Validation of the species sensitivity distribution in retrospective risk assessment of herbicides at the river basin scale—the Scheldt river basin case study

Species sensitivity distribution (SSD) is commonly used in prospective risk assessment to derive predicted no-effect concentrations, toxicity exposure ratios, and environmental quality standards for individual chemicals such as pesticides. The application of SSD in the retrospective risk assessment of chemical mixtures at the river basin scale (i.e., the estimation of “multiple substance potentially affected fractions” [msPAFs]) has been suggested, but detailed critical assessment of such an application is missing. The present study investigated the impact of different data validation approaches in a retrospective model case study focused on seven herbicides monitored at the Scheldt river basin (Belgium) between 1998 and 2009. The study demonstrated the successful application of the SSD approach. Relatively high impacts of herbicides on aquatic primary producers were predicted. Often, up to 40 % of the primary producer communities were affected, as predicted by chronic msPAF, and in some cases, the predicted impacts were even more pronounced. The risks posed by the studied herbicides decreased during the 1998–2009 period, along with decreasing concentrations of highly toxic pesticides such as simazine or isoproturon. Various data validation approaches (the removal of duplicate values and outliers, the testing of different exposure durations and purities of studied herbicides, etc.) substantially affected SSD at the level of individual studied compounds. However, the time-consuming validation procedures had only a minor impact on the outcomes of the retrospective risk assessment of herbicide mixtures at the river basin scale. Selection of the appropriate taxonomic group for SSD calculation and selection of the species-specific endpoint (i.e., the most sensitive or average value per species) were the most critical steps affecting the final risk values predicted. The present validation study provides a methodological basis for the practical use of SSD in the retrospective risk assessment of chemical mixtures.