Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria

Traditional toxicity testing cannot keep pace with the rapid growth of synthetic chemicals, creating major data gaps that hinder the development of water quality criteria (WQC) for emerging contaminants. This study developed a machine learning model integrating compound- and organism-related features to enable cross-compound and cross-species toxicity prediction. The model demonstrated strong robustness and generalization, outperforming the Interspecies Correlation Estimation application in cross-species prediction, particularly across large taxonomic distances. SHAP analysis identified water solubility and lipophilicity as dominant predictors, with organism-related features also contributing substantially. The model predicted the acute toxicity of 30 representative per- and polyfluoroalkyl substances (PFAS) across 181 aquatic species. Habitat-informed species selection was then used to derive ecologically relevant 5% hazardous concentrations (HC5), which were generally higher in saltwater than in freshwater. Cross-regional comparisons further indicated that salinity may modulate fish sensitivity to PFAS. HC5 estimates for China were higher than those for North America and Europe, potentially reflecting inter-regional differences in species sensitivity, with Chinese species appearing comparatively more tolerant. Finally, site-specific WQC for perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) were derived for the Great Lakes using predicted sensitivities of 76 dominant native species, providing greater ecological relevance than existing criteria.