In recent years, the co-pollution of surface ozone (O3) and fine particulate matter (PM2.5) has emerged as a critical concern within specific regions of China's atmospheric environment. This study employed a comprehensive approach by integrating statistical analysis with the interpretable ensemble machine learning model. Delving deeply into the intricate mechanisms behind O3 and PM2.5 co-pollution in Lanzhou city from 2019 to 2022, the research synthesized and analyzed an array of data sources, including ground observations, a multi-parameter lidar system, and meteorological data. Our findings, derived from ground observations to vertical distribution, unequivocally confirm that the enhancement of atmospheric oxidation capacity serves as a critical driver in the genesis of secondary particles, playing a substantial role in the augmented levels of O3 and PM2.5 experienced during the warm season. Moreover, the impact of local weather patterns is indispensable as it precipitates a relatively stable mid-level atmosphere, culminating in elevated surface concentrations of both PM2.5 and O3. Overall, this study emphatically underscores the importance of adopting a comprehensive approach to address these environmental challenges.
Keywords: Atmospheric oxidation; Interpretable ensemble machine learning model; Local weather patterns; O(3)/PM(2.5) co-pollution; Vertical distribution.
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