Eight emerging and six traditional organophosphate esters (OPEs) were investigated in office air from Hangzhou China with all the traditional OPEs and 5 out of 8 emerging OPEs detected. The median concentrations of ∑_traditional OPEs and ∑_emerging OPEs were 61,200 and 5.81 pg/m³, respectively. Butylphenyl diphenyl phosphate (BPDPP) and trisisopropyltrisphenyl phosphate (TIPPP) were observed for the first time in indoor air and Chinese office, respectively. The levels of ∑_traditional OPEs decreased in the following order: summer > autumn > spring > winter. Conversely, no obvious trends were observed for emerging OPEs. ∑_traditional OPEs (p < 0.001) and tri(chloroisopropyl) phosphate (TCIPP) (p < 0.01) concentrations were positively correlated with temperature. Interestingly, ∑_emerging OPEs and the individual emerging OPEs analytes had no significant correlations with temperature (p > 0.05). ∑_traditional OPEs, tris(2-chloroethyl) phosphate (TCEP) and TCIPP levels were significantly positively correlated with relative humidity (p < 0.05), while ∑_emerging OPEs levels were negatively correlated with relative humidity (p < 0.05). Median intakes of traditional OPEs were estimated to be several orders of magnitude lower than the corresponding Reference Dose (RfD) values. The distinct environmental behaviors of emerging OPEs compared to traditional OPEs, coupled with the lack of established RfD values for them, underscore the need for their risk evaluations in future.

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.