Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China

Megacity Hangzhou, located in eastern China, has experienced severe O3 pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O3 deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O3 formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021–2022. The EML model achieved better performance than the single model, with R2 values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O3 variability with the contribution of 57 %–60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O3 formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM2.5), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM2.5 and NOx were also important and increased with the O3 deterioration. The impact of seven emission sources on O3 formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O3 pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O3 concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O3 pollution at different event scales, supporting the formulation of control measures. [Display omitted] •The overall performance of ensemble model outperforms the single model.•EML-SHAP can rapidly quantify key drivers on O3 for different time scale.•UV Radiation and relative humidity were main drivers for O3 formation.•The effect of vehicle exhaust was main but biogenic emission increased largely with elevated O3 levels.