Passage of the Food Quality Protection Act in 1996 has greatly increased the need for data on pesticide concentrations in drinking water. In many parts of the U.S., drinking water is derived wholly or partly from surface water sources. Ideally, potential exposure to pesticides would be assessed by directly measuring pesticide concentrations in these source waters. However, the high cost of monitoring and analysis makes this possible for only a relatively small number of the approximately 7000 such sources nationwide. Thus, there is a need for the development of techniques for estimating pesticide concentrations in surface waters through the use of simulation or empirical models. Data from the U.S. Geological Survey’s NAWQA program were used to develop empirical regression models for prediction of pesticide concentrations in U.S. streams. Annual mean concentrations and specific percentiles of the annual distribution of concentrations were used as the response variables, with pesticide use intensity in the watershed and various watershed characteristics as explanatory variables. Initial work has focused on atrazine and several other widely used herbicides. The models were applied to a subset of U.S. drinking water sources to illustrate the potential use of the models for addressing current risk assessment needs. Preliminary work has been done on extension of the regression methods to additional pesticides and on development of a multi-compound model incorporating pesticide properties. See water.wr.usgs.gov/pnsp/
Learning Objectives: At the conclusion of the session, the particpant will be able to construct regression relations to predict pesticide concentrations in streams.
Keywords: Drinking Water Quality, Pesticide Contamination
Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: U.S. Geological Survey
I have a significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.
Relationship: Employment