Development of a US national-scale, mixed-source, pesticide, rural well database for use in drinking water risk assessment: an atrazine case study.

For pesticide registrations in the USA under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), as implemented by the United States Environmental Protection Agency, drinking water risk assessments for groundwater sources are based on standard scenario modeling concentration estimates. The conceptual model for the drinking water protection goals is defined in terms of (1) a rural well in or near a relatively high pesticide use area, a shallow well (4-10 m); (2) long-term, single-station weather data; (3) soils characterized as highly leachable; (4) upper-end or surrogate, worst-case environmental fate parameters; and (5) maximum, annual use rates repeated every year. To date, monitoring data have not been quantitatively incorporated into FIFRA drinking water risk assessment; even though considerable, US national-scale temporal and spatial data for some chemistries exists.

Coupling field-scale and watershed models for regulatory modeling of pesticide aquatic exposures in streams.

Estimating exposure in receiving waterbodies is a key step in the regulatory process to evaluate potential ecological risks posed by the use of agricultural pesticides. The United States Environmental Protection Agency (USEPA) currently uses the Variable Volume Water Model (VVWM) to predict environmental concentrations of pesticides in static waterbodies (ponds) that receive edge-of-field runoff inputs from the Pesticide Root Zone Model (PRZM). This regulatory model, however, does not adequately characterize potential pesticide concentrations in flowing water systems (streams and rivers) drained from watershed areas.

Development of a mixed-source, single pesticide database for use in ecological risk assessment: quality control and data standardization practices.

Inclusion of pesticide monitoring data in pesticide risk assessment is important yet challenging for several reasons, including infrequent or irregular data collection, disparate sources procedures and associated monitoring periods, and interpretation of the data itself in a policy context. These challenges alone, left unaddressed, will likely introduce unintentional and unforeseen risk assessment conclusions. While individual water quality monitoring programs report standard operating procedures and quality control practices for their own data, cross-checking data for duplicated data from one database to another does not routinely occur.