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Modeling and Predicting Pesticide Exposures
Citation:
VALLERO, D. A., S. ISUKAPALLI, V. G. ZARTARIAN, T. R. MCCURDY, T. MCKONE, P. Georgopoulos, AND C. C. DARY. Modeling and Predicting Pesticide Exposures. Edition 3, Chapter 44, Robert Krieger (ed.), Hayes Handbook of Pesticide Toxicology. Elsevier Science, New York, NY, 1:995-1020, (2010).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Models provide a means for representing a real system in an understandable way. They take many forms, beginning with conceptual models that explain the way a system works, such as delineation of all the factors and parameters of how a pesticide particle moves in the air after a spraying event. Conceptual models help to identify the major influences on where a chemical is likely to be found in the environment, and as such, need to be developed to help target sources of data needed to assess an environmental problem. In general, developing a model requires two main steps: First, a model of the domain and the processes being studied must be defined. Then, at the model boundaries, a model of the boundary conditions is especially needed to represent the influencing environment surrounding the study domain. Research scientists often develop physical or dynamic models to estimate the location where a chemical would be expected to move under controlled conditions, only on a much smaller scale. For example, the U.S. Environmental Protection Agency (EPA) uses chambers or even full size test homes to model the fate of pesticide after application. Like all models, the dynamic model's accuracy is dictated by the degree to which the actual conditions can be simulated and the quality of the information that is used.