Recent advances in mechanistic modeling of vegetated filter strips (VFS) have made it possible to incorporate VFS mitigation into environmental exposure assessments (EEAs). However, outside of fixed efficiency approaches, there are no widely adopted and standardized procedures for incorporating VFS quantitative mitigation into long-term, higher-tier EEAs. A source of hesitation involves the use of empirical equations for predicting pesticide trapping by the VFS. A recent study evaluated existing empirical equations and a mechanistic mass-balance approach using the most extensive field database available of VFS pesticide efficiency from single-event storms. That study concluded that an updated empirical equation (Sabbagh equation) and a mechanistic mass-balance approach performed reasonably well. The objective of this research was to study the effect of upscaling the VFS trapping equations from single events into long-term EEAs. The U.S. EPA Pesticide in Water Calculator (PWC) model linked with the Vegetative Filter Strip MODeling system (VFSMOD) long-term EEA modeling framework (30 yr) was updated to incorporate the alternative trapping equations and tested VFS mitigation results under contrasting agroecological settings with varying erosion/sediment transport conditions. Differences in both acute and chronic 90th percentile estimated environmental exposure concentrations (EECs) were relatively small when comparing predictions using the four pesticide trapping equations. A global sensitivity analysis (GSA) also indicated that selection of a specific trapping equation for predicting EECs was less important than other important input factors such as the VFS length and pesticide properties. However, in terms of the percent reductions in EECs, the choice of pesticide trapping equation was as important as the VFS length. This research builds upon the conclusion of previous single-event studies that the mechanistic mass-balance and refit Sabbagh empirical equation were both valid for EEAs. The mass balance approach represents a reasonable option for regulatory agencies that prefer mechanistic approaches.

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http://dx.doi.org/10.1016/j.watres.2019.114983DOI Listing

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