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

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 (O3) 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 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 NOx‐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 NOx changes reported here provide a better understanding of urban O3 pollution; for example, reductions in NOx emissions are suggested to have resulted in an increase in surface O3 by approximately 7 ppb in the Sichuan Basin in 2014–2020.

The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe

“…In this study, we explore the capability of DL to make O 3 predictions across different spatial and temporal domains using a model with integrated CNNs and LSTM neural networks. The architecture of this DL model has been applied in recent studies to investigate the changes in CO, nitrogen oxide (NO x ) and O 3 (Chen et al., 2023; Han et al., 2022; He et al., 2022a). The DL model was trained and validated with surface O 3 observations in China and the US in 2015–2018.…”

“…Recent studies demonstrated applications of data‐driven techniques to provide air quality forecasts (Bi et al., 2022; Zhang et al., 2023), spatial extensions of atmospheric observations (Liu et al., 2022; Wei et al., 2023), more accurate or rapid CTM simulations (Shen et al., 2022; Xing et al., 2020), and atmospheric pollutant emission estimates (He et al., 2022b; Xing et al., 2022). Furthermore, recent studies have highlighted the importance of data‐driven techniques to provide a better understanding of atmospheric ozone (O 3 ), for example, the contributions of meteorological and anthropogenic sources to observed O 3 changes (Chen et al., 2023; Wang et al., 2023; Weng et al., 2022).…”