Discovery of an End-to-End Pattern for Contaminant-Oriented Advanced Oxidation Processes Catalyzed by Biochar with Explainable Machine Learning.

The utilization of biochar-catalyzed peroxymonosulfate in advanced oxidation processes (BC-PMS AOPs) is widely acknowledged as an effective and economical method for mitigating emerging contaminants (ECs). Especially, state-of-the-art machine learning (ML) technology has been employed to accurately predict the reaction rate constants of EC degradation in BC-PMS AOPs, primarily focusing on three aspects: performance prediction, operating condition optimization, and mechanism interpretation. However, its real application in specific degradation optimization targeting different ECs is seldom considered, hindering the realization of contaminant-oriented BC-PMS AOPs. Herein, we propose a hierarchical ML pipeline to achieve an end-to-end (E2E) pattern for addressing this issue. First, the overall XGB model, trained with the comprehensive data set, can perform well in predicting the reaction constants of EC degradation in BC-PMS AOPs, additionally providing the basis for further analysis of various ECs. Then, the submodels trained with different EC clusters can offer specific strategies for the selection of the optimum option for BC-PMS AOPs of specific ECs with different HOMO-LUMO gaps, thus forming an E2E operating pattern for BC-PMS AOPs. This study not only increases our understanding of contaminant-oriented optimization of AOPs but also successfully bridges the gap between ML model development and its environmental application.