Update of vegetative filter strip scenarios for pesticide risk assessment in Europe

Environmental risk assessment procedures for the authorization of pesticides at European Union (EU) and national level must keep pace with scientific progress and must be updated regularly. The standardized FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) approach for pesticide exposure assessment in surface waters consists of four steps. Mitigation measures for spray drift and surface runoff (vegetative filter strips, VFS) can be assessed in Step 4. The SWAN (Surface Water Assessment eNabler) tool for higher-tier exposure assessment implements fixed VFS efficiencies according to the FOCUS Landscape and Mitigation report. Since 2012, SWAN has also included the option to apply runoff mitigation using the mechanistic, event-based model VFSMOD. Since then, substantial scientific efforts have been made to further improve VFSMOD and its underlying assumptions and process descriptions. Moreover, new EU soil profile and land cover data have become available. The objective of this study was therefore to derive updated SWAN-VFSMOD scenarios for Europe. VFSMOD was run for 1897 combinations of FOCUS scenario and soil profile, two rainfall/runoff events, two example compounds, a VFS length of 10 m and two different lower boundary options. Spatial cumulative distribution functions (CDFs) of pesticide load reduction efficiency ∆P were subsequently calculated for each scenario. The four CDFs corresponding to the most relevant input factor combination were selected. In each case, the soil profile corresponding to the 10th percentile ∆P was used for parameterizing VFSMOD in SWAN. The new SWAN-VFSMOD scenarios were found to be more conservative than the previous ones. The largest proportion of the observed decrease in ∆P was due to the replacement of the pesticide trapping equation. Compared with the current standard fixed efficiency approach, the new approach yielded predominantly less conservative, but occasionally more conservative ∆P and exposure estimates, which reflects an increase in realism and accuracy.