Data- and model-based urban O3 responses to NOx changes in China and the United States

Here we present a comparative analysis of urban ozone (O-3) responses to nitrogen oxide (NOx) 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 O-3 daily variabilities with respect to independent O-3 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 NOx-limited regimes. Sensitivity analysis indicates that the inconsistent O-3 responses are partially caused by the inaccurate representation of O-3 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 O-3 precursor concentrations and meteorological conditions.