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PROBABILISTIC AQUATIC EXPOSURE ASSESSMENT FOR PESTICIDES 1: FOUNDATIONS
Citation:
Burns, L A. PROBABILISTIC AQUATIC EXPOSURE ASSESSMENT FOR PESTICIDES 1: FOUNDATIONS. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-01/071 (NTIS PB2003-106605), 2001.
Impact/Purpose:
Extend existing model technologies to accommodate the full range of transport, fate and food chain contamination pathways, and their biogeographical variants, present in agricultural landscapes and watersheds. Assemble the range of datasets needed to execute risk assessments with appropriate geographic specificity in support of pesticide safety evaluations. Develop software integration technologies, user interfaces, and reporting capabilities for direct application to the EPA risk assessment paradigm in a statistical and probabilistic decision framework.
Description:
Models that capture underlying mechanisms and processes are necessary for reliable extrapolation of laboratory chemical data to field conditions. For validation, these models require a major revision of the conventional model testing paradigm to better recognize the conflict between model user's and model developer's risk (as Type I and Type II errors) in statistical testing of model predictions. The predictive reliability of the models must be hypothesized and tested by methods that lead to conclusions of the form the model predictions are within a factor-of-two of reality at least 95% of the time. Once predictive reliability is established, it can be treated as a method error within a probabilistic risk assessment framework. This report, developed under APM 131 (Develop a Probability-Based Methodology for Conducting Regional Aquatic Ecosystem Exposure and Vulnerability Assessments for Pesticides), describes a step-by-step process for establishing the predictive reliability of exposure models. Monte Carlo simulation is the preferred method for capturing variability in environmental driving forces and uncertainty in chemical measurements. Latin Hypercube Sampling (LHS) software is under development to promote efficient computer simulation studies and production of tabular and graphical outputs. Desirable outputs include exposure metrics tailored to available toxicological data expressed as distribution functions (pdf, cdf) and, if needed, empirical distribution functions suitable for use in Monte Carlo risk assessments combining exposure and effects distributions. ORD numerical models for pesticide exposure supported under this research program include a model of spray drift (AgDisp), a cropland pesticide persistence model (PRZM), a surface water exposure model (EXAMS), and a model of fish bioaccumulation (BASS). A unified climatological database for these models is being assembled by combining two National Weather Service (NWS) products: the Solar and Meteorological Surface Observation Network (SAMSON) data for 1961-1990, and the Hourly United States Weather Observations (HUSWO) data for 1990-1995. Together these NWS products provide coordinated access to solar radiation, sky cover, temperature, relative humidity, station atmospheric pressure, wind direction and speed, and precipitation. By using observational data for the models, trace-matching Monte Carlo simulation studies can transmit the effects of environmental variability directly to exposure metrics, by-passing issues of correlation (covariance) among external driving forces. Additional datasets in preparation include soils and land-use (planted crops) data summarized for the State divisions of Major Land Resource Areas (MLRA), derived from National Resource Inventory (NRI) studies.