Data‐ and Model‐Based Urban O<sub>3</sub> Responses to NO<sub>x</sub> Changes in China and the United States
Xiaokang Chen, Min Wang, Tai‐Long He, Zhe Jiang, Yuqiang Zhang, Li Zhou, Jane Liu, Hong Liao, H. M. Worden, Dylan B. A. Jones, Dongyang Chen, Qinwen Tan, Yanan Shen
Abstract
Abstract Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of urban ozone (O 3 ) 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 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 NO x ‐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. The O 3 responses to NO x changes reported here provide a better understanding of urban O 3 pollution; for example, reductions in NO x emissions are suggested to have resulted in an increase in surface O 3 by approximately 7 ppb in the Sichuan Basin in 2014–2020.