PM_2.5 pollution modulates the response of ozone formation to VOC emitted from various sources: Insights from machine...

Numerous studies have linked ozone (O₃) production to its precursors and fine particulate matter (PM_2.5), while the complex interaction effects of PM_2.5 and volatile organic compounds (VOCs) on O₃ remain poorly understood. A systematic approach based on an interpretable machine learning (ML) model was utilized to evaluate the primary driving factors that impact O₃ and to elucidate how changes in PM_2.5, VOCs from different sources, NO_x, and meteorological conditions either promote or inhibit O₃ formation through their individual and synergistic effects in a tropical coastal city, Haikou, from 2019 to 2020. The results suggest that under low PM_2.5 levels, alongside the linear O₃-PM_2.5 relationship observed, O₃ formation is suppressed by PM_2.5 with higher proportions of traffic-derived aerosol. Vehicle VOC emissions contributed maximally to O₃ formation at midday, despite the lowest concentration. VOCs from fossil fuel combustion and industry emissions, which have opposing effects on O₃, act as inhibitors and promoters by inducing diverse photochemical regimes. As PM_2.5 pollution escalates, the impact of these VOCs reverses, becoming more pronounced in shaping O₃ variation. Sensitivity analysis reveals that the O₃ formation regime is VOC-limited, and effective regional O₃ mitigation requires prioritizing substantial VOC reductions to offset enhanced VOC sensitivity induced by the co-reduction in PM_2.5, with a focus on industrial and vehicular emissions, and subsequently, fossil fuel combustion once PM_2.5 is effectively controlled. This study underscores the potential of the SHAP-based ML approach to decode the intricate O₃-NO_x-VOCs-PM_2.5 interplay, considering both meteorological and atmospheric compositional variations.