Bisphenol A (BPA), representing a class of organic pollutants, finds extensive applications in the pharmaceutical industry. However, its widespread use poses a significant hazard to both ecosystem integrity and human health. Advanced oxidation processes (AOPs) based on peroxymonosulfate (PMS) via heterogeneous catalysts are frequently proposed for treating persistent pollutants. In this study, the degradation performance of BPA in an oxidation system of PMS activated by transition metal sites anchored nitrogen-doped carbonaceous substrate (M-N-C) materials was investigated. As heterogeneous catalysts targeting the activation of peroxymonosulfate (PMS), M-N-C materials emerge as promising contenders poised to overcome the limitations encountered with traditional carbon materials, which often exhibit insufficient activity in the PMS activation process. Nevertheless, the amalgamation of metal sites during the synthesis process presents a formidable challenge to the structural design of M-N-C. Herein, employing ZIF-8 as the precursor of carbonaceous support, metal ions can readily penetrate the cage structure of the substrate, and the N-rich linkers serve as effective ligands for anchoring metal cations, thereby overcoming the awkward limitation. The research results of this study indicate BPA in water matrix can be effectively removed in the M-N-C/PMS system, in which the obtained nitrogen-rich ZIF-8-derived Cu-N-C presented excellent activity and stability on the PMS activation, as well as the outstanding resistance towards the variation of environmental factors. Moreover, the biological toxicity of BPA and its degradation intermediates were investigated via the Toxicity Estimation Software Tool (T.E.S.T.) based on the ECOSAR system.

Pharmaceuticals and personal care products (PPCPs) are continuously introduced into aquatic environments, raising concerns about potential health risks from long-term exposure. Metal-organic frameworks (MOFs), owing to their abundant adsorption sites and tunable pore structures, show considerable potential for PPCP removal. However, experimental screening across diverse adsorbents and pollutants is time-consuming. This study developed a machine-learning framework to predict the adsorption capacity of PPCPs by MOFs at contact time and identify the key factors influencing model predictions. In total, 7324 adsorption records were collected; 7075 records covering 100 MOFs and 39 PPCPs were used for model development and internal evaluation, and 249 additional records were reserved for external validation. MACCS keys, binary Morgan fingerprints (BMF), and count-based Morgan fingerprints (CMF) were applied to encode PPCP structures, and eight machine learning models were systematically compared. Among all model–fingerprint combinations, BMF–CatBoost achieved the best predictive performance, with a test-set R² of 0.9723, and was selected as the optimal model. SHAP and accumulated local effects (ALE) analyses identified key factors and favorable response ranges associated with enhanced adsorption capacity. Overall, this study provides a data-driven methodology for rapid adsorption capacity prediction, key factor identification, and experimental condition optimization in MOF-based PPCP adsorption.