Co-Occurring Extremes of PM 2.5 and Ozone in Warm Seasons of the Yangtze River Delta of China: Insights from Explainable Machine Learning

作者：Yan Lyu, Danni Wu, Fuliang Han, Huiying Zhang, Fangfang Lv, Azhen Kang, Yijia Hu, Xinyu Pang · 发表于：ACS ES&T Air · 年份：2025 · DOI：10.1021/acsestair.4c00314 · 被引用次数：5 · 研究领域：Climate variability and models、Air Quality Monitoring and Forecasting、Atmospheric chemistry and aerosols

Recently, summertime PM 2.5 and ozone extremes were reported to frequently co-occur in southern China. In this study, we further demonstrate that their co-occurring extremes can spread into warm seasons in the Yangtze River Delta (YRD) region of China. The annual co-occurrence frequency ranged from 26% to 50% in the YRD from 2015 to 2022, with higher frequencies observed in coastal cities. Notably, the co-occurrence frequency was higher during the COVID-19 pandemic, implying that such co-occurrence may be more spatially widespread with continuous PM 2.5 reduction in China. Taking the pandemic period as an example, we leveraged a machine learning algorithm (i.e., Random Forest) coupled with SHapley Additive ExPlanation (SHAP) to identify higher relative importance of solar radiation-related variables (e.g., surface net solar radiation) during co-occurrence periods compared to non-co-occurrence periods in the YRD. Additionally, incorporating volatile organic compounds (VOCs) measurements, we further showed the higher relative importance of VOCs to the extremes of ozone and PM 2.5 through a case study at Shaoxing (a typical city in the YRD). Overall, the findings highlight the increasing potentials for co-occurring extremes with ongoing PM 2.5 reductions in the YRD and suggest that reducing VOCs (e.g., halocarbons) may help mitigate these extremes in the future.