Data- and Model-Based Urban O₃ Responses to NO_x Changes in China and the United States

Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of urban ozone (O₃) responses to nitrogen oxide (NO_x) changes in China and the United States (US) over 2015–2020 by integrating various data- and model-based methods. The data-based deep learning (DL) model exhibited good performance in simulating urban air quality: the correlation coefficients (R) of O₃ daily variabilities with respect to independent O₃ observations are 0.88 and 0.79 over N. China, 0.87 and 0.90 over S. China, and 0.87 and 0.49 over E. United States by the DL and GEOS-Chem chemical transport models, respectively. Furthermore, the data-based methods suggest volatile organic compound (VOC)-limited regimes in urban areas over northern inland China and transitional regimes over eastern US urban areas; in contrast, GEOS-Chem model suggests strong NO_x-limited regimes. 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, while the data-based methods are driven by the variabilities in local O₃ precursor concentrations and meteorological conditions. The O₃ responses to NO_x changes reported here provide a better understanding of urban O₃ pollution; for example, reductions in NO_x emissions are suggested to have resulted in an increase in surface O₃ by approximately 7 ppb in the Sichuan Basin in 2014–2020.