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Title: AGROCHEMICAL ENVIRONMENTAL FATE CONCEPTUAL MODELS: TOOLS FOR FOOD AND WATER SAFETY RISK MANAGERS

Author
item Wauchope, Robert - Don

Submitted to: American Chemical Society SE/SW Regional Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 9/7/2004
Publication Date: 11/9/2004
Citation: Wauchope, R.D. 2004. Agrochemical environmental fate conceptual models: tools for food and water safety risk managers. American Chemical Society SE/SW Regional Meeting, Triangle Park, NC, November 9-12, 2004.

Interpretive Summary:

Technical Abstract: Agrochemical risk management is an increasingly complex enterprise. Our knowledge of processes and toxic mechanisms grows and legislation evolves to reflect that knowledge. Risk management involves an analysis and weighing, in an integrated way, all the many processes and exposure/toxicity relationships needed to describe the risks related to an agrochemical introduction into the environment. How and where will the chemical be used? How does it subsequently dissipate in the environment? What are the species, from microbes to humans, potentially effected? What are the processes by which they are exposed? What are the hazard (toxicity) relationships for each species by each route of exposure? This complex weighing process cannot be done without efficient communication between several disciplines including environmental chemistry, toxicology, geosciences and ecology, and these disciplines ordinarily use different languages. Construction of a conceptual model of processes, exposures, and hazards allows for an explicit context within which these disciplines can cooperate. In this presentation current developments of single- and multiple-chemical conceptual models will be described. These models provide an approach to evaluating the potential adverse effects of agrochemicals on environmental biota, and prediction of human exposure via food, water, air and soil. Their potential to be made more analytical'i.e., mathematical'and move in the direction of absolute rather than only relative predictions will be explored.