Deriving the Predicted No Effect Concentrations of 35 Pesticides by the Qsar-Ssd Method

The widespread use of pesticides results in their frequent detection in water bodies and other environmental media. Pesticide residues in water may pose environmental risks, and reliable predicted no effect concentrations (PNEC) must be obtained when the environmental risks of pesticides are assessed. Species sensitivity distribution (SSD) is an important method for the derivation of chemical PNECs. Construction of an SSD model for deriving a regulatory-prescribed PNEC is considered to require sufficient toxicity data to various species including at least eight families in three phyla, suitable nonlinear fitting functions and assessment factors (AFs) that lower the calculated SSD-based PNEC to address uncertainties in the number and quality of the input data. However, most chemicals lack the toxicity data for some species, while some chemicals, although with sufficient toxicity data, cannot find suitable fitting functions, thus hinder the construction of effective SSD models. To this end, the established QSAR models were applied to predict toxicity of chemicals to specific species to fill in the toxicity data gaps required for SSD and selecting multiple nonlinear functions to optimize the SSD model, a new method called QSAR-SSD was developed in this study and successfully used to derive the acute PNECs of 35 pesticides. Three QSAR models were used to predict the toxicities of six pesticides with less data. Nine two-parameter nonlinear functions were used to fit the toxicity-cumulative probability data one by one to determine the optimal SSD models. The hazardous concentrations at the cumulative probability of 5% and 10% of various pesticides (HC5 and HC10) were calculated by the optimal SSD models. It is found that HC10 has a good power function relationship with HC5, so it is recommended using HC10 to derive PNEC, which may be more plausible, especially if the toxicity data is insufficient