Machine learning-based disentanglement of meteorological and anthropogenic drivers of ozone pollution in the Chengdu-Chongqing urban agglomeration
摘要
To clarify the relative contributions of meteorological conditions and anthropogenic activities to ozone (O3) pollution in the Chengdu–Chongqing urban agglomeration, this study systematically quantified their impacts on the daily maximum 8-h average ozone concentration (O3-8 h) during 2015–2024. Using ground-based observations and ERA5-Land reanalysis data, we combined the Light Gradient Boosting Machine (LightGBM) machine learning model, the SHAP (SHapley Additive exPlanations) interpretability framework, a meteorological normalization approach, and controlled-variable perturbation experiments to investigate the drivers of O₃ pollution and compare the differences among interpretative methods. The results showed that the LightGBM model reproduced O₃-8 h well, with a cross-validated R2 of 0.88. Meteorological normalization analysis indicated that meteorological factors dominated the interannual variability of O3-8 h, with an average contribution of 70.5%, while anthropogenic emissions contributed 29.5%. Meteorological conditions generally suppressed O3 formation during 2015–2018 but shifted to a promoting effect after 2019, with a markedly enhanced influence during 2022–2024. SHAP analysis identified surface solar radiation (ssrd), 2-m air temperature (t2m), and soil moisture (swvl1) as the key meteorological drivers, with contribution rates of 34.0%, 16.4%, and 6.0%, respectively. In contrast, controlled-variable perturbation experiments showed that O3-8 h was most sensitive to temperature, whereas its response to solar radiation was weaker and nonlinear. In addition, satellite-based formaldehyde-to-nitrogen dioxide ratio analysis indicated a shift in ozone formation sensitivity from a VOCs-limited regime toward a NOx-limited regime during 2019–2024. These findings provide support for regional ozone control strategies.