Revealing the drivers of surface ozone pollution by explainable machine learning and satellite observations in Hangzhou Bay, China

Surface ozone (O3) pollution is an emerging concern in China. Hangzhou Bay (HZB), where the petrochemical industry is clustered, has become one of China's most O3 polluted areas due to exposure to volatile organic compounds (VOCs) emissions and land-sea breezes. It is urgently need to investigate the multiple drivers of surface O3 generation in HZB more specifically. The spatial distribution of O3 trends from April to September (2015–2022) in HZB depicts a general upward trend, with an observed trend of 0.26 μg/m3 a−1, where meteorological factors contribute to 54°% based on the stepwise multiple linear regression (MLR). Ensembled machine learning is more efficient and accurate, especially the Light Gradient Boosting model (LightGBM, R2 = 0.84) outperforms other machine learning algorithms. The Shapley additive explanation (SHAP) technique allows for more in-depth quantification of the contribution of specific factors to O3 trends. The results of the LightGBM-SHAP algorithm present that solar radiation plays a leading role in O3 generation. More importantly, stronger solar radiation can still lead to high O3 concentration accumulation even at lower temperature based on the interaction of SHAP values. For the precursor's emissions, the ratio of formaldehyde-to-NO2 (HCHO/NO2) obtained from the Tropospheric Monitoring Instrument (TROPOMI) satellite observations, shows the study area is located in the VOCs-limited and transitional regimes, highlighting that VOCs control is more cost-effective.