PM2.5 pollution modulates the response of ozone formation to VOC emitted from various sources: Insights from machine learning

Abstract

Numerous studies have linked ozone (O3) production to its precursors and fine particulate matter (PM2.5), while the complex interaction effects of PM2.5 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 PM2.5, VOCs from different sources, NOx, 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 PM2.5 levels, alongside the linear O3-PM2.5 relationship observed, O3 formation is suppressed by PM2.5 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 PM2.5 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 PM2.5, with a focus on industrial and vehicular emissions, and subsequently, fossil fuel combustion once PM2.5 is effectively controlled. This study underscores the potential of the SHAP-based ML approach to decode the intricate O3-NOx-VOCs-PM2.5 interplay, considering both meteorological and atmospheric compositional variations.