Deep learning-derived anthropogenic and meteorological drivers of surface ozone change in China

Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of the anthropogenic and meteorological drivers of surface ozone (O₃) change in China by integrating deep learning (DL) and chemical transport model (CTM) methods. The DL method suggests volatile organic compound (VOC)-limited regimes in urban areas over northern inland China in contrast to strong nitrogen oxides (NO_x)-limited regimes in GEOS-Chem simulations. Sensitivity analysis indicates that the inconsistent O₃ responses are partially caused by the inaccurate representation of O₃ precursor concentrations at the locations of urban air quality stations in the simulations. The DL method exhibits possible weakened anthropogenic contributions to surface O₃ rise in the North China Plain, for example, 1.53 and 0.54 ppb/y in 2015-2019 and 2019-2021, respectively. Similarly, GEOS-Chem simulations suggest an accelerated decrease in surface O₃ concentrations driven by the decline in nitrogen dioxide (NO₂) concentrations. Furthermore, both DL and GEOS-Chem models suggest the reverse of meteorological contributions to the observed O₃ change in the North China Plain in 2019-2021, which is mainly resulted from the reversed changes in meteorological variables in surface air temperature and relative humidity. This work highlights the importance of DL as a supplement to CTM-based analysis. The derived O₃ drivers are helpful for making effective regulatory policies to control O₃ pollution in China.