Abstract

Clarifying the driving forces of O3 and fine particulate matter (PM2.5) co-pollution is important to perform their synergistic control. This work investigated the co-pollution of O3 and PM2.5 in Hainan Province using an observation-based model and explainable machine learning. The O3 and PM2.5 pollution that occurs in winter is affected by the wintertime East Asian Monsoon. The O3 formation shifts from a NOx-limited regime with a low O3 production rate (PO3) in the non-pollution season to a transition regime with a high PO3 in the pollution season due to an increase in NOx concentrations. Increased O3 and atmospheric oxidation capacity promote the conversion from gas-phase precursors to aerosols. Meanwhile, the high concentration of particulate nitrate favors HONO formation via photolysis, in turn facilitating O3 production. Machine learning reveals that NOx promotes O3 and PM2.5 co-pollution during the pollution period. The PO3 shows an upward trend at the observation site from 2018 to 2022 due to the inappropriate reduction of volatile organic compounds (VOCs) and NOx in the upwind areas. Our results suggest that a deep reduction of NOx should benefit both O3 and PM2.5 pollution control in Hainan and bring new insights into improving air quality in other regions of China in the future.

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