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Air pollution exposure is closely linked to population age and socioeconomic status. Population aging and imbalance in regional economy are thus anticipated to have important implications on ozone (O3)‐related health impacts. Here we provide a driver analysis for O3 mortality burden due to respiratory disease in China over 2013–2050 driven by population aging and regional inequity. Unexpectedly, we find that population aging is estimated to result in dramatic rises in annual O3 mortality burden in China; by 56, 101–137, and 298–485 thousand over the periods 2013–2020, 2020–2030, and 2030–2050, respectively. This reflects the exponential rise in baseline mortality rates with increasing age. The aging‐induced mortality burden rise in 2030–2050 is surprisingly large, as it is comparable to the net national mortality burden due to O3 exposure in 2030 (359–399 thousand yr−1). The health impacts of O3 pollution, shown as mortality burden per capita, are inequitably distributed, with more severe effects in less developed provinces than their developed counterparts by 23.1% and 21.5% in 2019 and 2030, respectively. However, the regional inequity in O3 mortality burden is expected to be mitigated in 2050. This temporal variation reflects evolving demographic dividend characterized by a larger proportion of younger individuals in developed regions. These findings are critical for targeted improvement of healthcare services to ensure the sustainability of social development. Plain Language Summary: Air pollution exposure is closely linked to the age distribution and socioeconomic status of the population. The ongoing process of population aging and the pronounced disparities in regional economic growth across China are thus anticipated to have substantial implications for the health effects stemming from ozone (O3) exposure. Our finding reveals that population aging is projected to lead to a significant upsurge in annual O3 mortality burden; the health impacts of O3 pollution, measured in terms of mortality burden per capita, exhibit significant disparities, with more severe effects in less developed provinces than their developed counterparts. These results hold critical implications for the targeted improvement of healthcare services, ensuring the sustainable development of society in China over the coming decades. Key Points: Ozone‐related mortality burden are increasing in the future except for the SSP1 scenarioThe mortality burden increases were mainly driven by the population aging in ChinaThe O3 mortality burden per capita, are inequitably distributed, with more severe effects in developed regions [ABSTRACT FROM AUTHOR]

Data‐driven methods have been extensively applied to predict atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O3) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O3 observations in China and the US in 2015–2018. The DL model was applied to predict hourly surface O3 over three continents in 2015–2022. Compared to baseline simulations using GEOS‐Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 μg/m3 with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 μg/m3 with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015–2018 and 2019–2022, respectively. The comparable performances between DL and GC indicate the potential of DL to make reliable predictions over spatial and temporal domains where a wealth of local observations for training is not available. Plain Language Summary: Machine learning techniques have been extensively applied in the field of atmospheric science. It provides an efficient way of integrating data and predicting atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O3) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O3 observations in China and the US in 2015–2018. We then applied the DL model to predict surface O3 concentrations in China, the US and Europe in 2015–2022. Our analysis exhibits comparable performances between DL and chemical transport models for surface O3 predictions in Europe. This indicates the potential of DL models to extend predictions across spatial and temporal domains. Key Points: Deep learning exhibits acceptable capabilities of spatial and temporal extrapolations for surface O3 predictionsComparable performances between deep learning and GEOS‐Chem models with respect to independent O3 observationsGood performance of deep learning for rapid hourly O3 predictions [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Ozone (O3) at the surface level is an important pollutant with complicated photochemistry. Here we analyze the responses of urban O3 concentrations to nitrogen oxide (NOx) changes in China and the United States (US) over 2015–2020. The data‐based methods suggest that NOx emission declines have resulted in increases in urban O3 concentrations over northern inland China, while urban O3 concentrations over eastern US are less sensitive to NOx changes. In contrast, GEOS‐Chem simulation suggests that NOx emission declines can result in dramatic decreases in urban O3 concentrations over both China and the US. The derived O3 responses in this work are helpful for making effective regulatory policies to control urban O3 pollution. Key Points: Data‐based methods suggest VOC‐limited regimes over northern inland China and transitional regimes over eastern United States urban areasGEOS‐Chem simulation suggests strong NOx‐limited regimesData‐based methods have the potential to provide new insights for air quality regulations as a supplement to Chemical transport models [ABSTRACT FROM AUTHOR]

Observational data indicate increasing trends of surface ozone (O3) in China, despite emission controls that have resulted in reductions of precursor emissions. Here, we explore the cause of this contradiction, through analysis of surface observations (2014–2019) in China and historical observation record in the United States (US, 1990–2019). Our observation‐based analysis indicates that the reductions of nitrogen oxides (NOx) emissions led to increase of surface O3 in North China Plain (NCP) and Yangtze River Delta (YRD) of around 8 ppb. However, NOx controls resulted in shift of O3 chemical regimes over NCP and YRD, with turning points between NOx‐ and volatile organic compound (VOC)‐limited regimes around 2019, while model simulations suggest transitional or NOx‐limited regimes over the rest of China. The impacts of high fine particles (PM2.5) on O3 formation has declined because of the reduction of PM2.5 concentrations. Stricter NOx controls can mitigate O3 pollutions over industrialized areas in China. Plain Language Summary: Ozone (O3) in surface air is an important pollutant with adverse effects on human health and vegetation growth. Here, we explore the sustainable pathway to control O3 pollution in China through analysis of observations in China (2014–2019) and the United States (US, 1990–2019). We find that the reductions of nitrogen oxides (NOx) emissions have led to increase of surface O3 in North China Plain (NCP) and Yangtze River Delta (YRD) by about 8 ppb. However, the severe O3 pollution in China can be mitigated because NOx controls led to the shift of O3 chemical regimes over NCP and YRD to transitional regime around 2019. In addition, the impacts of high fine particles (PM2.5) on O3 formation has declined because of the reduction of PM2.5 concentrations. Key Points: Reductions of nitrogen oxides (NOx) led to increase of surface ozone (O3) in North China Plain and Yangtze River DeltaShift of O3 chemical regimes with turning points between NOx‐ and volatile organic compound‐limited regimes around 2019The impacts of high fine particles (PM2.5) on O3 formation has declined with reduction of PM2.5 concentrations [ABSTRACT FROM AUTHOR]