Uncertainty analysis in pesticide residue risk assessment in drinking water
Abstract
Pesticide residues in drinking water can vary significantly from day to day. However, water quality monitoring performed under the Safe Drinking Water Act (SDWA) at most community water systems (CWSs) is typically limited to four data points per year over a few years. Due to this limited sampling, likely maximum residues may be underestimated in risk assessment. In this work, a statistical methodology is proposed to study two types of uncertainties in observed samples and their propagated effect in risk estimates. The methodology was demonstrated using data from 16 CWSs that have three independent databases of atrazine residue to estimate the uncertainty of risk in infants and children. The results showed that in 85% of the CWSs, chronic risks predicted with the proposed approach may be two- to four-folds higher than that predicted with the current approach, wheras intermediate risks may be two- to three-folds higher in 50% of the CWSs. In 12% of the CWSs, however, the proposed methodology showed a lower intermediate risk. A closed-form solution of propagated uncertainty was developed to demonstrate the number of years (seasons) of data and sampling frequency needed to reduce the uncertainty of risk estimates. In general, this methodology provided good insight into the importance of addressing uncertainty of observed water quality data and the need to predict likely maximum residues in risk assessment.
