Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning

The science-informed design of 'green' carbonaceous materials (e.g., biochar and activated carbon) with high removal capacity of recalcitrant organic contaminants (e.g., pharmaceuticals and personal care products (PPCPs)) is indispensable for promoting sustainable wastewater treatment. In this study, machine learning (ML) incorporating PPCPs and biochar properties as well as adsorption conditions were applied to build adsorption prediction models and explore the contributions of various biochar's inherent properties to their PPCPs adsorption capacity. The results demonstrated that the models developed by detailed biochar properties (e.g., surface functionality and hierarchical porous structure) from advanced microscopic and spectroscopic techniques were more accurate (i.e., the root-mean-square error decreased by 18-24%) than those by general information such as bulk elemental composition and total pore volume. The relative importance of surface carbon functionalities ranked in the order of C-O bond > C--O bond > non-polar carbon for predicting the adsorption capacity. According to the partial dependence analysis, the average pore diameters of adsorbents that were larger than the maximum diameter of PPCPs molecules by 1.5-fold to 2.5-fold favored the PPCPs adsorption. This study reveals new insights into the adsorption of PPCPs and provides a comprehensive reference for the sustainable engineering of biochar adsorbents for PPCPs wastewater treatment.