Articles | Volume 24, issue 2
https://doi.org/10.5194/acp-24-1177-2024
© Author(s) 2024. 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-24-1177-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Meteorological characteristics of extreme ozone pollution events in China and their future predictions
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Yang Zhou
Shanghai Baoshan Meteorology Bureau, Shanghai, China
Hailong Wang
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Mengyun Li
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Huimin Li
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Pinya Wang
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
Hong Liao
Joint International Research Laboratory of Climate and Environment Change, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China
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32 citations as recorded by crossref.
- Evaluating high-ozone concentration events in the megacity of Tehran, Iran using WRF-Chem simulations and satellite observations S. Roozitalab et al. https://doi.org/10.1007/s11869-026-01965-y
- Efficient Diagnosis of Spatiotemporal Evolution and Driving Factors of Surface Ozone Pollution Episodes: An Application in Jiangsu Province, China H. Wang et al. https://doi.org/10.1021/acs.est.6c03665
- Changes in Urban–Nonurban Ozone Disparities across China during 2000–2024 L. Guo et al. https://doi.org/10.1021/acs.estlett.6c00315
- Assessing the Association Between Unfavorable Meteorological Conditions and Severe PM2.5 and Ozone Pollution Y. Zhou et al. https://doi.org/10.3390/atmos17020194
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- Leveraging U.S. regulatory strategies for China’s dual challenge: Coordinating PM2.5 and ozone mitigation Y. Nie et al. https://doi.org/10.1016/j.jes.2026.03.033
- The Characteristics, Sources, and Health Risks of Volatile Organic Compounds in an Industrial Area of Nanjing T. Tan et al. https://doi.org/10.3390/toxics12120868
- Evaluating spatiotemporal variations and exposure risk of ground-level ozone concentrations across China from 2000 to 2020 using high-resolution satellite-derived data Q. He et al. https://doi.org/10.5194/acp-25-6663-2025
- Tropospheric ozone trends and attributions over East and Southeast Asia in 1995–2019: an integrated assessment using statistical methods, machine learning models, and multiple chemical transport models X. Lu et al. https://doi.org/10.5194/acp-25-7991-2025
- Surface ozone pollution over the Tibet, China: Characteristics, drivers, and source analysis X. Wang et al. https://doi.org/10.1016/j.jhazmat.2026.142130
- Integrating causal inference and interpretable automated machine learning to identify drivers of near-surface ozone concentration in eastern China J. Shi et al. https://doi.org/10.1016/j.envsoft.2025.106782
- A global assessment of intensified heatwaves and air quality P. Wang et al. https://doi.org/10.1016/j.crsus.2025.100559
- Distinct climate drivers governing the dominant interannual variability of co-occurrences of heat and ozone pollution extremes over northern and southern urban clusters in Eastern China X. Zhong et al. https://doi.org/10.1016/j.atmosres.2025.108621
- Comparison of Surface Ozone Variability in Mountainous Forest Areas and Lowland Urban Areas in Southeast China X. Jiang et al. https://doi.org/10.3390/atmos15050519
- Ultra-high-resolution modeling of ground-level ozone for long-term exposure risk assessment driven by GTR-transformer J. Chen et al. https://doi.org/10.1016/j.envint.2025.109705
- Using machine learning to unravel chemical and meteorological effects on ground-level ozone: Insights for ozone-climate control strategies Z. Li et al. https://doi.org/10.1016/j.envint.2025.109567
- Amplification of Surface Ozone by the Aerosol Direct Effect under COVID-19 Lockdown in Shanghai, China Z. Sun et al. https://doi.org/10.1021/acsestair.5c00130
- Regulation of the western pacific subtropical high in regional ozone pollution in the Yangtze river delta region, china: Local accumulation and regional transport L. Shen et al. https://doi.org/10.1016/j.atmosres.2025.108669
- One-step retrieval of ground-level ozone concentrations from OMI hyperspectral observations using machine learning L. Sun et al. https://doi.org/10.1016/j.jclepro.2026.148302
- VOCs-driven ozone extremes during dry and wet heatwaves in the Jiangsu–Shandong–Henan–Anhui Boundary: Integrating meteorological forcings and SHAP interpretation C. Wang et al. https://doi.org/10.1016/j.atmosres.2025.108396
- Analysis of the Relationship between Local Circulation Patterns and Ground-Level Ozone Concentrations in Chungcheongnam-do S. Ma & S. Lee https://doi.org/10.5572/KOSAE.2025.41.5.778
- Daily Time Series Analysis of Ambient Ozone and Fine Particulate Matter Levels in Corpus Christi, Texas D. Sanchez-Warren et al. https://doi.org/10.1007/s11270-025-08778-2
- Unique impacts of strong and westward-extended western Pacific subtropical high on ozone pollution over eastern China M. Li et al. https://doi.org/10.1016/j.envpol.2024.124515
- Drivers of ozone episodes during clean and polluted days in eastern China: Insights into precursor characterization, source apportionment, and associated health risks H. Zhang et al. https://doi.org/10.1016/j.envint.2026.110318
- Carbonyl Compounds Observed at a Suburban Site during an Unusual Wintertime Ozone Pollution Event in Guangzhou A. Ge et al. https://doi.org/10.3390/atmos15101235
- Seasonal Prediction of Ozone Pollution in Central-East China Using Machine Learning S. Yang et al. https://doi.org/10.1007/s41810-025-00310-7
- Machine Learning Analysis of Particulate Matter Driving Contributions in the Southern Sichuan Basin X. Jing et al. https://doi.org/10.1595/205651326X17695054422285
- Multi-scale analysis of ozone pollution Shanghai based on meteorological normalization: A case study of multi-model collaborative machine learning framework and MCM coupling mechanism M. Wu et al. https://doi.org/10.1016/j.atmosenv.2026.121943
- Machine Learning-Based Bias-Corrected Future Projections of Ozone Concentrations from a Chemistry-Climate Model Y. Ni et al. https://doi.org/10.1021/acs.est.5c11992
- Enhanced understanding of atmospheric blocking modulation on ozone dynamics within a high-resolution Earth system model W. Kou et al. https://doi.org/10.5194/acp-25-3029-2025
- Explainable machine learning identifies the lower-tropospheric thermal gradient as the dominant seasonal driver of surface ozone in the Yangtze River Delta, China Z. Li et al. https://doi.org/10.1088/2515-7620/ae675f
- Tracking surface ozone responses to clean air actions under a warming climate in China using machine learning J. Fang et al. https://doi.org/10.5194/acp-26-851-2026
Saved (final revised paper)
Latest update: 31 Jul 2026
Short summary
This study reveals that extreme ozone pollution over the North China Plain and Yangtze River Delta is due to the chemical production related to hot and dry conditions, and the regional transport explains the ozone pollution over the Sichuan Basin and Pearl River Delta. The frequency of meteorological conditions of the extreme ozone pollution increases from the past to the future. The sustainable scenario is the optimal path to retaining clean air in China in the future.
This study reveals that extreme ozone pollution over the North China Plain and Yangtze River...
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