Using precise data sets on farming and pesticide properties to verify a diffuse pollution hydrological model for predicting pesticide concentration

Selecting pesticides for inclusion in drinking water quality guidelines on the basis of detection probability and ranking

Pesticides released into the environment may pose both ecological and human health risks. Governments set the regulations and guidelines for the allowable levels of active components of pesticides in various exposure sources, including drinking water. Several pesticide risk indicators have been developed using various methodologies, but such indicators are seldom used for the selection of pesticides to be included in national regulations and guidelines. The aim of the current study was to use risk indicators for the selection of pesticides to be included in regulations and guidelines. Twenty-four risk indicators were created, and a detection rate was defined to judge which indicators were the best for selection. The combination of two indicators (local sales of a pesticide for the purposes of either rice farming or other farming, divided by the guideline value and annual precipitation, and amended with the scores from the physical and chemical properties of the pesticide) gave the highest detection rates. In this case study, this procedure was used to evaluate 134 pesticides that are currently unregulated in the Japanese Drinking Water Quality Guidelines, from which 44 were selected as pesticides to be added to the primary group in the guidelines. The detection probability of the 44 pesticides was more than 72%. Among the 102 pesticides currently in the primary group, 17 were selected for withdrawal from the group.

Selecting analytical target pesticides in monitoring: Sensitivity analysis and scoring

Measuring river water concentrations of all pesticides applied in a catchment area is a daunting task. This study aims to develop new score tables for selecting analytical target pesticides. Sensitivity analyses were conducted using a diffuse pollution hydrologic model to quantitatively evaluate the influence of pesticide properties (e.g., log K(OC), degradability [half-life]) on concentrations of rice-farming pesticides in river water. Using the results of the analyses, score tables were systematically designed for the pesticide properties such that the sum of the scores for a particular pesticide, designated as the contamination index, was proportional to the expected/predicted concentration of that pesticide in river water. The contamination indexes for pesticides applied in three river basins were calculated and compared with the corresponding observed pesticide concentrations. Correlations between contamination indexes and observed concentrations were fairly good. Pesticides were ranked according to the quotients obtained by dividing the pesticide concentrations predicted from the contamination indexes by the corresponding drinking-water quality guideline values, and pesticide candidates to be monitored were successfully selected on the basis of a threshold quotient.