标题：Machine learning-based disentanglement of meteorological and anthropogenic drivers of ozone pollution in the Chengdu-Chongqing urban agglomeration

作者：Xiaoyong Liu, Hongrui Li, Wei Wei, Hui Gao, Kai Zhang, Xiangchao Cui

来源出版物：Environmental Monitoring and Assessment

DOI：10.1007/S10661-026-15373-3

出版年：2026

文献类型：Article

语种：英文

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 NO_x-limited regime during 2019–2024. These findings provide support for regional ozone control strategies.

关键词：Chengdu-Chongqing region;Ozone; Anthropogenic emissions; Meteorological conditions; Machine learning

影响因子：3.6

论文链接：https://doi.org/ 10.1007/S10661-026-15373-3

（地理科学学院 刘媛心/撰稿 崔向超/初审 闫军辉/复审 韩勇/终审）