Source-Specific Oxidative Potential of PM2.5 in Xi'an: Roles of Water-Soluble Metals Revealed by DTT Assay and Interpretable Machine Learning.

Oxidative stress is a central mechanism underlying the toxicity of fine particulate matter (PM2.5); however, the source-specific chemical drivers of particle-associated oxidative potential remain incompletely understood. In this study, the oxidative potential (OP) of ambient PM2.5 in Xi'an was investigated during winter and summer using the dithiothreitol (DTT) assay, with particular emphasis on the toxicological roles of water-soluble metals and emission sources. PM2.5 exhibited significantly higher volume-normalized OP (DTTv) in winter, indicating an enhanced particle-associated oxidative stress burden during the heating period. Notably, although water-soluble metals accounted for only a minor fraction of PM2.5 mass, interpretable machine learning analysis (XGBoost-SHAP) identified potassium and manganese as dominant contributors to OP, highlighting the importance of biomass burning tracers and redox-active transition metals in particle-mediated reactive oxygen species generation. Source apportionment further revealed pronounced seasonal contrasts: dust sources contributed substantially to wintertime OP primarily due to their large mass loading, whereas traffic-related emissions dominated OP in summer owing to their high intrinsic oxidative toxicity. Overall, these findings suggest that variations in PM2.5 oxidative potential are more closely associated with chemical composition and source-specific oxidative activity than with particle mass alone, providing additional insight into the factors influencing PM-related health risks.