Using machine learning to unravel chemical and meteorological effects on ground-level ozone: Insights for ozone-climate control strategies

In the context of climate change, various countries/regions across East Asia have witnessed severe ground-level ozone (O₃) pollution, which poses potential health risks to the public. The complex relationships between O₃ and its drivers, including the precursors and meteorological variables, are not yet fully understood. Revealing the impact of multiple drivers on O₃ is crucial for providing evidence-based information for pollution control. In the present study, we evaluated the influence of key chemical-aerosol (e.g., volatile organic compounds, PM_2.5, NO_x) and meteorological drivers (e.g., air temperature, relative humidity) on ground-level O₃ pollution at Tucheng site in New Taipei, Northern Taiwan, using fine-resolution atmospheric composition measurements and machine learning. The developed random forest machine learning models performed well, with 10-fold cross-validation R² values above 0.867. The results reveal seasonal disparities on chemical and meteorological effects on ground-level O₃ between winter and summer. Aggregated SHAP values indicated that chemical (e.g., NO_x and VOCs) and aerosol variables (i.e., PM_2.5) accounted for 82.4 % of the explained variance in winter O₃ predictions and 62.1 % in summer. Meteorological variables (e.g., temperature, relative humidity) contributed the remaining variance, highlighting seasonally shifting sensitivities. Across seasons, temperature, 1,2,3-Trimethylbenzene, NO_x, t-2-Butene, and relative humidity were identified as the dominant drivers of ground-level O₃ predictions, reflecting their modelled associations with elevated O₃ concentrations. The machine learning-based modelling framework developed in this study can be easily adapted to new sampling sites with minor modifications if necessary.