Pesticide monitoring data are useful for refining ecological and drinking water exposure assessments. Most monitoring data are not sampled daily, thus a challenge to risk assessors is how to address the concern of using non-daily data to estimate daily and upper-bound concentrations and associated confidence intervals. We developed approaches to estimate these concentrations on non-sampled days from existing monitoring data from more than 100 watersheds over multiple years. We will present a survey sampling approach for inference of upper-bound values; a bias factor (BF) approach to estimate potential upper-bound and daily concentrations from less frequently sampled datasets; and a PRZM-Hybrid modelling approach to complete daily chemo-graphs to estimate sampling gaps between two sampling events. We will demonstrate how less frequent, non-daily monitoring data can be coupled with BF and/or PRZM-Hybrid modelling output to estimate potential maximum shorter duration concentrations and to extend this knowledge to predict exposure to other pesticides and other watersheds with limited monitoring data.

Surface water monitoring and modelling of pesticides: Approaches to estimate upper-bound and daily concentrations from non-daily monitoring

248th National Meeting (2014)

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