Screening estimates of bioaccumulation factors for 4950 per- and polyfluoroalkyl substances in aquatic species.

The considerable variability in bioaccumulation factors (BAFs) of per- and polyfluoroalkyl substances (PFAS) across aquatic species, driven by the diversity of PFAS, complex water conditions, and species differences, underscores the resource-intensive nature of relying on experimental data. To develop a robust and effective approach for predicting BAFs, a predictive framework using a three-level stacking deep ensemble learning model was established. Initially, we compiled a substantial dataset of BAFs, encompassing a wide variety of PFAS across both marine and freshwater species. The stacking model demonstrated strong performance, achieving R-squared (R2) values of 0.94 and 0.89, and root-mean-square errors (RMSE) of 0.88 and 1.17 for training and testing, respectively. External validation revealed that 60 % and 90 % of predictions fell within 2-fold and 4-fold differences, respectively, from the observed values. Using this model, we predicted BAFs for 4950 PFAS in 54 global edible fish species, with the predicted median BAF values ranging from 22 L/kg to 477.09 L/kg. The results indicated that PFAS with multiple functional groups (e.g., benzene rings and ketones) exhibited higher BAFs. Finally, an accessible online tool (https://pfasbaf.hhra.net/) was launched to facilitate BAF predictions. This newly released application promises to offer valuable support for environmental risk management and policymaking efforts.