Characteristics of secondary inorganic aerosols and contributions to PM pollution based on machine learning approach in Shandong Province.

Primary emissions of particulate matter and gaseous pollutants, such as SO and NO have decreased in China following the implementation of a series of policies by the Chinese government to address air pollution. However, controlling secondary inorganic aerosol pollution requires attention. This study examined the characteristics of the secondary conversion of nitrate (NO) and sulfate (SO) in three coastal cities of Shandong Province, namely Binzhou (BZ), Dongying (DY), and Weifang (WF), and an inland city, Jinan (JN), during December 2021. Furthermore, the Shapley Additive Explanation (SHAP), an interpretable attribution technique, was adopted to accurately calculate the contributions of secondary formations to PM The nitrogen oxidation rate exhibited a significant dependence on the concentration of O. High humidity facilitates sulfur oxidation. Compared to BZ, DY, and WF, the secondary conversion of NO and SO was more intense in JN. The light-gradient boosting model outperformed the random forest and extreme-gradient boosting models, achieving a mean R value of 0.92. PM pollution events in BZ, DY, and WF were primarily attributable to biomass burning, whereas pollution in Jinan was contributed by the secondary formation of NO and vehicle emissions. Machine learning and the SHAP interpretable attribution technique offer a precise analysis of the causes of air pollution, showing high potential for addressing environmental concerns.