Abstract:
Release of emerging pollutants such as pesticides, phthalates, and substituted phenols and anilines is detrimental threat for the aquatic environment. Registration, Evaluation, Authorization and Restriction of CHemicals (REACH) regulation requires algal toxicity data for regulatory risk assessment purposes. Quantitative Structure–Toxicity Relationships (QSTRs) are well accepted tools for data gap-filling. Therefore, studying the toxic effects of chemicals on algae via experimental and in silico methods would provide invaluable information for the chemicals with no toxicity data; and the knowledge gained through this study forms a scientific basis towards the protection of aquatic ecosystems. In the present study, the 96-h algal toxicity tests were performed with nitro-, chloro-, methoxy-, and methyl- substituted phenols and anilines to Chlorella vulgaris. Merging these data with the previously reported toxicity data of our laboratory enabled a high quality single source algal toxicity data for toxicity modeling. Consequently, models for the prediction of acute toxicity and low-toxic-effect concentrations were developed and verified based on the principles of OECD. Interspecies models were also developed using algae-algae and algae ciliate toxicity data. Developed models displayed decent predictivity and have a high potential to assess the toxicity of untested phenols and anilines on C. vulgaris within the applicability domain of models.
