Key toxic components and sources affecting oxidative potential of atmospheric particulate matter using interpretable machine learning: Insights from fog episodes.

Fog significantly affects the air quality and human health. To investigate the health effects and mechanisms of atmospheric fine particulate matter (PM) during fog episodes, PM samples were collected from the coastal suburb of Qingdao during different seasons from 2021 to 2022, with the major chemical composition in PM analyzed. The oxidative potential (OP) of PM was determined using the dithiothreitol (DTT) method. A positive matrix factorization model was adopted for PM. Interpretable machine learning (IML) was used to reveal and quantify the key components and sources affecting OP. PM exhibited higher oxidative toxicity during fog episodes. Water-soluble organic carbon (WSOC), NH, K, and water-soluble Fe positively affected the enhancement of DTT (volume-based DTT activity) during fog episodes. The IML analysis demonstrated that WSOC and K contributed significantly to DTT, with values of 0.31 ± 0.34 and 0.27 ± 0.22 nmol min m, respectively. Regarding the sources, coal combustion and biomass burning contributed significantly to DTT (0.40 ± 0.38 and 0.39 ± 0.36 nmol min m, respectively), indicating the significant influence of combustion-related sources on OP. This study provides new insights into the effects of PM compositions and sources on OP by applying IML models.