankton vs. residence time at differing nitrogen loading rates with the assumption of a flushing time-dependent denitrification rate. In this scenario, denitrification suppresses phytoplankton level due to nutrient limitation, especially at longer time scales. Horizontal dotted lines correspond to trophic state boundaries suggested by the National Estuarine Eutrophication Assessment (Bricker et al. 1999): <5, 5-20, 20-60 and >60 μg chl l-1 for low-, medium-, high-, and hyper-eutrophic states. The NPZ model as configured here characterizes phytoplankton biomass in its nitrogen equivalents, which correspond to values of 0.03, 0.12, and 0.37 mg N l-1 for the trophic boundaries listed above. (Swaney et al., in press)
Conclusions:
Through the process of developing the above models, analyzing supporting data, and performing associated statistical analyses, we have drawn several conclusions about the relationship between nitrogen inputs to watersheds and coastal ecosystem dynamics. Primary findings include:
- Net anthropogenic nitrogen inputs to the landscape (NANI) which include atmospheric N deposition, agricultural N fixation, fertilizer application, and the nitrogen content of net food and feed imports to watersheds, are primary determinants of the variation of nitrogen export in rivers over watersheds of the northeastern US and other regions of the world
- The relationship between NANI and riverine N fluxes is strongly governed by climatically-driven factors, primarily streamflow or precipitation and temperature
- At large regional scales, nitrogen retention in watersheds is affected strongly by temperature, probably due to its effect on denitrification
- Coastal ecosystem processes are jointly affected by nutrient loads and residence time, both of which are driven by the magnitude and variation of river flows to the coast. This implies that coastal ecosystems will see significant impacts as a result of ongoing climate and land use changes.
Appendix 1. Quality assurance requirements following 40 C.F.R. 30.54
A. Activities performed
The primary goals of the project were to 1) extend a watershed loading model to the problem of assessing multi-stressor impacts on estuaries as mediated by alterations in estuarine residence time and associated conditions (salinity, nutrient, and turbidity levels and fluctuations) and 2) to examine these impacts using modeling and statistical approaches. As such, the project relied heavily on “secondary data, ” i.e. data previously published in peer-reviewed journals or grey literature, or collected as part of federal, state, or international monitoring and data collection programs, such as meteorological and water quality data. QA/QC of data used in the project was maintained as follows: 1) sources of all data was noted and cited in project documents and publications; 2) non-peer-reviewed publications or databases complied with standard QA procedures. For purposes of analysis, the 95% confidence interval was used as the standard for acceptance or rejection of hypotheses.
B. Study design
Simulations and statistical analyses focused primarily on 16 watersheds from the northeastern US (Boyer et al., 2002), of which 11 or 12 had adequate nitrogen flux data with which to compare to simulation results. Some analyses focused on individual watersheds or subwatersheds, primarily the Susquehanna and upper Susquehanna, when data were available, to take advantage of collaborations with researchers working on projects in the region. Other analyses, performed in collaboration with an international group of colleagues, extended the work beyond US watersheds to include European watersheds in order to evaluate the efficacy and generality of the approach beyond the NE US. Additional data was collected from the peer-reviewed literature as necessary to characterize reasonable parameter ranges for the NPZ model.
C. Procedures for the handling and custody of samples.
No physical, chemical or biological samples were handled during this study, which consists of computer simulation and data analysis as described above.
D. Procedures that will be used in the calibration and performance evaluation of all analytical instrumentation.
No analytical (laboratory or field) instrumentation were used in this study.
E. Procedures for data reduction and reporting.
Statistical and other data analyses beyond model development were conducted using standard, commercial software packages(Statistica, MATLAB, Excel, ARCView) and included conventional statistical analyses (exploratory data analyses, simple and multivariate regression, t-tests, analysis of variance and other statistical methods). Models were developed in Visual Basic (Microsoft, 2002) to facilitate dissemination to users across several different platforms (the models are operable as Excel VBA programs) and to facilitate adaptation to alternate software environments. Model validation used conventional model performance criteria such as Nash-Sutcliffe efficiency, R2, and others described in Reckhow et al. (1990).
F. Quantitative and/or qualitative procedures used to evaluate the success of the project.
See section E. above. Results of this project were published in peer-reviewed journals following standard practice, as well as on the project website, and presented at scientific meetings.
References:
Boyer, E. W., C. L. Goodale, N. A. Jaworski and R. W. Howarth. 2002. Anthropogenic nitrogen sources and relationships to riverine nitrogen export in the northeastern U.S.A. Biogeochemistry 57/58: 137-169.
Chang, H. 2004. Water quality impacts of climate and land use changes in Southeastern Pennsylvania. The Professional Geographer. 56(2): 240-257.
Dai, T., R. L. Wetzel, Tyler R. L. Christensen and E. A. Lewis. 2000. BasinSim 1.0 A Windows-Based Watershed Modeling Package User’s Guide SRAMSOE #362. (Computer program manual). Virginia Institute of Marine Science, School of Marine Science, College of William & Mary, Gloucester Point, VA. Available at http://www.vims.edu/bio/vimsida/basinsim.html.
Dodd, R. C. and J. P. Tippett. 1994. Nutrient Modeling and Management in the Tar-Pamlico River Basin. Research Triangle Institute. Unpublished Report.
Evans, B. M., S. A. Sheeder, and D. W. Lehning. 2003. A Spatial Technique for Estimating Streambank Erosion Based on Watershed Characteristics. J. Spatial Hydrology, Vol. 3, No. 1.
Evans, B. M., D. W. Lehning, K. J. Corradini, G. W. Petersen, E. Nizeyimana, J. M. Hamlett, P. D. Robillard, and R. L. Day. 2002. A Comprehensive GIS-Based Modeling Approach for Predicting Nutrient Loads in Watersheds. J. Spatial Hydrology 2(2): 1-13.
Haith, D. A., R. Mandel and R. S. Wu. 1996. Generalized Watershed Loading Functions. VERSION 2.0 User’s Manual. Cornell University, Ithaca NY (corrected & reprinted: January, 1996).
Haith, D. A., and Shoemaker, L. L. 1987. Generalized watershed loading functions for stream flow nutrients. Water Resources Bulletin 23(3): 471-478.
Howarth, R. W., G. Billen, D. P. Swaney, A. Townsend, N. Jaworski, K. Lajtha, J. A. Downing, R. Elmgren, N. Caraco, T. Jordan, F. Berendse, J. Freney, V. Kudeyarov, P. Murdoch, Zhu Zhao-liang. 1996. Riverine Inputs of Nitrogen to the North Atlantic Ocean: Fluxes and Human Influences. Biogeochemistry 35: 75-139.
Howarth, R. W, J. R. Fruci, and D. Sherman. 1991. Inputs of sediment and carbon to an estuarine ecosystem: Influence of land use. Ecol. Appl. 1: 27-39.
Howarth, R. W., D. P. Swaney, E. W. Boyer, R. M. Marino, N. Jaworski and C. L. Goodale. 2006. The influence of climate on average nitrogen export from large watersheds in the Northeastern United States. Biogeochemistry 79: 163-186.
Lee, Kuang-Yao, T. R. Fisher, T. E. Jordan, D. L. Correll, and D. E. Weller. 2000. Modeling the hydrochemistry of the Choptank River basin using GWLF and Arc/Info: 1. Model calibration and validation. Biogeochemistry 49: 143-173.
Lee, Kuang-Yao, T. R. Fisher, and E. P. Rochelle-Newall. 2001. Modeling the hydrochemistry of the Choptank River basin using GWLF and Arc/Info: 2. Model validation and application. Biogeochemistry 56: 311-348.
Nash, J. E., and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models, I, A discussion of principles. J. Hydrol. 10: 282-290.
Ning, S.-K., K.-Y. Jeng and N.-B. Chang. 2002. Evaluation of non-point sources pollution impacts by integrated 3S information technologies and GWLF modeling. Water Science and Technology 46(6-7): 217-224.
Reckhow, K., J.T. Clemens, and R.C. Dodd. 1990. Statistical evaluation of mechanistic water quality models, J. Env. Eng. ASCE. 116:250-268
Schneiderman, E. M., D. C. Pierson, D. G. Lounsbury, and M. S. Zion. 2002. Modeling the Hydrochemistry of the Cannonsville Watershed with Generalized Watershed Loading Functions (GWLF). Journal of the American Water Resources Association. 38(5): 1323-1347.
Smedberg, E., C.-M. Mörth, D. P. Swaney, C. Humborg. 2006. Modelling hydrology and silicon-carbon interactions in taiga and tundra biomes from a landscape perspective - Implications for global warming feedbacks. Global Biogeochemical Cycles 20, doi:10.1029/2005GB002567.
Swaney, D. P., D. Sherman, and R. W. Howarth. 1996. Modeling Water, Sediment, and Organic Carbon Discharges in the Hudson/Mohawk Basin: Coupling to Terrestrial Sources. Estuaries 19(4): 833-847.
Journal Articles on this Report : 12 Displayed | Download in RIS Format
Other project views: | All 59 publications | 21 publications in selected types | All 12 journal articles |
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Boyer EW, Howarth RW, Galloway JN, Dentener FJ, Green PA, Vorosmarty CJ. Riverine nitrogen export from the continents to the coasts. Global Biogeochemical Cycles 2006;20:GB1S91, doi:10.1029/2005GB002537. |
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David MB, McIsaac GF, Howarth RW, Goodale CL, Drinkwater LE. Fertilizer: complex issue calls for informed debate. Nature 2004;427(6970):99. |
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Filoso S, Martinelli LA, Howarth RW, Boyer EW, Dentener F. Human activities changing the nitrogen cycle in Brazil. Biogeochemistry 2006;79(1-2):61-89. |
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Galloway JN, Aber JD, Erisman JW, Seitzinger SP, Howarth RW, Cowling EB, Cosby BJ. The nitrogen cascade. BioScience 2003;53(4):341-356. |
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Galloway JN, Dentener FJ, Capone DG, Boyer EW, Howarth RW, Seitzinger SP, Asner GP, Cleveland CC, Green PA, Holland EA, Karl DM, Michaels AF, Porter JH, Townsend AR, Voosmarty CJ. Nitrogen cycles: past, present, and future. Biogeochemistry 2004;70(2):153-226. |
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Howarth RW. The development of policy approaches for reducing nitrogen pollution to coastal waters of the USA. Science in China Series C: Life Sciences 2005;48(S1):791-806. |
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Howarth RW, Swaney DP, Boyer EW, Marino R, Jaworski N, Goodale C. The influence of climate on average nitrogen export from large watersheds in the Northeastern United States. Biogeochemistry 2006;79(1-2):163-186. |
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Martinelli LA, Howarth RW, Cuevas E, Filoso S, Austin AT, Donoso L, Huszar V, Keeney D, Lara LL, Llerena C, McIssac G, Medina E, Ortiz-Zayas J, Scavia D, Schindler DW, Soto D, Townsend A. Sources of reactive nitrogen affecting ecosystems in Latin America and the Caribbean: current trends and future perspectives. Biogeochemistry 2006;79(1-2):3-24. |
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Morth C-M, Humborg C, Eriksson H, Danielsson A, Medina MR, Lofgren S, Swaney DP, Rahm L. Modeling riverine nutrient transport to the Baltic Sea: a large-scale approach. Ambio 2007;36(2-3):124-133. |
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Scavia D, Justic D, Bierman Jr. VJ. Reducing hypoxia in the Gulf of Mexico: advice from three models. Estuaries and Coasts 2004;27(3):419-425. |
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Smedberg E, Morth C-M, Swaney DP, Humborg C. Modeling hydrology and silicon-carbon interactions in taiga and tundra biomes from a landscape perspective:implications for global warming feedbacks. Global Biogeochemical Cycles 2006;20:GB2014, doi:10.1029/2005GB002567. |
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Swaney DP, Scavia D, Howarth RW, Marino RM. Estuarine classification and response to nitrogen loading:insights from simple ecological models. Estuarine, Coastal and Shelf Science 2008;77(2):253-263. |
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Supplemental Keywords:
RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, Chemistry, climate change, Air Pollution Effects, Monitoring/Modeling, Regional/Scaling, Environmental Monitoring, Ecological Risk Assessment, Atmosphere, anthropogenic stress, coastal ecosystem, eutrophication, aquatic species vulnerability, biodiversity, environmental measurement, ecosystem assessment, meteorology, climatic influence, global change, anthropogenic, climate models, UV radiation, environmental stress, coastal ecosystems, plankton, ecological models, climate model, nutrient fluxes, Global Climate Change, land use, regional anthropogenic stresses, atmospheric chemistry, stressor response modelProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.