Articles | Volume 26, issue 2
https://doi.org/10.5194/acp-26-851-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-851-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jan 2026
Research article |
| 19 Jan 2026
| 19 Jan 2026
Tracking surface ozone responses to clean air actions under a warming climate in China using machine learning
Jie Fang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Didier Hauglustaine
Laboratoire des Sciences du Climat et de l'Environnement, CNRS-CEA-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Ming Wang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jingyi Li
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Haiwei Li
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Junfeng Wang
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yun Wu
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
Mindong Chen
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Xinlei Ge
School of Energy and Environment, Southeast University, Nanjing 210096, China
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Cited
11 citations as recorded by crossref.
- Innovative synergy coefficient revealed the negative PM2.5–O3 synergistic pollution in China over the past decade L. Zhao et al. https://doi.org/10.1016/j.jclepro.2026.149510
- Meteorological Drivers and Chemical Evolution of Ozone Pollution in the North China Plain H. Huo et al. https://doi.org/10.3390/atmos17080789
- Long-term ozone formation sensitivity in China: spatiotemporal evolution and machine learning attribution J. Lin et al. https://doi.org/10.5194/acp-26-7081-2026
- From numerical advection schemes to hybrid Eulerian– artificial intelligence models: Application to atmospheric dispersion across turbulence regimes A. Ajdour et al. https://doi.org/10.1016/j.ijheatfluidflow.2026.110630
- Divergent diurnal dynamics and regional applicability of GEMS-based ozone sensitivity indicators (FNR vs. GNR) across Central and Eastern China J. Wang et al. https://doi.org/10.1016/j.jhazmat.2026.143650
- Drivers of interannual variability in autumn ozone and its precursors in Shanghai during 2018–2023 X. Chen et al. https://doi.org/10.1016/j.atmosenv.2026.122405
- Meteorological normalization of surface ozone variability across warm and cold seasons in China during 2015–2024 Z. Ye et al. https://doi.org/10.5194/acp-26-15291-2026
- Changes in Urban–Nonurban Ozone Disparities across China during 2000–2024 L. Guo et al. https://doi.org/10.1021/acs.estlett.6c00315
- Multiscale driving mechanisms of PM2.5 and O3 in China: Insights from reconstruction, spatial clustering, and ensemble modeling M. Dong et al. https://doi.org/10.1016/j.eiar.2026.108580
- Contrasting Responses of Peak Summer Surface Ozone to Anthropogenic Emission Changes Across Two Major Emission Hotspots in Eastern China Y. He et al. https://doi.org/10.3390/atmos17090885
- Topographic modulation of high-temperature ozone suppression: Core-periphery threshold structure in the Sichuan Basin S. Wang et al. https://doi.org/10.1016/j.envpol.2026.129161
11 citations as recorded by crossref.
- Innovative synergy coefficient revealed the negative PM2.5–O3 synergistic pollution in China over the past decade L. Zhao et al. https://doi.org/10.1016/j.jclepro.2026.149510
- Meteorological Drivers and Chemical Evolution of Ozone Pollution in the North China Plain H. Huo et al. https://doi.org/10.3390/atmos17080789
- Long-term ozone formation sensitivity in China: spatiotemporal evolution and machine learning attribution J. Lin et al. https://doi.org/10.5194/acp-26-7081-2026
- From numerical advection schemes to hybrid Eulerian– artificial intelligence models: Application to atmospheric dispersion across turbulence regimes A. Ajdour et al. https://doi.org/10.1016/j.ijheatfluidflow.2026.110630
- Divergent diurnal dynamics and regional applicability of GEMS-based ozone sensitivity indicators (FNR vs. GNR) across Central and Eastern China J. Wang et al. https://doi.org/10.1016/j.jhazmat.2026.143650
- Drivers of interannual variability in autumn ozone and its precursors in Shanghai during 2018–2023 X. Chen et al. https://doi.org/10.1016/j.atmosenv.2026.122405
- Meteorological normalization of surface ozone variability across warm and cold seasons in China during 2015–2024 Z. Ye et al. https://doi.org/10.5194/acp-26-15291-2026
- Changes in Urban–Nonurban Ozone Disparities across China during 2000–2024 L. Guo et al. https://doi.org/10.1021/acs.estlett.6c00315
- Multiscale driving mechanisms of PM2.5 and O3 in China: Insights from reconstruction, spatial clustering, and ensemble modeling M. Dong et al. https://doi.org/10.1016/j.eiar.2026.108580
- Contrasting Responses of Peak Summer Surface Ozone to Anthropogenic Emission Changes Across Two Major Emission Hotspots in Eastern China Y. He et al. https://doi.org/10.3390/atmos17090885
- Topographic modulation of high-temperature ozone suppression: Core-periphery threshold structure in the Sichuan Basin S. Wang et al. https://doi.org/10.1016/j.envpol.2026.129161
Saved (final revised paper)
Latest update: 02 Nov 2026
Short summary
Surface ozone pollution is a pressing global challenge driven by human activities and a warming climate. Using nationwide observations (2013–2023) across China together with satellite data, we developed a new machine learning approach to decouple the impacts of emission controls and weather changes. Our results show that while emission reductions improved ozone in some regions, climate change is increasingly offsetting these gains, underscoring the need for joint air quality and climate actions.
Surface ozone pollution is a pressing global challenge driven by human activities and a warming...
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